Japan Aerospace Exploration Agency (JAXA) currently operates six Earth observation missions for water cycle and climate studies, disaster mitigation, and various application studies including weather forecasts. One of six missions, the Global Precipitation Measurement (GPM) is an international mission to achieve highly accurate and highly frequent global precipitation observations (Hou et al. 2014, Skofronick-Jackson et al. 2017). The GPM mission consists of the GPM Core Observatory jointly developed by U.S. and Japan and Constellation Satellites that carry microwave radiometers and provided by the GPM partner agencies. The GPM Core Observatory, launched on February 2014, carries the Dual-frequency Precipitation Radar (DPR) by JAXA and the National Institute of Information and Communications Technology (NICT) (Kojima et al. 2012, Iguchi 2020).

Regarding future satellite missions, Global Change Observation Mission - Water "SHIZUKU" (GCOM-W) follow-on mission (AMSR3) with high-frequency channels (166 & 183 GHz) will be installed on the Global Observing Satellite for Greenhouse gases and Water cycle (GOSAT-GW) satellite (Kasahara et al. 2020). Japan will provide the world's first satellite-based cloud vertical motion information by the Cloud Profiling Radar (CPR) to the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE), Europe-Japan joint mission (Illingworth et al. 2015, Wehr et al. 2023). JAXA is currently conducting R&D of the Precipitation Measuring Mission carrying the Ku-band Doppler Precipitation Radar to succeed and expand currently operating GPM/DPR.

It is also required to evolve combined use of multi-satellite to provide the “best” information to users. Under the GPM mission, the Global Satellite Mapping for Precipitation (GSMaP) produces high-resolution and frequent global rainfall map based on multi-satellite passive microwave radiometer observations with information from the Geostationary InfraRed (IR) instruments (Kubota et al. 2020). Output product of GSMaP algorithm is 0.1-degree grid for horizontal resolution and 1-hour for temporal resolution. The GSMaP near-real-time version product (GSMaP_NRT) has been in operation at JAXA since November 2007 in near-real-time basis, and browse images and binary data available at JAXA GSMaP web site (http://sharaku.eorc.jaxa.jp/GSMaP/).

JAXA also collaborates with model development community to expand satellite data utilization in various fields. With the goal of providing reliable water cycle information and achieving integrated water resources management, JAXA has developed the global hydrological simulation system “Today’s Earth (TE)” under the joint research with University of Tokyo (Ma et al. 2021). To provide the products with better accuracy, rainfall from the GSMaP is used for TE-Global GSMaP version. The Over 50 hydrological variables are now accessible through the web page and ftp site of the “TE-Global” system (https://www.eorc.jaxa.jp/water/).

JAXA continues to provide useful satellite-based information related to the global water cycle.

How to cite: Oki, R., Kubota, T., Kachi, M., Yamamoto, K., and Yamaji, M.: JAXA Earth Observation Overview for measuring the Global Water Cycle, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4687, https://doi.org/10.5194/egusphere-egu23-4687, 2023.

Periodic reprocessing of the complete GPM data suite is an integral part of thescience processing strategy for the GPM mission. The first publicly available data was identified by the data product version V04 in 2014. In 2017 product version V05 for the radiometer products and in 2018 V06 for the radar products were completed.This reprocessing cycle extended the radar data  back to the TRMM era (1997) and radiometer data back to 1987.  It also officially integrated a considerable amount of earlier data into the GPM data suite. Reprocessing cycle V07 began at the end of 2021 with the radar based data and completed in May 2022 with all the radiometer data. The V07 version had significant changes in radar data because of a change in the Ka radar scanning strategy in May 2018 that allowed the Ka radar to cover the  entire swath of the Ku radar. This allowed dual frequency retrieval across the entire Ku swath rather than just in the interior.  The GPROF precipitation retrieval for the radiometers reverted back to a probablistic output. This presentation will summarize these major changes as well as other algorithm changes that improved the precipitation retrieval.  It will  also present other data that the mission makes available to users. 

How to cite: Stocker, E. F., Kwiatkowski, J., and Kelley, O.: V07 Reprocessing of Global Precipitation Measurement (GPM) Mission Data Suite, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2893, https://doi.org/10.5194/egusphere-egu23-2893, 2023.

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.  GPM carries a state of the art dual-frequency precipitation radar and a multi-channel passive microwave radiometer that acts not only to enhance the radar’s retrieval capability, but also as a reference for a constellation of existing satellites carrying passive microwave sensors.  In April 2022, GPM approved V 7 of its precipitation products starting with GMI and continuing with the constellation of radiometers.  The precipitation products from these sensors are consistent by design and show relatively minor differences in the mean global sense.  Validation results will be shown for work done over the Continental United States using a Radar/Gauge composite as truth, and Kwajalein atoll to represent truth over tropical oceans.  The validation results are a necessary but not sufficient component to quantify the algorithm’s uncertainties.  Good results for bias, MAR and RMSE are demonstrated.  Validation results, however, are only able to assess errors at their own sites, and systematic errors, in particular, are not actually systematic, but regime dependent errors that vary as a function of how well the algorithm assumptions are captured at the validation sites.   This talk will explore ways of validating not by location, but by precipitation states that, the environment that the precipitation evolves in, as a way of obtaining robust statistics of individual precipitation states that are universal and can be applied with confidence to areas outside the validation domain.

How to cite: Kummerow, C.: The GPM Radiometer Algorithm - from Validation to Uncertainties, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-954, https://doi.org/10.5194/egusphere-egu23-954, 2023.

Real-time production of the second generation CMORPH (CMORPH2) has been migrated to and executed at a NOAA / NWS required operational environment, the NWS/NCEP Central Operation (NCO) and Climate Prediction Center (CPC) Compute Farm (CF) effective December 2022.  CMORPH2 real-time production was routinely implemented on a research and development environment at NOAA/CPC since April 2017. Successful migration of the production system to the 7/24 operational environment ensures the production of the high-resolution, high-quality global precipitation analysis at a much higher stability and reduced production latency of one hour, satisfying a requirement for an observational analysis to be infused into operational forecast models and routine field operations.

Inputs to the CMORPH2 real-time production include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard more than 10 low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).  These inputs are first inter-calibrated to ensure quantitative consistencies. The inter-calibrated PMW retrievals and IR-based precipitation estimates are then propagated from their respective observation times to the target analysis time along the cloud motion vectors from both the forward and backward directions. The propagated PMW and IR based precipitation estimates are finally integrated into a single field of global precipitation through the Kalman Filter framework. In addition to the total precipitation, fraction of solid precipitation is computed from the surface air temperature and other surface meteorological variables using the algorithm of Sims and Liu (2015).

The CMORPH2 satellite precipitation analysis is constructed on a 0.05^o latitude/longitude over the entire globe (90^oS-90^oN) and in a 30-minute temporal resolution. The real-time production is first generated at a very short latency of one hour and then refreshed with any newly available inputs once every 30 minutes up to 12 hours of latency for improved accuracy when inouts from all sources are available in most cases. The CMORPH2 real-time production is utilized by several important users including the NWS Aviation center (AWC), Weather Prediction Center (WPC), and NWS Alaska Office, and pushed to the AWIPS for field applications. 

Work is under way to examine the CMORPH2 real-time production as a function of region, season, precipitation type, and production latency, and to further improve the CMORPH2 through infusing the PMW precipitation retrievals from the NOAA Direct Broadcast (DB) systems and refining the GEO IR based precipitation estimates. Results will be reported at the 2023 EGU Meetings.

How to cite: Xie, P., Sinsky, E., Wu, S., DeWitt, D., Garrett, D., and Wang, W.: Operational Real-Time Production of CMORPH2, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2792, https://doi.org/10.5194/egusphere-egu23-2792, 2023.

The Global Precipitation Measurement (GPM) mission combines passive microwave (PMW) and infrared (IR) satellite data, together with other data to create the Integrated Multi-satellitE Retrievals for (IMERG) precipitation product on a (nearly) global 0.1° half-hour grid.  Experience with Version 06 datasets revealed deficiencies that the algorithm team has addressed in creating the new Version 07 datasets.

