The vertical structure of precipitating clouds plays a vital role in shaping the rainfall characteristics of the surrounding region. Based on the dual-frequency space-borne precipitation radar observation placed on the Global Precipitation Measurement (GPM) satellite for the years 2014-2023, we examined the vertical profiles of precipitating clouds over three different regions of India (Western Ghats, Central India, and Arabian Sea), based on the dual-frequency space-borne precipitation radar observation placed on the Global Precipitation Measurement (GPM) satellite for the years 2014-2023. Vertical distribution of radar reflectivity (Z), rain rate (R), mass-weighted mean diameter (Dm), and normalized intercept parameter (Nw) with altitude for the convective and stratiform clouds for each region is determined. The distribution shows considerable variation with altitude due to the difference in microphysical properties of precipitating clouds with cloud type and topography. Intense convective cloud formation is dominated over the Western Ghats region with high echo tops (>10 km), near-surface Z > 40 dBZ, large R and bigger rain droplets (high Dm) due to strong orographic lifting and enhanced collision-coalescence process in the precipitating clouds. Over the Central India region, deep convective precipitating clouds often form during the monsoon depression, exhibiting echo top above 12 km and considerable variation in rain droplet diameter due to intense updrafts with increased concentration of ice particles. However, relatively weak marine convective clouds were observed over the Arabian Sea, having echo tops of up to 8 km, small R, low near-surface Z, and significant concentrations of smaller rain droplets (low Dm). Stratiform cloud vertical profiles are uniform with little variation. Regional comparison showing the domination of different microphysical processes for stratiform and convective precipitation in all three regions.
How to cite: Kumar, A. and Pattanaik, D. R.: Vertical profiles of precipitating clouds in Monsoon Regions using the GPM satellite , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-836, https://doi.org/10.5194/egusphere-egu25-836, 2025.
During 2024 two important events impacted the processing of the IMERG near-realtime product. On 1 June 2024 the NRT was converted to use the RedHat RHEL 8 operating system. Equally importantly the system was converted to use python 3. Coupled with these system updates, the IMERG NRT algorithms were updated to V07B. This update had been implemented almost a year earlier for the Final IMERG product (appx. 3 months latency from realtime). This much improved algorithm corrected some issues with V06 and added features some of which are discussed in this paper. In October 2024 IMERG products were extended back to January of 1998. Until V07 IMERG only extended back to May 2000. This restriction was due to IR products used as part of IMERG processing not being available before 2000. This paper will provide information about this early phase processing and provide images that illuminate processing for this period. The paper will also discuss the plans and status of availability of NRT version of the 1998-2000 data for early and late versions of IMERG V07B.
How to cite: Stocker, E. F., West, J., Song, Y., and Kelley, O.: The V07B Near-realtime (NRT) update of IMERG and extension of record to January 1998, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-162, https://doi.org/10.5194/egusphere-egu25-162, 2025.
Available evidence indicates that accurate electromagnetic (EM) single scattering properties (SSPs) obtained from hydrometeors with realistic morphology are crucial for simulated signals to align with radar and radiometer observations (across all frequencies). Melting hydrometeors are of particular interest. Although they are confined to the melting layer, occupying only a few radar range gates, their greatly enhanced reflectivity and extinction obscure the EM signal of rainfall from below when observed from space, increasing uncertainty in surface precipitation estimates.
All recent efforts to enhance the realism of melting hydrometeor models and their SSPs have been constrained by computational costs and uncertainties in the scattering solutions. The Discrete Dipole Approximation (DDA) method, for its versatility with target geometry, has been applied to realistic solid hydrometeors, achieving unprecedented consistency in active (radar) and passive (radiometer) retrievals of snowfall. However, when applied to melting hydrometeors of mixed liquid-solid composition with high refractive contrast, DDA methods reveal their limitations, producing significant and varying uncertainties depending on dipole resolution and liquid mass fraction.
To tackle these challenges in the relevant microwave spectrum for the full range of hydrometeors, we developed MIDAS, a numerically efficient 3D full-wave model for scattering by complexly shaped scatterers. Its core concept involves devising a direct-solver-based domain decomposition for the Method of Moment based on the volume integral equation to solve the EM scattering of electrically large and arbitrarily shaped scatterers. MIDAS has demonstrated not only a significant computational advantage over DDA-based codes when applied to realistic solid snow particles but also a greater potential to overcome DDA’s limitations concerning melting hydrometeors
Indeed, promising initial results indicate that MIDAS outperforms the DDA code ADDA in calculating the SSPs of heterogeneous particles. We observe a good agreement, with relative differences below 2%, among MIDAS, ADDA, and Mie solutions for the scattering by heterogeneous (ice and water) 2-layer spheres and melting hydrometeors, provided the dipole size for MIDAS and ADDA is 5 times smaller than required by the normal criterion. However, MIDAS is 30 times faster than ADDA when SSPs are computed for 703 particle orientations.
Furthermore, as we understand the need to economize further to meet the demands and constraints of melting hydrometeors, we have implemented adaptive mesh in MIDAS. The concept involves using a cell size inversely proportional to the material’s (i.e., water or ice) refractive index and ensuring compliance with the stricter validity criterion for liquid water without over-meshing the solid ice components of the melting hydrometeor. Initial results obtained with a mixed-resolution mesh where the finer mesh's cell size is half that of the coarser mesh are promising. The mere reduction in cell size by a factor of two for the liquid water portion significantly decreases computation costs, shortening the total computing time from 13.75 hours to 6.15 hours for the entire melting process (25 melting stages). The outcomes of this ongoing research will directly enhance the accuracy of SSPs for melting hydrometeors and provide a robust characterization of the uncertainties related to hydrometeor scattering in precipitation retrievals.
How to cite: Kuo, K.-S., Fenni, I., and Roussel, H.: Progress Toward Addressing the Challenge of Mixed-phase Precipitation for the GPM Combined Algorithms , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14561, https://doi.org/10.5194/egusphere-egu25-14561, 2025.
The Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset is computed by the U.S. Science Team of the NASA-JAXA Global Precipitation Measurement (GPM) mission. It provides global satellite precipitation estimates for a wide range of scientific research and societal applications. Using a constellation of low-Earth orbit passive microwave and geosynchronous-orbit infrared satellites of opportunity provided by domestic and international partners, IMERG supplies precipitation estimates at high spatial and temporal resolution globally (0.1° every half hour), Three separate Runs, with increasing latencies, are generated to fit the diverse needs of the scientific and applications communities.
The presentation focuses on the future of IMERG for the upcoming V08 and thereafter. This includes issues remaining from V07 development, new issues identified in analysis of the V07 time series, calibration shifts due to the GPM Core Observatory (GPM-CO) orbit boost (and in retrospect the Tropical Rainfall Measuring Mission [TRMM] orbit boost and the TRMM Precipitation Radar’s A/B electronics switch), the advent of SmallSats capable of observing precipitation, the advent of machine learning algorithms, and priorities stemming from the approaching end of the GPM-CO satellite (circa 2032). The complete retrospective processing that will accompany the introduction of Version 08 is planned as the last upgrade before the end of mission.
The goal of this work is to continue the progress that the GPM mission and the IMERG products have realized over the past decade, especially over regions with limited ground observations. We emphasize our continuing goal of providing the scientific and applications communities with a long record of reliable high-resolution precipitation observations, and invite discussion on the next generation of sustained observations and algorithms for global satellite precipitation.
How to cite: Huffman, G., Bolvin, D., Joyce, R., Nelkin, E., and Tan, J.: Plans for IMERG V08 and Future Perspectives for Global Satellite Precipitation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7237, https://doi.org/10.5194/egusphere-egu25-7237, 2025.
Precipitation is one of the most important parameters in the earth system. China began to develop satellites dedicated to precipitation measurements in the second generation of the FENGYUN polar-orbiting meteorological satellite program (FY-3). The first of total two rainfall missions scheduled, FY-3G, was successfully launched on 16 April 2023 and became the world’s third satellite to measure precipitation with space-borne radar after the TRMM in 1997 and GPM in 2014. In this presentation, we will illustrate the scientific products and validation program.
The instruments on the FY-3G satellite can produce important geophysical parameters, including precipitation, atmospheric profiles, various clouds products and so on. As the core remote sensing instrument on the Fengyun rainfall mission, PMR(Precipitation Measurement Radar) can provide the 3D structure of precipitation, invert to obtain accurate information such as precipitation intensity and precipitation type, and improve the space-based precipitation measurement capability. Products such as bright band detection, precipitation type, precipitation phase state, precipitation rate, and latent heating will be processed to generate.19 kinds of scientific products have been publicly released and can be obtained through the dedicated website, FENGYUN Satellite Data Center (http://satellite.nsmc.org.cn/portalsite/default.aspx)..
The FY-3G Precipitation Measurement Radar (PMR) are comparable to Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR). Ground-based weather radar (GR) data are used to perform a comparative analysis of the reflectivity consistency between PMR and DPR satellite-ground radar observations. The results indicate that PMR and DPR are all systematic higher than GR. PMR and DPR are 1.15 dB and 1.56 dB higher than CINRAD reflectivity respectively, while 1.73 dB and 2.85 dB higher with NEXRAD with uncertainty round 2 dB. Stratiform samples exhibits the smallest biases, with reflectivity differences further reduced below the bright band (BB). PMR precipitation classification result aligns well with DPR. Through ground-based comparisons with CINRAD and NEXRAD, the FY-3G PMR exhibits relatively small differences. This makes it well-suited for joint global precipitation observations alongside the DPR.
As a pioneer of China's rainfall missions, FY-3G will greatly improves our ability to provide global precipitation measurements, understand Earth's water and energy cycle, and forecast extreme events for the benefit of society.
How to cite: Chen, L. and Zhang, P. and the FY3G product technology team: The Fengyun rainfall mission FY3G: the scientific products and validation progress, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14175, https://doi.org/10.5194/egusphere-egu25-14175, 2025.
The INvestigation of Convective UpdraftS (INCUS) is a NASA Earth Venture mission (EVM-3) that will provide the first global systematic investigation into convective mass flux, the vertical transport of air and water, and its evolution within deep tropical convection. The overarching goal of the INCUS mission is to understand why, when and where tropical convective storms form, and why only some storms produce extreme weather. INCUS is led by PI Susan van den Heever of Colorado State University (CSU), in collaboration with NASA/Jet Propulsion Laboratory (JPL), Blue Canyon Technologies, and Tendeg Systems. INCUS consists of a series of three small satellites flying in formation, each carrying a Ka-band radar based on RainCube and one cross-track scanning radiometer based on TEMPEST. A novel time-differencing approach among the three satellites flown in close succession (30, and 90, and 120 seconds apart) will provide the first estimates of convective mass flux across the tropics.
The success of the INCUS EVM-3 proposal to NASA relied on the prior success of two pathfinder CubeSat missions: RainCube, the first weather radar on a CubeSat, led by NASA/JPL, and the Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D) mission, led by CSU, producing the first global (up to 58 degrees latitude) observations from a multi-frequency microwave radiometer on a CubeSat, operating for nearly three years in LEO.
TEMPEST-D, a NASA Earth Venture Technology mission, produced global atmospheric science data, a well-calibrated, highly stable radiometer over three years of operations. TEMPEST-D brightness temperatures were validated using scientific and operational microwave sensors, including GPM/GMI and four MHS sensors, operating at similar frequencies to TEMPEST-D channels at 87, 164, 174, 178 and 181 GHz. Using the double-difference approach, TEMPEST-D performance was shown to be comparable to or better than much larger scientific and operational sensors, in calibration accuracy, precision, stability and instrument noise, during its nearly 3-year mission.
A duplicate TEMPEST sensor produced alongside TEMPEST-D was integrated with the Compact Ocean Wind Vector Radiometer (COWVR) from NASA/JPL and launched by the U.S. Space Force to demonstrate low-cost space technologies to improve global weather forecasting. COWVR/TEMPEST were launched on the STP-H8 mission on December 21, 2021, and have performed coordinated observations of Earth’s oceans and atmosphere from the ISS since January 7, 2022. Retrievals of water vapor profiles, clouds, and precipitation from COWVR/TEMPEST-H8 are being performed in collaboration between JPL and CSU.
Previous studies have validated the accuracy and precision of TEMPEST-D brightness temperatures using clear-sky oceanic observations. Recent advances extended the validation of TEMPEST-D and TEMPEST-H8 brightness temperature observations over tropical cyclones using GPM/GMI brightness temperatures and GPM/DPR vertical cumulative reflectivity.
Prior studies demonstrated accurate quantitative precipitation estimation using machine learning over CONUS. Recent advances expanded this capability to a global basis using GPM/GMI and AMSR-2 datasets for training and validation and IMERG rain rates for cross comparison. The heritage of TEMPEST-D and TEMPEST-H8 will be used to demonstrate the potential for remote sensing of precipitation from the Dynamic Microwave Radiometer on the INCUS mission.
How to cite: Reising, S. C., Chandrasekar, V., Radhakrishnan, C., Brown, S. T., and van den Heever, S.: Potential for Remote Sensing of Precipitation using the Dynamic Microwave Radiometer on the NASA INCUS Mission based on the Heritage of TEMPEST Scientific Results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14923, https://doi.org/10.5194/egusphere-egu25-14923, 2025.
In this study, we produced surface precipitation data at a high spatial resolution of 0.1° by integrating observations from two spaceborne radars, TRMM PR and GPM DPR KuPR, spanning a 25-year period. This dataset allowed us to analyze the seasonal variation in diurnal peaks, enhancing our understanding of spatiotemporal precipitation patterns and their detectability. The precipitation data were classified based on the horizontal scale for the individual precipitation systems, represented by the area-equivalent diameter of consecutive precipitation regions. Certain grid points in the equatorial and mid-latitude zones lacked sufficient long-term, time-resolved samples due to limited satellite overpasses. For example, in mid-latitude regions such as Europe, observations by DPR alone provided fewer than 10 passes under the current conditions for averaging time.
To address these limitations, we applied a running average technique and imputed missing values to minimize outlier impacts. Seasonal changes in the timing of maximum precipitation were then categorized into distinct clusters, revealing key spatiotemporal patterns. On the Tibetan Plateau, small-scale precipitation systems predominantly generate early afternoon peaks throughout the year, while in winter, morning rain frequently occurs in certain valleys. In the southern foothills of the Himalayas, precipitation peaks in the morning, whereas evening showers are observed in the southernmost regions during summer. In the southwestern part of Japan, which is heavily influenced by the ocean, large-scale precipitation dominates during the rainy season, with morning rainfall prevailing, while midsummer shows a shift toward afternoon peaks. Additionally, medium-scale precipitation systems tend to follow small-scale systems by a few hours, while large-scale systems exceeding 100 km in diameter exhibit distinct timing patterns.
