The NASA and the JAXA performed orbit boost maneuvers in November 2023 that raised an altitude of the Global Precipitation Measurement (GPM) Core Observatory from 400 km to 435 km to extend its lifetime. Effects of the orbit boost on the spaceborne precipitation radar have been investigated in the Tropical Rainfall Measuring Mission (TRMM) performed in August 2001. This study evaluates effects on DPR observations due to the GPM orbit boost.

Firstly, spacecraft altitudes of the GPM Core Observatory were analyzed during the period from 13rd October to 17th November 2023. The minimum altitudes were changed from about 400 km to about 435 km by the orbit boost. The averaged altitudes were changed from about 407 km to about 442 km by it. Thus, 407km and 442km were adopted as typical averaged satellite altitudes in pre-boost and the post-boost, respectively.

Spatial resolution at the nadir and swath width is changed at 5.04km×5.04km and 255.8 km at satellite altitude of 407 km to 5.48km×5.48km and 277.9 km at satellite altitude of 442 km, respectively.  Distances between adjacent footprints in the cross-track direction between the pre-boost and the post-boost using observation data and they confirmed that changes of the sampling were larger in the cross-track direction (about 5 km to 5.5 km at the nadir).

It was found that the DPR coverage tendency was changed by the GPM orbit boost. In pre-boost, DPR achieved 100% coverage in 8 days. On the other hand, with post-boost, the coverage was still 99.9834% after 24 days, slightly less than 100%. This coverage trend is expected to change with satellite maneuvers. The maneuver is expected to change the orbit elements, thereby covering all locations.

The sensitivity degradation of the DPR is expected owing to the increase of satellite altitude. Measured radar reflectivity factor (Zm) at storm top height (STH) over the ocean for is used as an indicator of the sensitivity. With analyzing Zm at STH over the ocean, the sensitivity degradation was found for about 0.8-0.9dB for KuPR, and about 0.7-0.9dB for KaPR.

How to cite: Kubota, T., Masaki, T., Kikuchi, G., Ito, M., Higashiuwatoko, T., Kanemaru, K., Takahashi, N., Yamamoto, K., Furukawa, K., and Nio, T.: Early evaluation of effects on Dual-frequency Precipitation Radar observations by the orbit boost of the GPM Core Observatory , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4232, https://doi.org/10.5194/egusphere-egu24-4232, 2024.

Understanding historical evolution and future projections of drought are crucial for Madagascar, which experiences drought almost every year. Not only it contributes to the economic development of the area but it also helps to mitigate direct and indirect impacts of drought on human’s lives and natural ecosystems. To begin with, it is crucial to use accurate datasets for assessing drought in order to get reliable findings. However, Madagascar lacks reliable station datasets. Here, we present the first evaluation of performance of available observed precipitation datasets over the country: gridded precipitation datasets from gauge-based, reanalysis and satellite estimates. Among the 15 analyzed datasets, CHIRPS (Climate Hazards Group Infrared Precipitation with Station data version 2) and ERA5 (European Centre for Medium-Range Weather Forecasts reanalysis fifth generation- Land dataset) the lowest biases compared to the rest. Thus, they are used as the reference for evaluating the performance of CMIP6 HighResMIP simulations. The assessment employs diverse methods, accompanied by the use of the Taylor skill score for ranking the overall performance of the models. The results show that EC-Earth3P-HR, ECMWF-IFS-HR, ECMWF-IFS-LR and HadGEM3-GC31-MM perform the best. The evaluated precipitation datasets are used in current ongoing research of recent drought evolution and its impact on vegetation over Madagascar. Preliminarily results show that the SPI (Standard Precipitation Index) exhibit decreasing trend for all chosen SPI scales (SPI3, SPI6 and SPI12). This indicates that the occurrence of drought over Madagascar has amplified within the study period of 1981 to 2022. Eventually, the evaluation of future projections of drought over the Island would be the next goal to be tackled in order to provide bases for planning appropriate measures in lessening the impact of drought, building effective adaptation strategies and structuring climate change policies.

How to cite: Randriatsara, H. H.-R. H. and Holtanova, E.: Precipitation over Madagascar: Assessment of observed datasets and CMIP6 HighResMIP models for further analysis of drought and its impact on vegetation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-199, https://doi.org/10.5194/egusphere-egu24-199, 2024.

A Multiscale Analysis of a Nocturnal Extreme Rainfall Event of 14 July 2017 in Northeast China

Gaili Wang¹, Da-Lin Zhang^2,1, and Jisong Sun¹

¹State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Science, Beijing, China