Input precipitation estimates from the Goddard Profiling (GPROF) algorithm (which retrieves precipitation from passive microwave sensor data), the GPM Combined Radar-Radiometer Algorithm (CORRA), and the new Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR) algorithm (retrieving precipitation from IR data) all represent advances over the V06 inputs.  For V07, several issues have been addressed, and many algorithm improvements have been implemented.  These include refining the Kalman filter to approximately preserve the local histogram of precipitation rates (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN), and applying the Kalman filter even when a PMW overpass occurs.  Furthermore, a long-standing bug in the geolocation that shifted grid values 0.1° to the east in the latitude band 70°N-S has been corrected.  Other changes in V07 include a hierarchical selection among motion vector sources to address deficiencies in the precipitation propagation near orography, an update to the precipitation phase specification for improved consistency with current inputs, and climatological adjustment of the near-real-time Early and Late Runs to the Final Run (which includes monthly precipitation gauge analyses).  Extensive development work was directed at unexpected biases in the V06 products, leading to 1) calibrations that now employ the entire swath widths of CORRA and GPROF GPM Microwave Imager (GMI) precipitation estimates (rather than spatially coincident data), and 2) coarsening the CORRA resolution to approximately match the GPROF-GMI footprint scale.  The latter provides more consistent histograms for building the calibrations.

It is anticipated that the retrospective analysis for V07 will be well underway at the time of the meeting.  Changes between V06 and V07 will illustrate the cumulative result of the improvements implemented in V07.  The current status of processing and plans for future development will also be discussed.

How to cite: Huffman, G., Bolvin, D., Joyce, R., Nelkin, E., and Tan, J.: Lessons Learned in V07 IMERG, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2961, https://doi.org/10.5194/egusphere-egu23-2961, 2023.

Snow, on the ground and falling to the surface, plays an important part in the global water cycle, yet the measurement of it remains problematic. The estimation of falling snow (or frozen precipitation) is difficult and relies upon the interpretation of observations from passive microwave sensors. However, the relationship(s) between the passive microwave observations and falling snow at/near the surface is very much dependent upon the type of snow (ice) crystals present which vary greatly between different precipitating weather systems. To date, much work has concentrated observations from current sensors using the 150/166 GHz and the 183.31 GHz water vapour channels to extract the scattering signature associated with frozen hydrometeors. However, the TROPICS pathfinder mission, launched in June 2021 into a near polar orbit, carries a new radiometer that provides an opportunity to assess the impact of including observations at 204.8 GHz. Such measurements are more sensitive to ice particles, resulting in a greater scattering signature, while being less sensitive to the water vapour around the 183.31 GHz region. Preliminary results are encouraging and will be presented here. A number of case studies, including lake-effect snowfall (November 2022) and widespread snow across North America (mid-December 2022), will be explored in detail. These initial results show observations at 204.8 GHz provide additional information that improves snow delineation and estimation: this is of great significance for the upcoming EUMETSAT EPS-SG missions.

How to cite: Milani, L. and Kidd, C.: Assessing the potential of frequencies around 205 GHz for improving snowfall estimation., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1484, https://doi.org/10.5194/egusphere-egu23-1484, 2023.

The Temporal Experiment for Storms and Tropical Systems – Demonstration (TEMPEST-D) mission demonstrated the first global observations from a multi-frequency microwave radiometer on a CubeSat.  The TEMPEST-D CubeSat was deployed from the ISS in July 2018 and operated in low Earth orbit nearly continuously for three years until it re-entered the Earth’s atmosphere in June 2021. This NASA Earth Venture Technology mission exceeded expectations in terms of scientific data quality, instrument calibration, radiometer stability, and mission duration. TEMPEST-D brightness temperatures were validated using scientific and operational microwave sensors, including GPM/GMI and four MHS sensors on NOAA and ESA/EUMETSAT satellites. These comparison sensors operate at similar frequencies to TEMPEST-D, observing at the 87 GHz window channel, and at 164, 174, 178 and 181 GHz for water vapor sounding, cloud water and precipitation retrievals. TEMPEST-D was shown to have comparable or better performance to much larger operational sensors, in terms of calibration accuracy, precision, stability and instrument noise, during its nearly 3-year mission.

TEMPEST-D performed detailed observations of the microphysics of hurricanes, typhoons and tropical cyclones during three consecutive hurricane seasons. Nearly simultaneous observations by TEMPEST-D and JPL’s RainCube weather radar demonstrated a high degree of correlation between complementary passive and active microwave measurements of convective storms and tropical cyclones from the two CubeSats.  TEMPEST-D periodically performed along-track scanning measurements, providing the first space-borne demonstration of “hyperspectral” microwave sounding observations to retrieve the height of the planetary boundary layer with high precision.

The highly successful, stable operation of the TEMPEST-D instrument on a 6U CubeSat for nearly three years suggests myriad future opportunities to enhance microwave sounding and imaging of water vapor, clouds and precipitation. During the TEMPEST-D development, a nearly identical TEMPEST sensor was produced for risk reduction. The second sensor was delivered to the U.S. Space Force and integrated with NASA/JPL’s Compact Ocean Wind Vector Radiometer (COWVR). On December 21, 2021, COWVR and TEMPEST were launched from KSC as part of STP-H8 for 3 years of operations on the ISS. COWVR and TEMPEST-H8 have performed coordinated observations of Earth’s oceans and atmosphere from the ISS since January 7, 2022. TDRSS allows for near real-time communications from the ISS to ground, and STP-H8 plans to ingest COWVR and TEMPEST microwave observations into short- and medium-term weather forecasting models.

Quantitative precipitation estimates from TEMPEST-D on-orbit observations have been produced using a machine-learning approach.  Precipitation retrievals over continental storms as well as land-falling hurricanes demonstrated excellent agreement with multiradar/multisensor system (MRMS) quantitative precipitation estimates (QPE). These retrievals are currently being expanded from CONUS-only to a global basis using the IMERG precipitation dataset.  Similar techniques are being applied to TEMPEST-H8 observations from the ISS to provide retrievals of water vapor profiles, cloud liquid water, cloud ice water, and precipitation.

How to cite: Reising, S. C., Chandrasekar, V., Radhakrishnan, C., Brown, S. T., Gaier, T. C., and Padmanabhan, S.: Recent Advances in Quantitative Precipitation Estimation using Passive Microwave Observations from the Temporal Experiment for Storms and Tropical Systems (TEMPEST), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11068, https://doi.org/10.5194/egusphere-egu23-11068, 2023.

A significant portion of the atmospheric particulate matters are irregular without symmetry, e.g., dust particles, ice crystals, snowflakes, etc. Some of them are of heterogeneous composition, e.g., melting hydrometeors and droplets with inclusions. Solving the electromagnetic scattering problem for these particles are computationally intensive. It becomes impractical and very inefficient to repeatedly solve the same problem. Moreover, due to the lack of symmetry, every orientation of the particle has a unique solution, generating considerably greater volumes of data than the solutions for particles with nice symmetry. Storing the solutions and making them accessible is far more sensible, economical, and thus sustainable. The Particle and Single-Scattering Database, PaSS-DB, initiative aims to catalog, warehouse, and disseminate these atmospheric particles and their electromagnetic scattering solutions using an enterprise-grade database management system and a web interface. We report the early progress of the effort in this presentation.

How to cite: Kuo, K.-S., Fenni, I., Schrom, R., Adams, I., Ton, D. H., Huffman, G., and Braun, S.: NASA Particle and Single-Scattering Database (PaSS-DB) in Support of Particulate Matter Retrievals, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11345, https://doi.org/10.5194/egusphere-egu23-11345, 2023.

