AS1.31

Since its founding in 1989, the Global Precipitation Climatology Centre (GPCC) has been producing global precipitation analyses based on land surface in-situ measurements. In the now over 30 years the underlying database has been continuously expanded and includes a high station density and large temporal coverage. Due to the semi-automatic quality control routinely performed on the incoming station data, the GPCC database has a very high quality. Today, the GPCC holds data from 123,000 stations, about three quarters of them having long time series.

The core of the analyses is formed by data from the global meteorological and hydrological services, which provided their records to the GPCC, as well as global and regional data collections.  In addition, the GPCC receives SYNOP and CLIMAT reports via the WMO-GTS. These form a supplement for the high quality precipitation analyses and the basis for the near real-time evaluations.

Quality control activities include cross-referencing stations from different sources, flagging of data errors, and correcting temporally or spatially offset data. This data then forms the basis for the following interpolation and product generation.

In near real time, the 'First Guess Monthly', 'First Guess Daily', 'Monitoring Product', ‘Provisional Daily Precipitation Analysis’ and the 'GPCC Drought Index' are generated. These are based on WMO-GTS data and monthly data generated by the CPC (NOAA).

With a 2-3 year update cycle, the high quality data products are generated with intensive quality control and built on the entire GPCC data base. These non-real time products consist of the 'Full Data Monthly', 'Full Data Daily', 'Climatology', and 'HOMPRA-Europe' and are now available in the 2020 version.

All gridded datasets presented in this paper are freely available in netcdf format on the GPCC website https://gpcc.dwd.de and referenced by a digital object identifier (DOI). The site also provides an overview of all datasets, as well as a detailed description and further references for each dataset.

How to cite: Rustemeier, E., Schneider, U., Ziese, M., Finger, P., and Becker, A.: Updated gridded datasets version 2020 provided by the Global Precipitation Climatology Centre (GPCC), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14611, https://doi.org/10.5194/egusphere-egu21-14611, 2021.

As is known, rainfall varies spatially and temporally with regard to intensity and frequency. Floods, related to extreme rainfall cases, cause stress on geophysical system and community if climate change is considered. For this reason determining of extreme rainfall patterns is very important. While obtaining three dimensional status of hydrometors in atmosphere is not possible only by using ground station networks, it is possible by using weather radars. Therefore, weather radars provide significant contribution to studies about getting cloud and rainfall patterns. The aim of this study is to investigate spatial patterns of extreme rainfall events in Antalya and Muğla cities which are located on the Mediterranean coast of Türkiye. Firstly, hourly rainfall (RN1) and rain rate (SRI) products of 2 C band doppler radars and raingauge data between 2015 and 2020 will be processed by a software named MeteoRadar which is developed by İstanbul Technical University. It is capable of reading, decoding, parallel processing and visualization. Secondly, extreme rainfall patterns will be obtained over 2 study areas. Finally, after validation by using raingauge data, results will be discussed in detail.

Key Words: Antalya, Extreme rainfall, MeteoRadar, Muğla, RN1, SRI, Weather radar.

How to cite: Yapıcı, E., Öztopal, A., and Erdi, E.: Investigation of Extreme Rainfall Patterns around Antalya and Muğla Cities in Türkiye by Using C Band Doppler Radar Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14529, https://doi.org/10.5194/egusphere-egu21-14529, 2021.

Extreme precipitation events are expected to occur more frequently in a warming climate. Understanding their structure and predicting the exact time and location of precipitation events still remains a challenge because of the high temporal and spatial variability of rainfall. Nationwide weather radar networks are a common tool for investigating precipitation events and their spatial and temporal structure. The German Weather Service (DWD) provides a nationwide climatological radar data set from 2001 to 2020. A reprocessing procedure has been applied to reflectivity measurements in order to obtain precipitation estimates as homogeneous as possible. With an object-oriented analysis, all precipitation events for 11 different durations from 1 to 72 hours exceeding DWD’s official warning level for heavy precipitation have been detected and statistically analysed.

We will present a comprehensive analysis of all heavy precipitation events that occurred in Germany between 2001 and 2020. We examined their size, duration, location, spatial structure and distribution as well as regional and climatological differences and demonstrate how this information is collected in an online tool for easy access. An assessment of how well these heavy precipitation events were captured by DWD’s network of precipitation stations will be given. Finally, we will present the possibility to use the event detection procedure as an operational tool for assessing and classifying heavy precipitation events and their potential impact in near real-time.

How to cite: Lengfeld, K., Walawender, E., Winterrath, T., Weigl, E., and Becker, A.: Statistics of 20 years of heavy precipitation events in Germany from radar data , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2266, https://doi.org/10.5194/egusphere-egu21-2266, 2021.

Spaceborne-radar precipitation products at high altitudes entail close attention to geographically inherent retrieval uncertainties. The lowest levels free from surface clutter are ~1 km higher in rugged mountainous areas than those over flatlands. The clutter-removal filter masks precipitation echoes at altitudes below 3 km from the surface at the swath edge over narrow valleys in the Himalayas. In this study, precipitation profiles at levels with clutter interference were estimated using an a priori precipitation profile dataset based on near-nadir observations. The corrected precipitation dataset was generated based on the Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) product at a spatial resolution of 0.01° around the Trambau Glacier terminus in the Nepal Himalayas, where ground observation sites were installed in 2016. The occurrence frequency of precipitation was considerably small compared with the in situ observation because of limitations in the sensor sensitivity. The occurrence frequency of light precipitation is increased by the Dual-frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) Core Observatory, and the low-level precipitation profile correction mitigates underestimation bias by ~10%. In this presentation, the detectability of fine-scale precipitation climatology and the local characteristics of its diurnal variation at high altitudes are discussed based the combination of the TRMM PR and GPM DPR products.

How to cite: Hirose, M. and Fujinami, H.: Spatial patterns of high-elevation precipitation observed through spaceborne precipitation radars, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15910, https://doi.org/10.5194/egusphere-egu21-15910, 2021.

One of the World Climate Research Programme Grand Challenges is to evaluate whether existing observations are enough to underpin the assessment of weather and climate extremes. In this study, we focus on extreme associated with Atmospheric Rivers (ARs). ARs are characterized by intense moisture transport usually from the tropics to the extra-tropics. They can either be beneficial, providing critical water supply, or hazardous, when excessive precipitation accumulation leads to floods. Here, we examine the uncertainty in gridded precipitation products included in the Frequent Rainfall Observations on GridS (FROGS) database during two atmospheric river events in distinct Mediterranean climates: one in California, USA, and another in Portugal. FROGS is composed of gridded daily-precipitation products on a common 1∘×1∘ grid to facilitate intercomparison and assessment exercises. The database includes satellite, ground-based and reanalysis products. Results show that the precipitation products based on satellite data, individually or combined with other products, perform least well in capturing daily precipitation totals over land during both cases studied here. The reanalysis and the gauge-based products show the best agreement with local ground stations. As expected, there is an overall underestimation of precipitation by the different products. For the Portuguese AR, the multi-product ensembles reveal mean absolute percentage errors between -25% and -60%. For the Western US case, the range is from -60% to -100 %. 