46 South Street Zhongguancun, Beijing, China 100081

² Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, Maryland

 Abstract

A multiscale observational analysis of a nocturnal extreme rainfall event that occurred at Changtu in Northeast China on 14 July 2017 is performed using global analysis, automated surface observations, Doppler radar, rawinsonde and disdrometer data. Results show that the large-scale environment was characterized by high convective available potential energy and precipitable water, moderate convective inhibition, and a southwesterly low-level jet (LLJ) capped by an inversion layer. The first and subsequent convective cells developed along a quasi-stationary surface convergence zone in a convection-void region of a previously dissipated meso-a-scale convective line. Continuous convective initiation through backbuilding at the western end and the subsequent merging of eastward-moving convective cells led to the formation of a near-zonally oriented meso-b-scale rainband, with reflectivity exceeding 45 dBZ (i.e., convective core intensity). This quasi-stationary rainband was maintained along the convergence zone by the LLJ of warm-moist air, aided by local topographical lifting and convectively generated outflows. A maximum hourly rainfall amount of 96 mm occurred during 0200-0300 BST as individual convective cores with a melting layer of >55 dBZ reflectivity moved across Changtu with little intermittency. The extreme-rain-producing stage was characterized with near-saturated vertical columns, and rapid number concentration increases of all raindrop sizes. It is concluded that the formation of the meso-b-scale rainband with continuous convective backbuilding, and the subsequent echo-training of convective cores with growing intensity and width as well as significant fallouts of frozen particles accounted for the generation of this extreme rainfall event. This extreme event was enhanced by local topography and the formation of a mesovortex of 20~30 km in diameter.

How to cite: Wang, G.: A Multiscale Analysis of a Nocturnal Extreme Rainfall Event of 14 July 2017 in Northeast China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4235, https://doi.org/10.5194/egusphere-egu24-4235, 2024.

Freezing rain is a meteorological phenomenon in which precipitation melts in the upper atmosphere, transforming into super-cooled droplets near the ground due to lower temperatures. In Northern Europe and North America, strong winter storms often accompany freezing rain, leading to road or facility damage. In Korea, several traffic accident have also occurred due to road icing caused by freezing rain, demanding the development of monitoring technologies for enhanced safety measures. In order to provide the information about the road hazard warning and ensuring safety, we analyzed the atmospheric condition and dual-polarimetric characteristics for road icing and developed the algorithm to detect the potential refreezing rain area by using dual polarization radar and 3-D wet-bulb temperature.
 We selected road icing accidents including precipitation and inversion layer events from 2019 to 2021, and analyzed the changes in surface temperatures and wet-bulb temperatures at surface and the hydrometeor classified using dual-polarization variables at upper layer. The hydrometeor at the accident sites were classified with rain or super-cooled droplet, and wet-bulb temperatures ranged between -2 to 1.5 degrees. This information was used to determine the potential refreezing rain area. The inversion layer was also analyzed by the calculation of 3-dimensional wet-bulb temperatures through multi-quadratic interpolation using various observations (AWS, sounding, Buoy, etc.) and the Korea Local Analysis and Prediction System (KLAPS). The dual-polarization variables were employed to classify the hydrometeor type and investigate the possibility of ice particle melting within the inversion layer. The area for potential refreezing rain was designated as dangerous/cautious zones based on ground temperature conditions when snow particles melted within the inversion layer.
The performance of the algorithm for potential refreezing rain areas was evaluated during cold seasons when incidents of refreezing rain, often referred to as black-ice events occurred. We analyzed the hourly and monthly frequencies of detecting dangerous/cautious zones during traffic accidents caused by refreezing rain.

※ This research was supported by the "Development of radar based severe weather monitoring technology (KMA2021-03121)" of "Development of integrated application technology for Korea weather radar" project funded by the Weather Radar Center, Korea Meteorological Administration.

How to cite: Kwon, S., Lee, J.-E., Lee, S., and Choi, H.-J.: Evaluation of detecting algorithm for potential refreezing rain area using the road icing accidents report , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5053, https://doi.org/10.5194/egusphere-egu24-5053, 2024.

As a primary component of the Earth’s hydrological cycle, precipitation plays a central role in many environmental processes and human activities. The availability of reliable precipitation data is essential for many sectors and applications, such as water resources management, flood and drought risk analysis, or hydrological modeling. In this study, we evaluate the characteristics of different precipitation datasets based on distinct methodologies and sources. This is in the context of high-resolution hindcasts and prototypical daily forecasts with the integrated hydrological model ParFlow over a central European model domain, where precipitation is a first order driver as part of the atmospheric forcing. Our objective is to determine, how closely precipitation from the ECMWF HRES numerical weather prediction matches in-situ observations, and how HRES compares to other precipitation products, some of which might be suitable for a bias adjustment of the hydrological model inputs. The European Climate Assessment & Dataset (ECA&D) in-situ daily precipitation observation dataset of 5072 stations in our ParFlow model domain serves as the reference. The time span of the comparison is from 2014 to 2022. Aside from ECMWF HRES, the evaluation includes at present data at different spatio-temporal resolutions: The ERA5 reanalysis as a background dataset, the HYRAS interpolated hydrometeorological raster data from the German Weather Service (DWD), the meteorological radar data product OPERA, a European composite dataset from EUMETNET, and the radar data product RADOLAN from DWD. Due to the spatial coverage of some datasets, the analysis is restricted to Germany constituting a subset of the hydrological model domain. The initial part of this evaluation uses only daily data, and precipitation products are compared at station locations. The spatial distribution and temporal variability is assessed with annual and seasonal sums, mean errors, and spatial correlation coefficients. Precipitation intensity is analyzed through the spatial distribution of the typical climate indices. The temporal characteristics of precipitation is determined through the precipitation fraction, i.e., the number of moderately wet days (75th percentile), very wet days (95th percentile), and consecutive wet days. Perkin's skill score is used for the comparison of the empirical distributions. While preliminary results indicate that HRES agrees well with the observational reference data, some form of bias adjustment may still be necessary.