This study evaluates efficacy of rainfall kinetic energy (KE) and rain intensity (I) relationship through field observations under different wind conditions.  KE-I relationship is critical for various applications.  For example, rain-induced soil erosion, for which KE is an important indicator of potential rain splash erosion, may cause severe environmental and agricultural issues during, especially, heavy rainfall events.  Fertilizers removed from the soil due to rain-induced erosion pollute waterbodies, silt up the basin, and reduce basin capacity that may trigger flooding.  In this study, the KE-I relationship was investigated using raindrop size and fall speed measurements by a disdrometer and wind speed measurements by a 3D Ultrasonic anemometer.  Rainfall events considered in this study were selected from a 3-year long field campaign conducted at our outdoor rainfall laboratory located on the West campus of the University of Texas at San Antonio, Texas, USA.  Using these measurements, different KE-I relationships reported in the literature were evaluated through statistical analyses.  A new parameterization for the KE-I relationship was also developed based upon our field observations, and wind-induced effects on KE-I relationship are discussed.  The results of this investigation with potential implications will be discussed in this presentation.  This material is based upon work supported by the National Science Foundation under Grants No. AGS-1741250.

How to cite: Testik, F. and Saha, R.: Assessing Rainfall Kinetic Energy – Rain Intensity Relationship through Field Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4299, https://doi.org/10.5194/egusphere-egu23-4299, 2023.

Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.

How to cite: Dong, J., T.Crow, W., Chen, X., Tangdamrongsub, N., Gao, M., Sun, S., Qiu, J., Wei, L., Gao, H., and Duan, Z.: Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4247, https://doi.org/10.5194/egusphere-egu23-4247, 2023.

This study introduces a new machine learning-based probabilistic quantitative precipitation estimate (PQPE) retrieval that uses observations from the GOES-16 Advanced Baseline Imager (ABI) across the CONUS at 5 min temporal resolution and ~2 km spatial resolution. It is developed and evaluated using the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system as a benchmark, and features Convolutional Neural Network (CNN) machine learning. Key advances include (1) the design of a three-dimensional CNN model to retrieve the distribution of precipitation rate instead of a single deterministic value; (2) a comprehensive set of predictors based on spatio-temporal information from infrared ABI channels and complemented by environmental conditions from Numerical Weather Prediction (NWP) models; and (3) using probabilities of occurrence for different precipitation type (e.g., convective and stratiform), retrieved from a separate machine learning  model as predictors in the CNN model. Precipitation type predictors allow a single model to be used seamlessly for all precipitation types. The analysis reveals that combining predictors based on satellite and NWP data leads to improved performance, with the greatest improvement in the stratiform precipitation type. The use of probabilities of precipitation type as predictors contributes significantly to the improved performance of the quantitative precipitation retrievals. Furthermore, improvements in conditional biases are demonstrated for all precipitation rates when compared to a deterministic CNN model.

How to cite: Upadhyaya, S. A., Kirstetter, P.-E., and Kuligowski, R. J.: Machine Learning-based Probabilistic Precipitation Estimation with the GOES-16 Advanced Baseline Imager, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2945, https://doi.org/10.5194/egusphere-egu23-2945, 2023.

Precipitations are a crucial and extremely valuable source of water for humanity’s food production and for its own consumption but droughts or even floods can be potential causes of considerable damages to crops, infrastructures or properties and life-threatening situation. Precipitations are characterised by a high spatial and temporal variabilities. Hence, multiple rain gauges, precipitation radars and satellite-based estimates gridded datasets are necessary to quantify rain accumulation at local, regional and global scales.

Rain gauges observations tend to observe the amount of precipitable water locally providing rain rates for an equivalent surface of less than 1 m². Weather radars estimate the rain fields over a surface at high spatio-temporal resolution (~1km for space and several minutes for the time). Finally, the satellite-related estimates of precipitation, mainly based on the estimates made by infrared (IR) and passive or active microwave (PMW or AMW) sensors, have been developed and studied in the last decades. Moreover, new innovative satellite skills provide rain estimates from the attenuation of broadband communication satellite link signals at Ka-band.

This work evaluated the behaviour of two precipitation radar products: the French radar product PANTHERE and the pan-European radar mosaic product OPERA, and 11 satellite precipitation products: GHE, PDIR, IMERG Early v6, IMERG Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v8, GSMaP-NRT-GC v8, GSMaP-NOW, GSMaP-NOW-GC and Databourg. This study has been performed against the observation made the RADOME rain gauge network with more than 500 stations over the French territory. All datasets are aggregated at hourly temporal resolution and are compared using a point-to-pixel method during two notable case studies: a 3-days April 2022 event and a 4-days June 2022 event. It is shown that radar products tend to be more reliable on the estimation of precipitation accumulation while PMW datasets tend to underestimate rain rates compared to the observations whereas IR datasets has the potential to overestimate these values.

How to cite: Causse, A., Baray, J.-L., Planche, C., and Buisson, E.: Evaluation of precipitation satellite products and ground-based radars during two case studies over France in 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3385, https://doi.org/10.5194/egusphere-egu23-3385, 2023.

Quantitative precipitation estimates obtained from satellite data are of critical importance to research and applications. Not only is precipitation a key component of important water and energy cycles, but the immediate societal benefits offered by reliable products are undeniable.

The complex terrain of the Peruvian Andes creates significant challenges to precipitation retrievals from space and to the establishment of dense ground monitoring networks. Nonetheless, for the communities and authorities of the region (home to nearly one third of the Peruvian population), this information is vital.

The performance of the quasi-global Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) V06 was assessed in Peru as part of Project e-Andes. Data covering the period between 2011 and 2020 were compared against gauge data from 35 stations maintained by SENAMHI. The gauges are located in the Andes and arid Pacific coast. Mean Pearson correlation values ranged from 0.34 (Early, Daily), to 0.80 (Final, Monthly), showing a clear improvement with temporal aggregation and from Early to Final runs. The trend was also observed across other metrics including Bias, RMSE, and MAE.

 Challenges to the validation of IMERG Final in sparsely gauged regions is also discussed. The study was an important component of capacity-building efforts and the development of user networks. The information provides important guidance for the development of monitoring services that incorporate both IMERG and gauge networks to create estimates with reduced bias.

How to cite: Mantas, V. and Caro, C.: Performance of quantitative precipitation estimates in the complex terrain of the Peruvian Andes. IMERG V06 and the development of user-driven downstream applications., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7860, https://doi.org/10.5194/egusphere-egu23-7860, 2023.

In semi-arid contexts, the strong spatiotemporal fluctuation of rainfall and the sparsity of the rain gauge (RG) measurement networks are the main limitations for water resources management. Freely available satellite-based rainfall estimates can be a potential source of information to cop data limitations over poorly gauged regions. Thus, the main aim of this work was to investigate how eight Spatial Rainfall Products (SRP, ARC-2, CHIRPSp25, CHIRPSp5, CMORPH-CRT, GPM-IMERG, PERSIANN-CDR, RFE-2, and TRMM-3B42) can be able to reproduce the observed monthly rainfall over a semi-arid context. The SRP estimates were directly evaluated against the RG observations. Then, bias correction techniques were used to account for the bias in the SRPs. The results indicated that the SRPs poorly correlate with the daily rainfall patterns (with Pearson Correlation Coefficients (PCCs) mostly below 0.5) but agreed with the monthly observations. The agreement was stronger over the lowlands than over the mountainous region. Overall, out of all the considered SRPs, IMERG (with a short-term record) and PERSIANN (with a long-term record) performed the best. Still, the monthly SRP estimates were significantly biased as the large rainfall totals were frequently underestimated. However, when the bias correction was applied remarkable improvement in the SRP’s performance was observed. The different adopted correction techniques yielded close results, with a slight prevalence of the Cumulative Distribution Function (CDF) over the Linear Scaling (LS), and Simple Linear Regression (SLR) techniques. Still, to reliably adjust the bias in the SRP estimates, LS and SLR should be preferred over the CDF technique, as they demonstrated more spatially consistent performance after validation.

How to cite: Ouatiki, H., Boudhar, A., and Chehbouni, A.: Can Bias correction Techniques Improve Remote Sensing-based Rainfall Estimates in a Semi-Arid Context: Case of the Oum Er-Rbia River Basin in Morocco, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8537, https://doi.org/10.5194/egusphere-egu23-8537, 2023.