Acknowledgments

The financial support for this work was possible through the following FCT project: HOLMODRIVE—North Atlantic Atmospheric Patterns Influence on Western Iberia Climate: From the Late Glacial to the Present (PTDC/CTA-GEO/29029/2017). A.M. Ramos was supported by the Scientific Employment Stimulus 2017 from Fundação para a Ciência e a Tecnologia (FCT, CEECIND/00027/2017). 

How to cite: Ramos, A. M., Roca, R., Soares, P. M. M., Wilson, A. M., Trigo, R. M., and Ralph, M.: Uncertainty in different precipitation products in the case of two atmospheric river events, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3167, https://doi.org/10.5194/egusphere-egu21-3167, 2021.

Microwave backhaul links from cellular communication networks provide a valuable “opportunistic” source of high-resolution space–time rainfall information, complementing traditional in situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Over the past decade, a growing community of researchers has, in close collaboration with cellular communication companies, developed retrieval algorithms to convert the raw microwave link signals, stored operationally by their network management systems, to hydrometeorologically useful rainfall estimates. Operational meteorological and hydrological services as well as private consulting firms are showing an increased interest in using this complementary source of rainfall information to improve the products and services they provide to end users from different sectors, from water management and weather prediction to agriculture and traffic control. The greatest potential of these opportunistic environmental sensors lies in those geographical areas over the land surface of the Earth with few rain gauges and no weather radars: often mountainous and urban areas, but especially low- to middle-income regions, which are generally in (sub)tropical climates. 

Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka, a middle-income country having a tropical climate. This is performed for a 3.5-month period based on CML data from on average 1140 link paths. CML rainfall maps are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the Dual-frequency Precipitation Radar on board the Global Precipitation Measurement Core Observatory satellite. The results confirm the potential of CMLs for real-time tropical rainfall monitoring. This holds a promise for, e.g., ground validation of or merging with satellite precipitation products.

How to cite: Overeem, A., Leijnse, H., van Leth, T., Bogerd, L., Priebe, J., Tricarico, D., Droste, A., and Uijlenhoet, R.: Rainfall monitoring in Sri Lanka employing commercial microwave links, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7793, https://doi.org/10.5194/egusphere-egu21-7793, 2021.

Spaceborne passive microwave sensors have been developed to improve the knowledge of precipitation systems based on channels that interact directly with hydrometeors in clouds. In particular, understanding the global distribution of precipitation is one of the main missions. Prior to these precipitation studies, many researchers tend to implement the rain/no-rain classification (RNC) procedure. As a simple way, the polarized corrected temperature at 89 GHz (PCT89) from passive microwave radiometry has been widely used to identify rain pixels. The PCT89 can estimate the scattering intensity accompanied by precipitating clouds while minimizing the effects of the surface at high resolution, however, the diversity of the hydrometeor distributions can be a problem in the use of a consistent cut-off threshold. Therefore, the purpose of this study is to evaluate differences in the accuracy of the PCT-based RNC method induced by the various hydrometeor distributions and to present a new perspective to users so that it can be used appropriately. Precipitation data observed by the global precipitation measurement (GPM) microwave imager (GMI) for the period from January to December of 2015 in the tropics were used in the study. Based on the classification algorithm of the GPM dual precipitation radar (DPR), the precipitation data were subdivided into 11 types (3 stratiform types, 4 convective types, and others), and then a statistical verification was attempted to ensure that the cut-off threshold was appropriate. The PCT89-based RNC method leads to an increase of 70% and 54% in the number of two significant stratiform types compared to the DPR precipitation flag. On the other hand, the convective types decreased by up to 53%. Although regional diversity could lead to systematic differences in the verification, they did not exceed magnitudes of the difference between precipitation types. Therefore, this study suggests that the precipitations identified by the PCT89-based RNC method have features that enhance the bias toward the stratiform type.

How to cite: Kim, J. and Shin, D.-B.: Evaluation of the precipitation-type dependent uncertainty in rain/no-rain classification using PCT from GPM/GMI data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1908, https://doi.org/10.5194/egusphere-egu21-1908, 2021.

Precipitation is an extremely important variable for society. While intense and persistent rainfall are responsible for causing floods and landslides, its absence is also a factor of concern, such as droughts. For an efficient rainfall monitoring over a certain region, sub-daily measurements of this variable are required to understand the physical processes which modulate the so-called Precipitation Diurnal Cycle (PDC). Over Brazil, due to the low density of ground observational data, both from rain gauges networks (most of them available on daily basis) and weather radars, it is necessary to use satellite-based rainfall estimation products. However, the error for those techniques on sub-daily scale are still high. In this context, this study analyzes Artificial Intelligence techniques, specifically Artificial Neural Networks (ANN), for downscaling daily to a sub-daily scale precipitation data using multiple datasets. The main information from daily retrievals comes from a satellites-based technique corrected by rain gauges, called MERGE which was developed by INPE in Brazil. MERGE has an available dataset of 20 years. In order to better represent the characteristics of the diurnal cycle and the physical processes of the different regions of the country we applied two different types of ANN, the Deep Neural Network (DNN) and the Recurrent Neural Network (RNN). The target is a sub-daily rainfall with temporal resolution of 3 hours. Meteorological variables with physical relationship with the rain in previous studies were selected, like infrared brightness temperature from GOES satellite, hourly precipitation estimates from microwave sensors (IMERG), and environmental data (e.g. humidity, wind, etc) from ERA reanalysis. Also, we used topography and location information for the whole area. Each of the chosen variables was pre-processed, producing averages (or accumulated) values and other 3-hour temporal resolution measurements. Correlation between them and the accumulated observed rain at the same time were analyzed. The results were evaluated for different regions, seasons, and times. Results obtained by the ANNs are in a better agreement when compared to IMERG product (the reference). For results with less input data (e.g. without wind information), to save computer time, the DNN has the best performance, especially when trained with data from all regions. DNN obtained an MSE of 11.09 mm and RNN shows a value of 11.88 mm. However, the rain screening (areas with rainfall) is slightly better for IMERG, but with a superestimation of the precipitation. Also, DNN shows better results for all the different regions of Brazil as well as for the different seasons. BIAS for RNN is better for hours with low precipitation, while DNN and IMERG are better for rainy periods (18 and 21 GMT). However, BIAS differences between DNN and RNN are very small and MSE shows a slightly better values to DNN for all times. Therefore, DNN was chosen as the best ANN. Sensitivity tests will be carried out to determine the best DNN configuration without considering computational costs. For its improved version, with the inclusion of more meteorological variables, DNN performed better in all aspects, including rain screening, when compared to IMERG.

How to cite: Batista, R., Calheiros, A., and Vila, D.: Daily to Sub-daily precipitation downscaling based on multiple datasets using artificial neural networks in Brazil, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13654, https://doi.org/10.5194/egusphere-egu21-13654, 2021.

