AS1.25
Orals: Fri, 28 Apr | Room M1
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, 24–28 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, 24–28 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, 24–28 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 0.05o latitude/longitude over the entire globe (90oS-90oN) 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, 24–28 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, 24–28 Apr 2023, EGU23-2961, https://doi.org/10.5194/egusphere-egu23-2961, 2023.
Snowfall detection and quantification are challenging tasks in the Earth science field. Ground-based instruments provide only punctual measurements, and therefore the development of satellite-based snowfall retrieval methods is necessary for the implementation of a global monitoring system of snowfall. In particular, Passive Microwave (PMW) radiometric measurements are used for snowfall remote sensing: the retrieval is based on the scattering effect of the snowflakes visible in the high-frequency channels (> 80 GHz) on the upwelling radiation. However, the detection is made difficult by the weakness of this signature and by the contamination by the background surface emission/scattering signal. This phenomenon is particularly evident at high latitudes, where the prevalence of very light snowfall events and the extremely cold and dry environmental conditions make snowfall retrieval very difficult. The exploitation of operational microwave sounders on near-polar orbits such as the Advanced Technology Microwave Sounder (ATMS) allows for a very good coverage at high latitudes. Moreover, the wide range of channels (from 22 GHz to 183 GHz), allows for a radiometric characterization of the surface at the time of the overpass.
In this work the High lAtitude sNow Detection and rEtrieval aLgorithm for ATMS (HANDEL-ATMS), a new snowfall retrieval algorithm developed especially for high latitude environmental conditions and based on the ATMS observations, is described. The algorithm is based on the use of ATMS-CPR coincidence dataset, i. e. a dataset where each ATMS multichannel observation is associated with a vertical snow profile obtained by the CloudSat Cloud Profiling Radar (CPR) and therefore it is possible to analyze the relationship between the vertical precipitation structure and the PMW measurements in a direct way, without using simulated datasets.
The algorithm is composed of three main modules. The first module, based on the PMW Empirical clod Surface Classification Algorithm (PESCA), exploits ATMS low-frequency channel observations to obtain the surface classification and radiometric characterization at the time of the overpass. The second module estimates a set of clear-sky simulated brightness temperatures (TBs) by exploiting the previously derived surface radiometric properties. The clear-sky TBs set is compared with the observed TBs to highlight the snowfall signature. The third module is composed of four neural networks, which have been tuned against the CPR snowfall products. These networks, which exploit the deviation of the ATMS TBs from the clear-sky simulated TBs and the PESCA surface classification flag as inputs, return as outputs a snowfall detection flag and the surface snowfall rate estimate. HANDEL-ATMS shows very good detection capabilities - POD = 0.83, FAR = 0.18 and HSS=0.68. Estimation error statistics show an overestimation of very light snowfall events, but a good agreement for more intense events with respect to CPR snowfall products. The analysis of the results for an independent ATMS-CPR coincidence dataset and of selected snowfall events evidence the capability of HANDEL-ATMS to well detect and estimate snowfall also in presence of extreme environmental conditions typical of higher latitudes – dry and cold atmosphere and snow-covered background surface.
How to cite: Camplani, A., Casella, D., Sanò, P., and Panegrossi, G.: The High lAtitude sNowfall DEtection aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at the high latitudes, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6662, https://doi.org/10.5194/egusphere-egu23-6662, 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 Apr 2023, EGU23-11499, https://doi.org/10.5194/egusphere-egu23-11499, 2023.
The specific differential phase Kdp is defined as the slope of range profiles of the differential propagation phase shift Φdp between horizontal and vertical polarization states observed by polarimetric radar. The Kdp 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 Kdp 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 Kdp and related rainfall of several heavy rain events in autumn. The application of Kdp to quantitative rainfall estimation is also illustrated.
The result shows that the value of Kdp > 2° km-1 is closely related to the movement and intensity of the severe rainfall area (> 60 mm h-1). Kdp > 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 Kdp >1.5° km-1 reaches the melting level, or there is a core area with Kdp >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). Kdp > 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, 24–28 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, 24–28 Apr 2023, EGU23-1718, https://doi.org/10.5194/egusphere-egu23-1718, 2023.
The GPM science implementation plan articulates the new frontiers of space-based precipitation observations, including new insights into vertical storm structure and microphysics [1]. The DPR algorithms are a key part of the process as discussed in the GPM science implementation the last updated DPR Algorithm Theoretical Basis Document (GPM/DPR ATBD 2021) document. Dual-frequency precipitation radar (DPR) onboard the GPM satellite has extended scan pattern to full swath mode for both Ku- and Ka-band since May 2018.
The objective of the level-2 DPR algorithms are to derive radar only meteorological quantities meaning general characteristics of the precipitation, correction for attenuation and estimation of precipitation water content, rainfall rate and, when dual-wavelength data are available, information on the particle size distributions in rain and snow. The DPR Level-2 algorithms consist of several modules including the classification (CSF) module. Currently, our team plays a key role in the international DPR algorithm development group and is responsible for the dual-frequency algorithms development in the classification module. These algorithms include rain type classification, melting region detection, surface snowfall identification, graupel and hail detection etc. [2]-[4].
It is a unique advantage for space radar to provide a hydrometeor type over the globe while ground based observations are limited to the regions of deployment. Among the algorithms of current DPR classification module, most of the products are two dimensional with either a “flag” or “type” (or etc.) on a Latitude / Longitude surface. In this research, we will add a range-bin based hydrometeor type for DPR full swath data to enhance the classification module. This three-dimensional hydrometeor identification feature is the next frontier built upon our knowledge of hydrometeor classification development for DPR.
This algorithm has been applied to various precipitation types and validated successfully with either ground based weather radar or airborne weather radar observations from field experiments such as OLYMPEX. Validation for cases with extreme hydrometeor type as hail are also performed with GMI-based approach and illustrate meaningful comparisons.
How to cite: Chandrasekar, C. V. and Le, M.: Hydrometeor Identification for GPM DPR, EGU General Assembly 2023, Vienna, Austria, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 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, 24–28 Apr 2023, EGU23-184, https://doi.org/10.5194/egusphere-egu23-184, 2023.