These findings underscore the diverse precipitation regimes shaped by geographical features and prevailing winds, highlighting the need to assess the value and challenges of leveraging high-resolution precipitation climate datasets.
How to cite: Hirose, M. and Mantas, V.: Detecting seasonal differences in the variations in diurnal precipitation using spaceborne Ku-band radars, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9231, https://doi.org/10.5194/egusphere-egu25-9231, 2025.
The National Science Foundation (NSF) of the United States approved the Airborne Phased Array Radar (APAR) Mid-scale Research Infrastructure-2 proposal in 2023 to develop the next generation airborne polarimetric, Doppler weather radar mounted on the NSF/National Center for Atmospheric Research (NCAR) C-130 aircraft. Development of anew observing system is critical for the advancement of scientific understanding of weather phenomena. These instruments establish a proving ground for future operational transition while also providing tools for the research community. One of the issues with developing new instrumentation is the unknown performance characteristics of the instrument and the subsequent unknowns in uncertainty in measurements.
The APAR Observing Simulation, Processing, and Research Environment (AOSPRE) was developed to simulate APAR's measurement capabilities for heavy precipitation and high-impact weather events. Using Cloud Model 1 (CM1) and Weather Research and Forecasting (WRF) model output to provide various storms of interest and their surrounding environments, simulated NCAR C-130 flights are operated within the model space. Radar moments and dual-Pol variables are determined using the Cloud Resolving Model Radar Simulator (CR-SIM). Three-dimensional dual-Doppler radar winds can be retrieved from the Spline Analysis at Mesoscale Utilizing Radar and Aircraft Instrumentation (SAMURAI). The output can be examined directly or passed through additional tools to analyze various aspects of the data collected during each flight.
AOSPRE is linked to a NSF NCAR wide INtegrating Field Observations and Research Models (INFORM) to (1) establish and support best practices and methods for comparisons between models and observations, (2) exploit, assess and quantify the impacts of integrating observations and models to improve understanding of the prediction and predictability of the Earth system, and (3) improve the design, planning, deployment strategy of field programs and instrument development. The AOSPRE will be expanded into a field program planning tools as wells as a post campaign re-analysis tool with DA capability.
AOSPRE is developed as an open-source software. The first version of AOSPRE software has been released to the research and operational community in the last quarter of 2024. This paper will provide an overview of the AOSPRE and report the recent development of the AOS to better simulate the characteristics of a phased array radar on a moving platform. In addition, the authors will outline how AOSPRE will be used as a component in the future APAR data analysis software system.
How to cite: Lee, W.-C., Klotz, B., Manning, K., and Vivekanandan, J.: The Airborne Phased Array Radar (APAR) Observing Simulation, Processing, and Research Environment (AOSPRE), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14218, https://doi.org/10.5194/egusphere-egu25-14218, 2025.
Wet downbursts are commonly associated with heavy rain. Because the precipitation flux is proportional to the summed product of the mass and downward velocity of precipitating hydrometeors, the rain rate within downbursts can be significantly amplified due to the increased fall velocities. All existing radar methodologies for rainfall estimation assume that the fall velocity of raindrops is equal to their terminal velocity in still air. This results in a strong underestimation of precipitation in the presence of downbursts or microbursts.
Hail and graupel play an important role in generating downbursts via precipitation loading and negative buoyancy caused by melting of ice hydrometeors. These effects are quantified in the framework of our 1D cloud model with spectral bin microphysics that explicitly treats melting, sublimation, and evaporation for various size distributions of ice particles aloft and vertical thermodynamic profiles. The cloud model is coupled with an advanced polarimetric radar forward operator and generates vertical profiles of radar variables such as radar reflectivity Z, specific differential phase KDP, and specific attenuation A used in modern radar QPE methods.
KDP is the primary radar variable used for rain rate (R) estimation when rain is mixed with hail. However, the parameters of the power-law R(KDP) relation may vary depending on the predominant hail size. For example, storms producing a large amount of small hail (SPLASH storms) in high concentration are frequently characterized by anomalously high values of KDP. On the other hand, the effects of diabatic cooling that determine the strength of the downdraft (along with precipitation loading) are stronger for SPLASH storms.
The major points of this study will be illustrated by the results of model simulations and polarimetric radar observations of hail-bearing storms.
How to cite: Ryzhkov, A. and Carlin, J.: The impact of downburst and hail on the accuracy of polarimetric radar rainfall estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4467, https://doi.org/10.5194/egusphere-egu25-4467, 2025.
The Global Precipitation Measurement (GPM) mission was launched in February 2014 as a joint mission between JAXA from Japan and NASA from the United States. By applying the insight provided by the GPM radars, the program has contributed enormously to the quality of the passive microwave radiometer time series that now spans almost 40 years. This talk will examine the long time series of precipitation from 3 approaches. The first is an uncertainty analysis based upon first principles. It shows that time series can be homogenized, but that potential changes in convective organization over annual time scale must be included as a source of uncertainty in order to homogenize the time series of different satellites. This is verified with the second approach that focuses on closing the water budget on regional scales. While not as direct, it also hints strongly at the fact that our current time series overestimate precipitation when convection is better organized into large Mesoscale Convective Complexes. The final approach seeks to correlate biases with large scale meteorological conditions to also show that biases due to convective organization are predictable. While not applied in any product yet, this insight may serve as a blueprint for gaining confidence in our time series of precipitation where even a 1% change/decade in global precipitation is more than currently expected from observed warming trends.
How to cite: Kummerow, C.: Precipitation Uncertainties at Climate Time Scales , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1657, https://doi.org/10.5194/egusphere-egu25-1657, 2025.
Due to the limitation of remote sensing observation, the research on the three-dimensional precipitation in Northwest China is insufficient. However, the launch of GPM provides convenience for the study of precipitation structures and types in Northwest China. In this work, the spatio-temporal distributions of convective and stratiform precipitation and corresponding thermal structures are analyzed during summer of 2014-2019 in Northwest China based on GPM observations, EAR5 reanalysis and IGRA2 datasets. The result shows that the stratiform precipitation is dominant in Northwest China and four representative sub-regions are divided to further discuss (Tianshan Mountain, Tarim Basin, Qilian Mountain and eastern part of Northwest China). The storm tops of convective precipitation are generally 2-3 km higher than those of stratiform precipitation, and the storm top reaches the maximum in the Tianshan Mountain (16 km) and the lowest in the Tarim Basin (10 km). Moreover, the maximum rain rate of convective precipitation below 4 km occurs in the Tianshan Mountain, while maximum rain rate of stratiform precipitation occurs in the eastern part of Northwest China. The maximum latent heating of both precipitation types occurs at 4-6 km. The peak frequency of convective precipitation mainly appears in the afternoon, whereas the diurnal variation of stratiform precipitation displays a bimodal peak (in the early morning and evening). Furthermore, the intensities of both precipitation types vary with the total column water vapor and follow an approximate quadratic function relationship. The precipitation conversion of convective precipitation and CAPE are obviously larger than those of stratiform precipitation. There is convergence in the lower troposphere and divergence in the upper troposphere, which is favorable to occurrence of precipitation (except for Tarim Basin). Additionally, the positive temperature and humidification are significant in the lower-middle troposphere during process of both precipitation types. This study aims to reveal the features of convective and stratiform precipitation from the perspective of GPM remote sensing observation and provide reference for numerical simulation in arid-semiarid regions.
How to cite: Wang, R.: Characteristics of different precipitation types and corresponding thermal structures in Northwest China in summer derived from GPM observation, ERA5 and IGRA2, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5351, https://doi.org/10.5194/egusphere-egu25-5351, 2025.
This study focuses on the development of a new high-resolution gridded rainfall dataset for Senegal, which is essential for rainfed agriculture, which is sensitive to climate variability. Given the limited number of rain gauges, the research will evaluate 17 publicly available gridded rainfall datasets (P-datasets) against data from 21 stations of the Senegalese National Meteorological Service (ANACIM) over a 17-year period (2005-2021). The evaluation uses several agroclimatic indices, including rainfall onset and cessation, rainy season duration, and extreme events. The results show that the reliability of the P-datasets varies significantly depending on the metrics used. For total precipitation, ARC2, CHIRPS, ERA5 and RFEv2 were found to be the most reliable datasets. ERA5 achieved the highest Kling-Gupta Efficiency (KGE) value of 0.81 at the daily scale. In terms of agroclimatic parameters, ARC2, CHIRPS and RFEv2 excelled in accurately representing the start (KGE ≥ 0.45) and end (KGE ≥ 0.39) dates of the rainy season. However, the P datasets generally overestimate rainfall events and struggle to identify dry spells. The newly constructed merged dataset (M-dataset) showed over 100% improvement in correlation for daily estimates and a significant reduction in bias of 99.19% for ARC2, 80% for CHIRPS and 90.57% for RFEv2. This research provides critical insight into the selection of appropriate datasets to improve climate information for agricultural decision making in Senegal.
How to cite: Asse, M.: Reliability assessment of 17 gridded rainfall dataset for the construction of a daily high-resolution reanalysis (4km) across Senegal for agroclimatic applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-539, https://doi.org/10.5194/egusphere-egu25-539, 2025.
It has been widely reported that precipitation tends to occur more in the form of rainfall rather than snowfall under global warming, as expected from theory. However, the observed data across China from 1961 to 2022 show that the rainfall to total precipitation rate decreased, especially in Northwest and Northeast China. This study investigates this paradox from perspective of the time-variable observational errors in the precipitation observation. The national standard gauge without a wind shield has long been used in China. There is an undercatch issue with the gauge caused by the turbulence generated when wind blows over it. This issue is more severe for snow and is exacerbated during strong winds. To improve the accuracy of snowfall measurement, new weighing gauges with one-layer wind shield have been deployed since 2009 in China. The authors conducted wind-induced error corrections for rainfall and snowfall at approximately 2300 national weather stations. It was found that the national mean annual precipitation, rainfall, snowfall were 794 mm, 763 mm, and 27 mm before correction and were 854 mm, 810 mm, and 40 mm after correction. After correction, the national mean rainfall to total precipitation rate showed an increasing trend (0.17 %/decade) from 1961 to 2022 instead of a decreasing trend (-0.04 %/decade) in the raw data. Especially, the trend of rainfall proportions in the Northwest China and the Northeast China changed from significant negative to positive. The key reason for this change is that wind-induced error decreased due to a reduction in surface wind speed, which is amplified by the instrument replacement. This is more obvious for snowfall.
How to cite: Li, Y. and Wang, K.: Time-variable observational errors explain the spurious rainfall and snowfall proportion trend in China under global warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2830, https://doi.org/10.5194/egusphere-egu25-2830, 2025.
The Middle East, characterized by dry climates and water scarcity, has seen significant changes in precipitation patterns over the past few decades. This research investigated the temporal and spatial changes of precipitation during the statistical period of 1981-2023 in the Middle East using CHIRPS satellite images. Analysis of average monthly rainfall in the Middle East showed that January, January, March, and December were the wettest months, and June, July, August, and September were the driest months. An upward trend of rainfall was observed in all months except February. This trend was especially significant in June, September, July, and August. The months of January, April, May, and June showed the highest annual increase in rainfall. Also, based on the results of seasonal rainfall, the winter season had the highest average rainfall, followed by spring and summer, which showed the highest slope of rainfall changes. Based on the results of the visual trend of precipitation in summer, regions such as southeast and eastern Anatolia in Turkey, Basra, and various regions of Iraq and Iran experienced a significant decrease in rainfall with a trend of approximately 0.25 mm. Likewise, during the fall, this trend continued in the northern regions of Iran, Yemen, Oman, and parts of Türkiye, Iraq, Egypt, and Syria. Parts of Lebanon and northern Iraq have experienced a significant decrease in some places during the winter season. A part of the north of Matrouh province in Egypt, southwest (Khuzestan), and north (Mazandaran, Gilan, and Ardabil) of Iran have experienced an increase in rainfall up to .5 mm in the winter season. In general, according to the picture of the annual changes in precipitation, the northern half of the Middle East in the countries of Iran, Turkey, Syria, and northern Iraq has seen a decrease in precipitation, and the southern half of the Middle East and northern Turkey in the Black Sea geographical region have seen an increase in precipitation over 43 years. have experienced in the past.
How to cite: Rousta, I. and Ólafsson, H.: Assessing Spatio-Temporal Precipitation Variations in the Middle East (1981-2023) Using Remote Sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19134, https://doi.org/10.5194/egusphere-egu25-19134, 2025.
The Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR) (Ku- and Ka-band) provides vertically resolved information on rain and ice water under moderate to heavy precipitation conditions across the tropics and mid-latitudes (Hou et al. 2014, Skofronick-Jackson et al. 2017). Owing to the unique asynchronous orbit of the GPM Core Observatory with the DPR and the GPM Microwave Imager (GMI), its orbital ground tracks intersect with those of many other sun-synchronous satellites. The CloudSat - GPM coincidence dataset (CSATGPM; Turk et al. 2021), focusing on intersections with the W-band cloud radar onboard CloudSat, which excels at observing clouds and light precipitation, offers "pseudo three-frequency" radar profiles of near-coincident observations. In addition, simultaneous observations by CloudSat and the Tropical Rainfall Measuring Mission (TRMM; Kummerow et al. 1998) satellite, the predecessor of GPM, are also available (CSATTRMM; Turk et al. 2021), which includes a larger number of cases compared to CSATGPM, as it covers the period before CloudSat transitioned to day-time only operation in 2011. These datasets have been utilized for many scientific purposes, such as studies of cold-season precipitation, ice microphysics, and light rainfall.
The Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite (Illingworth et al., 2015; Wehr et al., 2023), launched in May 2024, is equipped with four sensors employing different observation methods: radar, lidar, imager, and radiometer. In particular, the Cloud-Profiling Radar (CPR), developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT), is the first spaceborne W-band radar with Doppler capability. It continues the cloud and precipitation observations performed by the CloudSat while introducing the novel measurements of vertical cloud motion from space. Building on the CSATGPM dataset, we are constructing a coincident observation dataset for the EarthCARE era.
From August to December 2024, the two satellites recorded several hundred coincident observation events per month, with approximately one-third of these events detecting precipitation on both satellites. An examination of the vertical profiles of radar reflectivity revealed that while the DPR detected large raindrops and snow particles in advanced stages of growth, the CPR captured detailed features within clouds at higher altitudes. In stratiform precipitation cases, Doppler velocity observations from the CPR showed slower downward motion at altitudes above the bright band detected by the DPR, and faster downward motion at lower altitudes. Furthermore, in addition to using DFR from three-frequency observations during the CloudSat era to classify solid precipitation particles, the incorporation of Doppler velocity as a new constraint suggests the potential for more advanced microphysical analysis of ice particles.