The high spatial, temporal, and spectral resolutions from the new generation of GEO satellites provide opportunities to map precipitation more accurately and enhance our understanding of precipitation processes. The research question addressed in this study is: Which predictors derived from satellite observations are significant in estimating the occurrence of a given precipitation process? Several indices from the Advanced Baseline Imager (ABI) sensor onboard the Geostationary Observing Environmental Satellite (GOES)-16 are derived and matched with surface precipitation types from the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system across the conterminous United States (CONUS). A machine learning (ML) based Random Forest (RF) classification is developed with several categories of predictors, such as ABI brightness temperatures (Tb) from five channels, spectral channel differences and textures, and environmental variables from the Rapid Refresh numerical forecast model (NWP).

The developed RF model displays overall classification accuracy of around 75%. Investigating the model shows that the absence of precipitation (no-precipitation) and convective types are better detected using GOES-16 derived predictors, while the detection of stratiform types is better with the NWP predictors. Simple Tbs detect no-precipitation and hail types correctly, whereas Tb textures contribute to the classification accuracy of warm stratiform and convective precipitation types. The accuracy of all precipitation types identification significantly improved with the addition of NWP predictors along with GOES-16 derived predictors. Overall, the analysis provided new insights on the monitoring of precipitation with GEO satellites and showed novel ways to diagnose ML models.

How to cite: Upadhyaya, S. and Kirstetter, P.-E.: Machine Learning based Precipitation Types Classification from GEO Satellite Observations: Diagnostic Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10366, https://doi.org/10.5194/egusphere-egu21-10366, 2021.

Four years (2015-2018), Joss-Waldvogel disdrometer (JWD) data are utilized for the statistical analysis of Raindrop size distribution (RSD) of pre-monsoon and monsoon season over the Western Ghats. JWD Instrument installed at High Altitude Cloud Physics Laboratory (HACPL, 17.92°N, 73.66°E), Mahabaleshwar in the core of heavy rainfall region of Western Ghats. Variation in raindrop size distribution characteristics features in pre-monsoon and monsoon season for convective and stratiform precipitation of windward side of Western Ghats analysis, using long-term in-situ JWD instrument data done. Convective and stratiform rainfall classification is based on the number of concentrations of rain droplets and rain rates. Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim data sets are also integrated with disdrometer data to establish microphysical and dynamical features of pre-monsoon and monsoon season rain. Long-term trends of rain droplet size spectra are not studied until now over the Western Ghats.   Rain droplet spectra of pre-monsoon and monsoon seasons show notable differences. The rain droplets of monsoon display considerably higher divergence compared to pre-monsoon rainfall.  Monsoon rainfall has a higher concentration of smaller drops, while pre-monsoon rainfall contains a significantly higher concentration of large droplets. RSD classified on the rain rate demonstrates a higher mass-weighted mean diameter (D_m) and a lower normalized intercept parameter (log₁₀N_w) in monsoon than winter. Similarly, the Diurnal variation of RSD reveals higher D_m with a lower value of log₁₀N_w in pre-monsoon season. Also, in both seasons, the higher value of mean D_m in convective precipitation than stratiform.  Convective activities with increased ground temperature alter RSD in pre-monsoon season rather than monsoon season through droplet classification, evaporation, and collision-coalescence processes.

How to cite: Kumar, A., Srivastava, A. K., Chakravarty, K., and Srivastava, M. K.: Rain Droplets Size Distributions Statistical analysis for pre-Monsoon and Monsoon Season over the Western Ghats, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6231, https://doi.org/10.5194/egusphere-egu21-6231, 2021.

Ground-based precipitation observations are the most critical data that provide essential information for the sustainable planning, management, and utilization of water resources.  In recent decades, a declining trend in the density of rain gauges has been noted worldwide. It could be due to budgetary constraints and an increase in the availability of remote sensing precipitation products. However, the latter have quantitative uncertainties and biases compared to ground data. Precipitation gauge networks (PGNs) are essential for collecting accurate in-situ data, which are necessary to discern valuable information on spatiotemporal variability of rainfall for a plethora of applications, including bias correction of satellite-based precipitation products. Moreover, an effective network avoids redundancy of data as irrelevant, insufficient, or inefficient data in the incorrect location/time can impede data collection quality. Hence an efficient network design should account for factors such as spatiotemporal variability and non-stationarity in precipitation time-series, physiographic characteristics, and socio-economic aspects, including population density and land-use/land cover patterns. A network design methodology is proposed which tries to address all these factors through a two-level clustering procedure. It harnesses the advantages of the Bayesian framework for regionalization of the study area based on precipitation characteristics in the first level. It integrates information from multiple clustering options in the second level to account for uncertainties in the restructuring of a PGN. The methodology suggests using ground-based precipitation observations and multiple satellite/space precipitation products to identify potential locations for installing new rain gauges and/or decommissioning of existing gauges to effectively re-design an existing network.  Advantages of multiple satellite-based precipitation products (e.g., CHIRPS, IMERG) is being used for expansion of existing network if the adequacy criterions are not satisfied. The methodology could readily be used for areas extending over hundreds and thousands of square kilometers. Its potential is illustrated through a case study on a PGN comprising 1128 gauges in Karnataka state (191,791 km²) of India.  Adequacy of the gauge network is assessed, and recommendations are made for restructuring the PGN by considering the World Meteorological Organization’s (WMO) minimum density criterion. Analysis in the first stage is based on precipitation characteristics discerned from India Meteorological department data extending over 39 years.  In the second level, multiple partitional clustering algorithms are considered for arriving at optimal network density to meet the WMO criterion. The study is of significance, as effective/efficient PGNs that provide accurate and non-redundant ground-based observations are essential for studies focusing on different applications such as sustainable agricultural water management, detection of climate variability, and forecasting floods and droughts.

How to cite: Vijay, S. and vv, S.: Optimal design of precipitation gauge network using a two-stage clustering procedure utilizing satellite-based data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3769, https://doi.org/10.5194/egusphere-egu21-3769, 2021.

The near surface rain (NSR) dataset of the Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) and the Global Precipitation Mission (GPM) Dual Precipitation Radar (DPR) was validated using around 40 tipping bucket raingauges installed over the northeastern Indian subcontinent, and disdrometers in the Meghalaya Plateau, India. The comparison during 2006-2014 showed significant overestimation of TRMM PR in Assam and Bengal plains during pre-monsoon season (March to May), and significant underestimation of TRMM PR over the Indian subcontinent during monsoon season (June to September). Whereas, the comparison during 2014-2019 showed significant overestimation of GPM DPR over only Meghalaya during monsoon season. The validation of rain-drop size distribution parameters: Dm and Nw showed positive correlation between GPM DPR derived values and Parsivel disdrometers observed ones, while unrealistic concentration of Nw on 30-40 dB was derived by GPM DPR. In the southern slope of the Meghala Plateau, NSR of TRMM PR at Cherrapunji, where is known as the heaviest rainfall station, on the plateau observed smaller rainfall than that in the adjacent valley. However, newly installed raingauges in the valley showed rather less rainfall than that on the plateau. The validity of the satellite derived rainfall distribution over the complicated terrain are discussed.