In most ungauged areas, validation of precipitation gridded satellite products is fundamental to provide precipitation information with high accuracy and spatial-temporal resolution. Drâa river basin (DRB) in southeastern Morocco is one of ten driest watersheds worldwide with a limited network of rain-measured stations. However, literatures investigations have shown the necessity of strong climatic datasets to conduct water resources management. In this study, five satellite precipitation products with high spatio-temporal resolution were evaluated including: the latest version of Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS V2.0), the atmospheric reanalysis dataset for the global climate by the ECMWF (ERA5-Land), the latest version of Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.2), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) and Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT V3.1). The evaluation was conducted in terms of their performance in capturing occurred precipitation events and reliable amounts. The Monthly and seasonal precipitation amounts determined by these products at different altitude levels were evaluated against available rain gauge observations for a period of 81 months using point-to-pixel method and with reference to continuous, categorical and volumetrics indices. The only available station belonging to SYNOP weather network (Surface Synoptic Observations) with more than 40 years of data was included to advance our conclusion regarding the performance of a given P-dataset. The achieved results show that the best performance was obtained for ERA5-Land and MSWEP V2.2 for monthly and seasonal time-step, respectively, while CHIRPS V2.0 followed by TAMSAT V3.1 and PERSIANN-CCS-CDR perform the worst. The expected results will cover the performance of each P-dataset for different sub-seasons, elevation, intensities and their ability to detect extreme rain events. The outcomes of this investigation provide valuable information in one of the most scarcely gauged and arid Moroccan watersheds, indicating which P-dataset could be an alternative to rain gauges measurement.
Key words: Drâa river basin (DRB), ungauged arid areas, precipitation satellite products, ground observations.
How to cite: Khettouch, A., Hssaisoune, M., Hermans, T., Aouijil, A., and Bouchaou, L.: Assessment of preselected Satellites and Reanalysis Precipitation Products using ground observations over the Upper and the Middle Drâa catchment, central-east of Morocco., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6602, https://doi.org/10.5194/egusphere-egu23-6602, 2023.
Posters on site: Thu, 27 Apr, 16:15–18:00 | Hall X5
A major challenge for measuring precipitation from space is the need to properly capture the spatial and temporal variability of precipitation. This requires that all available observations from precipitation-capable sensors are exploited. Passive microwave sensors are fundamental in providing reliable observations since they relate to the precipitation particles themselves. Passive microwave sounding instruments have been developed for cubesats, such as for the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission. The TROPICS pathfinder was launched in June 2021 into a polar orbit, carrying the TROPICS Millimeter-wave Sounder (TMS) with frequencies ranging from 91.655 to 204.8 GHz. The Precipitation Retrieval and Profiling Scheme (PRPS) has been adapted for use with the TMS based upon a 4-channel ATMS-DPR a priori database. This surrogate database is built upon coincident observations from the NPP and NOAA-20 ATMS sensors and the precipitation retrievals from the GPM DPR. The main limitation of the current database is that it is limited to just four closely matched channels, one at 89/91 GHz and the three around the 183.31 GHz water vapour absorption lines. The limited number of TMS vs DPR observations early in the mission precluded their use to generate a viable database: however, a reasonable number of coincident observations are now available to provide an insight into the use of the full range of TMS channels in the retrieval scheme. A total of about 1200 coincident TROPICS/GPM overpasses are available since launch, providing about 2 million matched footprints. Comparisons between the full 12-channel TMS database and the 4-channel ATMS database show a good degree of improvement, although refinement of channel weighting is deemed necessary, not least due to the high inter-channel correlations present within the 118 and 183 GHz channel groups. However, validation of the resulting precipitation products against the IMERG precipitation product indicate that the retrievals from the TMS are comparable as those from similar cross-track sounding instruments (such as GPROF retrievals from the MHS and ATMS).
How to cite: Kidd, C., Matsui, T., Blackwell, W., Braun, S., Leslie, R., and Griffith, Z.: Advancing Precipitation Measurements from the NASA TROPICS Mission., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8700, https://doi.org/10.5194/egusphere-egu23-8700, 2023.
The tropical ocean, which receives a substantial volume of precipitation and evapotranspiration, has a greater impact on Earth's energy and water balance. Yet, considerable studies have been focused on the Earth's terrestrial precipitation, whereas very little attention has been given to the oceanic region. Despite the advancement of satellite and reanalysis precipitation estimates, relatively few studies explored these estimates over the oceans. In this context, we quantitatively evaluate and inter-compare the state-of-the-art satellite, reanalysis, and merged precipitation products over the tropical oceans. We use a suite of various gridded and well-known precipitation datasets such as the Integrated Multisatellite Retrieval for Global Precipitation Measurement (IMERG), Global satellite Mapping of Precipitation (GSMaP), European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis v5 (ERA5), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) products to comprehensively estimate the tropical oceanic precipitation and its spatial and temporal variation. In particular, we are looking into the estimates of the total mean precipitation and its inter-annual variation. In addition, the discrepancies among various precipitation estimates are analysed as a function of different spatial and temporal scale to assess their uncertainty over tropical ocean for the first time. This study will provide deep insights into the precipitation characteristic and its spatio-temporal variability across the tropical ocean. Moreover, such information will help to revisit the estimation of the global water budget components in the near future.
How to cite: Pradhan, R. K. and Markonis, Y.: Multi-source assessment of uncertainty over the tropical ocean, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-394, https://doi.org/10.5194/egusphere-egu23-394, 2023.
When snow particles fall and pass through the melting layer, they melt from the surface and change into rain particles. At this time, due to the difference in permittivity between ice and water, reflectivity appears large, and this is called the bright band (BB). Since the BB causes confusion in the precipitation estimation of weather radar, many studies on BB detection have been conducted to improve the quantitative precipitation estimation accuracy of weather radar and the performance of numerical models. Wind profiler radar (WPR) is useful for vertical structure analysis of mesoscale convective systems with excellent temporal and vertical spatial resolution. We analyzed the bright band of Typhoon HAISHEN passing near the Korean Peninsula from 00 UTC on September 06 to 00 UTC on September 07, 2020 by 9-site WPR. As the HAISHEN approached, distinct precipitation patterns and BB were detected with vertical signal to noise ratio and vertical radial velocity. The BB height gradually increased as the typhoon approached, and decreased as it moved away. Therefore, the precipitation converged in the front of the typhoon and originated from a stratiform structure. It is known that the BB height depends on local factors such as ground temperature and topography. However, the BB height in Jeju Island and Chupungnyeong, located on the mountain boundary at about 240 m above sea level, showed 3-4 km and 4-5 km, respectively. In order to understand the structure of a rapidly developing precipitation cell, it is necessary to investigate the dynamic parameters which are optimal to retrieve based on WPR.
How to cite: Lee, K. H., Kwon, B. H., Kim, Y., Seo, J., and Lee, G. M.: Variation in bright band height during the passage of a typhoon observed with wind profiler radars, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3081, https://doi.org/10.5194/egusphere-egu23-3081, 2023.