The combination of active observations from the W-band radar and 13-channel (10–183 GHz) GMI is also useful for algorithm development and evaluation, sensitivity studies of snow and light rain, cloud process studies, and radiative transfer simulations. In this presentation, we will also introduce preliminary results from coincident observations of the EarthCARE/CPR and the GMI radiometer.
How to cite: Aoki, S., Kubota, T., and Turk, F. J.: Development of EarthCARE - GPM coincidence dataset with combination of spaceborne cloud and precipitation radars, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7635, https://doi.org/10.5194/egusphere-egu25-7635, 2025.
In Dual-frequency Precipitation Radar (DPR), the observed radar reflectivity factor cannot be used near the ground surface because of the main lobe clutter. Therefore, the standard algorithm V07 estimates the precipitation rate in the main-lobe clutter region, assuming that the attenuation-corrected radar reflectivity factor does not change in the beam direction. On the other hand, Hirose et al. (2021) produced a database of vertical profiles of precipitation rate (DB21) using observations near the nadir, where the effect of main lobe clutter is relatively small, and applied it to observations outside the nadir to estimate precipitation rate at ground surface level. This estimation method extrapolates precipitation rate profiles estimated by the standard algorithm, and its consistency with other estimates, such as path-integrated attenuation, is not guaranteed.
In this study, we improved the precipitation rate estimation method in the standard algorithm using DB21 and its updated database (DB24). When the standard algorithm estimates using different parameters, the precipitation rate profile in the main lobe clutter region changes in the beam direction according to DB21 or DB24. The precipitation rate estimates obtained in this manner were consistent with those of the other estimates.
Experiments conducted for all orbits in June 2018 showed that the surface altitude precipitation rate of the dual-frequency algorithm increased by 6.6% (10.5%) compared with V07 when using DB21 (DB24). For DB24, the database classification by precipitation rate at the reference altitude (2.25 km or 3.25 km) was added. In addition, the classification of the database by precipitation rate gradient between the reference altitude and 0.5 km higher was subdivided. As a result, the downward increase in precipitation rate, especially in heavy precipitation, can be more easily expressed. The estimated precipitation rate at the clutter-free bottom was 1.2% lower for DB24 than for V07. This is due to the need to compensate for the general increase in precipitation rate in the main lobe clutter region, because the conditions of the surface reference technique remain the same.
How to cite: Seto, S. and Hirose, M.: Improved Precipitation Rate Profile Estimation Method for the Main-lobe Clutter Region in GPM/DPR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10838, https://doi.org/10.5194/egusphere-egu25-10838, 2025.
A vertical description of the profiles of precipitation is a long-term goal of atmospheric research and precipitation science. The detailed hydrometeor identification products have great potential for constraining four-dimensional distributions of bulk hydrometeors and thus microphysical conversion processes for evaluating cloud-resolving models (CRMs), which is an important tool in the weather and climate research community. New three-dimensional hydrometeor type product will be available in the classification module in the next version (V8) of GPM DPR level 2 algorithm. This is a unique advantage for the space borne radar to provide a three-dimensional hydrometeor type over the globe while ground based observations are limited to the regions of deployment. The products developed by our team in the classification module allow us to have the potential to take a big step forward adding vertical profile of hydrometeors for DPR full swath data. Although GPM DPR has fine vertical resolutions in dual-frequency observations, most of the algorithms or products developed are 2 dimensional with either a “flag” or “type” (or etc.) on a 2-dimentional surface. These products include 1) Stratiform, convective rain separation; 2) detection of melting regions; 3) Developing a surface snowfall identification algorithm; (4) Developing a graupel and hail identification algorithm; and 5) the hail identification algorithm.
How to cite: Chandrasekar, C. V. and Le, M.: Recent Progress On Hydrometeor Identification Product For GPM DPR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12666, https://doi.org/10.5194/egusphere-egu25-12666, 2025.
In the past decades, ground-based weather radars gained popularity for enhancing the understanding of precipitation systems, the accuracy of the Quantitative Precipitation Estimation (QPE) and for serving as input in numerical weather models. Nevertheless, they are prone to errors from various sources, including significant calibration errors. Previous research showed that the Ku-band precipitation radar aboard the Global Precipitation Measurement Mission Dual-Precipitation Radar (GPM DPR) is effective for calibrating ground-based radars. Several studies proposed the alignment of ground-based radar reflectivities with those from the GPM DPR to achieve their absolute calibration. This study performs the absolute calibration of the Rizoelia (LCA) and Nata (PFO) radars in Cyprus for approximately six years of observations (October 2017 to May 2023), assessing and comparing volume-matching thresholds and data filtering techniques. The results indicate that excluding reflectivities within the melting layer and adding a 250 m buffer consistently improved calibration for both radars. The selected calibration schemes were combined, and the resulting offsets were compared against stable radar parameters to identify stable calibration periods. Future work will include disdrometer data and expand the analysis to quantitative precipitation estimation.
Acknowledgements
The authors acknowledge the ‘EXCELSIOR’: ERATOSTHENES: EΧcellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.
The authors also acknowledge the Department of Meteorology of the Republic of Cyprus for providing the X-band radar data.
How to cite: Loulli, E., Michaelides, S., Bühl, J., Loukas, A., and Hadjimitsis, D. G.: GPM DPR-Based Calibration of two Ground-based Weather Radars, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12943, https://doi.org/10.5194/egusphere-egu25-12943, 2025.
The Global Precipitation Climatology Project (GPCP) is a popular combined satellite-gauge precipitation dataset in which the long-term CDR standards of consistency and homogeneity are emphasized. This presentation is composed of four major parts: (1) a brief overview of the latest GPCP Daily and Monthly products (V3.2) and satellite-gauge input data sets used in them; (2) comparison of the GPCP V3.2 products with the previous version of GPCP Daily (V1.3) and Monthly (V2.3) products and highlighting major changes; (3) assessment of the GPCP V3.2 products over the oceans using Passive Aquatic Listeners (PALs), over sea ice using snow depth data from a combination of ICESat-2 and Cryosat-2 observations plus ERA5 estimates, and over Antarctica using CloudSat; (4) insights from the latest GPM (V07) products as they are related to GPCP and the update of GPCP to GPCP V3.3. Several major changes occurred in GPCP V3.2, including: (1) moving from Monthly 2.5°x2.5° and Daily 1.0°x 1.0° spatial resolution in V2.3 to 0.5°x0.5° for both Monthly and Daily products; (2) calibrations to climatologies based on high-accuracy satellite missions, including TRMM, CloudSat, GPM, and GRACE; and (3) use of new precipitation retrieval and calibration methods. Compared to V2.3, GPCP V3.2 shows about a 6.5% increase in global oceanic and about a 4.5% increase in global (land and ocean) precipitation rates with some major changes over the ocean between 40°S and 60°S. Similar to V2.3, near-zero global precipitation trend is observed in V3.2. However, regional trends, which are substantial, remain generally similar between V2.3 and V3.2. Evaluations over the oceans using PALs showed that GPCP V3.2 substantially outperforms GPCP V2.3 in representing rain occurrence and rain intensity at daily scale, likely due to the use of IMERG in the GPCP V3.2 Daily product. Our study suggests that GPCP V3.2 generally captures the snowfall accumulation pattern over sea ice, compared to that obtained from the combined ICESat-2 and Cryosat-2 observations, as well as that from ERA5. However, this set of products shows considerable differences in the amount of snowfall accumulation, with ERA5 often showing the highest values. We will end the presentation by briefly discussing our plans for further improvement of GPCP, including higher spatial and temporal resolution, lower latency, and the use of more-advanced gauge analysis and precipitation retrieval methods.
How to cite: Behrangi, A., Huffman, G. J., Adler, R. F., Song, Y., Bolvin, D. T., Nelkin, E. J., and Gu, G.: The latest GPCP Daily and Monthly Products: Current Status, Assessments, and the Future Plans, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4886, https://doi.org/10.5194/egusphere-egu25-4886, 2025.
The Polarimetric Radio Occultation (PRO) technique involves tracking signals emitted by navigation satellites (GPS, Galileo, Beidou…) from a Low Earth Orbit (LEO) satellite as it rises or sets behind the Earth’s limb. This method extends the capabilities of the standard Radio Occultation (RO) technique by employing two orthogonal linear polarizations—horizontal (H) and vertical (V)—thereby providing relevant information about atmospheric hydrometeors. Furthermore, the traditional RO products (vertical profiles of thermodynamic variables) are simultaneously measured, becoming the first technique to provide both type of observations.
This technique has been under testing since 2018 aboard the Spanish PAZ satellite, a mission that successfully demonstrated the GNSS-PRO concept. Moreover, since 2023, it has been implemented on three Spire global commercial CubeSats and one PlanetiQ satellite. The polarimetric capability of PRO enables to retrieve the observable called differential phase shift, defined as the difference between the phase delays associated with the horizontal and vertical polarizations. Intense precipitation events, characterized by non-spherically symmetric hydrometeors, exhibit a positive differential phase shift when these observations pass through such phenomena, showing sensitivity to microphysical properties related to these events.
The primary hypothesis, that PRO is sensitive to oblate raindrops, has already been validated. Unexpectedly, the technique also demonstrated sensitivity to frozen hydrometeors, further expanding its potential applications. The validation of PRO has been successfully achieved through comparisons with both two-dimensional datasets, such as IMERG-GPM products, and three-dimensional datasets, including observations from NEXRAD polarimetric radars.
Current analyses focus on evaluating the sensitivity of PRO to various microphysical parameterizations obtained from the Weather Research and Forecasting (WRF) model. Additionally, its sensitivity to specific particle habits is being examined using the Atmospheric Radiative Transfer Simulator (ARTS) database. The study is centered on Atmospheric Rivers (AR) to investigate how variations in microphysical parameterizations influence the ability of PRO to detect and characterize hydrometeors. Preliminary results indicate that some specific parameterizations and particle habits better compare to PRO actual observations. These findings aim to enhance our understanding of the processes associated with extreme weather systems and advance the application of PRO in atmospheric science.
How to cite: Paz, A., Padullés, R., and Cardellach, E.: Evaluating the sensitivity of GNSS-PRO to different microphysical assumptions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6647, https://doi.org/10.5194/egusphere-egu25-6647, 2025.
Snowfall is an important indicator of climate change, affecting surface albedo, glaciers, sea ice, freshwater storage, cloud lifetime, and ecosystems. Accurate measurements of snowfall at high latitudes are particularly important for estimating the mass balance of ice sheets; however, snowfall is difficult to quantify from both in situ and remotely sensed measurements.
Today, global snowfall products are mostly based on space-borne cloud radar observations such as CloudSat and now EarthCARE. However, these products suffer from systematic and random errors due to poor spatio-temporal sampling, the inability to observe snowfall near the surface due to ground clutter, and retrieval uncertainties due to insufficient information content of the observations.
WIVERN (WInd VElocity Radar Nephoscope) is one of the two remaining ESA Earth Explorer 11 candidate missions, with the final selection in July 2025. It is equipped with a 94 GHz conical scanning polarimetric Doppler radar and a 94 GHz passive radiometer. The main objective of the mission is to measure global horizontal winds in clouds, but it will also quantify cloud water content and precipitation rate.
Here we analyze WIVERN's potential to improve global snowfall products through the mission's unique design. Compared to CloudSat, WIVERN's 800 km swath provides 70 times better coverage including sampling closer to the poles and its 42 off-zenith angle significantly reduces the radar blind zone near the surface (especially over the ocean). In addition, WIVERN's radar includes polarimetric measurements and is accompanied by a radiometric mode, which can further improve the estimation of snowfall rates. Our results show that the WIVERN sampling strategy significantly reduces the uncertainty in polar snowfall estimates, making it a valuable product for climate model evaluation and as an input to surface mass balance models of the major ice sheets at the regional and seasonal spatio-temporal scales.
How to cite: Maahn, M., Battaglia, A., Coppola, M., Hörnig, S., Kollias, P., Lhermitte, S., Maherndl, N., Montopoli, M., Scarsi, F., Tridon, F., and Illingworth, A.: How can global snowfall estimates be improved by ESA's proposed Earth Explorer 11 WIVERN mission?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15261, https://doi.org/10.5194/egusphere-egu25-15261, 2025.
Non-Catching Gauges (NCGs) are increasingly employed to study precipitation microphysics and often serve as ground-based references for validating radar and satellite measurements. Their growing popularity is also due to their minimal maintenance requirements, that counterbalance their higher costs. However, wind-induced biases significantly affect NCG measurements by potentially diverting hydrometeors away from the instrument sensing area. This bias, already a concern for traditional catching gauges, is even more pronounced for NCGs due to their complex shapes, usually not radially symmetric (see e.g. Chinchella et al. 2024). This study investigates the wind-induced bias of measurements taken by the OTT Parsivel2 disdrometer using Computational Fluid Dynamics (CFD) simulations coupled with a Lagrangian particle tracking model implemented in the OpenFOAM software. CFD simulations provide the wind velocity field around the instrument body for different combinations of wind speed and direction by numerically solving the Unsteady Reynolds-Averaged Navier-Stokes equations, using a k-ω SST turbulence model and a local time-stepping approach. Results show that wind parallel to the laser beam causes maximum disturbance on the instrument sensing area, while wind perpendicular to the laser beam minimizes the disturbance. Hydrometeor trajectories are modelled starting from the simulated velocity fields, by releasing drops ranging from 0.25 mm to 8 mm in diameter within the computational domain. The trajectories are tracked until the drops either reach the gauge, exit the domain, or fall below the sensing area. From these simulations, the Catch Ratio (CR) is calculated, representing the ratio of the number of droplets reaching the instrument sensing area in the presence of wind and their number in undisturbed conditions. For wind parallel to the laser beam, limited overcatch is shown at low wind speed (1–2.5 m/s), while severe undercatch occurs at high wind speed. For wind perpendicular to the laser beam, the bias is limited, with minor overcatch observed at high wind speed. By fitting the CR, adjustments to the measurements can be applied, provided the wind speed and direction are known at the installation site. Since the CRs strongly depend on the hydrometeors diameter, wind also affects the measured Drop Size Distribution (DSD), with small drops often missed entirely in certain wind conditions. Similar results are shown when integrating the CRs over the full range of drop sizes, obtaining the Collection Efficiency, that represent the ratio of the precipitation volume sensed by the instrument to the actual precipitation volume. The effect of free stream turbulence is also being tested by superimposing turbulent vortexes over the free stream flow at the inlet of the simulation domain. In conclusion, wind significantly affects the measurement of precipitation microphysical and integral properties, including the derived DSD and rainfall volume, when using the OTT Parsivel2 disdrometer. These biases can be mitigated by applying adjustments as a function of wind speed and direction, thereby improving the reliability of NCG measurements in windy conditions.