How to cite: Hammoudeh, S., Goergen, K., Belleflamme, A., and Kollet, S.: Evaluation of precipitation product characteristics over Germany for hydrologic model forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5606, https://doi.org/10.5194/egusphere-egu24-5606, 2024.

Cloud microphysics parameterization has several ice-crystal-related parameters that define the characteristic of ice crystal. Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) parameterization scheme adopts the fall velocity-diameter and mass-diameter relationships from Heymsfield and Iaquinta (2000, HI00 hereafter) with the assumed single-bullet shape of ice crystals, and the mean mass-weighted terminal velocity-mixing ratio relationship from Heymsfield and Donner (1990, HD90 hereafter). There are a total five parameters that define ice-crystal characteristics, and these parameters vary according to different shapes of ice crystals, contributing to uncertainties of simulated precipitation. To assess these uncertainties, we generate 50 sampling sets using Latin hypercube sampling within the recommended range from previous studies. Numerical experiments are conducted for two major types of winter precipitation, namely Air-mass Transformation (AT) and Ease-coast Terrain effect (ET) types, over the Korean peninsula. The simulation results indicate that parameters defining the mass-diameter relationship are most sensitive for simulating precipitation in the AT type, while parameters defining the fall velocity-diameter relationship are most sensitive for the ET type. Sensitivity experiments are designed by adjusting the sensitive parameters for each type by ±20% to mitigate biases in surface precipitation observed in the control experiments. In the AT type, the sensitivity experiment simulates more solid-phase precipitable hydrometeors, such as snow and graupel, resulting in increased precipitation over the region with a negative bias. Conversely, in the ET type, the sensitivity experiment reduces the amount of snow and graupel, leading to a decrease in precipitation over the area with a positive bias. Our analysis underscores the high priority of tuning parameters related to ice-crystal characteristics to reduce uncertainty in precipitation simulations, depending on the type of winter precipitation.

Key words: Ice crystal, Uncertainty parameter, WDM6, Winter precipitation

Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00272424) and Korea Meteorological Administration Research and Development Program under Grant (RS-2023-00240346)

How to cite: Kim, K.-B. and Lim, K.-S. S.: Impact of Parameters Related to Ice Crystal on the Simulation of Winter Precipitation over the Korean Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6793, https://doi.org/10.5194/egusphere-egu24-6793, 2024.

The Tibetan Plateau, known as the 'Asian Water Tower,' has drawn significant attention to its hydrological cycle and associated atmospheric dynamics. The Qiang-tang Plateau, located in the northern part of the Tibetan Plateau's  endorheic basin (hereinafter referred to as the plateau), experiences notable climate and water cycle variations. The spatial characteristics of its precipitation determine the spatial patterns of hydrological elements and ecological environments in the Qiang-tang Plateau. However, its harsh environment and challenging conditions for station establishment have resulted in a severe scarcity of precipitation observation data. Presently, mainstream reanalysis products consistently overestimate precipitation levels on the plateau and fail to accurately simulate daily precipitation variations. To address this, utilizing data from 206 tipping-bucket rain gauges deployed across the plateau from 2017 to 2020, the study investigates rainfall events of different durations: short-term (1-3 hours), medium-term (4-6 hours), and long-term (7 hours or more).

The research reveals that the precipitation intensity at plateau sites is generally low, with short-term rainfall events being predominant. However, the contribution of short-term rainfall events increases spatially from the southeast edge to the inland of the plateau. Notably, the Qiang-tang Plateau exhibits a significantly higher proportion of short-term precipitation compared to other regions on the plateau. Furthermore, based on a newly established mountainous precipitation transect, it was discovered that as one ascends from the Gangdisi Mountains to the Qiang-tang Plateau, the contribution of short-term rainfall to the total precipitation significantly increases with elevation. Additionally, an analysis of mainstream reanalysis products (ERA5, MERRA2) and high-resolution model simulation data (HAR2) for different duration rainfall events indicates that reanalysis products consistently underestimate the contribution of short-term precipitation while overestimating long-term precipitation. HAR2 outperforms ERA5 specifically in the Qiang-tang Plateau and the northeast part of the plateau, whereas MERRA2 fails to capture the spatial heterogeneity of different duration rainfall events. Although reanalysis products can capture the diurnal peak of short-term precipitation, they tend to prematurely estimate the diurnal peak of long-term precipitation.

How to cite: Ling, X., Chen, Y., Yang, K., Li, X., and Zhou, X.: The analysis and evaluation of rainfall events of different durations in the Tibetan Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6976, https://doi.org/10.5194/egusphere-egu24-6976, 2024.

Accurate estimation of surface precipitation with high spatial and temporal resolution is crucial for disaster weather detection and decision-making regarding water resources management. Polarimetric weather radar is an important instrument for quantitative precipitation estimation (QPE). Conventional parametric approaches, such as the radar reflectivity (Z) and rain rate (R) relations, cannot fully represent the spatial and temporal variability of clouds and precipitation due to parameterization errors and dependence on raindrop size distribution (DSD). Furthermore, these relations estimate rainfall on a grid-by-grid basis, preventing the incorporation of spatial information into precipitation estimation.