The effectiveness of numerical weather prediction models in forecasting precipitation and temperature extremes is highly dependent on the correct combination of the parameterization schemes as well as the grid resolutions used. For precipitation, the parameterization schemes of microphysics, cumulus and the planetary boundary layer are decisive for the correct forecast of the event. The WRF model is one of the most widely used numerical weather prediction models to estimate such extreme phenomena. In order to identify the optimal combination of these parameterizations for the European region, WRF is used here to simulate eight extreme precipitation events that occurred in the Schleswig–Holstein and Baden–Wurttemberg regions in Germany. The events were selected from the German Weather Service (DWD) catalog and exceeded DWD severe weather warning level 3 (i.e., precipitation > 40 mm/h – W3). A two-way nesting approach is used with 9 and 3 km spatial resolutions. The initial and boundary conditions are obtained from the ERA5 dataset at 0.25° × 0.25° resolution. To model each event, thirty different parameterization configurations were used, accounting for all possible combinations of five microphysics (MP), three cumulus (CU), and two planetary boundary layer (PBL) parameterization methods, yielding a total of 240 simulations. To determine the performance skill of each setup, the multi-criteria decision analysis Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed. Six categorical and five statistical metrics are used as input in the TOPSIS algorithm to calculate each member's performance rank. The analysis conducted here revealed that an increase in the grid spatial resolution from 9 km to 3 km did not result in a substantial improvement in the accuracy of the forecast in time or in the estimation of precipitation intensity. When considering each event individually, the optimal combination, according to the TOPSIS ranking algorithm, is seasonally and geographically dependent, with specific members appearing more frequently in the top-ranking positions. When all events are treated as one to determine the best performing simulating members, the Morrison double-moment (MDM) scheme, along with the Grell-Freitas (GF) CU and the YSU PBL schemes, is found to be the most effective set up, followed by the WRF single-moment 5-class, along with the GF and the YSU schemes.
How to cite: Sotiropoulou, R.-E. P., Stergiou, I., and Tagaris, E.: Evaluation of WRF physics ensemble performance in forecasting extreme precipitation events over Germany , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4123, https://doi.org/10.5194/egusphere-egu23-4123, 2023.
Recent studies have shown that climate change is likely to affect the agricultural sector in Cyprus. In the present study, crop water requirements in each of the 1995–2004 and 2010-2019 decades are estimated, by employing the same methodology, and subsequently compared. The aim is to investigate whether there has been any significant change regarding the crop water requirements of the most water-intensive crops in Cyprus. For the estimation of the crop evapotranspiration, the FAO Penman-Monteith methodology is used, as this has been adapted to satellite data. Remote-sensing data from Landsat TM/ETM+/OLI were employed.
Five water-intensive crops are in the focus of this research: citrus, bananas, colocasi, potatoes, and avocado. For each of these crops, estimations of the crop evapotranspiration were performed, considering three agricultural areas on the island where these crops are grown: Pafos, Polis, and Famagusta.
The results have shown that there is no significant effect of climate variation on crop evapotranspiration, despite the fact that some climatic factors have exhibited a change on the island of Cyprus; the mean values for the most water-intensive trees/crops in Cyprus in the 1994–2004 decade have shown no significant difference from the mean values in the 2010–2019 decade, for all the crops and all agricultural areas. From the statistical analysis performed, it can be concluded that the climate change which has been documented in the past decades has not impacted significantly the crop evapotranspiration.
The authors would like to express their thanks to the Agricultural Research Institute for providing the proper resources for applying the specific methodology. Also, the authors acknowledge the ‘EXCELSIOR’: ERATOSTHENES: EΧcellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.
How to cite: Michaelides, S., Papadavid, G., Hadjimitsis, D., and Kountios, G.: Satellite remotely-sensed data for studying the impact of climate change on crop evapotranspiration in Cyprus, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4215, https://doi.org/10.5194/egusphere-egu23-4215, 2023.
Many studies have proposed methods to estimate the raindrop size distribution (DSD) parameters from polarimetric radar data as a part of rain attenuation correction and/or rainfall rate estimation algorithms, in which a modified gamma distribution model has been often used to characterize the natural variation of DSD. The parameters that determine the modified gamma DSD are a shape parameter μ, median volume diameter D0 or slope parameter Λ, and a number concentration N0 or its normalized version NW. While D0 (or Λ) and NW can be retrieved relatively straightforward from the polarimetric radar measurements, estimation of shape parameter is not an easy task. Instead, empirical relations including μ-Λ and/or μ-D0 relations derived from surface measurements of DSD are widely used to estimate μ implicitly.
Adachi et al. (2015) proposed a method to estimate the three parameters of the DSD from polarimetric radar data without any assumptions of relationship among the parameters. In that method, they assumed a constant shape parameter in a range profile. However, this assumption may not be satisfied if the radar is sampling mixed convective/stratiform echoes that simultaneously exist in a single profile. Theoretically, on the other hand, a shape parameter can be estimated from a correlation coefficient ρHV at each range gate (e.g., Thurai et al. 2008). However, to estimate shape parameters with a method of this kind, it is necessary to obtain a correlation coefficient with quite high accuracy, for which a very long sampling time is needed to apply it to radars to satisfy this condition for most radar measurements. The MRI C-band polarimetric radar is equipped with solid-state transmitters, which enable the radar to make observations of correlation coefficient with high accuracy in a relatively short time, especially in high-SNR regions. Thus, we have developed an algorithm to estimate a shape parameter at each range gate both from correlation coefficient and differential reflectivity along with D0 and NW, and compared it with surface measurements.
How to cite: Adachi, A., Kobayashi, T., and Umehara, A.: Estimation of raindrop size distribution from polarimetric radar measurements at C-band, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4731, https://doi.org/10.5194/egusphere-egu23-4731, 2023.