How to cite: Murata, F., Terao, T., Yamane, Y., Kiguchi, M., Fukushima, A., Tanoue, M., Kamimera, H., Syiemlieh, H. J., Cajee, L., Ahmed, S., Choudhury, S. A., Bhattacharya, P., Mahanta, R., and Hayashi, T.: Validation of satellite-borne precipitation radars by raingauges and disdrometers over the northeastern Indian subcontinent, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14066, https://doi.org/10.5194/egusphere-egu21-14066, 2021.

The replenishment of aquifers depends mainly on precipitation rates, which is of vital 19 importance for determining water budgets in arid and semi-arid regions. El-Qaa Plain in Sinai 20 Peninsula is such a region which experiences a constant population growth. The local water budget 21 equilibrium is negatively affected by relatively frequent light rain events. This study compares the 22 performance of two sets of satellite-based data of precipitation and in situ rainfall measurements. The 23 dates selected refer to rainfall events between 2015 and 2018. For this purpose, 0.1° and 0.25° spatial 24 resolution TMPA (TRMM Multi-satellite Precipitation Analysis) and IMERG (Integrated Multi-25 satellitE Retrievals for GPM) data were retrieved and analyzed, employing appropriate statistical 26 metrics. The best-performing data set was determined as the data source capable to most accurately 27 bridge gaps in the limited rain gauge records, embracing both frequent light-intensity rain events 28 and rarer heavy-intensity events. With light-intensity events the corresponding satellite-based data 29 sets differ the least and correlate more, while the greatest differences and weakest correlations are 30 noted for the heavy-intensity events. The satellite-based records best match those of the rain gauges 31 during light-intensity events, when compared to the heaviest ones. IMERG data exhibit a superior 32 performance than TMPA, in all rainfall intensities.

How to cite: Morsy, M., Scholten, T., Michaelides, S., Borg, E., Sherief, Y., and Dietrich, P.: Comparative analysis of TMPA and IMERG precipitation datasets in the arid environment of El- Qaa Plain, Sinai, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2705, https://doi.org/10.5194/egusphere-egu21-2705, 2021.

Keywords:

Scavenging process, Rainfall, Aerosols, Lag correlation, Rainfall-aerosol processes

Abstract

Rainfall and aerosols play major roles in the Earth climate system and substantially influence our life. Here, the focus is on the local near-surface aerosol/rainfall correlations with time-scales of minutes to days. We investigated 29 experiments including 14 specific rain events, with time resolutions of daily and 60, 30, 10 minutes at ten stations in Israel and California. The highest negative correlations were consistently at a positive lag of about 140-160 minutes where a positive lag means that the aerosol time-series follows that of the rain. The highest negative value is suggested to be the probable outcome of immediate scavenging along with the rise in aerosol concentration after rain depending on aerosol sources, hygroscopic growth and transport. The scavenging dominance is expressed by the mostly negative lag-correlation values in all experiments. In addition, the consistent lack of significant correlation found at negative lags suggest a weak aerosol effect on precipitation (Gryspeerdt et al., 2015).

Plain Language Summary: Rainfall and atmospheric particles (aerosols) play significant roles in the Earth atmosphere and largely influence our weather and climate. The relations between near-surface aerosol and rainfall on time scales of minutes to days are studied, employing correlations in 10 meteorological stations in Israel and California. The highest negative correlations were consistently at a positive lag of about 140-160 minutes. A positive lag means that the aerosol time-series follows that of the rain. The highest negative correlation value is suggested to be the outcome of scavenging along with the rise in aerosol concentration after rain depending on the sources of aerosols, hygroscopic growth and transport. Furthermore, our approach provides a more fundamental insight into the local, near-surface rain-aerosol interactions, in contrast to many aerosol-rainfall studies that are climatological and with the tele-connection approach (Alpert et al., 2008), which involves other processes over distances of a few km up to even large synoptic scales.

Relevant References:

Alpert, P., Halfon, N., & Levin, Z. (2008). Does Air Pollution Really Suppress Precipitation in Israel? Journal of Applied Meteorology and Climatology. https://doi.org/10.1175/2007jamc1803.1

Barkan, J., & Alpert, P. (2020). Red Snow occurrence in Eastern Europe - A case study. Weather. https://doi.org/10.1002/wea.3644

Gryspeerdt, E., Stier, P., White, B. A., & Kipling, Z. (2015). Wet scavenging limits the detection of aerosol effects on precipitation. Atmospheric Chemistry and Physics, 15(13), 7557–7570.

Tsidulko, M., Krichak, S. O., Alpert, P., Kakaliagou, O., Kallos, G., & Papadopoulos, A. (2002). Numerical study of a very intensive eastern Mediterranean dust storm, 13-16 March 1998. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2001jd001168

How to cite: Alpert, P., Shafir, H., and Elhacham, E.: An unknown maximum lag-correlation between rainfall and aerosols at 140-160 minutes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-317, https://doi.org/10.5194/egusphere-egu21-317, 2021.

During the last years we made great progress with the country-wide rainfall estimation from commerical microwave link (CML) data in Germany (Graf et al. 2020, Polz et al. 2020). Using the derived results in different applications has, however, revealed that undetected erratic behaviour of CML raw data is still limiting data quality and that data gaps during heavy rain can lead to underestimation of peak rain rates. Hence, we have extended our processing methods and, for the first time, have carried out a large-scale intercomparison with other available methods. Albeit we are constantly improving our CML rainfall estimation, we already apply these data to operationally generate rainfall maps for Germany, also in combination with radar data from the German Meteorological Service (DWD).

In this contribution we will present our current research on the following interconnected topics:

1. Detecting erratic signal fluctuations: In contrast to the existing methods that focus on detecting rainy-periods in the noisy raw data we have developed a dedicated classification method for periods with erratic signal fluctuations, which can easily lead to rainfall overestimation from CMLs. Our method, which is based on an artificial neural network, is designed to reduce the number of falsely classified rainy periods during dry periods with strong signal fluctuation.

2. Large scale method intercomparison: For the first time, we compare the widely used RAINLINK algorithm, which is based on analysing data from nearby CMLs, with purely time-series based processing methods. First results show that both methods have advantages that, when combined, could improve the overall processing.

3. The effect and mitigation of data gaps during heavy rainfall: CML networks are designed so that very heavy rain events lead to a complete loss of signal, and hence to gaps in the data we use for rainfall estimation. We analyse the occurrence of these gaps and show the impact on CML-derived rainfall estimation as well as mitigation methods.

4. Real-time application: We use the CML data that we acquire in real-time to generate rainfall maps for Germany and merge the CML rainfall estimates with DWD radar data. Our approach is an extension of the existing RADOLAN-method. Results show that merging with the path-averaged CML rainfall information provides similar results than merging with gauges. In regions where the addition of CMLs significantly increases the density of observations, the joint Radar-gauge-CML product is expected to show improved quality.