Satellite precipitation estimates (SPE) offer an excellent way to complement information on the spatio-temporal distribution of precipitation in semi-arid regions, such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia is used to evaluate the performance of the Integrated Multisatellite Estimates for GPM (IMERG). The semi-hourly scale analysis considered five categories related to precipitation intensity (light, moderate, heavy, very heavy, and torrential) and an analysis of two case studies of extreme precipitation was performed. Results found indicate that IMERG tends to overestimate light precipitation, while showing underestimates (errors above 60%) of cumulative precipitation in the rest of the intensity thresholds. This behaviour is related to the variability of precipitation on a point scale provided by the rain gauges and the uncertainties generated in the meshing process of the IMERG products. For high precipitation intensities, a time lag appears between satellite estimates and observations, related to the fact that the estimated precipitation may transform differently from the actual cloud movement. In addition, errors may be directly associated with the lack of information from the passive microwave (PMW) sensor. Finally, it is concluded that while IMERG can capture the spatio-temporal variability of the region in general, it has significant shortcomings in the detection of extreme sub-daily precipitation events. This research has been funded by projects WISE-PreP (RTI2018-098693-B-C32) and ARTEMIS (PID2021-124253OB-I00) and the Institute for Water Research (IdRA) of the University of Barcelona.

How to cite: Peinó, E., Bech, J., and Udina, M.: Dependence of GPM IMERG products on precipitation intensity in Catalonia., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12109, https://doi.org/10.5194/egusphere-egu23-12109, 2023.

Over the past decades, spaceborne radiometers have proven to be valuable input to realize a global coverage of precipitation estimates. However, retrieving accurate shallow precipitation estimates from radiometers remains challenging. The signal related to precipitation formed close to the Earth’s surface is difficult to distinguish from dry weather, especially over land.  Despite the relatively low precipitation rates that are often associated with shallow precipitation, its persistent presence results in a significant contribution to the total amount of rainfall over the mid- and high latitudinal regions. Hence, correct identification is important.

This study aimed to improve our understanding of the radiometric signatures of shallow precipitation from passive microwave observations by implementing a Random Forest (RF) model. RF is chosen because of its limited risk of overfitting and the ability to physically interpret the resulting model structure and parameters. The RF model is applied to brightness temperature observations from all channels onboard the Global Precipitation Measurement (GPM) Microwave Imager (GMI) during 2017-2020 over The Netherlands (52°N). A high-quality gauge-adjusted radar product is used as reference. The echo top height retrieved from the two radars in The Netherlands (Herwijnen and Den Helder) are used to classify the GMI footprints to either dry, shallow (<3km) or non-shallow (>3km) regime.

Hyperparameter settings, such as the depth of the model, and choices such as the number of years the model is trained on or the threshold to classify footprint as dry, shallow, or non-shallow regime have a limited effect on the performance of the RF. In general, the model tends to wrongly classify dry footprints as wet (both shallow and non-shallow). The model showed a seasonal dependency, with the best performance in summer. Preliminary results also showed a strong seasonal effect when excluding all footprints within 40km distance of the coast. These results indicate that four different parameter sets representing each season are required. Furthermore, the specific years the model is trained or tested on are found to strongly affect its performance. Currently, additional variables (such as ERA5 freezing level, two-meter air temperature) and simultaneous observations from the GPM dual-frequency precipitation radar (DPR), are included to further improve and understand the performance of the RF model.

How to cite: Bogerd, L., Whan, K., Kidd, C., Kummerow, C., Petkovic, V., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Detecting shallow precipitation from conical-scanning radiometer observations using a Random Forest model over the Netherlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11506, https://doi.org/10.5194/egusphere-egu23-11506, 2023.

Within the framework of the GEWEX initiative  “Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment” (LIAISE), the WISE-PreP project was carried out to study precipitation processes aiming to characterize possible differences in precipitation induced by surface characteristics (irrigated vs non-irrigated areas) in NE Spain. Specific deployed instrumentation during the 2021 campaign included three sites equipped each with a vertical radar Doppler Micro Rain Radar (MRR) and a laser disdrometer (PARSIVEL), plus an additional PARSIVEL disdrometer, covering both irrigated and non-irrigated sites. Time series of vertical precipitation profiles and in-situ drop size distributions were recorded to study microphysical processes and related variables including precipitation intensity or convective vs stratiform rainfall regimes.

First results show higher accumulated precipitation in the non-irrigated area (eastern area) than those in irrigated area (western area) in summer 2021, a feature also observed in summers for a previous reference period (2010-2019). Maximum and minimum daily temperatures were higher in irrigated areas than in non-irrigated areas. Both results are consistent with current climatology based on monthly precipitation and temperature that indicate the existence of a zonal gradient that increases semi-arid conditions (drier and warmer) from the east to the west. Disdrometer derived 1-min rainfall rate distributions presented some differences between the irrigated and non-irrigated areas during summer, unlike the other seasons when surface conditions are more similar in both areas. An overview of additional results obtained with numerical simulations using the WRF model is also provided. This research was supported by projects WISE-PreP (RTI2018-098693-B-C32) and ARTEMIS (PID2021-124253OB-I00) and the Water Research Institute (IdRA) of the University of Barcelona.

How to cite: Bech, J., Udina, M., Peinó, E., Polls, F., García-Benadí, A., and Balagué, M.: Precipitation observations and simulations during the LIAISE-2021 field campaign, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9890, https://doi.org/10.5194/egusphere-egu23-9890, 2023.

Many studies have already shown that attenuation data from commercial microwave link (CML) networks can be used to derive rainfall information, also on a country-wide scale. Particularly in regions with coarse station networks and without radar coverage, CMLs provide an attractive solution to increase the spatial and temporal coverage of rainfall observations. There are, however, several challenges that we face when transferring the successful applications from Europe to developing countries. In this contribution we present recent results from dense CMLs networks in two African cities, discuss the challenges that we are facing when trying to expand CML rainfall estimation, and present potential solutions to tackle these challenges.

We show rainfall maps with temporal resolution of 15-minutes derived from CML networks in the city of Ouagadougou (Burkina Faso) and the city of Lusaka (Zambia). There is only one rain gauge for comparison in each city, which limits the options for validation. However, comparison of the CML-derived rainfall maps with the gauges shows good agreement. These results clearly show the large potential of the dense CML networks in African cities for rainfall observation. 

Country-wide rainfall estimation based on CML data in developing countries can not always be done in the same manner, as e.g. in Germany. Based on our experience, a large number of CMLs in developing countries are long 7-GHz CMLs. At these frequencies the path attenuation is less sensitive to rainfall and the long CMLs seem more prone to fluctuations during dry periods. This makes the data processing more challenging. We suggest that a combination of CML data processing with data from geostationary satellites is considered a basic requirement and not only an option for further improvement. While this combination is methodologically feasible, it implies large organizational efforts. Either large amounts of satellite data have to be moved to the individual institutions that do CML data processing, or CML data, which is hard to get access to, has to be transferred to an institution that has direct access to the satellite data.

To be able to bring rainfall estimation from a combination of CML and geostationary satellite data to an operational level, simplified access to CML data and concerted processing is required. We do not suggest a final solution, but we present ideas to initiate a discussion that should pave the way towards making operational usage of CML data in developing countries a reality.

How to cite: Chwala, C., Djibo, M., Graf, M., Polz, J., Zougmoré, F., and Kunstmann, H.: CML rainfall estimation in Africa: Recent results, challenges and suggested solutions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15222, https://doi.org/10.5194/egusphere-egu23-15222, 2023.