Extreme rainfall is characterized by a high spatio-temporal variability. This variability is exacerbated in mountain areas, such as the tropical Andes, where the complex orography and mesoscale atmospheric processes have an enormous influence on the rainfall processes. Particularly the analysis of extreme rainfall events in the Ecuadorian Andes has remained a challenge due the lack of high spatio-temporal resolution operational observing systems. However, the recent availability of rainfall radar data in this area enables an improvement of our knowledge about those extremes. Here, we presented a study that aims to identify specific types of extreme rainfall events based on a clustering approach and to analyze their spatio-temporal characteristics. The study is based on three years of data collected from an X-band scanning weather radar that was located at 4450 m a.s.l in the Tropical Andes of southern Ecuador, delivering high resolution (5min, 500m) data. Several extreme rainfall events were identified, which were selected based on a rainfall accumulation threshold and visual inspection. Then, extreme rainfall events characteristics (e.g., rain rate, duration, rainfall accumulation, hour and month of occurrence) were identified for each event. Then, the k-means clustering was applied to the events using their rainfall characteristics. The main idea of this algorithm is to cluster a set of objects by accounting for their similarities. In our presentation, we will show the three major types of extreme rainfall events resulting from our analysis as well as the marked differences in their rainfall characteristics. The first type of extreme-events showed the highest values of intensity and the lowest values of duration. Also, two extreme-events types showed predominant months and hours of occurrence. In addition, the site of occurrence of the spatial nucleus of maximum intensity of the first type was located at higher elevations. We will show that the typology of extreme rainfall events improves our knowledge about the spatio-temporal characteristics in the tropical Andes.
How to cite: Urgilés, G., Orellana-Alvear, J., Bendix, J., and Célleri, R.: Characterization of Extreme Rainfall Events Classes in the Tropical Andes by Using Weather Radar Data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6374, https://doi.org/10.5194/egusphere-egu23-6374, 2023.
Due to its geographic location and unique climatic circumstances, the Galápagos archipelago is renowned for its exceptional and high endemic biodiversity. Nevertheless, due to limited access to the permanent freshwater body in the archipelago, the freshwater budget is almost exclusively dependent on precipitation. However, with the lack of spatial and temporal distribution of rainfall information, it is not easy to understand the short- and long-term dynamics of rainfall in the Galápagos.
The poster presents the new satellite-based rainfall retrieval algorithm, the Galápagos Rainfall Retrieval (GRR), which offers the potential for a high spatio-temporal resolution (2 km, 10 min) rainfall product in near real-time for the Galápagos archipelago.
The algorithm is proposed to combine physical methods with machine learning in which sequences of Geostationary Earth Orbit infrared (GEO-IR) images are used to retrieve both cold season Garua drizzle and warm season convective rainfall.
In the first step, a threshold technique and spectral differences are used to identify the cloudy regions and distinguish the low, middle, and high clouds. Next, the cloud-covered region will go under a different entity-based classification method (e.g., slope test/ML algorithm) for each cloud type to detect the low stratus/Garua drizzle and potentially convective core regions. The next test follows for all detected potentially convective core regions based on cloud formation over time and space to examine whether they are likely to be decaying. If the convective core is classified as decaying, it is labelled stratiform rain; otherwise, it is labelled as the active convective core.
Finally, the rainfall assignment will be performed by training the random forest regression models. The convective and stratiform cells will be trained based on microwave-only IMERG-V06 rainfall data separately, meanwhile the cloudsat would be used to train the rainfall rate for the Garua detected regions. By the end, all of these steps are combined together as GRR product. The algorithm of GRR product will be developed for the time period 1/1/2022-1/1/2023 and then it would be applied to the entire available GOES-16 dataset.
The validation will be conduct by: i) independent microwave-only IMERG-V06 rainfall data/cloudsat not used for model training ii) recently installed automated weather stations (AWS) network with high temporal resolution of 10 minutes covering a W-E and luff-lee transects over three islands (Isabela, S. Cruz, S. Cristóbal).
How to cite: Turini, N., Delgado Maldonado, B., and Bendix, J.: High spatio-temporal rainfall algorithm over Galápagos archipelago using multispectral GOES-16 infrared brightness temperatures: the GRR product, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6938, https://doi.org/10.5194/egusphere-egu23-6938, 2023.
Quantitative estimation of precipitation (QPE) of high resolution, accurate and in real-time, increases the potential of weather radars for many applications, such as flash flood forecasting and hydropower production and distribution management. Using polarimetric variables from dual-polarization weather radars has already shown significant improvements in quantitative precipitation estimation in many countries with diverse weather. In Brazil, in the past ten years, we have seen an increase in dual-polarization weather radar coverage, mostly S-Band and some X-Band, concentrated in the southern parts of the country, an area prone to severe weather with high precipitation and lightning due to mesoscale convective systems. This region's significant economic activity is agriculture and energy production, accounting for more than 33% of the hydro energy generation used in the country. Therefore, the improvement of precipitation estimation is a necessary goal. However, the use of weather radar's QPE depends on calibration, good fit with rain gauges and distrometers, good data filtering, target’s distance from the radar, orography (i.e., relative to the topography), and signal propagation, as well as other factors. A multi-sensor integration approach of remotely sensed precipitation estimation using weather satellites and weather radar with rain gauges improves the accuracy of hydrological models compared to a model using only rain gauge data. A quantitative precipitation estimation algorithm called SIPREC (System for Integrated PRECipitation) has been used operationally for more than 15 years, combining data from different sources, such as weather radar, rain gauge, and satellite. Precipitation estimates are obtained through an automated precipitation classification scheme based on reflectivity structures. This approach aggregates data from rain gauges by interpolation while maintaining the spatial distribution of the radar or satellite measurement. Statistical results indicate that the method can reduce radar and satellite data errors. This method is an essential advantage in an operational environment since it does not require frequent processing to update the weights as in other known schemes. However, this approach does not solve problems such as uncertainties related to Z-R estimation, spatial variability, and the one-hour temporal resolution. To improve the SIPREC algorithm, we used machine learning classification and regression methods to address the problem of precipitation estimation using dual polarization variables and rain gauge. An enhanced satellite precipitation estimation using GOES-16 data also replaced the previous dataset, and a new quality control algorithm for the network of weather radars was also applied to the dataset. A performance evaluation study shows improvements in precipitation estimation, primarily when used in real-time in an operational environment. This paper presents the results of this evaluation, with applications in severe weather events with high precipitation in the area.
How to cite: Beneti, C., Calvetti, L., Verdelho, F., Rocha Junior, R., Silva Junior, J., and Cebalhos, V.: Operational Quantitative Precipitation Estimation Algorithm in Southern Brazil - An Update Blending Dual Polarization Weather Radar Network with Raingauges and Satellite Data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8456, https://doi.org/10.5194/egusphere-egu23-8456, 2023.
Weather radars are a significant component of modern precipitation recordings,as they provide information with high spatial and temporal resolution. However, radars as a tool for weather applications emerged only after the 1950s. AI/ML methods have proven to be successful when it comes to determining patterns and connections between related fields in space and time. Moreover, AI/ML methods have exhibited remarkable skill in infilling missing climate information (see Kadow et al. 2020). Desired outcomes of the project include using these AI/ML techniques to build a spatial precipitation field by combining station and radar data. We will use data from two well-known datasets: RADOLAN and COSMO-REA2. The validity of this digital twin will be investigated by comparing its output with other reanalysis data (e.g. ERA5). Further evaluation can be carried out by testing the radar field’s accuracy in detecting extreme precipitation events in the past (e.g. heavy rain events in the summer of 2021 in Western Germany). We aim for the creation of a radar field that will be successfully projected in the past. Moreover, it will uncover new information on regional climatology, especially in areas where station data is sparse.