References:
Chinchella E., Cauteruccio, A., & Lanza, L. G. (2024). Quantifying the wind-induced bias of rainfall measurements for the Thies CLIMA optical disdrometer. Water Resources Research, 60(10), e2024WR037366. https://doi.org/10.1029/2024WR037366
How to cite: Chinchella, E., Cauteruccio, A., and Lanza, L. G.: Numerical evaluation of the wind-induced bias for the OTT Parsivel2 optical disdrometer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13347, https://doi.org/10.5194/egusphere-egu25-13347, 2025.
In the latest improvements to the Climate Hazards Infrared Precipitation with Stations dataset, CHIRPS3 (version 3), we address key shortcomings and validate the results against high gauge density datasets. The Climate Hazards Infrared Precipitation with Stations version 2 (CHIRPS2) is a widely-used 1981-present quasi-global 0.05° dataset that combines thermal infrared (TIR) geostationary satellite observations, a high-resolution climatology, and in situ rainfall gauge observations. While many studies have shown that CHIRPS2 performs well, we have identified and addressed an important shortcoming — a tendency to underestimate temporal precipitation variance. We also update and improve version 2 of the Climate Hazards Precipitation Climatology (CHPclimv2), and extend CHIRPS to 60°N/S. Finally, thousands of additional new time-varying stations are now included in CHIRPS3. Several countries in Africa, Central America and South America routinely contribute stations monthly.
We validate estimates using the high quality ‘Rainfall on a Gridded Network’ (REGEN) data set, comparing the performance of the CHIRP2 and CHIRP3 and similar products in 12 regions with high gauge densities. We also perform a validation study in Ethiopia. The usage section contrasts CHIRP2 and CHIRP3 performance in East Africa, during recent seasons associated with severe drought or extreme precipitation, to illustrate the value of the advancements made in the CHIRPS precipitation data product.
How to cite: Funk, C., Peterson, P., Harrison, L., Saldivar, R., Landsfeld, M., Davenport, F., Peterson, S., Turner, W., Alaso, D., Sonnier, A., Shukla, S., Zhou, E., Fink, A. H., Budde, M., Pedreros, D., Verdin, J., and Husak, G.: Innovative improvements supporting version 3 of the Climate Hazard Center Infrared Precipitation with Stations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13256, https://doi.org/10.5194/egusphere-egu25-13256, 2025.
The Advanced Quantitative Precipitation Information Project (AQPI) provides supplemental radar observations across the Greater San Francisco Bay Area – specifically, 4 X-band radars (3 already installed) and 1 C-band radar, by the end of 2025. These new radars complement the existing radar network by filling horizontal and vertical gaps in coverage caused by terrain blockage and distance from the existing radars. Additionally, the new radars operate at a much higher spatial and temporal resolution than the existing network. Together, these aspects provide for much more accurate estimation of rainfall rates and improved short-term forecast capability across the area. Local stakeholders and emergency managers can make direct use of the rainfall estimations, both in real time and integrated over various historical periods, as well as the improved forecasts to optimize any number of operations. These include emergency response, water and wastewater management, flood response, aquifer recharge, transportation efficiencies, and more. The data from AQPI radars can also be assimilated into short-range forecast models and used as an improved forcing dataset for hydrology models, especially those predicting streamflow for small and flashy basins across the area. A robust user interface provides data visualization and delivery, and will continue to mature as the program grows. Notably, AQPI represents a unique collaboration amongst local, state, and federal level entities from the academic, governmental, and private sectors.
This presentation will focus on key aspects of the program including an overview of system hardware, software, and the user interface that ties it all together. It will also highlight a case study that demonstrates the value of the AQPI radar precipitation estimates with respect to those of the existing network. And finally, it will describe a vision of the future of this important effort.
How to cite: Rutz, J., Vilela, R., Steen, M., Chandrasekaran, V., and Ralph, M.: The Advanced Quantitative Precipitation Information (AQPI) Project: Building a State-of-the-Art Precipitation Observation and Forecast System for the Greater San Francisco Bay Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-71, https://doi.org/10.5194/egusphere-egu25-71, 2025.
CombiPrecip is an operational algorithm of MeteoSwiss that combines in real-time raingauge measurements with radar precipitation estimates across a domain of 710x640km2, covering Switzerland and extending beyond the Swiss borders. The system utilizes a geostatistical approach known as kriging with external drift as an interpolation technique, offering probabilistic outcomes that provide both a mean value and a variance at each interpolated point. The purpose of our study is two-fold: (i) We investigate to what extent the kriging variance of CombiPrecip is a satisfactory measure of uncertainty of the kriging expected value. We answer this question through a probabilistic verification of a seven-year dataset against raingauge measurements. (ii) We present an algorithm which integrates the kriging expected value and variance of the CombiPrecip output with spatially autocorrelated noise fields to generate ensembles of N realistic members. The verification suggests that the probabilistic CombiPrecip output has skill, which remains satisfactory even for high precipitation intensities. The ensembles generated by this method can serve as valuable initial conditions for precipitation nowcasting systems. Moreover, the proposed ensemble-generation technique is not restricted to geostatistics-based applications and can be readily adapted to other approaches that produce probabilistic outputs.
How to cite: Ntoumos, A., Sideris, I., Gabella, M., Boscacci, M., Clementi, L., Germann, U., and Berne, A.: Kriging-variance based multi-member ensembles of radar-raingauge precipitation estimates: application in Switzerland , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13507, https://doi.org/10.5194/egusphere-egu25-13507, 2025.
We introduce Version 3 (V3) of the gridded near real-time Multi-Source Weighted-Ensemble Precipitation (MSWEP) product — the first fully global, machine learning-based precipitation (P) product, developed to address the growing demand for accurate precipitation data amid escalating climate challenges. MSWEP V3 provides hourly 0.1° resolution data from 1979 to the present, updated continuously with a latency of less than two hours. The development involves a two-stage process: first, baseline P fields are generated using machine learning model stacks that integrate satellite and (re)analysis P and air temperature products alongside static P-related variables, trained with hourly and daily observations from nearly 18,000 global gauges. Second, these fields are corrected using available daily gauge observations, accounting for gauge reporting times. To assess MSWEP V3's performance, we conducted an extensive evaluation of 19 gridded P products, using independent observations from almost 18,000 gauges excluded from training. MSWEP V3 (prior to gauge corrections) achieved a mean daily Kling-Gupta Efficiency (KGE) value of 0.69, outperforming all 18 other products evaluated. For comparison, other non-gauge-corrected products such as CHIRP, ERA5, GSMaP V8, and IMERG-L V7 achieved mean KGE values of 0.31, 0.61, 0.38, and 0.46, respectively. MSWEP V3 consistently ranked first or second across multiple metrics, including correlation, overall bias, peak bias, wet days bias, and the critical success index. Notably, MSWEP V3 (without gauge corrections) also outperformed several products that directly incorporate gauge observations, such as CHIRPS, CPC Unified, and IMERG-F V7, which achieved mean KGE values of 0.36, 0.54, and 0.62, respectively. Set for release in early 2025, we anticipate that MSWEP V3 will support climate research, water resource assessments, flood management, and numerous other applications.
How to cite: Beck, H., Wang, X., Fowler, H., Alharbi, R., and Miralles, D.: MSWEP V3: Enhancing Global Precipitation Estimates with Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20148, https://doi.org/10.5194/egusphere-egu25-20148, 2025.
Multi-source merging is essential for creating high-quality gridded precipitation datasets. Machine learning (ML) and/or deep learning (DL) algorithms have achieved inspiring success in this area. This study aims to explore two critical yet underexplored issues in ML-based precipitation merging, i.e., the selection of input features and evaluation benchmarks.
The first issue is about input features of ML models, which often include precipitation products such as satellite and reanalysis datasets, along with auxiliary features like topographical and meteorological variables. One major concern is data independence. Many precipitation products, particularly satellite datasets, are calibrated using similar gauge data, yet the impact of this interdependence on ML-based merging performance is largely unknown. Another challenge is the interaction between input features and regional characteristics, such as climatic regimes, topographical features and gauge density, which affects model generalization across regions or scales but receives little attention in current research.
The second issue relates to benchmark selection. Processes like merging, bias correction, downscaling, and interpolation often employ similar supervised learning frameworks: utilizing high-accuracy reference data (e.g., gauge observations) as training labels with various static or dynamic variables (e.g., latitude and longitude, low-accuracy precipitation products) as features. The ambiguous boundaries between these techniques leads to diverse benchmark choices, ranging from original datasets to sophisticated methods such as geographically weighted regression (GWR). This inconsistency fosters subjective and potentially misleading evaluations, impeding progress in merging precipitation datasets with ML methods.
We investigate these issues through a series of experiments merging five precipitation datasets and high-density gauge data in mainland China, using multiple ML methods including random forest, convolutional neural network and artificial neural network with self-attention modules. The experiments involve varying degrees of data dependence, across eight sub-regions with diverse geographical conditions and gauge densities, and are compared against several benchmark datasets and methods.
By controlling the data dependence, our findings highlight its impact on spatial estimation. Additionally, we identify optimal feature selections across different regions and gauge densities. Interestingly, in areas with low gauge density, simple feature sets without auxiliary environmental variables often outperform those with complex predictors. Moreover, our results show that the ML models function more as interpolation rather than merging, suggesting that complex interpolation algorithms such as GWR might serve as more fitting benchmarks. Our work offers critical insights not only for precipitation datasets but also applicable to a wide range of geoscience data, emphasizing the importance of comprehensive evaluations beyond simplistic comparisons and hasty conclusions.
How to cite: Xu, Y., Tang, G., Li, L., Xiong, W., and Wan, W.: Machine Learning-based Precipitation Merging: Selection of Input Features and Evaluation Benchmarks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3492, https://doi.org/10.5194/egusphere-egu25-3492, 2025.
We extract statistical precipitation features from precipitation events tracked in both space (two-dimensional, 2D) and time (one-dimensional, 1D) using NASA Global Precipitation Mission’s IMERG data product. These features can be used to ensure IMERG product consistencies (since the combination of instruments and algorithms used to derive this product evolves with time) and study decadal precipitation variations. They may even reveal climate change signals.
We use connected component labeling (CCL) for tracking. Two-dimensional (2D) connected precipitating components (with precipitation rate ≥ 0.1 mm/hr and an area coverage ≥ 25 grid cells, i.e., ~2500 km2) are identified first in each half-hour (spatial) slice of IMERG data. We consider the components touching the space or time boundary incomplete and discard them from temporal tracking. Spatially overlapping 2D connected components in adjacent half-hour time slices are considered to be of the same precipitating events and are given unique labels. A precipitation event thus may start as disjoint 2D components, experience merging and splitting, and eventually disappear.
We extract event-based precipitation features based on tracked events instead of spatially connected 2D components. This is a significant departure from previous precipitation feature studies ( e.g., Liu et al., 2008; Hayden et al., 2021), in which precipitation features are based on 2D connected components in a half-hour IMERG slice, i.e., in space only. Hayden et al. (2021) perform tracking based on the overlap in adjacent IMERG time slices of equivalent-area circular discs derived from these 2D connected components, which may or may not have overlapping precipitating cells.
We report in this presentation statistical precipitation features extracted from 10 years of Northern Hemisphere IMERG data (2014-2023). Such features include distributions of event duration, maximum area coverage, maximum precipitation rate, event-integrated precipitation volume, etc. We also report these features filtered by season and geographical region for more detailed analysis.
References
Hayden, L., Liu, C., and Liu, N.: Properties of Mesoscale Convective Systems Throughout Their Lifetimes Using IMERG, GPM, WWLLN, and a Simplified Tracking Algorithm, Journal of Geophysical Research: Atmospheres, 126, e2021JD035264, https://doi.org/10.1029/2021JD035264, 2021.
Liu, C., Zipser, E. J., Cecil, D. J., Nesbitt, S. W., and Sherwood, S.: A Cloud and Precipitation Feature Database from Nine Years of TRMM Observations, Journal of Applied Meteorology and Climatology, 47, 2712–2728, https://doi.org/10.1175/2008JAMC1890.1, 2008.
How to cite: Bauer, M., Kuo, K.-S., and Ton-That, D.-H.: Event-based Precipitation Features from GPM IMERG Data Product, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20488, https://doi.org/10.5194/egusphere-egu25-20488, 2025.
A paper examines the climatology of freezing rain events in Belgrade (Serbia) in the period from 1949 to 2022. This phenomenon occurs from October to March, most frequently in January and December, mostly at night (00–07 local time), and lasts up to 2 hours in 62% of cases. The onset of freezing rain events occurs most frequently between 00 and 01 local time (~ 16 %). The second maximum of these events is between 06 and 07 local time (~ 11 %). The vertical temperature profiles for days with freezing rain show that 60.42 % of all events have a characteristic “warm nose” at altitude (near the 850 hPa level), below which there is usually a temperature inversion and a supercooled layer of air near the ground. This result is consistent with the study [1], which found that in 30–40% of all vertical soundings there was no “warm nose” above the supercooled air layer on the ground. This study shows that the number of freezing rain events has decreased over time, which can be attributed to climate change. The analysis of the surface maps shows that freezing rain occurs under the same conditions as the local Košava wind: with almost meridional isobars and a typical southeasterly flow with strong pressure gradients between the low pressure area in the western Mediterranean and the anticyclone in the east. The analysis of the upper-level maps shows a wind shear with an almost westerly flow, which also indicates warm air advection in the analyzed layer.
[1] Carrière, J.-M., Lainard, C., Le Bot, C., Robart, F. 2000. A climatological study of surface freezing precipitation in Europe, Meteorol. Appl., 7, 229–238.
Keywords: freezing rain, climatology, Košava
Acknowledgement: This research was supported by the Science Fund of the Republic of Serbia, No. 7389, Project: "Extreme weather events in Serbia - analysis, modelling and impacts” - EXTREMES
How to cite: Kovačević, N., Veljović Koračin, K., and Putniković, S.: Study of freezing rain in Belgrade from 1949 to 2022, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-356, https://doi.org/10.5194/egusphere-egu25-356, 2025.
Serbia is located in the central part of the Balkan Peninsula, and is characterized by a continental climate in the north, a temperate continental climate in the central part and a modified Mediterranean climate in the south. The average annual precipitation is between 500 and 700 mm in the lowlands and over 1000 mm in the mountains. Novi Sad (45°20’ north latitude, 19°51’ east longitude, 84 m altitude) is located in the south of the Pannonian Plain and is the capital of the autonomous province of Vojvodina in northern Serbia. The average annual precipitation in the period 1961-2020 amounted to 655.5 mm.
The following extreme precipitation events were analysed based on daily precipitation data from 1961 to 2020 in Novi Sad: very heavy precipitation days (RR20), highest 1-day precipitation amount (Rx1d) and highest 3-day precipitation amount (Rx3d). The modified Mann-Kendall test (MMK) and the Sen’s slope method are used to examine the possible trends and their magnitudes. The generalised extreme value distribution (GEV) and the generalised Pareto distribution (GPD) were used for the analysis of extreme precipitation.