In recent years, machine learning has made rapid advancements in non-linear fitting and feature extracting. Since 2020, multiple studies constructed MLP or CNN-based QPE models that used polarimetric radar observations to retrieve precipitation. These researches have consistently demonstrated that machine learning algorithms perform better than traditional parametric methods in different regions and climatic conditions(Chen & Chandrasekar, 2021; Li et al., 2023; Osborne et al., 2023; Tian et al., 2020; Zhang et al., 2021; Zhou et al., 2023).

The aforementioned studies have highlighted the immense potential of deep learning for radar QPE, but they are based on S-band radar data. Because X-band radar has a shorter wavelength, the electromagnetic scattering characteristics of hydrometeors differ from those of S-band radar, especially for specific differential phase (kdp), which is closely related to rainfall. Furthermore, X-band radars have different spatial resolutions from S-band radars, which indicates that directly applying a model trained with S-band radar data to X-band radar data may introduce biases. Therefore, we develop a CNN-based QPE model using polarimetric measurements from X-band radars and compare its performance against traditional parametric methods. The input data for the CNN model is a matrix with dimensions (6, 9, 9). The matrix is composed of two matrices of size (3, 9, 9), which is the polarimetric measurements from the two lowest scan elevation angles and 9*9 surrounding range gates. This allows the input data to capture the spatial and physical characteristics of the precipitation field. The results reveal that the CNN-based model not only enhances the accuracy of radar QPE with a diminished bias but also provides a more precise depiction of the spatial distribution of precipitation in comparison to conventional methods.

How to cite: Zhou, R., Gong, A., Li, B., Qi, Y., and Ni, G.: Deep learning for X-band radar quantitative precipitation estimation using polarimetric measurements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7197, https://doi.org/10.5194/egusphere-egu24-7197, 2024.

Cloudburst are geographically localized extreme rainfall events where a large amount of rain falls within a few hours. The combination of small spatial scale, short duration and scarceness makes it difficult to reveal any systematic regional differences in occurrence. Here we estimate climatological cloudburst frequencies from the daily precipitation sums for a dense network of 161 historical Danish stations covering the period 1914-2010. We do this using supplementary sub-hourly precipitation observations from a modern network and relate the daily probability of cloudburst occurrence to the corresponding daily precipitation sum using binary regression. This allows a subsequent estimation of the cloudburst frequency from the daily sums from the historical observations. To validate the method, we use stations from the modern network that have been operating for 30 years or longer. For these stations, we demonstrate significant skill by comparing observed and estimated cloudburst frequencies with a jackknife procedure. We then apply the binary regression model using the 161 historical series as input and estimate climatological cloudburst frequencies throughout Denmark. We find large and systematic regional variations across Denmark. The methodology also allows determining temporal changes of cloudburst frequency and we find large differences across Denmark.

How to cite: Schmith, T., Thejll, P., Vejen, F., and Christiansen, B.: Regional variation of climatological cloudburst frequency estimated from historical observations of daily precipitation sums, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8019, https://doi.org/10.5194/egusphere-egu24-8019, 2024.

Urbanization activities and their effects in Cyprus are more pronounced in the last 35 years, leading to a drastic change of the local climate of Cyprus’ main cities. Indeed, the contrast of energy absorption between developed urban areas and surrounding rural areas results in a variation of the local climate. The monitoring of the Urban Heat Island (UHI) is essential in the effort to produce heat maps of the urban area of Limassol, a town on the south coast of Cyprus. The area affected by UHI must be examined systematically in order to extract information that is vital in assisting decision- and policy-makers to adopt effective mitigation strategies and improve urban planning. This study presents the findings from the literature review of studying the UHI using earth observation, and reports on the results of the UHI effects for the whole Cyprus area, by using Landsat-5/8 TM & Sentinel-3 satellite images. NDVI calculations were conducted to derive the Fraction of Vegetation (FV) and calculate Emissivity over the last 20 years (2003-2023). Urban heat Island determination between several cities in Cyprus is presented in this study.  The results of this study are intended for use by the local authorities in support of the proposed revision of the local plans for the area by proposing a new ‘sustainability index‘ that uses UHI for urban planning purposes.

How to cite: Soderiades, C., Tryfonos, K., Michaelides, S., Agapiou, A., and Hadjimitsis, D.: Study of the Urban Heat Island effect in Cyprus by using Earth Observation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8111, https://doi.org/10.5194/egusphere-egu24-8111, 2024.

Severe hailstorms are becoming more frequent in Central Europe showing increasing interannual variability. The Pre-Alpine and Alpine region seems to be especially affected due to its complex terrain, that initiates convection and can intensify many hail favoring processes. This results in increasingly strong large hail events, which are often very local phenomena. Ground-based observations from weather radars are most reliable for detecting hail, however, prove to be challenging in the Alpine region due to interference at mountain ranges.

Passive Microwave satellite observations offer a useful alternative for detecting hail: a probability for hail can directly be derived from Passive Microwave channels with a high spatial coverage. However, this data is only available at certain times during satellite overpasses, thus, capturing only a few of these events. The highest temporal coverage is given by visible, near-infrared and infrared data from MSG. Though not directly sensitive to hail its high spatiotemporal resolution can identify early stages of severe storm developments.