How to cite: Filippou, D., Plésiat, É., Meuer, J., Thiemann, H., Ludwig, T., and Kadow, C.: Machine Learning-driven Infilling of precipitation recordings over Germany, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9191, https://doi.org/10.5194/egusphere-egu23-9191, 2023.
The assessment and interpretation of an extreme hydrometeorological event and the consequent impacts such as flash floods, especially within urban environment, is of high interest, especially in the Anthropocene era. Global warming intensifies Urban Heat Island resulting in local intense convective precipitation over a densely urbanized area. Urbanization on one hand, increases the sensible heat flux and transports more water vapor, mixing it into the upper atmosphere and on the other hand, increases the surface roughness and as a consequence mechanical turbulence drives low-level convergence resulting in enhanced intensity and frequency of convectional extreme precipitation (Nastos and Zerefos, 2007; Li et al., 2023).
In this study, we focus on an extreme summer convective event on August 24, 2022, in Athens, Greece, where the precipitation intensity reached instantly 116 mm/h and the visibility was limited to 600m. More specifically, we analyzed and compared the H03B product of EUMETSAT Hydrology Satellite Applications Facility (HSAF) with the ground-based OTT PARSIVEL Laser-based optical Disdrometer measurements.
Product H03B is based on the infrared (IR) electromagnetic spectrum from the SEVIRI instrument on board of Meteosat Second Generation satellites. The product is generated at the 15-min imaging rate of SEVIRI, and the spatial resolution is consistent with the SEVIRI pixel. Precipitation is obtained by combining IR equivalent blackbody temperatures at 10.8 μm with rain rates from polar microwave measurements.
OTT PARSIVEL Disdrometer is ideal for simultaneous measurement of PARticle SIze and VELocity of all liquid and solid precipitation. It detects and identifies 8 different precipitation types as drizzle, mixed drizzle/rain, rain, mixed rain/snow, snow, snow grains, freezing rain and hail. Besides, it provides drop size distributions on the ground and a function to derive a local Z/R relation – ready to be used to adjust the radar data.
The findings of the combined analysis showed that, towards early warning of convective precipitation in urban areas, combined high temporal resolution measurements carried out by satellites and ground based disdrometer measurements could provide a high-performance regional early warning system.
Keywords: extreme convective precipitation, EUMETSAT HSAF, PARSIVEL, rain rate, Athens
Acknowledgement: This work is co-financed by Iceland, Liechtenstein, Norway European Economic Area (EEA) Grants 2014 – 2021 and Greek Public Investments Program.
References:
- Panagiotis T. Nastos, Christos S., Zerefos (2007), On extreme daily precipitation totals at Athens, Greece. Advances in Geosciences10, DOI: 5194/adgeo-10-59-2007
- Chenxi Li, Xihui Gu, Louise J. Slater, Jianyu Liu, Jianfeng Li, Xiang Zhang, Dongdong Kong (2023), Urbanization-Induced Increases in Heavy Precipitation are Magnified by Moist Heatwaves in an Urban Agglomeration of East China. Journal of Climate, 36 (2), 693–709, DOI: https://doi.org/10.1175/JCLI-D-22-0223.1
How to cite: Nastos, P. T. and Matsangouras, I. T.: Combined Analysis of a convective precipitation event in Athens, Greece, by utilizing the H03B product of EUMETSAT HSAF and ground-based OTT PARSIVEL Laser-based optical Disdrometer measurements, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9737, https://doi.org/10.5194/egusphere-egu23-9737, 2023.
The success of river restoration depends on precipitation and their hydrological effects. In the context of global warming, an increase in the frequency of extreme events has been observed, with a rapid alternation between periods of excess precipitation with floods and periods of deficit precipitation with drought. Understanding the spatio-temporal variability of the precipitation regime allows to set guidelines for river restoration at a large scale. The main aim of the study is to analyze the variability and observed changes in the precipitation regime in Romania. The research is based on the monthly amounts and maximum precipitation in 24 hours data recorded at 23 meteorological stations, which are part of the Regional Basic Synop Network (RBSN) and belong to the National Meteorological Administration (NMA), for the period 1981 – 2020. Based on these monthly data, the annual, seasonal and semestrial precipitation amounts were calculated. Standardized anomalies and decadal averages were also calculated both annually and for the months January, April, July and October, which are considered typical months from a climatic point of view. The analysis of changes in the precipitation regime is completed by calculating linear trends and statistical significance at annual and seasonal level by applying the Mann-Kendall statistical test. From the analysis of these monthly data of the precipitation amounts, it was found that from January or February to June or July, the monthly precipitation regime shows ascending values, which then gradually decrease until the end of the year. The annual precipitation amounts, at the level of Romania, decrease from west to east as a consequence of oceanic influence’s decrease in the same direction. The variation of the average decennial values of the precipitation amounts indicates increases and decreases from one decade to another, but without significant changes. The trend of average monthly precipitation amounts is predominantly decreasing, but without being statistically significant according to the Mann-Kendall test. In Romania, in the transitional temperate continental climate, the great variability of precipitation in time can be a drawback for self-sustainable river restoration.
D.M. (Oprea) Constantin, I.A. Șandor and G. Ioana-Toroimac were supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS - UEFISCDI, project number PN-III-P1-1.1-TE-2021-0600, within PNCDI III.
How to cite: Constantin (Oprea), D. M., Lüftner, G. D., Ilea, R. G., Șandor, I. A., and Ioana-Toroimac, G.: The observed changes in the precipitation regime in Romania – constraints for river restoration, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9756, https://doi.org/10.5194/egusphere-egu23-9756, 2023.
The aim of this study was to spatially downscale the Precipitation Estimates (PEs) from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG), over a complex region in Greece. For this purpose, the Multivariate Linear Regression (MLR) and the Residual Correction (RC) techniques were utilized, in conjunction with remote sensing cloud properties from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, onboard Aqua satellite, namely Cloud Optical Thickness (COT), Cloud Effective Radius (CER) and Cloud Water Path (CWP). The downscaled PEs were then validated using regional rain gauges’ measurements. According to our analysis, the 0.01o downscaled IMERG PEs were found to be more accurate than the original 0.1o IMERG data, over the region. In addition, the implementation of the RC technique to the 0.01o downscaled PEs was observed to improve the performance of the MLR downscaling method. This research was funded by the EU project titled: WATERLINE project id CHIST-ERA-19-CES-006.