A small number of RR20 was observed in all seasons except summer. The lowest number of heavy precipitation days was recorded in winter. The mean value of RR20 was 2.4 from 1961 to 1990 and 3.2 from 1991 to 2020 in summer, with a maximum value of 9 and 8, respectively. The highest 1-day and 3-day precipitation values were measured in summer. The highest values of Rx1d (121.9 mm) and Rx3d (149.4 mm) were observed in spring 2015. A significant positive trend was observed for Rx3d in all seasons, for Rx1d in spring, summer and fall and for RR20 in spring and fall. A positive but non-significant trend was observed for Rx1d in winter and for RR20 in summer. A significant positive trend was observed for all indices on an annual basis.
The GPD distribution was fitted to the daily precipitation series with a threshold of 20 mm in Novi Sad. The maximum likelihood estimates of the location and scale parameters were 30.36 and 197.7, respectively. Maximising the GEV log-likelihood for Rx1d and Rx3d leads to the estimation of the shape parameter 0.31 for Rx1d and 0.17 for Rx3d, respectively. The positive values of the shape parameter indicate that the Fréchet distribution was fitted to the highest 1-day and 3-day precipitation amounts in Novi Sad.
How to cite: Tosic, I., Alves da Silva, A. S., Putniković, S., Filipović, L., Djurdjević, V., Stosic, B., and Stosic, T.: Annual and seasonal extreme precipitation events in Novi Sad, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1549, https://doi.org/10.5194/egusphere-egu25-1549, 2025.
Snowfall significantly affects regional climate and water resources across mountainous and high-elevation regions, where it determines seasonal water availability and influences local hydrological processes. The high spatial and temporal heterogeneity of snowfall in complex terrain regions poses considerable challenges for conventional observation networks Satellite-based precipitation products provide an effective approach to monitor snowfall from regional to global scales. However, validating these products against ground observations remains essential for quantifying their uncertainties.
The Global Precipitation Measurement (GPM) mission's latest IMERG algorithm has been updated to improve its snowfall retrieval capabilities, incorporating enhanced detection methods and refined quantification procedures. However, comprehensive evaluation across different monitoring networks remains crucial for understanding its performance in various environmental conditions. This study examines IMERG V07B's snowfall estimation accuracy through systematic comparison with diverse ground-based monitoring networks over mainland China, including standard meteorological stations, automatic weather stations, and specialized snowfall observation sites.
By leveraging these multi-source observations, we investigate IMERG's performance not only in terms of snowfall amount but also in capturing the temporal characteristics of snowfall events, including intensity distribution and duration patterns. The evaluation is stratified by elevation zones and network density to assess the impact of topographic complexity and observation capability on validation results. Initial findings reveal varying degrees of estimation accuracy across different network types, with notable challenges in regions with complex terrain and sparse monitoring coverage. We also compare the differences in snowfall estimation between IMERG V06B and IMERG V07B.
Our comprehensive assessment provides valuable insights into the strengths and limitations of IMERG V07B's snowfall estimates across different monitoring environments, offering essential guidance for both algorithm refinement and product applications in various research and operational contexts.
How to cite: Li, X. and wang, N.: Evaluation of satellite-based snowfall estimates: A comprehensive assessment of IMERG V07B across diverse monitoring networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2362, https://doi.org/10.5194/egusphere-egu25-2362, 2025.
One of the primary challenges in satellite infrared (IR) quantitative precipitation estimates (QPEs) is accurately characterizing the nonlinear relationship between cloud properties and rainfall rates. This research proposes a deep neural network (DNN) method to classify clouds as rainy or non-rainy using brightness temperatures (BTs), reflectances (Refs), and cloud microphysical properties derived from the Advanced Himawari Imager (AHI) aboard the Himawari-8/9 satellite. The study incorporates cloud microphysical properties with BTs and Refs in the DNN model training process and conducts a comprehensive assessment of these features to elucidate their physical properties. The DNN-trained QPE models are validated by ground-based radar observation and compared to operational satellite-derived precipitation products like GSMaP and IMERG. The results indicate that including cloud microphysical properties enhances QPE model performance, with promising implications for real-time precipitation monitoring in East Asia.
How to cite: Liu, C.-Y. and Lin, M.-Y.: Application of Machine Learning Techniques in Satellite Precipitation Detection Using Himawari Spectral and Cloud Data , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2485, https://doi.org/10.5194/egusphere-egu25-2485, 2025.
Abstract: Accurate precipitation estimation is essential for various applications, including hydrological modelling, climate studies, and disaster management. Satellite-derived precipitation estimates are particularly valuable in regions with limited ground-based measurements, such as oceans and remote areas. However, challenges persist in improving the accuracy of these estimates, especially when relying solely on infrared (IR) satellite data. While microwave (MW) data has traditionally been favoured for precipitation estimation due to its strong correlation with precipitation[1], infrared (IR) data has become increasingly important, offering superior spatio-temporal coverage and resolution, essential for global observations.
This study explores the application of machine learning techniques to enhance IR-based precipitation estimates. Specifically, we employ U-Net, a convolutional neural network, known for its ability to capture spatial dependencies and local patterns in data, making it ideal for improving the spatial resolution and accuracy of precipitation estimates using only IR channels[2]. We leverage IR data from the MSG satellite to develop a model that enhances precipitation extraction from IR imagery alone
To achieve this, we utilise a database of IR brightness temperatures from three distinct IR channels (87, 108, and 120 μm). These channels capture a broad spectrum of thermal emissions, from cloud tops to deeper atmospheric layers, enabling the model to estimate precipitation rates more effectively[3]. These data are co-located with a radar mosaic from Météo-France, gauge-corrected for improved accuracy, which serves as a reference to evaluate the performance of the U-Net model and ensure alignment with actual measurements.
Our dataset spans 13 years, providing a diverse range of scenarios, including varying weather patterns and seasonal fluctuations. Initial results indicate that the U-Net model enhances precipitation estimation by accurately capturing spatial patterns, even with the inherent limitations of IR channels. In evaluating this approach, we consider a range of metrics specifically designed to address the unique characteristics of precipitation, such as intensity and spatial distribution. This targeted evaluation ensures a comprehensive assessment of the model's ability to account for the variability and intensity of precipitation, key challenges in accurate satellite-based Precipitation estimation.
These promising results highlight the potential of deep learning techniques to improve satellite-derived precipitation estimates from IR data. Looking ahead, we will explore the integration of microwave and IR satellite data to further refine the consistency and accuracy of these estimates. Additionally, we plan to investigate cutting-edge deep learning architectures tailored to this specific use case, aiming to optimise model performance and address the complexities of satellite-based precipitation retrieval.
References:
[1] Viltard, N., Sambath, V., Lepetit, P., Martini, A., Barthes, L., & Mallet, C. (2023). Evaluation of DRAIN, a deep-learning approach to rain retrieval from GPM passive microwave radiometer. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2023.3293932
[2] Wang, C., Tang, G., Xiong, W., Ma, Z., & Zhu, S. (2021). Infrared precipitation estimation using convolutional neural network for FengYun satellites. Journal of Hydrology, 603(C), 127113. https://doi.org/10.1016/j.jhydrol.2021.127113
[3]Sadeghi, Mojtaba, Nguyen, Phu, Hsu, Kuolin, & Sorooshian, Soroosh (2020). Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information. Environmental Modelling and Software, 134(C). https://doi.org/10.1016/j.envsoft.2020.104856.
How to cite: Meignin, M., Viltard, N., Barthès, L., and Mallet, C.: Enhancing IR-Based Satellite Precipitation Estimates Using Machine Learning., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6682, https://doi.org/10.5194/egusphere-egu25-6682, 2025.
Ongoing climate change continues to accelerate, significantly altering climatic conditions across the globe. These changes are evident not only in the rising mean global temperature but also in the increasing frequency and intensity of extreme weather events (IPCC, 2021). Extreme precipitation events, characterized by high multi-day precipitation totals, pose a severe risk to infrastructure, public safety, and property, even in the absence of climate change. This study presents a comprehensive seasonal regional frequency analysis (RFA) of maximum 2-day precipitation totals (Rx2D) in Slovakia using the L-moment approach (Hosking and Wallis, 1997). We analyzed 70 years (1951–2020) of precipitation data from 419 stations. The stations were grouped into homogeneous regions using a multi-regression approach and distance matrices, enabling the development of regional frequency curves. The L-moments ratio diagrams, 𝑍𝐷𝑖𝑠𝑡 measure, and Anderson-Darling goodness-of-fit tests were applied to identify the most suitable theoretical extreme-value distributions. The analysis identified nine distinct regions, with the generalized extreme value (GEV) distribution providing the best fit for Rx2D in eight of the nine clusters. The resulting regional frequency curves offer reliable estimates of extreme 2-day precipitation return values at any location within the study area. These findings are crucial for improving flood risk management, guiding infrastructure design, and supporting climate adaptation planning.
How to cite: Fasko, P., Markovič, L., and Onderka, M.: Regional frequency analysis of maximum 2-day precipitation in Slovakia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8738, https://doi.org/10.5194/egusphere-egu25-8738, 2025.
In the summer of 2023, North China was hit by an exceptionally intense precipitation storm caused by Typhoons Doksuri and Khanun, resulting in significant secondary disasters and underscoring the critical need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products, such as Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission, show great merits for enhancing forecasts. This study uses a dense rain gauge network as a benchmark to evaluate the performance of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products during the 2023 summer extreme precipitation event in North China. The satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER), IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), as well as two gauge-corrected products IMERG final run (IMERG_FR) and GSMaP gauge-adjusted (GSMaP_Gauge). The results show that the satellite-based QPE products, particularly IMERG_LR and GSMaP_MVK, show good performance in capturing the spatial distribution and overall rainfall amounts during the extreme precipitation event. However, they have significant under-detect high-intensity precipitation events in this region. The IMERG products generally outperform the GSMaP products, especially in terms of temporal rainfall measurement, but all products tend to underestimate rainfall. At high rainfall rates, while the detection ability is high, the false alarm ratios are also significantly elevated for all satellite-based QPE products. These findings highlight the need for further improvement of satellite-based QPE products for more accurate and reliable rainfall estimation.
How to cite: Liang, H., Li, Z., Chen, S., Li, X., Li, Y., and Wei, C.: Evaluation of Satellite-Based Precipitation Products in the 2023 Summer Extreme Precipitation Events Over North China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9513, https://doi.org/10.5194/egusphere-egu25-9513, 2025.
Radar-based measurements are crucial for accurately estimating precipitation and capturing the spatial variability of rainfall, which enhances both precipitation forecasting and hydrological modeling. This study focuses on quantitative precipitation estimation (QPE) using radar data in the Lombardy Region of northern Italy, examining the limitations of radar measurements and identifying optimal configurations. Specifically, data from two newly installed X-band radars with solid-state transmitters, operated by the Regional Environmental Protection Agency (ARPA Lombardia), were analyzed.
The goal of this research is to determine radar settings that maximize QPE performance at an operational level and explore post-processing methods to address radar limitations, particularly during extreme precipitation events that could lead to flooding. The methodology is twofold: first, to identify radar configurations that accurately correlate rainfall intensity with radar data, and second, to address radar challenges during severe events, focusing on attenuation correction, signal extinction, and the integration of third-party data sources.
Extreme and non-extreme precipitation events affecting the Milan hydraulic node were analyzed, highlighting opportunities to enhance the radar network through post-processing techniques that could aid future hydrological modeling. The study compares different QPE methods, including basic Z-R relationships and Z-R matching techniques based on previous research.
This work provides a foundation for optimizing operational QPE and proposes strategies for overcoming radar limitations during extreme weather events. Additionally, it supports future improvements, such as integrating real-time rain gauge data to enhance flood forecasting accuracy.
How to cite: Chaves González, N. A., Ceppi, A., De Michele, C., Ravazzani, G., and Cazzuli, O.: Improving Quantitative Precipitation Estimation with Solid-State X-Band Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9781, https://doi.org/10.5194/egusphere-egu25-9781, 2025.
The Tibetan Plateau and the adjacent mountain ranges are known as the "Asian Water Tower" (AWT) because they hold the third largest frozen water reserve in the world after the polar regions. This region plays a vital role in supplying water to nearly 2 billion people through rivers like the Indus, Ganges, Brahmaputra, Yangtze, and Yellow River.
Accurate precipitation data are essential for understanding hydrological processes in high mountain basins. However, in many mountainous areas, precipitation gauges are either sparse or absent due to the challenging environmental conditions. Moreover, the available precipitation gauges are often located in valleys and they are not adequate to represent the diverse topography of the territory. This underlines a significant gap in the existing precipitation datasets, since precipitation at high elevations is likely considerably underestimated.
In this study, we aim to address these challenges by analyzing an extensive area of High Mountain Asia (70°-100°E for longitude and 25°-40° N for latitude). Specifically, we examined two reanalysis datasets (ERA5 and HAR), two gauge-based datasets (GPCC and Aphrodite), and one satellite-derived dataset (PERSIANN) to evaluate their performance in capturing precipitation patterns.
At first, we compared the different datasets over the common period (1983-2007) evaluating their ability to reproduce the precipitation spatial distribution both at annual and seasonal level.
Then, due to the discrepancies in precipitation values over the area, particularly influenced by the complex orography, we decided to compare the datasets with the observational data available from the Copernicus Data Store (Global Land Surface Atmospheric Variables dataset, 1755–2020) and the runoff data provided by the GRDC (Global Runoff Data Centre) dataset as a reference.
When comparing gauge-based datasets with the observational data, there is consistency, whereas the other datasets tend to exhibit higher precipitation especially in areas with greater topographic complexity.
To compare precipitation values with the measured river flow, the total evaporation from the ERA5-Land dataset was taken into account to improve the estimates. The results indicate that reanalysis datasets are the most effective in simulating the hydrological balance while the gauge-based and the satellite datasets significantly underestimate precipitation.
How to cite: Spezza, A., Diolaiuti, G. A., Fugazza, D., Manara, V., and Maugeri, M.: Intercomparison of gauge based, reanalysis and satellite gridded precipitation datasets in High Mountain Asia: insights from observations and runoff data., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10031, https://doi.org/10.5194/egusphere-egu25-10031, 2025.
Precipitation is a critical variable for hydrological studies and water resource management. However, while rain gauges generally produce the most reliable observational results, their often-sparse distribution causes them not to be fully representative of some regions, especially large ones. In fact, in regions with a complex orography and scarce human settlements, rain gauges are usually not sufficient to provide data to resolve precipitation processes in simulation studies. Satellite retrievals have thus been used to create regular data grids, in order to fill in on lacking observations and to address the scarcity of stations in ungauged regions.