Recently, self-supervised machine learning approaches have been used to classify spatial cloud patterns from satellite measurements from MSG over the Atlantic and Germany. The model learns to sort similar cloud organization patterns into the same classes.

In this work, we aim at adapting this model to also include the temporal component to then classify the evolution of typical cloud patterns leading to severe hailstorms over the Alpine region. The framework will later be used to characterize changes in spatiotemporal evolution of large hail bearing systems and associated environmental conditions across a multi-year dataset. First steps are presented here including the investigation of the optimal training dataset using the available data sources.

How to cite: Bigalke, P., Acquistapace, C., and Corradini, D.: Investigation of climatic changes for hailstorms over the Alps using spatiotemporal satellite imagery and self-supervised machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10114, https://doi.org/10.5194/egusphere-egu24-10114, 2024.

In Zambia, like in many African countries, the dedicated rainfall observation network is sparse, whereas accurate information about rainfall is crucially needed. In such a data-poor country, opportunistic sensors like commercial microwave links (CMLs) can be very beneficial. However, the irregular spatial distribution and the fact that many CMLs are very long and operate at low frequencies are common characteristics for rural areas in Africa which make rainfall retrieval with CMLs challenging. In addition, the lack of reference data complicates the adoption and adjustment of existing CML processing methods. In particular, the detection of rain events in noisy CML data, which can have a significant effect on the resulting estimated rainfall amounts, requires special attention as the long low-frequency CMLs provide comparatively noisy data. One option to support CML data processing is the usage of satellite data.

We use level 1.5 data from Meteosat Second Generation (MSG) SEVIRI to generate a precipitation probability (PC) product, similar to the PC products from NWC SAF. Our PC product is generated by a convolutional neural network (CNN) which was trained with SEVIRI and high-resolution radar data in Germany and which was validated with station data in Burkina Faso. We use this PC product to improve the rain event detection during the data processing of almost 1000 CMLs with 15-minute min-max data over several months of the rainy season 2021/2022. In addition, we use two other rain event detection methods, the Python implementation of the nearby-link approach from RAINLINK and the simple rolling standard-deviation method. From the processed CML rainfall estimates, we produce interpolated rainfall maps which we then validate with rain gauge data.

Preliminary results show that the nearby-link and rolling standard-deviation method produce satisfactory results in urban regions where CML density is high and CML frequencies are larger than 10 GHz. The application of the SEVIRI-based PC product for improved CML data processing, in particular for the long low-frequency CMLs, is currently being investigated and we will present first results to analyze its potential and limitations.

How to cite: Blettner, N., Wiegels, R., Kunstmann, H., and Chwala, C.: Using commercial microwave links and SEVIRI observations for rainfall estimation in Zambia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10338, https://doi.org/10.5194/egusphere-egu24-10338, 2024.

The US Department of Defense (DoD) Meteorological Satellite Program (DMSP) has provided a long-term record of passive microwave observations from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMIS). These observations, available from 1987 to the present, provide the backbone of data used for global precipitation measurements. The SSM/I and SSMIS instruments have similar lower frequency channels (19.35-85.0 GHz vs 19.35-91.655 GHz), with the SSMIS having higher frequency channels at 150 GHz and three around 183.31 GHz.

The Precipitation Retrieval and Profiling Scheme (PRPS) is a retrieval scheme designed to be efficient and avoid the use of any external dynamic data sets, such as model information. This is particularly important for a truly independent data product that can be used for evaluating model performance. The PRPS was originally designed for use with cross track sounding instruments but has been adapted to other passive microwave sensors: here it has been adapted to utilise the SSMI and SSMIS Fundamental Climate Data Records generated by the EUMETSAT CM SAF. The PRPS-SSMIS relies upon an observational a priori database derived for each sensor paired with a database index file to provide a computationally efficient retrieval scheme.

This poster will present an outline of the PRPS-SSMIS scheme together with the validation and intercomparison of the resulting precipitation products. At present the databases for the retrieval scheme are based upon 7 years of observations (2016-2022) from SSMIS sensors on the F16, F17, F18 DMSP satellites, matched to co-incident and co-temporal measurements of precipitation from the Global Precipitation Measurement (GPM) mission’s Dual frequency Precipitation Radar (DPR). Comparisons are made at a number of scales: ‘climate’ scale comparisons are made against the GPCP v3.2 global precipitation product, through to instantaneous precipitation retrievals which are compared with surface radar over the US and Europe. In addition, comparisons are made with the Ferraro and GPROF precipitation products to assess consistency with other estimates. Overall, the PRPS-SSMIS retrievals tend to underestimate the precipitation, primarily due to the internal assumptions in the retrieval scheme as a result of the skewed distribution of precipitation occurrence and may easily be corrected. Correlations between the PRPS-SSMIS products and the GPCP are similar to those of the GPROF-SSMIS products, particularly when a comparable spatial resolution is used. Both the GPROF and PRPS scheme outperform the Ferraro precipitation product in terms of bias and correlation and are more consistent over time.

How to cite: Kidd, C., Niedorf, A., Konrad, H., Schröder, M., and Fennig, K.: Precipitation retrievals from the SSMIS using the PRPS scheme: formulation, validation and intercomparison., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12329, https://doi.org/10.5194/egusphere-egu24-12329, 2024.