How to cite: Stathopoulos, S. and Gemitzi, A.: Spatial downscaling of IMERG precipitation estimates using statistical techniques, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10873, https://doi.org/10.5194/egusphere-egu23-10873, 2023.
The daily life of everybody is affected by weather, especially by precipitation events. Climate projections indicate that the number and intensity of heavy rain events could increase in future. Therefore, the interest to improve QPE has increased rapidly, particularly for assurances, public infrastructure and flood risk management.
Currently, the QPE is calculated by using the RADOLAN algorithm of Deutscher Wetterdienst. This algorithm combines the data of 17 weather radars and roughly 1,200 rain gauges in Germany by adjusting the radar reflectivity to the precipitation amount measured at the ground. The adjustment process is done every 10 minutes using the hourly total of radar and rain gauge data. Due to the rain gauge data delivery the adjustment process is delayed by 25 minutes. For this reason, short convective precipitation events can only be observed insufficiently.
Therefore, the RADOLAN algorithm has to be adapted to improve the QPE based on shorter data accumulation time and contemporary data delivery. One approach is to use almost real-time available data from the telecommunication network. Rainfall leads to attenuation of the signal level of commercial microwave links (CMLs). The path integral of the attenuation along one sender-receiver pair can be related to a certain precipitation amount. Germany is covered by several thousands (~130,000 in total) of CMLs, which can potentially be used to quantify rainfall events. Especially in urban areas the density of CMLs exceeds the density of meteorological networks rain gauges clearly. Therefore, it becomes possible to observe convective extreme weather events with higher temporal and spatial resolution.
Within the project HoWa-PRO, the Deutscher Wetterdienst (DWD) collaborates with University of Augsburg, Karlsruhe Institute of Technology in Garmisch-Partenkirchen, and Ericsson. One of the first tasks is to set up a continuous data flow from Ericsson to DWD. To investigate different combinations of data sources and adjustment intervals, a fast, flexible and expandable software framework for combining and processing this data has to be developed.
We present first results of QPE after adjustment using different combinations of data e.g. CML+radar data and gauge+radar data. These results were analyzed and compared to show the potential of using opportunistic data from CMLs for radar adjustment.
How to cite: Wenzel, M., Vogel, C., Graf, M., Winterrath, T., and Chwala, C.: Development of a Python Framework (pyRadman) for QPE using radar and CML data at DWD, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13779, https://doi.org/10.5194/egusphere-egu23-13779, 2023.
The operational rainfall rate algorithm developed for the Advanced Meteorological Imager (AMI) onboard the GEO-KOMPSAT-2A (GK-2A) launched in 2018 uses the a-priori information including the microwave rainfall data from low-earth orbiting satellites and infrared (IR) brightness temperatures from geostationary satellites. The algorithm may better perform with a variety of a-priori information describing all possible precipitating systems. In addition, separation of physically different precipitating systems likely to improve the accuracy of retrieval process. However, it has been well known that such the separation can be hardly achieved based on the measurements of cloud top temperatures. This algorithm tried to utilize the radiative characteristics observed differently for different wavelengths in IR spectral regions. The characteristics include the different emissivity as a function of wavelength and cloud thickness. Using the brightness temperature difference (BTD) between IR channels the algorithm determines the thresholds of the BTDs discriminating five types of precipitating clouds: shallow, non-shallow-tall-cold, non-shallow-tall-colder, non-shallow-taller-cold, and non-shallow-taller-colder clouds. The algorithm also uses databases classified by latitudinal bands. The bands are separated with four latitudinal zones. The separation of database based on latitudes may have an effect of distinguishing the cloud types that can occur regionally. As an alternative approach for cloud classification, a machine learning algorithm with Gaussian mixture models (GMMs) has been developed. The GMMs separate four cloud types. This study compares rainfall retrievals based on two different cloud classification methods.
How to cite: Shin, D.-B. and Kim, D.-C.: Rainfall retrievals from AMI onboard GEO-KOMPSAT-2A satellite, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9806, https://doi.org/10.5194/egusphere-egu23-9806, 2023.
Data from commercial microwave links (CML) operated by mobile network providers is attenuated by precipitation. The line integral of the precipitation intensity can be calculated from this attenuation and is used as a complement to common precipitation measurements. A major issue in CML signal processing is the distinction between dry and wet time steps. In addition to time series-based methods, precipitation patterns based on weather radar or satellite are also used.
One aim of our work is to evaluate the suitability of MSG SEVIRI data as a wet/dry indicator. The advantage of SEVIRI is the almost global coverage with a resolution of about 4x6 km in the middle latitudes, every 15 minutes. We apply two products from NWCSAF that derive the probability of precipitation from a combination of SEVIRI channels. Our study is based on 3901 CMLs in Germany for the summer of 2021. We evaluate the performance differences at various precipitation intensities as well as between daytime and nighttime since fewer SEVIRI channels are available at night. Another aim of our work is to investigate the possibilities of improvements by a combination of time series and SEVIRI-based wet/dry detections.
Our results show at least equivalent performance of SEVIRI compared to common time-series-based methods. The Matthews Correlation Coefficient (MCC) values for combinations are even better. This is especially the case for light precipitation. In addition, there are also no deteriorations for individual CMLs compared to common methods.
How to cite: Wagner, A., Wiegels, R., Graf, M., Polz, J., and Chwala, C.: Combinations of CML wet/dry corrections based on MSG SEVIRI data in Germany, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14775, https://doi.org/10.5194/egusphere-egu23-14775, 2023.
Adequate spatial coverage of precipitation measurements is not available for large regions. Many countries are only equipped with networks of meteorological stations to measure precipitation based on sparse point measurements. Attenuation data from commercial microwave links (CML) allow precipitation estimates over existing networks, such as the cellular network. However, the processing of the data requires a distinction between wet and dry. Here, satellite data in countries of the Global South play a significant role, as they can be used in place of conventional reference data to distinguish between dry periods and precipitation events.
Deep Learning (DL) can be used to develop a dry indicator based on geostationary satellite data that can be applied for dry-wet classification in CML processing. A convolutional neural network is used to process visual and infrared cloud information from geostationary satellites and to create a dry indicator. Satellite derived products exist, such as the NWC SAF products PC and PC-Ph, and are utilized as baseline products. The baseline products and the DL based dry indicator developed in this work (DL product) are evaluated with radar and station data in Germany.