References:

Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020

Polz, J., Chwala, C., Graf, M., and Kunstmann, H.: Rain event detection in commercial microwave link attenuation data using convolutional neural networks, Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, 2020

How to cite: Chwala, C., Graf, M., Polz, J., Rothermel, S., Glawion, L., Winterrath, T., Smiatek, G., and Kunstmann, H.: Recent improvements of CML rainfall estimation and CML-Radar combination in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15207, https://doi.org/10.5194/egusphere-egu21-15207, 2021.

The accurate representation of snowfall is still a challenge for weather forecast and climate models. It mostly relies on the parameterization of microphysical processes that govern snowfall growth and decay. Recently, strong discrepancies have been pinpointed between different microphysical schemes in cold precipitations over Antarctica, questioning the reliability of  surface mass balance assessments. A better understanding and an improved parameterization of these processes require the acquisition of observational data, which nonetheless remains difficult in polar or mountainous regions due to the remote location and harsh meteorological conditions.

Polarimetric radars offer continuous measurements of precipitation with a large spatial coverage, retrieving information about the microphysical processes that govern its evolution. This study presents a new method, called Process Identification based on Vertical gradient Signs (PIVS), to spatially identify the occurrence of the dominant microphysical processes (aggregation and riming, crystal growth, sublimation) governing snowfall evolution from polarimetric radar scans.

We first propose a theoretical framework to asses in which meteorological conditions a vertical analysis of the radar signal reflects the underlying microphysical processes. Then PIVS identifies aggregation and riming, crystal growth and sublimation based on the sign of the local vertical gradients of reflectivity ZH and differential reflectivity ZDR

We then applied our method on two frontal snowfall cases, one in Adélie land, Antarctica and one in the Taebaeck mountains, South Korea. We successfully compare PIVS results with an hydrometeor classification and with snowflake observations using a Multi-Angle Snowflake Camera. In Antarctica, PIVS indicates that crystal growth dominates above 2500m a.g.l., aggregation and riming prevail between 1500m and 2500m a.g.l., and sublimation occurs mainly below, concurring with previous studies stating that snowflakes preferentially sublimate in the relatively dry katabatic boundary layer. In south Korea, the structure is similar although the altitudes are shifted, with aggregation and riming between 4000m and 4800m a.g.l., sublimation below and crystal growth above. Moreover, the statistical analysis of different radar variables provides quantitative information to further characterize the microphysical processes of interest.

Finally, we highlight further possible improvements of the method - notably the addition of complementary polarimetric variables - and illustrate the potential of PIVS to evaluate the microphysical schemes in numerical models. 

How to cite: Planat, N., Gehring, J., Vignon, E., and Berne, A.: Identification of snowfall microphysical processes from vertical gradients of polarimetric radar variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4171, https://doi.org/10.5194/egusphere-egu21-4171, 2021.

Passive microwave (MW) observation from low Earth-orbiting satellites is one of the major sources of information for global precipitation monitoring. Although various precipitation retrieval techniques based on passive MW observation have been developed, most of them focus on estimating precipitation rate at near surface height. Vertical profile information of precipitation is meaningful for process-based understanding of precipitation systems. Also, a previous study found that the use of the vertical precipitation profile information can improve sub-hourly surface precipitation estimates (Utsumi et al., 2019).

This study investigates the precipitation vertical profiles estimated by two passive MW algorithms, i.e., the Emissivity Principal Components (EPC) algorithm developed by authors (Turk et al., 2018; Utsumi et al., 2021) and the Goddard Profiling Algorithm (GPROF). The vertical profiles of condensed water content estimated by the two passive MW algorithms for the Global Precipitation Measurement Microwave Imager (GMI) observations are validation with the GMI + Dual-frequency Precipitation Radar combined algorithm (CMB) for June 2014 – May 2015. The condensed water content profiles estimated by the passive MW algorithms show biases in their magnitude (i.e., EPC underestimates the magnitude by 20 – 50% in the middle-to-high latitudes; GPROF overestimates the magnitude by 20 – 50% in the middle-to-high latitudes and more than 50% overestimation in the tropics). On the other hand, the shapes of the profiles are reproduced well by the passive MW algorithms. The relationship between the estimation performances of surface precipitation rate and vertical profiles are also investigated. It is shown that the error in the profile magnitude shows a clear positive relationship with the surface precipitation error. The estimation performance of the profile shapes also shows connection with the surface precipitation error. This result indicates that physically reasonable connections between the surface precipitation estimate and its associated profiles are achieved to some extent by the passive MW algorithms. This also implies that properly constraining physical parameters of the precipitation profiles would lead to the improvements of the surface precipitation estimates.

References

Utsumi, N., Kim, H., Turk, F. J., & Haddad, Ziad. S. (2019). Improving Satellite-Based Subhourly Surface Rain Estimates Using Vertical Rain Profile Information. Journal of Hydrometeorology, 20(5), 1015–1026.

Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., You, Y., & Ringerud, S. (2018). An observationally based method for stratifying a priori passive microwave observations in a Bayesian-based precipitation retrieval framework. Quarterly Journal of the Royal Meteorological Society, 144(S1), 145–164.

Utsumi, N., Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., & Kim, H. (2021). Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals. Journal of Hydrometeorology, 22(1), 95–112.

How to cite: Utsumi, N., Turk, F. J., Haddad, Z. S., Kirstetter, P.-E., and Kim, H.: Vertical precipitation profiles estimated by satellite-based passive microwave retrievals, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1961, https://doi.org/10.5194/egusphere-egu21-1961, 2021.

Current microwave precipitation retrieval algorithms utilize the instantaneous brightness temperature (TB) from a single satellite to estimate the precipitation rate. This study proposed to add the time-dimension into the precipitation estimation process by using the TB (or emissivity) temporal variation (ΔTB or Δe) derived from the Global Precipitation Measurement (GPM) microwave radiometer constellation.  Results showed that (1) ΔTB can improve the precipitation estimation over the cold surfaces (i.e., snow-covered region) through minimizing the microwave land surface emissivity’s influence; (2) Δe under the clear-sky conditions can accurately estimate the daily rainfall accumulation; and (3) ΔTB can be used to identify the liquid raindrop signature over the low surface emissivity areas. This study highlights the importance of maintaining the current passive microwave satellite constellation.

How to cite: You, Y., Peters-Lidard, C., Munchak, S., and Ringerud, S.: Improving Precipitation Retrieval by Brightness Temperature Temporal Variation (ΔTB): Definition, Computation, and Application, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-443, https://doi.org/10.5194/egusphere-egu21-443, 2021.