Extreme floods are one of the most severe natural disasters. In a recent example, long-lasting heavy rainfall over central Europe led to devastating flooding in several catchments in Germany and Belgium on the 14th and 15th of July 2021. The valley of the river Ahr in the German state of Rhineland-Palatinate was heavily affected by this flooding with over 100 casualties and a loss of over 20 billion Euros. Quantitative precipitation estimation (QPE) during this event was affected by several issues. Rain gauge measurements suffered from underestimation and were not available due to power outages towards the end of the event. Weather radar measurements underestimated the rainfall amount due to pronounced vertical gradients of precipitation below the melting layer. Rainfall products from these two sensors were not able to explain the discharge values within the Ahr catchment.A potential solution to improve the rainfall estimation for the Ahrtal event and radar rainfall estimation in general is to add additional rainfall information. This is commonly done by adjusting the radar derived rainfall fields to rain gauges. Here we use opportunistic sensors, namely commercial microwave links (CMLs), which have previously not been used for radar adjustment. We show QPE based on different radar products, each of them with and without an adjustment via CML rainfall estimates. We use the unadjusted RADOLAN product RY and two own polarimetric radar QPEs, of which one is enhanced with specific corrections based on MRR data and combined with a local X-Band gap-filling radar. We perform additive and multiplicative adjustment of the radar QPEs on an hourly basis with CML data, taking into account the path-averaging nature of the CML observations. Our results show that the CML adjustment significantly improves RADOLAN-RY and the polarimetric product without enhancement. The enhanced polarimetric product is already in very good agreement with the reference data and hence is not improved much. The applied enhancements from MRR and X-Band radar data are currently not suitable for operational usage, though. Radar-adjustment with CML data, which is available in real-time without delay, hence provides a suitable solution to improve operational QPE.

How to cite: Graf, M., Polz, J., Chen, J., Winterrath, T., Trömel, S., and Chwala, C.: Improved QPE for the Ahr flooding event using weather radar and CML data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14994, https://doi.org/10.5194/egusphere-egu23-14994, 2023.

A high temporal (hourly) and high spatial resolution (250-meter) multi-decadal (1991 - 2021) gridded dataset of mean temperature and precipitation is presented for the complex mountain area of Trentino-Alto Adige in the north-eastern region of the Alps in Italy. This dataset was obtained from more than 300 meteorological stations covering the entire region of Trentino-Alto Adige as well as the neighbouring countries and regions of Italy. The observed dataset underwent quality checks and quality control such as gross error limit check, two-sided continuity check, persistence check and others for both meteorological variables however, no gap-filling procedure was undertaken in order to keep the originality of the dataset. Using the processed dataset, kriging interpolation technique was used to generate a gridded hourly dataset of the meteorological metered variables on a high spatial resolution grid of 250m x 250m. The accuracy of the kriging dataset was evaluated by a leave-one-out cross-validation approach, wherein the station in consideration is omitted, to remove self-influence, and the time series is reconstructed using the neighbouring stations. For the entire time period and region, the hourly temperature and precipitation show no bias, with a mean absolute error (MAE) of about 1.2 °C and 0.1 mm, comparable with state-of-art high-resolution datasets. This new dataset provides valuable insight into the spatio-temporal distribution of temperature and precipitation for highly variable topography experiencing distinct climatic conditions for the region of Trentino-Alto Adige and it helps to validate climate models for monitoring climate change at the regional level. Moreover, gridded datasets support the modelling of extreme events and climate variance, which are crucial for a range of climate impact assessments.

How to cite: Dhawan, P., Dalla Torre, D., Menapace, A., Majone, B., and Righetti, M.: High-resolution gridded dataset of precipitation and temperature for Trentino-Alto Adige, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11499, https://doi.org/10.5194/egusphere-egu23-11499, 2023.

The specific differential phase K_dp is defined as the slope of range profiles of the differential propagation phase shift Φ_dp between horizontal and vertical polarization states observed by polarimetric radar. The K_dp is an important parameter for meteorological applications because it is proportional to precipitation particle concentrations and size and closely related to rain intensity. Past studies showed that the high K_dp value above the environmental 0 °C level potentially is an early indicator of heavy rain produced by summertime deep convection.

In autumn and winter, the stratiform precipitation system is the primary source of rainfall in north Taiwan. Additionally, embedded convective cells could lead to intense rain rates. But these cells’ top is not always developed higher than 0 °C level. This study uses a C-band polarimetric radar located in north Taiwan to discuss the evolution of K_dp and related rainfall of several heavy rain events in autumn. The application of K_dp to quantitative rainfall estimation is also illustrated.

The result shows that the value of K_dp > 2° km^-1 is closely related to the movement and intensity of the severe rainfall area (> 60 mm h^-1). K_dp > 2.0° km^-1 occurs for more than 30 minutes, which is related to the location of rainfall of 100mm in 3 hours. The development height of K_dp >1.5° km^-1 reaches the melting level, or there is a core area with K_dp >3.0° km^-1 below the melting level, which will cause local heavy rainfall on the ground in the next 10 to 20 minutes (>10 mm in 10 minutes). K_dp > 3.0° km^-1 occurs for more than 1 hour, which is related to the rainfall of up to 200mm in 3 hours.

How to cite: Jung, C.-J. and Jou, B. J.-D.: Application of specific differential phase as indicator for severe rainfall produced by shallow convection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11118, https://doi.org/10.5194/egusphere-egu23-11118, 2023.

The performance of various composite satellite precipitation products is severely limited by their individual passive microwave (PMW)-based retrieval uncertainties because the PMW sensors have difficulties in resolving heavy rain and/or shallow orographic precipitation systems, especially during small scale precipitation events. Characterizing the error structure of PMW retrievals is crucial to improving precipitation mapping at different space-time scales. This paper presents an ensemble learning framework to quantify the uncertainties associated with satellite precipitation products with an emphasis on orographic precipitation. A deep convolutional neural network is devised, which utilizes ground-based radar and gauge blended precipitation estimates as target labels to train satellite precipitation products in order to extract the uncertainty features involved in the satellite products. An ensemble strategy is designed to boost the performance of individually trained deep learning models. The ensemble model is then applied to multiple domains with different geophysical characteristics. The precipitation products derived using the NOAA/Climate Prediction Center morphing technique (CMORPH) over Taiwan and the coastal mountain region in the western United States are used to demonstrate the deep learning-based bias correction performance. The impact of topography on satellite-based precipitation retrievals is quantified. The results show that the orographic gradients have a strong influence on precipitation retrievals in complex terrain regions. The accuracy of CMORPH is dramatically enhanced after applying the ensemble learning-based bias correction technique, indicating the great potential of machine learning in improving satellite precipitation retrievals.

How to cite: Chen, H., Wang, L., Chen, Y.-L., Xie, P., Chen, C.-R., and Liao, T.: Deep learning for uncertainty quantification of satellite retrievals of precipitation: Case studies in two complex terrain regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1718, https://doi.org/10.5194/egusphere-egu23-1718, 2023.

How to cite: Chandrasekar, C. V. and Le, M.: Hydrometeor Identification for GPM DPR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10762, https://doi.org/10.5194/egusphere-egu23-10762, 2023.

The Dual-frequency Precipitation Radar (DPR) on NASA's core Global Precipitation Measurement satellite is the first space-borne instrument that offers an opportunity of a dual-wavelength radar retrieval of the size and the mass concentration of hydrometeors, i.e., two microphysical parameters that control the mass flux through the weather systems. Our focus is placed on DPR observations over the stratiform rain where the analysis revealed a sharp increase in mass flux from ice to rain phase in the official algorithm. This is inconsistent with the expectation that mass flux varies little across the bright band. The proposed algorithm imposes continuity of the precipitation rate across the bright band which additionally helps in deriving bulk ice density. It is based on Bayes' rule with riming parameterized by the “fill-in” model. The radar reflectivity are simulated using the scattering models corresponding to realistic snowflake shapes. The algorithm is validated using the co-located polarimetric radar data collected for the GPM ground validation program. In the future, this dataset will be used for multi-frequency radar studies that aim at constructing high quality training datasets for artificial intelligence algorithms that are necessary to analyse the huge volume of data that will be generated by upcoming space missions such as the one proposed by Tomorrow.io

How to cite: Mroz, K. and Battaglia, A.: Characteristics of ice over stratiform rain: Global statistics from the Dual-frequency Precipitation Radar and the proposed retrieval scheme, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15803, https://doi.org/10.5194/egusphere-egu23-15803, 2023.