The evaluation shows that the developed DL product improves the performance at day and especially at nighttime. Limitations in detecting the correct rain field area is reduced by the DL product. In total the DL product improves the Matthews Correlation Coefficient value by about 0.05 compared to the PC-Ph product.
How to cite: Wiegels, R., Wagner, A., Polz, J., Glawion, L., and Chwala, C.: An improved MSG SEVIRI wet-dry product based on a convolutional neural network, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14066, https://doi.org/10.5194/egusphere-egu23-14066, 2023.
Previous studies showed that conical scanning radiometers greatly outperform cross-track scanning radiometers for precipitation retrieval over ocean. This study demonstrates a novel approach to improve precipitation rates at the cross-track scanning radiometers’ observation time by propagating the conical scanning radiometers’ retrievals to the cross-track scanning radiometers’ observation time. The improved precipitation rate is a weighted average of original cross-track radiometers’ retrievals and retrievals propagated from a conical scanning radiometer. Results show that the morphed precipitation rates agree much better with the reference. The degree of improvement depends on several factors, including the propagated precipitation source, the time interval between the cross-track scanning radiometer and the conical scanning radiometer, the precipitation type (convective versus stratiform), the precipitation events’ size, and the geolocation. The study has potential to greatly improve high-impact weather systems monitoring (e.g., hurricanes) and multisatellite precipitation products. It may also enhance the usefulness of future satellite missions with cross-track scanning radiometers on board.
How to cite: You, Y., Huffman, G., Peters-Lidard, C., Munchak, J., Tan, J., Braun, S., Kidd, C., Ringerud, S., Blackwell, W., Xun Yang, J., Nelkin, E., and Dong, J.: Improving Cross-Track Scanning Radiometers’ Precipitation Retrieval over Ocean by Morphing, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17303, https://doi.org/10.5194/egusphere-egu23-17303, 2023.
Accurate and reliable real-time monitoring and dissemination of observations of atmospheric and hydrologic conditions in general is critical for a variety of research and real-time monitoring applications. Precipitation observations provide critical information for wide variety of applications ranging from drought monitoring for agriculture application and resource staging in extreme drought regions to water resource monitoring for freshwater allocation and hydroelectric power generation. In many regions of the World, especially in Africa, weather observation network density has been declining over the past few decades. The University Corporation for Atmospheric Research (UCAR) with support from USAID, is leading an initiative to develop low-cost hydrometeorological instrumentation (e.g., automatic weather stations, rain gauges, stream gauges) as solution to increase observation networks in sparsely observed regions of the world. We have started a new initiative to use low-cost observation network to support the Famine Early Warning Systems Network (FEWS NET: https://fews.net/). The goal of the project is to improve the number of observations (temporally and spatially) in these regions to improve the quality of the FEWS NET products. These data will be open and publicly available to the scientific community for other applications (e.g., satellite precipitation product evaluation, weather and hydrological prediction, etc.). The presentation will provide an overview of the low-cost observation technology and plans for the development of the precipitation monitoring network in Africa.
How to cite: Kucera, P. and Steinson, M.: Development of a Low-cost Precipitation Observation Network in Africa, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16922, https://doi.org/10.5194/egusphere-egu23-16922, 2023.
Following the success of MetOp, EUMETSAT Polar System Second Generation (EPS-SG) will provide satellite observations from polar orbit to support Numerical Weather Prediction and climate monitoring in the 2024 to mid-2040's timeframe. Designed to fly on board the EPS-SG satellite-B series and cover 19-183 GHz frequency range, Microwave Imager (MWI) is expected to deliver high-quality measurements of radiometric properties relevant to precipitation, clouds, near-surface ocean winds and snow/ice cover. With a goal to build an enterprise MWI retrieval in support to NOAA operational Environmental Data Records (EDRs) production, a development of new and adaptation of the existing microwave imager algorithm procedures are underway at University of Maryland. As part of this effort and to ensure timely delivery of day-1 retrievals, we simulate MWI level-1 data over prolonged periods of time (up to 12 months) using radiative transfer techniques. Two datasets will be presented. The first, oriented towards precipitation retrieval development, relies on Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) observations to construct a state vector in radiative transfer calculations. The second dataset relies exclusively on ERA5 parameters. Two radiative transfer models have been considered in the production of simulated MWI brightness temperatures: a) Community Radiative Transfer Model (CRTM) and b) Edington model. Each model uses MWI observation geometry, following DPR and GCOM-W1 AMSR2 sampling, respectively. To deliver the product, CRTM has been updated by, for this purpose derived, MWI coefficients using an idealized Spectral Response Function at each of the 26 channels. When compared to the common channels of AMSR2 sensor, the simulations reflect exceptionally high accuracy. In addition to the methodology and proxy data sets, preliminary results for MWI precipitation EDR retrieval will be presented.
How to cite: Petković, V., Stegmann, P., Meng, H., Ferraro, R., and Yang, J. X.: Building an EPS-SG Microwave Imager Retrieval Suite: Level-1 Proxy Data Record, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16639, https://doi.org/10.5194/egusphere-egu23-16639, 2023.
On 24-26 August 2022 Pakistan has suffered one of its most severe floods. The excessive monsoon rainfall throughout the summer, which was worsened by shorter bursts of extremely heavy rain that affected the regions of Sindh and Balochistan, directly contributed to the flooding. According to reports, Pakistan experienced more than three times its typical rainfall in August, making it the wettest month since 1961. The strong monsoon moist currents and thermal lows started from Arabian Sea penetrating into entire area of Balochistan and Sindh, and spread over upper and central parts of the country. The moist currents from the Bay of Bengal penetrating during the entire monsoon season
from mid-June to September resulted in floods and land sliding across Pakistan, causing human casualties as well as widespread destruction of homes and infraructure. Floods cannot be entirely avoided, but their harmful effects can be significantly managed with careful planning and adequate preparation. The use of operational satellite precipitation products could facilitate prompt and accurate monitoring (and forecasting) as well as the implementation of impact-minimizing strategies, reducing vulnerability to floods.