The Sahel has experienced an increase in the frequency and intensity of extreme rainfall events over the recent decades. These trends are expected to continue in the future. However the properties of these events have so far received little attention. In the present study, we define a heavy precipitating event (HPE) as the occurrence of daily-mean precipitation exceeding a given percentile (e.g., 99^th and higher) over a 1°x1° pixel and examine their spatial distribution, intensity, seasonality and interannual variability. We take advantage of an original reference dataset based on a rather high-density rain-gauge network over Burkina Faso (142 stations) to evaluate 22 precipitation gridded datasets often used in the literature, based on rain-gauge-only measurements, satellite measurements, or both. Our reference dataset documents the HPEs over Burkina Faso. The 99^th percentile identifies events greater than 26 mm d^-1 with a ~2.5 mm confidence interval depending on the number of stations within a 1°x1° pixel. The HPEs occur in phase with the West African monsoon annual cycle, more frequently during the monsoon core season and during wet years. The evaluation of the gridded rainfall products reveals that only two of the datasets, namely the rain-gauge-only based products GPCC-DDv1 and REGENv1, are able to properly reproduce all of the HPE features examined in the present work. A subset of the remaining rainfall products also provide satisfying skills over Burkina Faso, but generally only for a few HPE features examined here. In particular, we notice a general better performance for rainfall products that include rain-gauge data in the calibration process, while estimates using microwave sensor measurements are prone to overestimate the HPE intensity. The agreement among the 22 datasets is also assessed over the entire Sahel region. While the meridional gradient in HPE properties is well captured by the good performance subset, the zonal direction exhibit larger inter-products spread. This advocates for the need to continue similar evaluation with the available rain-gauge network available in West Africa, both to enhance the HPE documentation and understanding at the scale of the region and to help improve the rainfall dataset quality.

How to cite: Sanogo, S., Peyrillé, P., Roehrig, R., Guichard, F., and Ouedraogo, O.: Heavy precipitating events in satellites and rain-gauge products over the Sahel, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8261, https://doi.org/10.5194/egusphere-egu21-8261, 2021.

Simulations of the diurnal cycle of precipitation from CMIP6 models and the ERA5 reanalysis are evaluated against the observed diurnal cycle from NASA’s IMERG observations.  The IMERG observation product, which combines the GPM/TRMM microwave constellation, spaceborne infrared sensors and ground-based gauge measurements, provides 20+ years of gridded global precipitation estimates at 0.1˚ every half hour.  Using IMERG’s long precipitation record, the first multi-decade evaluation of the simulated diurnal cycle is conducted (IMERG and ERA5: 2000-2019; CMIP6: 1979-2008).  After spatial and temporal matching of IMERG to the hourly CMIP6 (NCAR-CESM2, CNRM-CM6-1, CNRM-ESM2-1) and ERA5 simulations, the diurnal cycle for boreal summer is compared between products across the globe (60˚N-S).  To avoid bias in the results, regions with yearly mean precipitation < 100 mm are excluded from all analyses, as well as regions with weak diurnal amplitudes when analysing the time of maximum precipitation.  CMIP6 and ERA5 simulations underestimate the observed diurnal amplitude over ocean (14-66% of the precipitation mean, for the 5^th-95^th percentile range), with varying performance over land (26-134%).  Maximum precipitation is observed to accumulate over land in the afternoon and at night (14-21 LST over flatter terrain, and 21-6 LST over mountainous regions), and in the morning over ocean (0-12 LST).  CMIP6 and ERA5 are identified to better simulate the time of maximum over ocean than over land, though typically earlier in the day than observed.  In particular, ERA5 and CMIP6 fail to capture the propagating night-time peaks in precipitation accumulation close to mountainous regions.  Further analyses over CONUS, which include the ground-based radar network, highlight the improved performance of models in regions susceptible to convection (e.g. the Rocky Mountains).  Furthermore, IMERG’s skill in capturing the diurnal cycle over CONUS is demonstrated, and the current capability of the GPM Core Observatory’s dual-frequency precipitation radar is assessed.

How to cite: Watters, D., Battaglia, A., and Allan, R.: The Diurnal Cycle of Precipitation: A Comparison of State-of-the-Art IMERG Observations, CMIP6 Models and ERA5 Reanalysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8891, https://doi.org/10.5194/egusphere-egu21-8891, 2021.

Water scarcity is a growing concern in arid and semi-arid regions of the World, locations where groundwater is the main source of freshwater. In order to preserve local water budgets, it is critical that accurate climatic data be acquired. Unfortunately, the majority of these arid regions feature a very limited number of rain gauges, reducing the reliability of the data produced. The present study offers a series of steps for overcoming the issue of data scarcity. Once resolved, this could then promote greatly needed hydrological studies on topics such as the spatiotemporal distribution of rainfall, the mitigation of flash floods hazards, or the minimization of soil erosion. In the present study, the DEM file and GPM (IMERG) data were used to identify the most suitable locations for a new network of rain gauges at the Eastern side of the Gulf of Suez. These two datasets were clustered using k-means clustering to produce an elbow graph whose elbow-shaped region offered several possible options for the number of optimum clusters at the test site. The authors chose three different cluster sizes (3, 6, and 9) and calculated the possible centroids for each size. Calculations resulted in 3 centroids, 6 centroids, and 9 centroids. These centroids were tested using the empirical cumulative distribution function (ECDF), once the sum of the GPM (IMERG) scenes, the scene limits, and the elevation map limits were determined. This test revealed gaps in all centroids mentioned. Consequently, the authors established nine clusters as the optimal size. Nine centroids were therefore taken, along with the existing five gauges, as a basis for standard error kriging. This allowed the authors to gradually minimize error via looping. The newly added points were tested with an ECDF. The complete spectrum of rainfall and elevation was efficiently covered by the 31 proposed rain gauge locations, and the five existing gauges.

How to cite: Michaelides, S., Morsy, M., Taghizadeh-Mehrjardi, R., Scholten, T., Dietrich, P., and Schmidt, K.: The potential of remote sensing data in rain gauge network optimization in the arid regions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8963, https://doi.org/10.5194/egusphere-egu21-8963, 2021.