Lack of knowledge about the physical processes controlling heavy precipitation arises from the limited number of simultaneous observations of the vertical structure of precipitation and its thermodynamic environment. These limitations are caused by the degradation that the signals of some spaced-based sensors suffer in presence of thick clouds or the lack of high vertical resolution thermodynamic measurements.
To overcome this problem, the ROHP (Radio Occultation and Heavy Precipitation) experiment provides high-quality thermodynamic profiles (temperature, pressure, water vapor pressure, etc.) and vertical information of the hydrometeors, simultaneously. This proof-of-concept experiment led by the Institut de Ciències de l’Espai (ICE-CSIC, IEEC) in collaboration with NOAA, UCAR, and NASA/Jet Propulsion Laboratory, is carried out aboard the Spanish low earth orbiter (LEO) PAZ. Its objective is to test the new Polarimetric Radio Occultation (PRO) concept, and it has been operating since 2018. The standard radio occultation technique consists of tracking the signals emitted by a Global Navigation Satellite System (GNSS) satellite from a LEO satellite that is rising or occulting behind the Earth’s limb. The novelty that PRO offers is that GNSS signals are collected using two different linearly polarized antennae (horizontal and vertical) as opposed to the standard technique, where GNSS signals are acquired using a circularly polarized antenna. Consequently, we can obtain an observable called the differential phase shift, defined as the difference in the accumulated phase delay between both polarizations (H-V). Since the hydrometeors surrounding heavy precipitation events stand out for being oblate spheroid-like, we will have an associated accumulated phase shift if rays are crossing heavy precipitation.
For the sake of continuing with the validation of the PRO technique, we make use of the polarimetric weather data provided by the Next Generation Weather Radars (NEXRAD). NEXRAD is the network of dual-polarized Doppler radars operating at the S-band, that covers all the United States territory. By comparing the differential phase shift obtained with PAZ and the observables from NEXRAD, we can analyze the polarimetry of both systems. In this study, we focus on the vertical structures of NEXRAD-provided specific differential phase shift (Kdp) that can be compared to the PAZ observable accounting for some geometry and frequency factors. This comparison will help us to better understand the PAZ observables, and ultimately to better understand the microphysics underlying heavy precipitation events.

How to cite: Paz Carracedo, A., Padullés Rulló, R., and Cardellach Galí, E.: Understanding the Polarimetric Radio Occultation observable differential phase shift with the help of the NEXRAD polarimetric weather radars, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5291, https://doi.org/10.5194/egusphere-egu23-5291, 2023.

In terms of spatial and temporal information, weather radar data offers notable advantages when used for rainfall statistics compared to rain gauge records. In the past, long-term statistics were only possible through the use of rain gauge records. However, the availability of more than one decade of radar data now allows for a more detailed analysis of rainfall variability in both space and time.

For radar-based statistical analyses to be a reliable alternative to rain gauge statistics, it is essential to have high-quality, consistent data that is free of errors. While advancements in technology, such as dual polarimetric estimation, have improved our ability to quantify rainfall intensities with radar, it is still necessary to utilize "ground truth" records from rain gauges to adjust and ensure the accuracy of the radar estimates. In extreme value statistics where long-term continuous records are required, it is necessary to not only utilize the latest technology but also to adjust and verify older data (e.g. pre dual-pol. data or data with lower spatial and temporal resolution)

This abstract present data from a 20-year radar series single radar from Denmark, where several analyses with regard to rainfall statistics have been conducted. We describe and quantify challenges in bias adjustment, advection interpolation to improve temporal resolution, duration-dependent biases, spatial scaling issues comparing points and pixels, subpixel variability, range dependence in rainfall estimates, etc.

We apply the data to develop extreme value statistics based on peak-over-threshold ranking and stochastic storm transposition. Furthermore, we examine spatial variability using radar-based areal reduction factors as well as spatial correlation analyses and severity diagrams. The analyses and application of radar data are conducted with an aim towards application in urban hydrology.

How to cite: Thorndahl, S. and Andersen, C. B.: Twenty years of radar data from a single C-band radar - Potentials and drawbacks in rainfall statistics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6110, https://doi.org/10.5194/egusphere-egu23-6110, 2023.

Raindrop size distributions (DSDs) are the main tool for describing and discussing rain microphysics. They play a crucial role in remote sensing of precipitation and extensive efforts have been devoted to measuring and modeling them. However, when it comes to DSDs in extreme rain, very few, reliable, results are available. Using numerical simulations, Srivastava (1978, 1982) and List (1988) theorized that for high enough rainfall intensities (>40 mm/h), DSDs should converge toward a stationary state where drop coalescence and breakup are in dynamical equilibrium with each other. In such conditions, the shape of the DSDs should be constant and the particle number concentration should be proportional to the rain rate. However, reliable evidence of such transitions toward a “number-controlled” remains scarce and many researchers have contested its existence.

In this study, high-quality DSD observations from a network of 7 optical disdrometers belonging to the Ruisdael observatory for Dutch atmospheric science are used to take a new, fresh look at the issue. The main research questions are:

• Is there empirical evidence for a transition from size to number-controlled regimes at high rainfall intensities in the Netherlands?
• What parametric model best fits DSDs at high rainfall rates?
• Can the super-CC scaling of sub-hourly rainfall extremes with temperature highlighted by Lenderink et al. (2008) be explained by changes in DSDs?

To address the questions above, we analyze characteristic drop sizes (Dm, D0), number concentrations (NT, Nw) and state variables (LWC, Z, Zdr) for different classes of rainfall intensities and temperatures and study the shape of DSDs by comparing the goodness of fit of various parametric DSD models. We look at non-parametric descriptors such as the relative number of small versus large drops and study the scaling laws linking different moments of the DSD in heavy rain using single and double-normalization frameworks to assess possible convergence toward number-controlled regime at higher intensities.

How to cite: Schleiss, M.: The microstructure of heavy rainfall in the Netherlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4933, https://doi.org/10.5194/egusphere-egu23-4933, 2023.

Wind is a recognised source of environmental bias in precipitation measurements, affecting both catching and non-catching instruments. The latter are a family of precipitation measuring instruments that do not require the collection of hydrometeors inside a reservoir (see e.g., Lanza et al., 2021). All instruments behave like bluff-body obstacles when exposed to wind and produce strong velocity gradients and turbulence near their sensing volume, with considerable impact on the measurement accuracy.

Among them, impact disdrometers present a roughly cylindrical shape, sometimes not fully radially symmetric, and operate by measuring the kinetic energy of incoming hydrometeors. In this work, the wind-induced measurement bias is assessed for the Vaisala WXT-520 gauge, for liquid precipitation, using Computational Fluid Dynamics (CFD) simulation and a Lagrangian Particle Tracking (LPT) model.

The OpenFOAM software was used to run CFD simulations, considering three different wind directions and seven different wind speeds (1, 2.5, 5, 7.5, 10, 15 and 20 m/s). CFD results showed significant updraft upstream of the instrument sensing area and a limited dependency on the wind direction. The numerical model was further validated using wind tunnel measurements performed in the DICCA laboratory on a real gauge.

The obtained airflow field was used as the basis for an uncoupled LPT model to compute trajectories of drops of various diameters (0.25, 0.5, 0.75 and from 1 to 8 mm) while approaching the instrument sensing area. Drops were injected in the simulation domain, starting from a regular grid, with a vertical velocity equal to their terminal velocity and a horizontal velocity equal to the undisturbed wind speed.

A Kinematic Catch Ratio (KCR) is defined as the ratio between the kinetic energy transferred to the sensor in windy conditions and the kinetic energy that would have been transferred in still air conditions. Results shows that at low wind speed (1 and 2.5 m/s) the reduction in fall velocity produced by the updraft reduces the total kinetic energy, resulting in KCR < 1, especially for the smaller drops. However, the increase in kinetic energy experienced by drops carried by strong wind is predominant with respect to the updraft, resulting in KCR values much larger than unity.

Analogously, the Kinematic Collection Efficiency (KCE) can be defined once a Drop Size Distribution (DSD) is chosen. KCE values showed a similar behaviour, with values close to unity at low wind speed, but significantly larger when increasing the wind speed.

Wind also affects the DSD sensed by the instrument, since drops with increased kinetic energy are detected as having a larger diameter. Therefore, the gauge tends to overestimate the number of drops at each drop size bin, showing a shift of the DSD towards the larger diameters, that increases with increasing the wind speed.