This study aimed to evaluate the skills of five satellite precipitation products in reproducing precipitation across three temporal scales (daily, seasonal, and annual) over Italy. These are the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run, the PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now), the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (HSAF H05), and the Soil Moisture to Rain (SM2Rain). To this purpose, precipitation data for the period 2000-2021 have been extracted from the National System for collection and processing of climate data (SCIA) gridded observational rainfall dataset provided by the Italian Environmental Protection Agency (ISPRA). After resampling all the different datasets to a common grid with spatial resolutions of 0.1°, the performance of the satellite products was then assessed using two distinct sets of statistical metrics. In particular, the accuracy of satellite products at a daily temporal resolution has been evaluated using performance metrics such as the Probability of Detection (POD), the False Alarm Ratio (FAR), the Success Ratio (1-FAR), the BIAS, and the Critical Success Index (CSI). Conversely, at annual and seasonal scales, the Root Mean Square Error (RMSE), the coefficient of determination (R²), and the standard deviation (SD), have been applied.
Results showed that GPM-IMERG Final Run satellite data performed better at a daily resolution both in capability (POD) and reliability (SR), except during the summer season, when the HSAF H05 demonstrates a better overall performance. Conversely, the PDIR-Now tends to overestimate rainfall events. As regards the annual and seasonal time scales, HSAF H05, GPM-IMERG, and SM2Rain demonstrate strong correlations with observed data at annual scale, with high R2 values (≥0.88) and generally low errors (SD and RMSE).
The procedure applied in this work is general and easily applicable where gridded data are available and might help scientists and policy makers to select among available datasets those best suited for further applications, even in areas with a complex orography and an inadequate amount of representative stations.
This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You – Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.
How to cite: Caloiero, T., Chiaravalloti, F., Coscarelli, R., and Pellicone, G.: Validation of satellite estimates of precipitation over Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10789, https://doi.org/10.5194/egusphere-egu25-10789, 2025.
The Kingdom of Saudi Arabia (KSA) initiated a Regional Cloud Seeding Program (RCSP) in 2022 to enhance precipitation over the southwestern and central parts of the country. This study presents an overview of RCSP cloud seeding operations conducted from 2022 to 2024, along with preliminary results on precipitation enhancement during the autumn season of 2022.
Precipitation data from the Global Interpolated Rainfall Estimation (GIRAFE) dataset, provided by EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF), were used to investigate the relationship between precipitation anomalies and cloud seeding operations. The analysis focused on identifying spatial patterns where high precipitation anomalies coincided with regions of elevated cloud seeding flare density. These regions were categorized to evaluate the potential influence of cloud seeding activities.
This exploratory study highlights spatial correlations between cloud seeding operations and rainfall patterns, without accounting for additional meteorological or synoptic variables. The findings contribute to understanding the spatial dynamics of cloud seeding operations and their potential role in enhancing precipitation in arid regions, offering valuable insights for the optimization of weather modification strategies.
How to cite: Matsangouras, I., Logothetis, S. A., and Albar, A. M.: Regional Cloud Seeding and Rainfall Enhancement in Saudi Arabia: Preliminary Observations and Insights from 2022 Using EUMETSAT CMSAF GIRAFEv1 CDR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11337, https://doi.org/10.5194/egusphere-egu25-11337, 2025.
In this study, future projections of changes in precipitation extremes over the CORDEX-Central Asia domain were analyzed using high-resolution (0.25° x 0.25°) outputs from NEX-GDDP-CMIP6 models under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. Initially, 17 models were compared against ERA5 reanalysis data, and the five models with the best statistical performance (i.e., IPSL-CM6A-LR, GFDL-ESM4, MRI-ESM2-0, ACCESS-CM2, and BCC-CSM2-MR) were selected. These models were employed to analyze various precipitation indices, including consecutive dry and wet days, heavy and very heavy precipitation days, maximum of annual maximum precipitation, annual 5-day maximum precipitation, wet and very wet days, and simple daily intensity for the periods of 2026–2050, 2051–2075, and 2076–2100 with respect to the reference period of 1981-2010. The findings indicate an overall decrease in the average number of consecutive dry days across the region. Similarly, a slight average increase in the number of consecutive wet days is expected, which could have significant implications for regional water resource management and agricultural activities. Furthermore, the number of heavy precipitation days is projected to increase on average, highlighting the risk of flooding. These analyses underscore significant changes in precipitation extremes due to future climate change in the CORDEX-Central Asia domain. These findings are critical for shaping regional climate adaptation strategies, offering valuable insights for policymakers in water resource management, agricultural planning, and disaster mitigation. By understanding these projected changes, regional resilience to climate impacts can be enhanced, reducing future risks and fostering sustainable development.
Acknowledgement: This research has been supported by Boğaziçi University Research Fund Grant Number 19367.
How to cite: Turp, M. T., An, N., Demiralay, Z., Avci, B. C., and Kurnaz, M. L.: Projected Changes in the CORDEX-Central Asia’s Precipitation Extremes Using the NEX-GDDP-CMIP6 Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12347, https://doi.org/10.5194/egusphere-egu25-12347, 2025.
Accurate estimation of rainfall quantiles at ungauged locations is critical for designing hydraulic infrastructure that can withstand extreme rainfall events over a broad range of timescales. However, short rainfall series often fail to capture the full variability and distribution of rainfall, leading to sampling bias. Geographical and climatological factors further complicate the estimation of rainfall frequencies in ungauged locations. To address these challenges, the concept of ergodicity in spatio-temporal patterns of rainfall extremes has been revisited. Ergodicity, in the context of stochastic processes, ensures that long-term time averages converge to the ensemble mean. This principle enables the pooling of rainfall data from multiple rain gauges within homogeneous regions to construct "regional" intensity-duration-frequency (IDF) curves. This mathematical framework has been investigated using normalized data from 100 rain gauges in Slovakia, with rainfall aggregated over time intervals ranging from 5 to 240 minutes. The analysis focused on low-probability rainfall events (p = 10-2 – 10-3) corresponding to recurrence intervals far exceeding the length of available records (approx. 15 years). Homogeneous regions were identified using fuzzy C-means clustering, revealing two homogenous clusters of rain gauges. Each cluster was assessed for ergodic behavior. To estimate the rainfall quantile for each cluster, the GEV distribution was applied to annual maximum series with parameters inferred using a Bayesian approach. Unique IDF curves were generated for each cluster, satisfying the criteria of ergodicity. These findings demonstrate the potential of the ergodicity-based approach to improve regional rainfall frequency estimates.
Acknowledgment: This work was supported by the Slovak Research and Development Agency under Contract No. APVV-23-0332.
How to cite: Onderka, M.: Application of ergodicity in regional rainfall frequency analysis , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12874, https://doi.org/10.5194/egusphere-egu25-12874, 2025.
Rain gauge measurement network of the Austrian national weather service operated by GeoSphere Austria comprises about 270 weather stations equipped with weighing rain gauges and, at a smaller part, with tipping bucket rain gauges. Each gauge is additionally equipped with a precipitation monitor that detects the beginning and the end of precipitation events. Precipitation data are checked for plausibility and completeness in several steps within a framework of an automated quality control tool called AQUAS (short for Austria Quality Service). The software was developed in 2016 at ZAMG (now GeoSphere Austria) in Vienna as part of the quality management in the area of real-time processing of near-surface observation data.
The basis for quality control procedure is formed by standard methods for checking meteorological and climatological data in accordance with the WMO recommendation (e.g. plausibility check, temporal, spatial and internal consistency check, etc.); in addition, test procedures are developed that take into account the specific errors in the measuring devices. The test methods are continuously improved and further developed within the framework of AQUAS. Individual system components are designed to test the incoming observation parameters in real time - at the time resolution of 10 minutes, for example, for wind or temperature data and down to 1 minute time resolution for precipitation data. In AQUAS, each parameter can be processed independently of the other measured variables of a weather station. In addition, data from other sources are implemented in AQUAS, such as radar and satellite data. Data from numerical weather prediction models and data from other measurement networks, such as hydrological network or another third-party network, can also be integrated.
Some examples for the operational use of AQUAS and the current state of research on quality control of precipitation data will be presented. As an example, a novel method for real-time quality control of 1-minute weighing gauge precipitation data is demonstrated, which detects missing gauge precipitation based on the observation of the precipitation monitor and the total weight changes of the rain gauge.
How to cite: Filipovic, N.: AQUAS - A quality control tool at GeoSphere Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17837, https://doi.org/10.5194/egusphere-egu25-17837, 2025.
Precipitation monitoring plays a key role in understanding Earth's climate system and its effects on sectors such as hydrology, water resource management, and agriculture. Satellite-based measurements, particularly through missions like the Global Precipitation Measurement (GPM), have significantly enhanced our ability to observe precipitation patterns globally. Onboard the GPM Core Observatory, the Dual-frequency Precipitation Radar (DPR), consisting of the Ku-band Precipitation Radar (KuPR), which operates at 13.6 GHz, and the Ka-band precipitation radar (KaPR) at 35.5 GHz. The DPR has proven to be an indispensable instrument for characterizing water cycle study applications. To extend the life of a satellite, in order to guarantee the continuity of observations, a common strategy is to increase the orbit altitude. For this reason, on November 7 and 8, 2023, the GPM Core Observatory performed two orbit boost maneuvers that raised its altitude from 407 km to 442 km. As a result of this orbital elevation, the observing parameters of the GPM DPR instruments underwent some changes, such as the increase of spatial resolution and of the minimum detectable rain rate, which has had an impact on some geophysical products. To ensure the accuracy and reliability of satellite data over time, the GPM mission supported a Ground Validation program, which aims to verify and improve precipitation retrieval algorithms over time using multiple ground based instruments. This study focuses on GPM DPR Level 2 Version 7, which is the first to incorporate a modified scan pattern for the KaPR, introduced on May 21, 2018. This adjustment enables the dual-frequency radar to operate across the full observation swath. This study compares the GPM DPR Version 7 products, specifically the earlier Version 7A (before the orbit boost) with Version 7C (after the orbit boost), over Italy, using data from a network of ground-based laser disdrometers networked by the GID (Gruppo Italiano Disdrometria, in Italian). The dual-frequency-based 2ADPR-FS, as well as the single-frequency-based 2AKa-FS and 2AKu-FS Version 7 Level 2 DPR products are used. GPM data from May 22, 2018, to November 30, 2024, were analyzed. The following variables have been investigated: reflectivity factors at the Ku and Ka bands corrected for attenuation, rainfall rate, and DSD parameters Dm and Nw. Statistical indices are used to assess the agreement between satellite observations and disdrometer data. After the orbit boost, dual frequency still presents a slightly better agreement with disdrometers with respect to single frequency products. Discrepancies, however, were noted in the performance of rainfall and microphysical parameters, especially in areas with complex terrain and disdrometers located at high altitudes. In general, the comparison of Version 7A and Version 7C products with disdrometers helped reveal the limited influence of the orbit boost on the quality of DPR products. The results suggest that an orbital adjustment, similar to those implemented for the GPM mission, can be effectively adopted by other missions aimed at reconstructing the 3D structure of clouds and precipitation, since extending the satellite's operational life results in only a negligible impact on the quality of the data products.
How to cite: Angeloni, S., Adirosi, E., Porcù, F., Montopoli, M., Baldini, L., Bracci, A., Capozzi, V., Annella, C., Budillon, G., Cazzuli, O., Minardi, G. P., Bechini, R., Campana, V., Cremonini, R., Luini, L., Nebuloni, R., Rizi, V., Valisa, P., Scapin, S., and David B., W. and the Sabina Angeloni: Evaluation of GPM DPR products after orbit boost using disdrometers over Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18722, https://doi.org/10.5194/egusphere-egu25-18722, 2025.
Satellite-based measurements are necessary to provide reliable measurements of clouds and precipitation on global scale. Starting from the NASA/JAXA TRMM and NASA CloudSat missions, and consolidated by the NASA/JAXA GPM (Global Precipitation Measurement) mission, radars on satellite are playing a growing important role allowing to cover remote and oceanic areas and to reveal the vertical structure of clouds and precipitation systems. The ESA/JAXA EarthCARE (Clouds, Aerosol and Radiation Explorer) satellite, in orbit since 28 May 2024, has on board a 94 GHz Cloud Profiling Radar (CPR) provided by JAXA and NICT, with Doppler capability that provides information on vertical cloud profiles and precipitation properties through complex algorithms that require physical assumptions. It is there important to validate both satellite quantitative products with independent measurements and the physical assumptions underlying the retrieval algorithms. Missions addressing clouds and precipitation have relied on field campaigns, using suborbital flights seeking for coincidence in locations of aircraft and satellite measurements, networks of instruments for long-term statistical validation, or supersites with multiple instruments capable of collecting high quality measurements correlative to satellite measurement.
Unlike other atmospheric parameters, clouds and even more, precipitation are significantly affected by spatial variability, even within a few kilometers and have an intrinsic intermittent nature. This fact poses specific challenges in obtaining an adequate quantity of significant correlative measurements during satellite overpasses from fixed installation. Satellite radar observations are validated with ground-based measurement devices, including raingauges and disdrometers (although the satellite measurement unaffected by ground clutter are several hundred meters above) or radars. Ground-based profiling radars operating at vertical incidence have adequate vertical resolution for matching satellite radar measurements but, depending on the wavelengths adopted on satellite and at ground, could differ in sensitivity and wavelengths. The spatial coincidence of individual satellite and ground-based profiles is unlikely. The GPM-GV program includes scanning weather radars, including operational ones, to match observations, considering the different sampling volume. They have a wider vertical resolution compared to radar profilers for most distances and lack sensitivity for clouds parameters. This study part of the project “Contribution to EarthCARE products VALidation during the commissioning phase from atmospheric observatories in Central MEDiterranean in Italy (EC-VALMED.it)“ funded by ASI consider available data collected from satellite along with datasets available in the two validation sites of Rome and Lampedusa. The evaluation of the influence of spatial variability of observed precipitation phenomena at small scale is the aim of this study, crucial to understand the representativeness of the two validation sites and to define the validation strategy to be followed to validate geophysical products of EarthCARE CPR. An experimental approach based on operational weather radar and satellite radar profiles, aims at pointing out the effect of non-coincident measurements, along effects of difference of wavelength between satellite and ground sensors, and the effect of blind zone close to the surface. To this purpose, L2 CPR data will be considered, together with measurements collected from the instruments at ground available in the validation sites (disdrometer, radar profilers) and quasi-coincident data from operational scanning radars.
How to cite: Baldini, L., Angeloni, S., Adirosi, E., Montopoli, M., Bracci, A., Pace, G., Meloni, D., Scarchilli, C., Ciardini, V., and Picchiani, M.: On validating EarthCARE CPR precipitation products with different instruments at ground, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18724, https://doi.org/10.5194/egusphere-egu25-18724, 2025.