Rainfall is a critical component of the Earth's water cycle, influencing global economic stability, access to food and freshwater, and daily life. Rain is also frequently used as a calibration target for various remote-sensing instruments. As such, timely and accurate observations of rainfall are vital for meteorological applications. The microphysical properties of rain are commonly characterized by the drop-size distribution (DSD), which determines the water content, intensity of precipitation, and kinetic energy of the rain.

Conventional methods for measuring DSD include in situ instruments such as optical disdrometers and polarimetric weather radars. Disdrometers measure the size and velocity of raindrops within a narrow laser beam, providing data only at the surface level and having uncertainties due to the limited sampling area. Polarimetric weather radars, on the other hand, can observe rain profiles over larger areas, but typically only capture higher moments of the DSD, which then require specialized retrieval methods to derive DSD properties. Such retrievals are typically based on known size-shape-velocity relations for raindrops and a scattering model. Polarimetric variables are of an especial value because they allow to decouple the contribution of shape, size, and concentration of raindrops to the observations. In addition, the polarimetric variables can be accurately calibrated. The results of retrieval based on the moments are, however, prone to uncertainties related to measurement errors and limited information content of the DSD moments.

Polarimetric Doppler cloud radars, operating at millimeter wavelengths, offer an alternative to traditional methods of the DSD estimation. They can measure the same set of parameters as weather radars but spectrally resolved, i.e. the cloud radar can separately measure droplets coexisting in the same volume but moving with different velocities relative to the radar. Since velocity of droplets is a proxy of their size, spectrally resolved measurements contain much more information about the underlying DSD.

This study explores the potential of polarimetric cloud radars to retrieve DSD profiles. We highlight the advantages of this approach, including the ability of the non-parametric estimation of DSD profiles. We also examine existing challenges, such as the impact of resonance effects on observations due to the comparable wavelength of cloud radars and droplet sizes. These effects require accurate representation in scattering models and size-shape-velocity relationships. Current literature lacks explanations for some observations, indicating a need for further research and development of retrieval methods based on spectral polarimetric cloud radar data.

How to cite: Myagkov, A., Nomokonova, T., and Frech, M.: Cloud radar spectral polarimetry for drop-size-distribution profiling: perspectives and challenges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12674, https://doi.org/10.5194/egusphere-egu24-12674, 2024.

Adjusting weather radar data with ground-based precipitation observations is an established way to overcome radar-specific uncertainties. Most commonly, rain gauge data is used for this task. Commercial microwave links (CMLs) deployed by mobile network operators offer another source of rainfall information that can be used to adjust weather radar data. One of the main advantages of CMLs for this task is the real-time availability of their data with a latency of less than a minute. In addition, their large number, with high densities in particular in urban regions, and the path-averaging nature of their measurements have the potential to improve radar adjustment at short aggregation times.

We developed the Python framework pyRADMAN which is capable of merging weather radar with rain gauge and CML data with selectable temporal aggregations from minutes to hours. The path-averaging nature of the CML data is considered when merging with the gridded radar data. Computational efficiency has been taken into consideration in all implementations allowing a full countrywide radar adjustment for Germany, including the required processing of CML rainfall estimates, within 2 minutes with a pure Python implementation. pyRADMAN has now been continuously operating at DWD in real time since August 2023. Currently, real-time data streams from the gridded weather radar composite (based on 17 radar sites), ~1500 rain gauges, and ~5000 CMLs are handled by pyRADMAN, and products consisting of different combinations of sensors are produced for several aggregation times and latencies. 

We will show the general concept of pyRADMAN and present results from merging radar data with rain gauge and CML data. Our analysis will consist of selected events and monthly statistics. Results will be shown for aggregation times from 5 to 60 minutes and latencies of production from 5 to 20 minutes (increasing the number of available rain gauges for merging with increasing latency).

How to cite: Graf, M., Chwala, C., Wenzel, M., Vogel, C., Kunstmann, H., and Winterrath, T.: The new real-time radar-gauge-CML adjustment system pyRADMAN at DWD, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12890, https://doi.org/10.5194/egusphere-egu24-12890, 2024.

Precipitation is a major energy resource in Iceland. This study employs the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset to examine how precipitation patterns have evolved across Iceland from 1982 to 2021 and forecast them for the period 2022-2050.  The data confirms the known basic pattern of substantial precipitation in the south, while the northern interior plains are relatively arid.  The maximum precipitation is found in the South-East, but values are lower than suggested by glaciological and runoff data.  There is a non-significant overarching trend in annual precipitation across the country. However, a statistically significant declining trend (R>0.3, p-value=0.05) is observed in the interior regions of the East and Northeast regions. Conversely, a statistically significant increasing trend (R>0.3, p-value=0.05) is detected in coastal areas of these two regions. Future forecasting (2022-2050) suggests a very slight increase in Iceland's annual precipitation (approximately 0.6 mm/year). The findings of this study underline the importance of local scale monitoring of precipitation and comparison of methods of assessment of true ground precipitation.

How to cite: Rousta, I., Dalvi, M., and Olafsson, H.: Trend analysis of remotely sensed and forecasted precipitation in Iceland 1982-2050, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14642, https://doi.org/10.5194/egusphere-egu24-14642, 2024.