The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) application facility on Support to Operational Hydrology and Water Management (H SAF) (http://h-saf.eumetsat.int/) provides operational satellite products of important hydrological parameters, including snow cover and water equivalent, soil moisture, surface rain rate and cumulated rainfall. Among these, H SAF generates a near-real time (NRT) product providing instantaneous surface precipitation rate over the Meteosat Second Generation (MSG) Indian Ocean Data Coverage (IODC) every 15 minutes at 3-5 km spatial resolution (H SAF product ID H63). H63 is based on the rapid update blending technique combining passive microwave (PMW) precipitation rate estimates and IR measurements from MSG SEVIRI. On the other hand, since the beginning of March 2014, the NASA Global Precipitation Measurement (GPM) mission (https://gpm.nasa.gov/missions/GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) product provides quasi-global (60 N-60 S) precipitation rate estimates every 30 min at 0.1°x0.1° spatial resolution, combining information from the GPM constellation of PMW radiometers available over the majority of the Earth's surface with IR satellite measurements.
This study proposes a comprehensive overview about the performance evaluation of IMERG (Early and Late run) and H63 products using rain gauges data in Pakistan, for the August 2022 Pakistan Flood event. Gauge-based rainfall data from the high-density climate station network of the Metrological Department Pakistan (PMD), are compared with NASA GPM IMERG rainfall products, and with H SAF and H63 rainfall product. Hourly and daily precipitation estimates are derived from the satellite products over specific regions entirely covered by 32 PMD stations. The goal of the study is to evaluate the performance of IMERG and H63 by means of a cross-comparison with rain gauges data (considered as the ground truth) using both statistical and graphical methods and analyze the results considering local environmental conditions. We also illustrate, through selected rainfall event cases and sub-regions, how insufficient coverage by PMW radiometers can lead to larger discrepancies in the IMERG and H63 estimates with respect to the ground measurements.
How to cite: Panegrossi, G., Batool, S., Casella, D., D'Adderio, L. P., Sanò, P., Sebastianelli, S., Giorgi, C., and Melfi, D.: Analysis of satellite precipitation products during the monsoon floods in Pakistan in 2022, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16155, https://doi.org/10.5194/egusphere-egu23-16155, 2023.
The monostatic field experiment ATTRRA2 uses six dual-polarized, customary CML antennas which operate at different frequency bands. The experiment is installed at the TERENO field site in Fendt, Germany. A mounted camera provides pictures of the antenna radomes, and, thereby, visual information about the wetness distribution on the antennas. On-site disdrometers provide a reference for the rainfall intensity. Transmit-receive signal level (TRSL) data commonly used for quantitative precipitation estimation (QPE) is recorded into a minutely sampled time series together with the so-called antenna mismatch factor. The latter provides additional information about the radome condition in this use case and, thereby, serves as a proxy measurement of the wet antenna attenuation (WAA). Careful analysis of the data yields new insights into the temporal dynamics of WAA, especially during dewy periods before actual rain and drying periods after rain events. A comparison with established WAA correction methods shows that the proposed one performs promisingly, especially during periods of high TRSL fluctuations. A remaining challenge is the long-term stability of the instrument which will be specifically addressed in the future.
How to cite: Tiede, J., Siart, U., Chwala, C., and Kunstmann, H.: New insights into the wet antenna attenuation effect based on data from a dedicated field experiment with CML antennas, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15447, https://doi.org/10.5194/egusphere-egu23-15447, 2023.
Posters virtual: Thu, 27 Apr, 16:15–18:00 | vHall AS
The rate of change of mass of a hailstone by diffusion is affected by its motions. In a stationary pure diffusive case the water vapor distribution around a spherical hailstone is spherically symmetric having rather weak water vapor gradients. However, when a hailstone falls in the air, the flow field and hence the water vapor distribution around the hailstone is asymmetric showing much stronger water vapor gradients which are high at the upstream side and lower at the rear side of the hailstone. When averaged over the whole surface area of the hailstone the mass transfer to or from the falling hailstone surface is always higher compared to a pure diffusive case. This convective enhancement is given by the ventilation coefficient. Thus, to reliably quantify growth or sublimation rates of falling hailstones with models, it is necessary to know their ventilation coefficients. The rate of change of mass is proportional to the rate of change of heat. Therefore, the growth or sublimation of hailstones has not only implications on the humidity of the ambient air but also the vertical temperature profile of the atmosphere and consequently cloud and storm dynamics.
However, there is a lack of experimental studies on the ventilation coefficient of spherical hailstones in the literature. There are just three experimental studies available – all dating back to the 1960’s – which investigate the heat and mass transfer of spherical and oblate hailstones, but all were measured under accretional growth and melting conditions, respectively.
Therefore, experiments in the Mainz vertical wind tunnel were carried out to determine ventilation coefficients of spherical hailstones during sublimation. We investigated stones with diameters between 1 and 3 cm or, equivalently, Reynolds numbers between 10.000 and 45.000. The spherical hailstones were produced by freezing water in moulds and introduced into the wind tunnel. While freely floating at their terminal velocities the hailstones lost mass due to sublimation. The temperature in the tunnel was set to -5°C and relative humidities were rather low, i.e. between 30 % and 50 % with respect to ice, to facilitate sublimation. The mass of the hailstones was measured before and after the wind tunnel measurements from which we calculated the rate of change of mass in the convective case. The recordings of temperature and dew point were used to calculate the rate of change of mass for the pure diffusive case. The ratio of these rates is, by definition, the ventilation coefficient, which was calculated and parameterized as a function of the Reynolds number.
How to cite: Theis, A., Werner, L., Mitra, S., Borrmann, S., and Szakáll, M.: A wind tunnel investigation on the ventilation coefficients of hailstones, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5991, https://doi.org/10.5194/egusphere-egu23-5991, 2023.
Peruvian Andes region has been proven in multiple studies to be one of a few regions have poor performance of many global precipitation estimations, due to its complex terrain and extreme interruption of atmospheric movement by the Andes mountain. This study provides an evaluation over two Peruvian local precipitation products PISCO and RAIN4PE, along with a regional dynamic downscaled WRF model simulation, and GPM-IMERG. The precipitation products were evaluated against local rain gauge data and used as the forcing data for CREST-VEC model to test the uncertainties of the precipitation products in a extremely dry region in Peru. This study readdress the accuracy issue of precipitation products in the Peruvian Andes region, and highlights the importance of using WRF modeling simulation to ‘fill-the-blank’ of heterogenous rain gauge distribution, when remote-sensing technologies fail to perform in this area.
How to cite: Chen, M., Huang, Y., Li, Z., Mamani Larico, A. J., Xue, M., Hong, Y., Hu, X.-M., Mayol Novoa, H., Martin, E., McPherson, R., Vitaliano Perez, A., and Yanqui Morales, I.: Statistical and hydrological evaluation of precipitation estimates and simulations from the remote sensing technologies and WRF modeling over the Peruvian Andes region, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17124, https://doi.org/10.5194/egusphere-egu23-17124, 2023.