References:

Lanza, L. G., Merlone, A., Cauteruccio, A., Chinchella, E., Stagnaro, M., Dobre, M., ... & Parrondo, M. (2021). Calibration of non‐catching precipitation measurement instruments: A review. Meteorological Applications, 28(3), e2002. https://doi.org/10.1002/met.2002

How to cite: Chinchella, E., Cauteruccio, A., and Lanza, L. G.: Assessing the wind-induced bias for an impact disdrometer using numerical simulation and wind tunnel experiments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7215, https://doi.org/10.5194/egusphere-egu23-7215, 2023.

How to cite: Amell, A., Eriksson, P., Hee, L., and Pfreundschuh, S.: Nearly instantaneous probabilistic retrievals of Rain over Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9544, https://doi.org/10.5194/egusphere-egu23-9544, 2023.

High spatiotemporal resolution satellite precipitation products (herein SPPs) have great potential for investigating rainfall dynamics, including diurnal rainfall (DR). However, the relative performances of these products are regionally specific and unknown in most places. This talk presents our recent works on evaluating and applying various SPPs in studying the summer DR events in Taiwan and Luzon (an Island of Philippines nearby Taiwan). In the first part of the talk, we evaluated the four post-real-time SPPs (including TRMM-3B42 v7, IMERG-F v5, IMERG-F v6, and GSMaP v7) in studying DR events in Taiwan. Our results show that IMERG-F v6 outperforms the other SPPs more accurately (both quantitatively and qualitatively) in depicting the summer rainfall variations in Taiwan at multiple timescales (including mean status, daily, and diurnal), using more than 400 rain-gauge observations as the baseline for comparison. IMERG-F v6 also performs better than other SPPs in capturing the characteristics of DR activities. In the second part of the talk, we further evaluated the near-real-time (NRT) products of IMERG v6 (i.e., IMERG-L and IMERG-E) and GSMaP v7 (i.e., GSMaP-NRT and GSMaP-Gauge-NRT) in depicting the variation in DR in Taiwan. Two sub-components of DR variation, daily mean (Pm) and anomalies (ΔP), were evaluated, and ΔP was further separated into diurnal (S1) and semi-diurnal (S2) harmonic modes. Compared with surface observations, all NRT products underestimated Pm and ΔP; however, IMERG products are relatively better than GSMaP products in most of the examined spatial characteristics. Furthermore, temporal analysis shows that only IMERG-E depicts the phase evolution of both S1 and S2, similar to surface observations. Finally, we showed some potential use of IMERG-F v6 and TRMM-3B42 v7 in studying the long-term changes in the summer DR activities over Luzon and its adjacent seas during 2000-2019.

How to cite: Huang, W.-R., Hsu, J., and Liu, P.-Y.: Evaluation and application of satellite precipitation products in studying the summer diurnal rainfall events in Taiwan and Luzon, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-915, https://doi.org/10.5194/egusphere-egu23-915, 2023.

Urban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that precipitation can be enhanced downwind of a city, albeit other locations of precipitation enhancement have also been reported. Among the proposed mechanisms that modify the precipitation, the thermodynamic and aerodynamic processes in the urban lower atmosphere interact with the synoptic conditions and could play a key role in determining the resulting spatial variability of precipitation. In addition, these processes are intricately shaped by urban form characteristics, such as the spatial extent of the impervious land. This study aims to unravel how different urban forms impact the spatial organizations of precipitation climatology under different synoptic conditions. We use the Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation data products and analyze the hourly precipitation maps for a selected set of cities across the continental United States from the years 2015 to 2021. Results suggest that a statistically significant downwind enhancement of precipitation does exist in about four-fifths of these cities, while the magnitude is comparable to previous findings. Additionally, we find that the precipitation distribution tends to be more clustered for higher wind speed; the location for precipitation maxima is located closer to the city center under low synoptic winds but shifts towards the urban-rural interface under high wind conditions. The magnitude of downwind precipitation enhancement is highly dependent on wind directions and is positively correlated with the city size for the south, southwest, and west directions. This study provides observational proof through a cross-city analysis that the spatial pattern of urban precipitation can be attributed to the modified atmospheric processes by distinct urban forms.

How to cite: Lu, Y., Li, Q., Yu, Z., Albertson, J., Chen, X., Chen, H., Pendergrass, A., and Hu, L.: Understanding the influence of urban form on the spatial pattern of precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2870, https://doi.org/10.5194/egusphere-egu23-2870, 2023.

This study investigated the impacts of low-level thermodynamic structure and water vapor on heavy rainfall events in the southern Korean Peninsula during the 2016 summer intensive observation period. An intensive dataset of mobile observation vehicle (MOVE), with high temporal resolution rawinsonde soundings and global navigation satellite system (GNSS) observations in Geochang (GC) supersite, was used. We divided study events into two heavy rainfall cases to compare the characteristics of representative summer heavy rainfall with different synoptic conditions. Case 1 has localized heavy rainfall associated with the Changma (summer monsoon) and Case 2 has convective instability. The temporal behavior of precipitable water vapor (PWV) retrieved from the MOVE-GNSS data demonstrated that during Case 1, heavy rainfall events experience a steep decrease after a long increasing trend. However, the most intense rainfall events occurred after a rapid increase in PWV during Case 2. In Case 1, the mean static stability at >2 km altitude was variable for all periods (in the order of after > before > during rainfall), whereas in Case 2, this was less variable with time and had generally higher convective instability close to the surface, compared with Case 1. In addition, Case 1 demonstrated the progression of a vertical wind structure connected with a quasi-stationary frontal passage (e.g., veering winds at low levels before rainfall), whereas Case 2 demonstrated a nearly homogeneous southwesterly wind from the surface to an altitude of 5 km.

How to cite: Kim, Y.-J. and Lim, B.: Study on the thermodynamic characteristics of heavy rainfall events in the southern Korean Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4680, https://doi.org/10.5194/egusphere-egu23-4680, 2023.

It is a well-established fact that different types of data (station, gridded, reanalysis) possess different statistical characteristics, e.g. for higher-order moments, extremes, and trends. In this contribution we examine the long-term changes in precipitation characteristics on different data sources over Europe. We calculate and display differences between the datasets and attempt to identify causes for the differences and for specific behavior of the datasets. We used data from stations across Europe (ECA&D project), gridded data (E-OBS) and reanalysis (NCEP/NCAR, JRA-55). We mainly analyze the trends of the seasonal total amount, intensity and probability of precipitation. Long-term trends of seasonal values of precipitation variables and their statistical significance are calculated by non-parametric methods (Mann-Kendall test, Kendall statistic). The analysis is conducted on a seasonal basis, with emphasis on winter and summer. We found that each of the datasets has its advantages and drawbacks. Trends in reanalysis deviate considerably from the other datasets mainly because the type and amount of data assimilated into them change in time. The weakness of the grid data sets is the unstable number of stations entering the interpolation in time, and the lack of representativeness of some climate stations is the main disadvantage of the station data.

How to cite: Beranova, R. and Huth, R.: Trends of precipitation variables on different datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5356, https://doi.org/10.5194/egusphere-egu23-5356, 2023.

Grid-based meteorological estimates are indispensable in a wide variety of contexts. In India, most of the existing precipitation datasets are deterministic and have limitations when it comes to expressing the inherent uncertainties in data. Existing gridded datasets for India were created using a similar process, which comprised a multi-stage quality check, followed by methods such as Shepard's interpolation and probabilistic interpolation. This paper focus on the development of ensemble based gridded product for India named as, Indian Meteorological Ensemble Dataset (IMED) for 30 years. Additionally, this paper also discusses the analysis of precipitation extremes over Indian region. IMED (Indian Meteorological Ensemble Dataset) creates a daily ensemble precipitation product for the specified grid using gauge station readings as input, together with spatial variables such as latitude, longitude, elevation, and slope for the period of 30 years from 1991 to 2020. Daily, thirty distinct ensemble members are generated with a resolution of 0.25 degrees. IMD (Indian Meteorological Department) gridded precipitation data, CHIRPS gridded precipitation data and ERA5 land precipitation are compared with the mean of the developed ensemble members. In addition, a sensitivity analysis carried out to find out the possible combination of input parameters such as search radius, number of neighbouring stations to be considered, and number of ensembles to be used etc. and found that the combination 80, 25, 30 respectively gives better performance in terms of the quality of developed dataset as well as the time complexity. The generated ensemble has generally strong reliability and discrimination of events of different magnitudes and it is comparable to other widely used hydrometeorological datasets, although there are significant distinctions. The correlation coefficient between IMED and station precipitation data is 0.972, which is greater than the correlation coefficient between IMD gridded precipitation data and station precipitation data. Ensemble precipitation datasets are especially useful in places with substantial meteorological uncertainty, since practically all available deterministic datasets encounter formidable difficulties in obtaining reliable estimations.