In remote areas with high mountains or challenging weather conditions, ground-based precipitation measurements cannot be performed by instruments that require extensive maintenance. Still, in-situ measurements are essential to validate model predictions or remote sensing measurement methods. Optical sensors (disdrometers and present weather sensors) constitute a good alternative to traditional methods, requiring little maintenance and having no moving parts that could deteriorate. However, these devices are calibrated in laboratory conditions that can be very different from the ones they experience in the field. Therefore, it is essential to improve our understanding of the reliability of these instruments when they operate in a challenging environment.
Users who rely on optical instruments are primarily interested in high-frequency reports of precipitation type (e.g. public road management, aviation) and precipitation rate (e.g. hydropower systems, agriculture). These variables are derived from semi-empirical knowledge together with measurements of hydrometeors’ sizes and fall velocities, that can both be affected by the wind or other instrumental systematic biases.
In this work, we analyze data collected at the former WMO-SPICE site of Haukeliseter in Telemark, Norway. This station is operated by MET Norway. Two to three models of different popular optical instruments (OTT Parsivel2, Thies Clima LPM, Vaisala PWD12 and PWD22), unevenly exposed to the wind, have been deployed there since September 2023, providing two winters of precipitation data to analyze. Haukeliseter is located in a mountainous area commonly experiencing strong winds and is covered with snow for about 6 months a year, making it an excellent location to study solid precipitation.
We perform a systematic intercomparison of these instruments to evaluate their level of agreement and, in turn, quantify their accuracy. A reference for the precipitation rates consisting of a Geonor rain gauge placed in a standard WMO-defined DFAR (double fence automated reference) setup is available. There exists no similar standard field reference for precipitation type detection. Where possible, human observations are used as a benchmark, but they are often available at a much lower time resolution than automatic measurements. To compensate for the lack of such long-term observations at Haukeliseter, a campaign of on-site high-frequency human observations of the precipitation type performed in early 2025 is used as a comparison reference. Preliminary results of the intercomparison and analysis from this winter’s measurement campaign will be presented.
How to cite: Gaban, R., Wolff, M., and Bhattarai, B. C.: An evaluation of the uncertainty of precipitation measurements from optical sensors at a Norwegian mountainous site., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19080, https://doi.org/10.5194/egusphere-egu25-19080, 2025.
This study investigates precipitation observed by collocated ground-based instruments at the rooftop observatory in Zhuhai, a coastal city located at the southern tip of the Pearl River Delta of Guangdong Province in South China. Two-year ground-based observations collected from a tipping-bucket rain gauge (RG), two laser disdrometers (PARSIVEL and Present Weather Sensor 100 (PWS)), and a vertically pointing Doppler Micro Rain Radar-2 (MRR), are analyzed in this study. The precipitation from January 2022 to December 2023 is classified into convective, stratiform, and undetermined rainfall categories with the PARSIVEL observations. Statistics are conducted to provide an overview of the rainfall at the coastal city after quality control. An insight into raindrop size distributions indicates that under stratiform rainfall events, measurement discrepancies between the observation instruments can be alleviated, and good consistency are found for the collocated deployment which mitigates uncertainties originating from spatial/temporal variabilities. The rainfall microphysics for convective and stratiform events are further characterized with the two-year dataset.
How to cite: Xie, X. and Li, X.: Convective and stratiform rainfall characteristics from ground-based observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3399, https://doi.org/10.5194/egusphere-egu25-3399, 2025.
We investigated the dependence of several characteristics of precipitation events on ground temperature. We defined the precipitation events as a time-continuous events based on hourly precipitation data, using a threshold of 0.2 mm/h for the rain / non-rain distribution. We considered ground temperature before the start of precipitation to ensure that the temperature was not affected by precipitation. We investigated the precipitation events for the warm half of the year (from April to September) and compared their characteristics for (i) precipitation events obtained from ground measurements for the period 1998-2019 from 97 precipitation stations, (ii) precipitation events based on reanalyses for a 25-year long period (1990-2014) and (iii) precipitation events using climate projections for three future periods (2026-2050, 2051-2075 and 2076-2100). The reanalyses and climate projections were calculated using a modified ALADIN NWP model with a horizontal resolution of 2.3 km.The results show a difference in the precipitation events determined from measured data as compared to those determined by the model. This is likely related to the significantly different number of the data and the fact that the model precipitation is smoother than the measured precipitation. The characteristics of precipitation events based on reanalyses and climate projections are similar in structure, but the projections show an increase in precipitation with increasing temperature in future to a certain temperature threshold.
How to cite: Sokol, Z., Řezáčová, D., and Skripniková, K.: Change in the distribution of precipitation events due to the temperature increase, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3513, https://doi.org/10.5194/egusphere-egu25-3513, 2025.
Land use and land cover (LULC) changes are known to significantly affect hydrological processes, directly influencing the frequency, magnitude, and spatial distribution of flood events. This study focuses on understanding the impact of LULC changes on flood dynamics in Hafr Al-Batin, Saudi Arabia, a region highly susceptible to flooding due to a combination of natural and anthropogenic factors. Remote sensing data, acquired from satellite imagery for multiple time periods, will be utilized to map and analyze changes in LULC using supervised classification techniques. These analyses will focus on key drivers of land use change, such as urban expansion, deforestation, agricultural intensification, and their role in altering watershed characteristics. To assess the hydrological implications of these changes, the Hydrologic Modeling System (HEC-HMS) will be used to simulate critical processes such as surface runoff, infiltration, and stream discharge to evaluate how LULC transitions influence flood patterns over time. Integrating historical flood data with HEC-HMS outputs will provide a comprehensive understanding of how LULC changes exacerbate or mitigate flood risks. The study aims to bridge gaps in knowledge regarding the interplay between land use dynamics and flood risks in arid and semi-arid regions like Hafr Al-Batin. The findings are expected to support the development of evidence-based land management strategies, sustainable watershed planning, and flood risk mitigation measures tailored to the region’s unique environmental and socio-economic context. Ultimately, this research seeks to contribute to disaster risk reduction efforts, helping to safeguard communities and infrastructure from the increasing threats posed by floods in the face of changing land use patterns.
How to cite: Al-Areeq, A. and Dirie, A.: Modeling the Impact of Land Use Dynamics on Flooding: A Case Study of Hafr Al-Batin, Saudi Arabia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4014, https://doi.org/10.5194/egusphere-egu25-4014, 2025.
Accurate precipitation estimation is vital for understanding hydrological processes, managing water resources, and mitigating climate-related risks. In arid regions such as the Kingdom of Saudi Arabia (KSA), where ground-based rainfall measurement networks are sparse, satellite-based precipitation products provide a valuable alternative. Since 2022, the National Center for Meteorology (NCM) has launched a cloud seeding program to increase rainfall across the KSA. Therefore, it is crucial to have a quality-assured precipitation dataset that covers the KSA both spatially and regionally, in order to monitor the precipitation patterns across the cloud seeding area of interest.
This study evaluates the performance of the latest Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) Version 07 (V07) precipitation products over KSA. The accuracy of IMERG V07 precipitation products (Early, Late, and Final) was evaluated on multiple temporal scales (daily and monthly) by using reference precipitation measurements from NCM’s ground-based rainfall measurement networks during 2001−2023. The error analysis was conducted using key performance metrics, including mean bias error (MBE), root mean square error (RMSE), correlation coefficient (CC), and categorical statistics such as the probability of detection (POD), false alarm ratio (FAR), and frequency bias index (FBI).
The performance of IMERG products compared to the ground-based rain gauge measurements indicated an adequately high correlation among all three products (daily: 0.74−0.85, monthly: 0.85−0.97), with the final product presenting the best performance. The three IMERG products suffer from systematic overestimation of daily and monthly precipitation (20.4−34.1%) across KSA. The two indices of precipitation detection ability, POD and FAR, presented records around 93−95% and 45−48%, respectively. The findings highlight the strengths and limitations of IMERG V07 for capturing precipitation patterns in arid environments and provide valuable insights for improving its application in hydrological modeling, climate, and cloud seeding studies in KSA.
How to cite: Logothetis, S.-A., Matsangouras, I., Alhmoud, M. I., Badrous, A. A., Alkhalaf, M. A., Aalismail, N. A., Basiakos, I., and Albar, A. M.: Ground validation of GPM-IMERG precipitation products across the Kingdom of Saudi Arabia , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4982, https://doi.org/10.5194/egusphere-egu25-4982, 2025.
Passive microwave (PMW) observations form the backbone of global precipitation measurements due to their relative directness of precipitation retrievals compared to those in the visible/infrared. The NASA/JAXA Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) missions have been central to advancing satellite precipitation measurements since TRMM was launched in 1997. Prior to TRMM, PMW estimates were derived primarily using observations from the US Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) sensors, first launched in 1987. The PMW data records preceding the SSM/I era are extremely important for providing long term precipitation measurements. Data from earlier missions could potentially extend the satellite precipitation data record back to 1973 and is therefore highly desirable, although these precipitation-capable sensors have not been fully exploited. Furthermore, while current PMW precipitation retrievals utilize a range of observations from both cross-track ‘sounders’ and conically scanning ‘imaging’ sensors, not all the available observations from these sensors are presently exploited.
This poster outlines the fundamental requirements of improving our measurements of global precipitation through exploiting and enhancing current and past precipitation-capable missions and their data using conventional and new methodologies. In particular, the different channel availability is shown to significantly affect the ability to provide accurate precipitation retrievals which impacts the generation of a consistent climate precipitation record. Extending the precipitation data record and fully exploiting the available observations has the potential to improve our knowledge of the Earth System and its’ water cycle, provide a greater understanding of our changing global climate, and gain better insights into the naturally occurring and human-induced changes.
How to cite: Kidd, C., Kroodsma, R., Petkovic, V., and Bogerd, L.: Extending and enhancing the satellite precipitation data record from passive microwave sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7426, https://doi.org/10.5194/egusphere-egu25-7426, 2025.
Italy, because of its complex orography and geography, is prone to extreme precipitation events, which may result in enormous damages and losses. It is therefore fundamental to monitor precipitation across the Italian territory and across time. Even though Italy has been a pioneer in developing meteorological observations, its rain gauge network – as those of many other countries in the World –is characterised by an uneven density and with records not always freely available online. Satellite and reanalysis precipitation datasets have the potential to overcome some of the issues characterising ground-based monitoring networks, but their performances vary widely across climate, topography and time scale. In this work we assess the performance of six remote sensing and reanalysis datasets (ERA5-Land, CERRA-Land, CHIRPS, CMORPH, IMERG, PERSIANN-CCS-CDR) in observing precipitation across the entire Italian peninsula, using the ground-based national records of the SCIA dataset as ground-truth. For our analysis, we compute common continuous and categorical metrics across different time scales (daily, monthly, annual). We then provide the best performing dataset at different spatial scales (i.e. watershed, administrative province, administrative region, nation-wide, Köppen-Geiger climate zone), providing useful insights for hydrological studies of various purposes. Our results show that at the national level, the two reanalysis datasets (CERRA-Land and ERA5-Land) outperform the satellite-based observations, having overall higher and consistent performances across the different climatic zones. Among the satellite datasets analysed, the most-performing is IMERG, while the least-performing in all the Italian climatic regions are CMORPH and PERSIANN-CCS-CDR, with the worst performances in the alpine and cold semi-arid climates, respectively.
How to cite: Moccia, B., Buonora, L., Bertini, C., Ridolfi, E., Russo, F., and Napolitano, F.: A Critical Assessment of Precipitation Datasets Over Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11059, https://doi.org/10.5194/egusphere-egu25-11059, 2025.
A reliable Numerical Weather Prediction (NWP) is useful to guide responsive actions for mitigating the impact of severe weather. The accuracy of the forecast given by NWP models depends also on the knowledge of the initial conditions, which can be improved by data assimilation.
In the last three decades, there has been a significant advancement of GNSS technology, which has broadened its range of applications, especially within the realm of the meteorology. GNSS-ZTD has proven to be an important source of water vapor data, which can be used to improve the weather forecast, in general, including intense precipitation events (Torcasio et al., 2023).
However, a relevant part of the information related to water vapor distribution remains still unexploited. In fact, routinely zenith tropospheric delays (ZTDs) are estimated off-line generally on an hourly basis at each GNSS site only, introducing the hypothesis of azimuthal isotropy of the troposphere.
The objective of the NEW-ARGENT (Improvement of NumErical Weather prediction through data Assimilation of Real-time GNSS-Estimated Non-isotropic Troposphere) project, funded by the Ministry of University and Research, is to assimilate the GNSS delay along slant path, to recover the local directional anisotropy. A possible way to recover, at least, part of the directional information given by GNSS observations, is through the assimilation of the gradients in the East and North directions (Zus et al., 2023). While for GNSS-ZTD data assimilation the WRFDA offers a specific tool, gradients assimilation has been recently added in a version of the WRFDA distributed by the link https://doi.org/10.5281/zenodo.10276429 and presented in the paper (Thundathil et al., 2024).
In this work we show the impact of GNSS gradient data assimilation in the WRF model for the month of September 2022 when several convective and intense storms occurred over Italy. Specifically, we compared the precipitation forecast at the short-range in four different experiments set-up: CTRL (control), without GNSS data assimilation, GNSS-ZTD, with the assimilation of GNSS zenith delay, GNSS-GRA, in which the gradients are assimilated, and GNSS-ZTD-GRA, in which both the gradients and the zenith total delay are assimilated. Simulations, lasting 12 each, are performed in a Very Short-term Forecast (VSF) approach. The first six hours are for spin-up and data assimilation (one analysis per hour), while the last six hours are considered as forecast phase.
Results show that the assimilation of the gradients, both alone and with the GNSS-ZTD, is beneficial for the improvement of precipitation forecast of convective events over Italy.
References
Thundathil, R. et al., 2024, https://doi.org/10.5194/gmd-17-3599-2024
Torcasio, R. C. et al., 2023, https://doi.org/10.5194/nhess-23-3319-2023
Zus, F. et al., 2023, https://doi.org/10.3390/rs15215114
Acknowledgments
This work has been realized in the project PRIN-PNRR NEW-ARGENT (Improvement of NumErical Weather prediction through data Assimilation of Real-time GNSS-Estimated Non-isotropic Troposphere) funded by the Ministry of University and Research contract number: P20228LMA2.
How to cite: Torcasio, R. C., Transerici, C., Realini, E., Crespi, M., and Federico, S.: Preliminary results of the assimilation of GNSS delays along slant paths, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11151, https://doi.org/10.5194/egusphere-egu25-11151, 2025.
The accurate assessment of precipitation is a critical challenge in meteorology due to the non-normal distribution characteristics commonly associated with precipitation data. This distribution can lead to significant errors in forecasting models, particularly concerning extreme precipitation events, which are both infrequent and increasingly influenced by climate change. The implications of climate change on the frequency and intensity of these extreme events further complicate the task of accurate prediction, necessitating improved methodologies for rainfall estimation.