The RadAlp experiment at the Grenoble region in the French Alps aims to advance the radar remote sensing techniques of precipitation in high mountain regions. Since 2016, two dual-polarimetric X-band radars, one on top of Mt Moucherotte (1901 m asl) and another in the Grenoble valley (220 m asl) are operated by Metro France and IGE respectively. High spatio-temporal variability of precipitation (e.g. intensity and phase) in the complex terrain requires high-resolution observations. X-band radar provides high spatial and temporal resolution imagery which makes it ideal for use in complex terrain but also comes with significant attenuation problems during heavy precipitation and in the melting layer (ML). The development of polarimetric techniques, especially differential phase shift (ϕDP) has helped to mitigate the power signal attenuation problem to a certain extent. The ϕDP is immune to attenuation due to rainfall, radar calibration errors and partial beam blockage, making it an attractive parameter for quantitative precipitation estimation (QPE) through attenuation correction of the reflectivity (Z). The ϕDP, however, is quite noisy and requires regularization. An iterative algorithm based on maximum allowed step sizes provides a robust solution in ϕDP regularization. In this study, we aim to understand the relationship between differential phase shift (ϕDP) and path integrated attenuation (PIA) at X-band. This relationship is crucial for quantitative precipitation estimation (QPE) using polarimetric techniques. Furthermore, this relationship is still poorly documented within the melting layer due to the complexity of the hydrometeors' distributions in terms of phase, size, shape and density. We use the mountain reference technique (MRT) for direct PIA estimations associated with the decrease in returns from mountain targets during precipitation events as compared to dry periods. The quasi-vertical profiles from the valley-based radar (XPORT) help to identify, characterize and follow the evolution of the melting layer. For the mountaintop radar (MOUC) stratiform events (59 days between Nov 2016 to Dec 2019) where the O° elevation angle beam passes through the melting layer are considered.  The PIA/ ϕDP ratios at different strata of the ML, snow-ML interface and ML-rain interface are studied. Initial results show that the PIA/ ϕDP ratio peaks at the levels of cross-correlation coefficient (ρHV) minima, remains strong in the upper part of the ML and tends to 0 towards the top of ML. Additionally, its value in rain (0.32 dB per deg) below the ML matches closely with the specific attenuation vs specific phase (k-KDP) relationship (0.29) derived from the disdrometer at ground level.  Its value increases steadily in the lower part of ML (peaks around 0.70 dB per deg), remains strong in the upper part of ML (0.5 - 0.6 dB per degree), and decreases rapidly to 0.13 dB per degree above the ML (in snow).

How to cite: Khanal, A. K., Delrieu, G., Boudevillain, B., Cazenave, F., and Yu, N.: Investigation of the relationship between differential phase shift and path integrated attenuation in the melting layer of precipitation of X-band radar, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15850, https://doi.org/10.5194/egusphere-egu21-15850, 2021.

In this study, the performance of ensemble precipitation forecasts of three numerical weather prediction (NWP) models within the TIGGE database as well as the integrated multi-satellite retrievals for global precipitation measurement (GPM), namely IMERG-RT V05B, for precipitation estimates were evaluated in recent severe floods in Iran over the March–April 2019 period. The evaluations were conducted in two modes: spatial distribution of precipitation and the dichotomous evaluation in four precipitation thresholds (25, 50, 75, and 100 mm per day). The results showed that the United Kingdom Met Office (UKMO) model, in terms of spatial coverage and satellite estimates as well as the precipitation amount, were closer to the observations. Although, generally, the models captured the spatial distribution of heavy precipitation events, the hot spots were not located in the correct area. The National Centers for Environmental Forecast (NCEP) model performed well at low precipitation thresholds, while at high thresholds, its performance decreased significantly. On the contrary, the accuracy of IMERG improved when the precipitation threshold increased. The UKMO had better forecasts than the other models at the 100 mm/day precipitation threshold, whereas the Medium-Range Weather Forecasts (ECMWF) had acceptable forecasts in all thresholds and was able to forecast precipitation events with a lower false alarm ratio and better detection when compared to other models. Although, the models and IMERG product underestimated or overestimated the amount of precipitation, but they were able to detect most extreme precipitation events. Overall, the results of this study show the IMERG precipitation estimates and NWP ensemble forecasts performed well in the three major flood events in spring 2019 in Iran. Given wide spread damages caused by the floods, the necessity of establishing an efficient flood warning system using the best precipitation products is advised.

How to cite: Aminyavari, S., Saghafian, B., and Sharifi, E.: Performance evaluation of ensemble precipitation forecasts and satellite products for the spring 2019 severe floods in Iran, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9089, https://doi.org/10.5194/egusphere-egu21-9089, 2021.

How to cite: Prat, O., Nelson, B., Leeper, R., and Embler, S.: Assessment of Cold-Season Precipitation Estimates Derived from Daily Satellite Precipitation Products over CONUS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13771, https://doi.org/10.5194/egusphere-egu21-13771, 2021.

The distinctive fingerprints of precipitation processes in multifrequency measurements from GOES-East and GPM sensors are characterized using ground-based observations (rain gauges, disdrometers, spectrometers, radars, etc.) and microphysical-dynamical models.  The focus is on low-level warm rain processes, including the life-cycle of hydrometeors from CN activation until they reach the land surface, not resolved by numerical weather prediction models and missed by remote observing systems on the ground or satellites.  That is, the Terra Obscura of orographic precipitation. We propose and demonstrate a framework to infer local physical-statistical constraints from satellite measurements to improve quantitative precipitation estimates (QPE) in complex terrain regions globally.

How to cite: Barros, A., Chavez, S., Ji, L., and Arulraj, M.: Fingerprinting Precipitation Processes in Remote-Sensing Observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13676, https://doi.org/10.5194/egusphere-egu21-13676, 2021.

The Radio Occultation and Heavy Precipitation (ROHP) experiment aboard the Spanish PAZ satellite was activated in May 2018 with the objective to demonstrate the Polarimetric Radio Occultation (PRO) concept for rain detection. This technique enhances standard RO by measuring GNSS signals at two orthogonal linear polarizations (H and V). Owing to hydrometeor asymmetry, electromagnetic signals propagating through regions of heavy precipitation would experience a differential phase delay expected to be measurable by the ROHP experiment.

After 2+ years of operations, the initial hypothesis has been verified and the main scientific goals have been achieved. Soon after the activation of the experiment it became clear that PRO observables were sensitive to heavy precipitation, showing positive signatures correlated with the presence and intensity of precipitation. After a thorough on-orbit calibration, it has been demonstrated that the PAZ polarimetric observable can be used as a proxy for heavy precipitation. Furthermore, PRO measurements were shown to be sensitive to the horizontally oriented frozen hydrometeors present throughout the vertical cloud extent, providing valuable information on the vertical structure of precipitating clouds.

In addition, PRO can retrieve standard thermodynamic RO products such as temperature, pressure, and water vapor. These products, provided with high vertical resolution, globally distributed and seamlessly over ocean and over land, make PRO observations a unique dataset, with potential applications ranging from the study of deep convection processes to the evaluation and diagnosis of NWP forecast models.

In this presentation we will report on the status of the experiment and current data availability. We will also show the results of the sensitivity studies to heavy precipitation and frozen particles, performed using collocated observations between PAZ and GPM-DPR, GPM-GMI, and other radiometers from the GPM constellation, as well as a-priory information from the Cloudsat radar. Finally, we will address potential level-2 products we can expect from PAZ observations.

How to cite: Padullés, R., Cardellach, E., Turk, F. J., Ao, C. O., Wang, K. N., De la Torre Juárez, M., and Oyola, M.: Sounding Heavy Precipitating Vertical Cloud Structures with Polarimetric Radio Occultations aboard PAZ, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9405, https://doi.org/10.5194/egusphere-egu21-9405, 2021.