How to cite: Peringiyil, A. and Saharia, M.: Analysis of Precipitation Extremes Using High Resolution Ensemble-based Dataset for India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5822, https://doi.org/10.5194/egusphere-egu23-5822, 2023.

The Tibetan Plateau (TP) plays a vital role in Asian hydrological climate. However, there is a lack of quantitative estimate on TP’s effect on snowfall over China. Some trending views include that the TP acts as a giant wall, blocking cold outbreaks and protecting southern China from severe snowstorm. Here, through topography experiments with and without the TP, we demonstrate that, compared to the world without the TP, the presence of the TP decreases snowfall in northern China by 60% due to drastically reduced moisture, while it promotes snowfall in southern China by 1500%, especially from November to March, through attracting cold air from the north and moisture from the south to southern China. The presence of the TP increases winter relative humidity substantially in southern China, which reduces human comfort. This work refutes some trending views and helps us correctly recognize TP’s role in China’s winter climate.

How to cite: Wang, L. and Yang, H.: Tibetan Plateau increases the snowfall in southern China: a refutation to some trending views, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7526, https://doi.org/10.5194/egusphere-egu23-7526, 2023.

The usefulness of satellite multi-sensor precipitation and other large-scale precipitation products in hydrologic applications can be hindered by substantial uncertainty. In parts of the world with few ground observations of precipitation, such uncertainty is difficult to quantify. At the same time, how to cope with the characterize and model the spatiotemporal structure of this uncertainty has been called a grand challenge within the precipitation community. We present progress on two fronts which, when combined, addresses this grand challenge. Rather than relying on ground reference data to quantify uncertainty in NASA’s IMERG precipitation dataset, we instead use the dual-frequency precipitation radar aboard the NASA/JAXA GPM platform. This uncertainty information is then fed into the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which uses an uncalibrated anisotropic and nonstationary spatiotemporal correlation modeling approach to stochastically generate ensemble precipitation fields that depict the uncertainty inherent in IMERG. We then use these ensemble fields to examine the effects of precipitation uncertainty in several hydrologic applications, including flood monitoring and prediction of water and energy fluxes. Ensemble-based hydrologic simulations outperform those based on IMERG and help reveal the spatiotemporal scales and hydrologic variables for which precipitation uncertainty is critical. The approach is compatible with other continental-to-global scale precipitation estimates such as those from numerical weather models, and can also be used in precipitation downscaling contexts. We are developing a set of open-source tools to facilitate its usage.

How to cite: Wright, D., Hartke, S., Li, Z., Peng, K., Alexander, A., and Liu, Y.: Estimating and Simulating Precipitation Uncertainty Data for Large-Scale Hydrologic Applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9527, https://doi.org/10.5194/egusphere-egu23-9527, 2023.

Precipitation is the most significant component of the global water and energy cycle associated directly with major Earth system processes- atmospheric circulation, clouds and water vapor and overall regulation of the biogeochemical cycle. Precipitation also has a major contribution in maintaining the socio-economic stability of the world as it is the primary source of freshwater and directly affects the food and water security. Extreme weather events associated with precipitation such as floods, droughts, landslides etc. are likely to intensify under current climate change scenarios which could induce mass migration and human conflicts due to unavailability of food and freshwater resources. Hence, accurate precipitation estimates are crucial in enhancing our understanding of the changing earth system processes and management of water resources through numerical weather predictions and hydrological forecasting. In this study, we evaluated the performance of a satellite-gauge merged rainfall product (GSMaP-IMD, 0.1×0.1) with a gauge based observational data (Indian Meteorological Department (IMD) daily gridded rainfall, 0.25×0.25) and two global satellite-based rainfall products- IMERG Final-run and GSMaP-CPC (standard JAXA product, GSMaP-Gauge) over 4 major river basins of Western India for the southwest monsoon period during 2000-2020.  

Results indicate that GSMaP-IMD better represents the overall distribution of rainfall over the river basins. The cumulative rainfall distribution over the study area is represented more realistically than other two datasets, especially at higher rainfall intensity (mm/day). GSMaP-IMD has smaller root mean squared error and higher correlation coefficient value than IMERG and GSMaP-CPC during the observation period. The distribution of low and moderate rainfall improved remarkably in case of GSMaP-IMD compared to the other products. Temporally, higher rainfall events are not represented accurately by IMERG and GSMaP-CPC which is improved in GSMaP-IMD. Overall, it is observed that IMERG overestimated the high rainfall events while GSMaP-CPC underestimated it whereas GSMaP-IMD showed improvement in estimating the events over the study area. The probability of detecting true rainfall events is further improved in GSMaP-IMD for all the basins. IMERG shows higher false rainfall bias over regions with high rainfall intensity which is reduced in GSMaP-CPC and further improved in GSMaP-IMD. The total ability of a dataset to capture actual rainfall events (Critical Success Index) is further enhanced for GSMaP-IMD. Finally, IMERG shows a large negative bias in detecting low rainfall events while GSMaP-CPC shows large positive bias in detecting high rainfall spatially. This systematic error is reduced in the GSMaP-IMD rainfall product. The results indicate that the IMERG and GSMaP-CPC have difficulties in detecting low and high rainfall events and further have systematic error which is due to the orographic effects and regional characteristics in southwest monsoon. Overall, satellite-gauge merged rainfall dataset performed better than the satellite-based products over major river basins of Western India. The integration of in-situ rainfall data from gauges and radars in future with satellite products at regional scale is found to improve the bias characteristics in IMERG and GsMAP-CPC which is significant in improving the availability of rainfall dataset for regional hydrological modelling applications, numerical weather predictions and water resource management. 

How to cite: Choubey, S., Kumari, R., Chander, S., and Kumar, P.: Analysis of Various Gauge Adjusted Merged Satellite Rainfall Products : A study for Major River Basins of Western India., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13962, https://doi.org/10.5194/egusphere-egu23-13962, 2023.

India has tropical monsoon climate with significant regional variability in rainfall and temperature, where precipitation is closely connected to precipitable water vapour (PWV). Here, the satellite and reanalysis data are applied to study the spatial and temporal changes of PWV over India in 1980–2020. We have also analysed its potential drivers such as precipitation, surface temperature and evapotranspiration during the same period. The distribution of annual PWV depicts the highest values over the east coast (40–50 mm) and lowest in western Himalaya (< 10 mm). The seasonal distribution shows highest PWV during monsoon (June-July-August-September, about 40–65 mm). Similarly, the monthly cycle of PWV shows the lowest amount in January, which gradually increases with time until it peaks in July, and then decreases thereafter. Interannual variations in PWV show a peak in 1997–1998, which is related to the strong El-Nino Southern Oscillation (ENSO) event during that period. Among the sources, sinks and drivers, evapotranspiration (0.6-–0.9), precipitation (0.7–0.9) and surface temperature (0.5–0.6) are highly correlated with PWV throughout India. The PWV trends in India are found significantly positive (0.6–0.9), which can be attributed to recent increase in surface temperature and thus the rise in atmospheric moisture.  This is concern for regional climate change as PWV is directly connected to water vapour and thus, to temperature and climate.

How to cite: Sarkar, S.: Long-term changes in precipitable water vapour over India derived from satellite and reanalyses data for the past four decades (1980–2020), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-184, https://doi.org/10.5194/egusphere-egu23-184, 2023.