In the context of Greece, the challenge is intensified by a sparse network of precipitation observation stations. This limited data availability, coupled with the region's inherent geographical variability—characterized by diverse topographic features such as mountains and valleys—creates additional hurdles in the generation of reliable precipitation datasets. Consequently, the objective of this study is not only to address these challenges but also to create a high-resolution precipitation database specifically for Greece, employing advanced statistical interpolation techniques.
To achieve this, we systematically investigate a range of interpolation techniques aimed at generating high-resolution gridded daily precipitation datasets across the Greek territory. Our approach utilizes a comprehensive dataset of meteorological stations, which forms the backbone of our analysis. In addition, we incorporate geographical variables derived from satellite-based elevation data and integrate precipitation data sourced from the ERA5 atmospheric reanalysis, a product known for its high spatial and temporal resolution.
Three distinct modeling approaches are developed throughout this research.
- General Additive Model and Indicator Kriging: In the first approach, we employ a General Additive Model combined with an Indicator Kriging methodology, relying predominantly on the station data and a limited selection of geographical variables. This foundational model serves as the baseline for understanding the initial relationships between observed precipitation and geographical factors.
- Incorporation of ERA5 Data: The second iteration enriches the interpolation methodology by blending ERA5 reanalysis data with the observational datasets. In this stage, we expand the geographical variables used, allowing for a more nuanced understanding of precipitation patterns in relation to the diverse topography of Greece.
- Multi-Model Interpolation Framework: Lastly, we introduce a novel modeling framework that not only integrates ERA5 data and an array of geographical datasets but also employs a multi-model interpolation process. This strategic approach utilizes different models tailored to predict precipitation during distinct thresholds. By addressing various precipitation intensity levels, this model enhances the ability to accurately forecast both average and extreme precipitation events.
The results of this study demonstrate that the inclusion of ERA5 data can significantly enhance the accuracy of the interpolated precipitation, particularly in regions where the observational station dataset is sparse. Moreover, the implementation of multi-model interpolation techniques—where distinct models are utilized for different precipitation thresholds—offers substantial improvements in the accuracy of both total precipitation forecasting and the modeling of extreme precipitation events. This multi-faceted approach effectively addresses crucial limitations exhibited in previous modeling efforts, thereby contributing valuable insights and robust methodologies to the field of meteorological research in Greece.
How to cite: Nastos, P. T., Ntagkounakis, G., Kapsomenakis, J., and Chasiotis, A.: Development of a high-resolution database for daily precipitation in Greece, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13282, https://doi.org/10.5194/egusphere-egu25-13282, 2025.
Precipitation is a critical physical quantity in a variety of disciplines and is observed globally. Ground-based observations of precipitation typically use precipitation gauges; however, various losses must be considered. Of particular note is the importance of wind-induced undercatch in the context of solid precipitation. In addition, climate change may change snowfall to rainfall, increasing the importance of accurate precipitation observations.
The objective of this study is to evaluate the accuracy of precipitation observations, including snowfall, and to provide basic information for precipitation correction methods.
The present study focuses on wind-induced undercatch loss and estimates the capture rate of precipitation gauges by comparing them with observations from snow-weight gauges and lysimeters. This approach differs from the commonly used double-fence intercomparison reference.
These observations were made in a mountainous area of Japan (Hokkaido, 439 m above sea level) and included observations other than precipitation.
In addition, observations were made during the summer season to estimate the capture rate for precipitation.
Precipitation was verified by careful comparison of precipitation gauge readings with lysimeter readings and snow weight meter increments.
The study site is located in one of the snowiest regions of the world.
The region's water resources are primarily derived from the snowpack, underscoring the need for accurate snowpack estimates.
The measurement period began in November 2023 and data through mid-April 2025 are used in the following presentation.
The effectiveness of the lysimeter in capturing precipitation was found to be approximately 95%, a result consistent with previous studies conducted in Japan.
The cumulative capture rate (for the entire winter) for snowfall was about 60%, which is reasonable.
The presentation will also include a comparison with the capture rate estimate by Yokoyama (2003).
How to cite: Yamada, T., Mizugaki, S., and Yamada, T.: Precipitation observations using snow weight gauges and lysimeters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14491, https://doi.org/10.5194/egusphere-egu25-14491, 2025.
Developing Intensity-Duration-Frequency (IDF) curves is critical for effective water resource management, infrastructure design, and flood risk mitigation, particularly in arid and semi-arid regions where rainfall events are infrequent but intense. This study focuses on the development of IDF curves for Makkah, Saudi Arabia, an arid region characterized by limited ground rainfall station coverage. To address the data scarcity, a hybrid approach combining ground-based rainfall records and remote sensing data from the Integrated Multi-satellite Retrievals for GPM (IMERG) was employed. Ground station data provided localized accuracy, while IMERG data offered spatial and temporal completeness, compensating for the sparse ground observations. The analysis involved statistical techniques to calibrate and validate remote sensing data against ground measurements, followed by the derivation of IDF relationships through probabilistic modeling. The resulting IDF curves provide insights into extreme rainfall events, enhancing the understanding of hydrological patterns in arid regions and supporting climate resilience initiatives. This methodology underscores the utility of integrating remote sensing with traditional ground-based observations to overcome data limitations in resource-constrained environments.
How to cite: Dirie, A. and Al-Areeq, A.: Enhancing Flood Resilience through Developed Intensity Duration Frequency (IDF) Curves for Makkah, Saudi Arabia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14578, https://doi.org/10.5194/egusphere-egu25-14578, 2025.
Precipitation extremes have caused considerable damages in the Berlin-Brandenburg metropolitan region (Germany) during recent decades. To assess the climatological trends in intensity and probability of occurrence of these events, long-term precipitation data sets are an important prerequisite. Often, such investigations are based on measurements or interpolations of ground station networks. However, due to the high spatial variability of precipitation and its extremes, even relatively dense station networks may not be sufficient to accurately represent small-scale events. The utilization of long-term data, derived from numerical model simulations, has the potential to facilitate an area-wide evaluation. Therefore, we analyzed the spatial distribution of precipitation extremes in Berlin-Brandenburg based on observations by ground measurements and the capabilities of a gridded, reanalysis-based data set for assessment of spatial patterns in changes in seasonal return levels.
The data used consists of 43 years of daily rain gauge measurements by 227 stations of the German Weather Service (DWD) and precipitation data of the Central Europe Refined Analysis version 2 (CER v2, Bart et al., 2024). The CER v2 is a WRF-based dynamical downscaling of ERA-5 for the Berlin-Brandenburg region with a maximum spatial resolution of 2 km. For both data sets we fitted a Generalized Pareto Distribution using a time-dependent seasonal threshold and scale parameter to estimate the 2-, 5- and 10-year return levels at each location (station, grid point). After evaluating the goodness-of-fit at each station the magnitude and change in return levels was compared between both data sets.
The estimated changes in return levels for the CER v2 data correspond relatively well to changes estimated from the DWD stations. However, the CER v2 return levels themselves were on average 10-18% higher. The spatial patterns show an increase in the intensity of the 2-, 5- and 10-year events during summer months in both data sets and a decrease across the region during spring. The spatial variability of the rates of change is particularly high in winter and fall. Overall, the results show that reanalysis-based data could provide an important complement in the assessment of changes in regional precipitation extremes.
Bart, F., Schmidt, B., Wang, X., Holtmann, A., Meier, F., Otto, M., Scherer, D., 2024. The Central Europe Refined analysis version 2 (CER v2): evaluating three decades of high-resolution precipitation data for the Berlin-Brandenburg metropolitan region. metz. https://doi.org/10.1127/metz/2024/1233
How to cite: Bart, F., Schmidt, B., Meier, F., Rust, H., Fenner, D., and Scherer, D.: Assessing climatological trends in daily precipitation extremes in Berlin-Brandenburg, Germany, using 43 years of station-based and reanalysis-based data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15581, https://doi.org/10.5194/egusphere-egu25-15581, 2025.
Ice-related microphysical processes play a central role in global precipitation formation, but remain complex and difficult to quantify. Among these, riming, a mechanism by which supercooled liquid droplets freeze onto falling ice particles, significantly influences precipitation properties (essentially particle’s mass and fall speed). However, accurate measurements of riming remain difficult to obtain due to the limitations of remote sensing.
Current radar-based riming retrievals mostly rely on the higher fall velocity of rimed particles of the vertically-pointing radar Doppler spectrum. However, these retrievals are easily disturbed by vertical air motions and turbulence limiting their applicability in e.g. complex terrain.
Here, we explore the potential of slanted polarimetric W-band cloud radar observations for quantifying riming. Slanted view gives the opportunity to extend the retrieval capability over larger areas and more importantly enables the potential of polarimetry in case of horizontally aligned hydrometeros. As a reference, we use a normalized rime mass retrieval approach combining a 94 GHz Doppler cloud radar and in situ snowfall measurements from the Video In Situ Snowfall Sensor (VISSS). The instrument was deployed during the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign.
Our analysis shows a strong relation between the normalized rime mass, column-integrated radar reflectivity (Ze), and the differential phase shift (ΦDP) from slanted polarimetric W-band radar observations. This suggests that polarimetric radar measurements, particularly the combination of Ze and ΦDP, can be used to estimate rimed mass with comparable performances than the joint use of radar and direct in-situ measurements.
This finding is particularly relevant in view of the forthcoming selection of ESA's Earth Explorer 11 mission, with WIVERN (WInd VElocity Radar Nephoscope) as one of the final candidates. WIVERN includes a conically scanning 94 GHz Doppler radar with polarimetric capabilities. Our results indicate that the correlation between ΦDP and Ze could potentially be used to retrieve rimed mass from spaceborne observations, opening a new way to study ice-related microphysical processes on a global scale.
How to cite: Hörnig, S., Battaglia, A., Maahn, M., Maherndl, N., and Montopoli, M.: Investigating the Potential for Rimed Mass Retrieval Using Polarimetric Cloud Radar Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15807, https://doi.org/10.5194/egusphere-egu25-15807, 2025.
Temperature and humidity are extremely important physical parameters of the atmosphere that can directly constrain atmospheric state variables, form the basis for data assimilation and are routine indicators for weather forecasting. Temperature and humidity parameters directly affect the interaction of solar shortwave radiation with longwave radiation in the earth-air system, which in turn affects the global balance of radiative energy. Therefore, accurate and rapid acquisition of temperature and humidity profiles in the atmosphere is of great significance for human production and life, climate and environmental monitoring and ecosystem assessment. Since the particles in the atmosphere, such as ice, clouds, rain and snow, have a certain attenuation effect on the surface microwave radiation, based on the high vertical resolution and high spectral resolution observation radiation in the test area obtained by the aircraft platform, the Fine Spectrum Microwave Atmospheric Sounder (FSMAS) can obtain different atmospheric information from different channels, so as to obtain more accurate information on the distribution of water vapour and its changes, and invert the atmospheric water vapour contours and precipitation information.
How to cite: He, J.: Detection and mining of water vapour and precipitation information based on microwave hyperspectral techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4412, https://doi.org/10.5194/egusphere-egu25-4412, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 5
EGU25-17395 | ECS | Posters virtual | VPS2
Development and application of the calibration device of precipitation phenomenometerTue, 29 Apr, 14:00–15:45 (CEST) | vP5.10
In order to solve the problem of quantity traceability of precipitation phenomenon instrument, a precipitation phenomenon checking device was developed. By simulating the precipitation particles of 4.3 mm and 9.5 mm, corresponding to the velocities of 2m/s, 7M/s and 12M/s respectively, the on-site verification of the precipitation phenomenometer and the test program of the upper computer software are carried out, the relevant particle channels are recorded and displayed in the map, and the performance of the precipitation phenomenometer is judged automatically. It has many advantages, such as complete function, reasonable design, easy to carry, friendly software interface, one-button detection, automatically judge whether the equipment is qualified, and according to the template to generate a verification report. The practical application proves that the device provides a strong support for the meteorological department's equipment support personnel to carry out the verification work of the precipitation phenomenometer, improves the working efficiency, and plays a role in supervising and inspecting the quality of the precipitation phenomenometer's equipment, it has a good application prospect in the field verification of precipitation phenomenometer.
How to cite: Han, Y.: Development and application of the calibration device of precipitation phenomenometer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17395, https://doi.org/10.5194/egusphere-egu25-17395, 2025.
EGU25-3935 | Posters virtual | VPS2
Characteristics of environmental parameters of short-term heavy rainfall in the Yangtze River Delta region in summerTue, 29 Apr, 14:00–15:45 (CEST) | vP5.11
Based on the minute by minute precipitation observation data from 46 national weather stations in the Yangtze River Delta region of China and hourly ERA5 reanalysis data from June to August 2018 to 2021, the temporal and spatial characteristics and environmental parameters of short-term heavy precipitation were analyzed. The short-term heavy rainfall was classified and compared according to the 19 environmental parameters representing water vapor, dynamic and thermal conditions. The results showed that:(1) There were more short-term heavy rainfall in the Yangtze River Delta in summer, and 58.7% of the weather stations appeared more than 5 times a year on average; most of short-term heavy rainfall appeared in August, accounting for 40.7%; From 14:00 PM to 17:00 PM was the high incidence period of short-term heavy rainfall; The duration of short-term heavy rainfall was mostly within 60 minutes, accounting for 85.9%, and the longest process lasted 282 minutes.(2) At the beginning of short-term heavy rainfall, water vapor was sufficient, PWAT generally exceeded 63mm, and the relative humidity at 850 hPa and 700 hPa exceeded 80%; The energy condition was good, and the average value of cape was 1516.9 J/kg; The vertical wind shear of 0-6 km was mainly distributed in the range of 8.1~16.7 m/s, belonging to medium weak or weak intensity; The thickness of warm clouds was large, most of which were more than 4395.2 m, which was conducive to higher precipitation efficiency.(3) The environmental parameters of the three types of short-term heavy rainfall were quite different. The water vapor of the first type was mainly concentrated in the lower layer, with high cloud base height and large Cape value, 75% of which was more than 1700 J/kg. The thermal conditions were prominent, and the dynamic effect was weak. The water vapor of the whole layer of the second type was sufficient, and the Cape value was high, with an average value of 1401.1 J/kg, the uplift condition of the middle and low layers was the best of the three types. The water vapor, thermal and dynamic effects were relatively balanced; The third type was rich in water vapor, with prominent water vapor conditions, large vertical wind shear in the lower layer and weak thermal effect.
How to cite: Zhang, C. and Peng, L.: Characteristics of environmental parameters of short-term heavy rainfall in the Yangtze River Delta region in summer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3935, https://doi.org/10.5194/egusphere-egu25-3935, 2025.
