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 K_DP, and specific attenuation A used in modern radar QPE methods.

K_DP 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(K_DP) 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 K_DP. 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.

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.