Urmia Lake is the largest hypersaline lake in Western Asia and it is currently facing severe desiccation. Immediate action is necessary to prevent irreversible damage to the environment and economy. The lake covers the majority of the Urmia Lake watershed. This study aimed to analyze the changes in Land Surface Temperature (LST) during the day and night in the area using MODIS 1 km, 8 days, version 061 (MOD11A2) images. The study also looked at water level variations using TOPEX/POSEIDON and Jason 1, 2, and 3, and precipitation variations using CHIRPS images from the period of 2001-2023. The results indicate that the water level of Urmia Lake has significantly declined by about 10 meters in the last few decades. Approximately 95 percent of the lake has dried up. The continuous declining trend of the water level started in 2001 and has led to an increase in LST day, about 0.03 ℃/year, and a decrease in LST night, about 0.07 ℃/year. Precipitation variations did not show any significant trend during the study period. Due to the high salt content caused by the lake drying up, the area is becoming a center for salty dust that can negatively affect the surrounding habitats. The trend of precipitation variations suggests that climate is not the primary factor responsible for the lake's desiccation.

How to cite: Dalvi, M., Rousta, I., and Olafsson, H.: Remotely sensed assessment of Urmia Lake drying up; Climate change or anthropogenic effects?!, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14760, https://doi.org/10.5194/egusphere-egu24-14760, 2024.

Rainfall estimation from commercial microwave link (CML) attenuation data has matured and is being implemented by several European meteorological services. Individual studies have also confirmed its applicability in developing countries. But data collection and data access remain cumbersome, requiring to start from scratch in each country and in each cooperation with a new mobile network operator (MNO). More often than not the precious CML attenuation data that is produced for monitoring purposes is not stored on a long-term basis and thus is lost forever if no cooperation with researchers or meteorological services incentivizes archiving.

To avoid further loss of data and to allow to better scale up CML data acquisition and data collection across different countries, we propose to start the global CML data collection initiative (GCDCI). The GCDCI will provide containerized templates for the required IT systems for CML data collection, archiving and monitoring, as well as template documents for the required legal agreements. Each MNO will get a separate cloud-based compute and storage infrastructure which they can use to do long-term monitoring and analysis of their network, providing an incentive for them to transfer their data to the GCDCI platform. Potentially, access for third parties, based on trilateral agreements with GCDCI and individual MNOs, could be implemented, e.g. to allow the development of derived products by the private sector. A central compute infrastructure, only accessible by GCDCI staff, will access data from the individual instances of the MNOs and do a centralized CML data processing. Potentially the centralized processing can be combined with real-time satellite data to both enhance the CML data processing as well as the generation of rainfall products from satellite data.

With our poster we want to spark a discussion about this approach and start forming a consortium to put it into operation as a not-for-profit organization with inspirations from initiatives like TAHMO and GPCC.

How to cite: Chwala, C., Uijlenhoet, R., Overeem, A., Winterrath, T., and van de Giesen, N.: The global CML data collection initiative GCDCI: The solution for scaling up CML rainfall estimation in developing countries?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17076, https://doi.org/10.5194/egusphere-egu24-17076, 2024.

Accurate precipitation (P) estimates are crucial for a wide range of applications, including water resource management, disaster risk reduction, agricultural planning, and infrastructure development. Over the past few decades, numerous gridded P products have been developed, with varying temporal and spatial resolutions, derived from diverse data sources, and employing different methodologies and algorithms. However, these products frequently exhibit significant uncertainties, errors, and biases, underscoring the importance of selecting the most suitable product for each application. In this study, we conducted a comprehensive evaluation of the strengths and weaknesses of over 20 freely available global gridded P products. We used the European RADar CLIMatology (EURADCLIM) gauge-radar dataset, the US Stage-IV gauge-radar product, and observations from approximately 20,000 global stations as ground truth. Our assessment included several new products, such as PDIR-Now and GPM+SM2RAIN, as well as an experimental Random Forest (RF) model, a potential new version of the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product. For the assessment, we employed a broad range of performance metrics sensitive to various aspects of P time series, including the versatile Kling-Gupta Efficiency (KGE) and its components (correlation, bias, and variability), as well as the Critical Success Index (CSI), wet day bias, peak bias, and trend error. Additionally, we assessed the relative performance in different physiographic regions, seasons, and P regimes, and among various product types (satellite, (re)analysis, gauge, and combinations thereof). The RF model showed the best overall performance, achieving a mean CSI of 0.42. In comparison, the current MSWEP version, CHIRP, ERA5, GSMaP and IMERG achieved mean CSI values of 0.40, 0.21, 0.36, 0.32, and 0.32, respectively. Our study highlights the stark differences in performance among various state-of-the-art P products and provides a baseline for the development of new machine learning-based P products.

How to cite: Wang, X., Beck, H., and Alharbi, R.: Global Performance Assessment of 20+ Precipitation Products Using Radar Data and Gauge Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19469, https://doi.org/10.5194/egusphere-egu24-19469, 2024.

The phenomenon of climate change, with its unique alterations in global temperatures and weather trends, presents a mounting obstacle for accurate weather prediction and climate simulation. This study uses the Weather Research and Forecasting (WRF) model to investigate the impact of variations of Sea Surface Temperature (SST) during the rainy season (17 May to 31 Oct 2016). The research aims to quantify the effect of changes in SST (0.5 to 2.0 degrees Celsius) in a climate-sensitive period. Utilising model configured for Thailand's specific geographic and climatic conditions, the study integrates SST data derived from satellite measurements and observations assess temperature, precipitation, and extreme weather events. Our results indicate the pronounced sensitivity of the WRF model to SST variations, with notable discrepancies in predicting rainfall patterns and temperature anomalies. These findings emphasise that SST is a critical factor in climate modelling and the need for accurate SST input in forecasting models, especially in the context of climate change. The study contributes to a better understanding of the WRF model's capabilities and limitations in simulating seasonal climate variations in tropical regions. It may also stress the importance of the governments to engage in effective water and irrigation management strategies, including improved drainage systems and adaptive agricultural practices, to mitigate climate change impacts like flooding and drought. Further research is recommended for other seasons and extended periods for a deeper understanding of the WRF model's performance against evolving climate dynamics.

How to cite: Lerdrittipong, S., Zhong, J., Widmann, M., Bradley, C., and Dixon, S.: Investigating SST's Role in Seasonal Climate Variations: A WRF Model Analysis in the Tropical Zone, Thailand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21379, https://doi.org/10.5194/egusphere-egu24-21379, 2024.

Severe weather events, particularly hailstorms with large hydrometeors, cause heavy losses worldwide. The south of France is one of the European regions most affected by these hydrometeors and is also one of the most studied because an extensive hailpad network of detection devices that has been in operation there for more than three decades. These direct observations are extremely useful because provide a very complete and reliable "ground truth". Space-based sensors are becoming increasingly important in monitoring hailstorms. Global Precipitation Measurement (GPM) is an international mission designed to advance precipitation measurements from multispectral sensors. The GPM core satellite carries a powerful and unprecedented Dual-Frequency Precipitation Radar (DPR) for studying 3D precipitation characteristics. Furthermore, it improves the accuracy of precipitation estimation and facilitates the analysis of the microphysical structure of clouds. The objective of the present work was to evaluate the DPR sensor capability in identifying hailstorms. Data from more than 1000 hailpads during eight field campaigns in southern France were used. We identified eight hailstorms over France where DPR data were coincident with ground-based observations from hailpad network during 2014–2021. In addition, variables provided by the DPR sensor indicative of hail presence were studied. The Ku band demonstrated greater capacity in identifying hailstorms. Storms with larger reflectivity values (≥50 dBZ, Ku band), both near the surface and throughout the vertical column, were those with a more clearly defined vertical structure and thus more powerful convective development. The intensity of these hailstorms was confirmed with the ground-based data. This work could contribute to enhancing the detection and prediction of hailstorms, thereby helping to mitigate the associated risks.

How to cite: Rivero Ordaz, L., Merino, A., Navarro, A., Tapiador, F. J., Sánchez, J. L., and García-Ortega, E.: Identification and characterization of hailstorms over France using DPR-GPM sensor, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21680, https://doi.org/10.5194/egusphere-egu24-21680, 2024.

Dual-frequency precipitation radar (DPR) placed on the Global Precipitation Measurement (GPM) satellite provides a three-dimensional distribution of precipitation between 65⁰N- 65⁰S. The availability of precipitation parameters at the spatial resolution of 0.1⁰*0.1⁰ and temporal resolution of 30 minutes can be used to investigate the microphysical process responsible for the precipitation. We analyzed the GPM-DPR level 2 V07 observed data collected over the Southern region of India and the surrounding Oceanic region to understand the precipitation characteristics in the pre-monsoon and monsoon seasons. India Meteorological Department (IMD) gridded rainfall data at the resolution 0.25⁰ is used to validate GPM-DPR data over the landmass region. There is significant variation in the temporal and spatial distribution of reflectivity (Z), rain rate (R), and DSD parameters such as mass-weighted mean diameter (D_m) and normalized intercept parameter (N_w). In the monsoon season, higher precipitation frequency provides considerable accumulated precipitation throughout India. However, the frequency of intense rainfall is higher in the pre-monsoon season than in the monsoon season, as most of rain events occur over the Ocean instead of land. The mean of R, Z, and D_m is small, and a large N_w value is observed in the monsoon season, as stratiform clouds (more than 68%) contribution in monsoon precipitation is more than convective clouds. The distribution of average D_m, Z, and R in pre-monsoon indicates the presence of bigger rain droplets, possibly due to the enhancement in the collision-coalescence process and slow-down of the break-up process. The share of convective clouds in overall precipitation on the land surface increased in the pre-monsoon season. The fluctuation in D_m not only occurs with topography, season, and R, but also with the concentration of heavy ice precipitation particles above the bright band and microphysical process. Simultaneously, in both pre-monsoon and monsoon seasons, a modest relationship was detected between the incidence of heavy precipitation and maximum echo top reflectivity.

How to cite: Kumar, A., Srivastava, A. K., and Srivastava, M. K.: Regional Variation in Precipitation characteristics observed by space-borne Precipitation Radar , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-736, https://doi.org/10.5194/egusphere-egu24-736, 2024.