There has been a noticeable increase in the application of artificial intelligence (AI) algorithms in various areas, in the recent past. One such area is the prediction of rainfall over a region. This application has seen crucial advancement with the use of deep sequential learning algorithms. This new approach to rainfall prediction has also helped increase the utilization of satellite data for prediction. As, AI based prediction algorithms are based on data, the characteristics of it dominates the accuracy of the prediction. And one such characteristic is the information content in the data being used. This information content is classified into redundant information (information of past states in the current state) and new information. The performance of the AI based rainfall prediction depends on the amount of redundant information present in the data being used for training the AI model, more the redundant information (less the new information content) more accurate will be the prediction. Various entropy based measure have been used to quantify the new information content in the data, like permutation entropy, sample entropy, wavelet entropy, etc. This study uses a new measure called the Wavelet Entropy Energy Measure (WEEM). One of the advantages of WEEM is that it considers the dynamics of the process spread across different time scales, which other information measures have not considered explicitly. Since, the dynamics of rainfall is multi-scalar in nature, WEEM is a suitable measure for it. The main goal of this study is to find out the amount of information being generated by INSAT-3D and IMERG rainfall at each time step over the North Eastern Region of India, which will dictate the suitability of the two rainfall product to be used for AI based rainfall prediction.

How to cite: Chakravorty, A., Kundu, S. S., and Raju, P. L. N.: Information content in the rainfall observed by INSAT-3D and IMERG: An intercomparison study over the north eastern region of India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14100, https://doi.org/10.5194/egusphere-egu21-14100, 2021.

The sparsity of rain gauge (RG) data over Africa is a known impediment to the assessments of hydro-meteorological risks and of the skill of numerical weather prediction (NWP) models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms.  Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–2018, this study performs a multi-scale evaluation of gauge-calibrated SREs, namely, Integrated Multi-satellite Retrieval for Global Precipitation Measurement (GPM) (IMERG), Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). Skills were assessed from daily to annual timescales, for extreme daily precipitation, and for the TMPA and IMERG near real-time (NRT) products. Results show that: 1) the satellite products reproduce the annual rainfall pattern and seasonal rainfall cycle well, despite exhibiting biases of up to 9%; 2) IMERG is the best overall for shorter temporal scales (daily, pentadal and dekadal) while MSWEP and CHIRPS perform best at the monthly and annual timesteps, respectively; 3) the SREs’ performance, especially in MSWEP, shows high spatial variability likely due to the variation of weights assigned during gauge calibration; 4) all the SREs miss between 57% (IMERG NRT)  and 83 (CHIRPS) of daily extreme rainfall events recorded in the RGs; 5) IMERG NRT outperforms all the other products regarding extreme event detection and accuracy; and 6) for assessing return values of daily extreme values, IMERG and MSWEP are satisfactory while the use of CHIRPS cannot be recommended. The study highlights some improvements of IMERG over its predecessor TMPA and the potential of Multi-Source Weighted-Ensembles products such as MSWEP for flood risk assessment and validation of NWP rainfall forecasts over East Africa.

How to cite: Ageet, S., Fink, A., and Maranan, M.: Validation of Satellite Rainfall Estimates over Equatorial East Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10858, https://doi.org/10.5194/egusphere-egu21-10858, 2021.

It is important to evaluate and improve the cloud properties in global non-hydrostatic models like a Nonhydrostatic ICosahedral Atmospheric Model (NICAM, Satoh et al. 2014) using observation data. One of the methods is a radiance-based evaluation using satellite data and a satellite simulator (here Joint simulator, Hashino et al. 2013), which avoids making different settings of the microphysics between retrieval algorithms and NICAM.

The satellite data with active sensors has a limitation to observe the specific case of cloud and precipitation systems. And it is needed to validate satellite observations using in-situ observation. There are intensive observation stations over the Tokyo area, whose domain size is 100 km×100 km. For examples, the High Spectral Resolution Lidar (HSRL, 355 nm), Doppler lidar, and the Cloud Profiling Radar (CPR, 94 GHz) are located in Tokyo. The WInd profiler Network and Data Acquisition System (WINDAS) data is available in Kawaguchiko, Mito, and Kumagaya. Several polarimetric radars cover this area like C-band, Ka band, and X-band phased array.  The ULTIMATE (ULTra sIte for Measuring Atmosphere of Tokyo metropolitan Environment) is proposed to verify and improve high-resolution numerical simulations based on these observation data. In this study, we introduce the preliminary evaluation results of NICAM and applications of the Joint simulator related to the ULTIMATE project.

How to cite: Roh, W. and Satoh, M.: An introduction to the ULTIMATE project in Japan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11901, https://doi.org/10.5194/egusphere-egu21-11901, 2021.

A stepwise evaluation method and a comprehensive scoring approach are proposed and applied to select a model setup and physics parameterizations of the Weather Research and Forecasting (WRF) model for high-resolution precipitation simulations. The ERA5 reanalysis data were dynamically downscaled to 1-km resolution for the topographically complex domain of the eastern Mediterranean island of Cyprus. The performance of the simulations was examined for three domain configurations, two model initialization frequencies and 18 combinations of atmospheric physics parameterizations (members). Two continuous scores, i.e., Bias and Mean Absolute Error (MAE) and two categorical scores, i.e., the Pierce Skill Score (PSS) and a new Extreme Event Score (EES) were used for the evaluation. The EES combines hits and frequency bias and it was compared with other commonly used verification scores. A composite scaled score (CSS) was used to identify the five best performing members.

The EES was shown to be a complete evaluator of the simulation of extremes. The least errors in mean daily and monthly precipitation amounts and daily extremes were found for the domain configuration with the largest extent and three nested domains. A 5-day initialization frequency did not improve precipitation, relative to 30-day continuous simulations. The use of multiple and comprehensive evaluation measures for the assessment of WRF performance allowed a more complete evaluation of the different properties of simulated precipitation, such as daily and monthly volumes and daily extremes, for different dynamical downscaling options and model configurations. The scores obtained for the selected five members for a three-month simulation period ranged for BIAS from zero to -25%, for MAE around 2 mm, for PSS from 0.25 to 0.52 and for EES from 0.19 to 0.26. The CSS ranged from 0.56 to 0.83 for the same members. The proposed stepwise approach can be applied to select an efficient set of WRF multi-physics configurations that accounts for these properties of precipitation and that can be used as input for hydrologic applications.

How to cite: Sofokleous, I., Bruggeman, A., Michaelides, S., Hadjinicolaou, P., Zittis, G., and Camera, C.: Comprehensive Methodology for the Evaluation of High-Resolution WRF Multi-Physics Precipitation Simulations for Small, Topographically Complex Domains , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7845, https://doi.org/10.5194/egusphere-egu21-7845, 2021.

Climate models project a strong increase in Arctic precipitation as well as an increase in the ratio of liquid to solid precipitation for the 21^st century. While previous studies have explored past trends in precipitation, relatively little is known about the trends in the ratio of liquid to solid precipitation. A regression analysis of the ratio of liquid to solid precipitation in East Greenland will be conducted to better understand if and how precipitation as well as the relative fractions of snow and rain in precipitation have changed in the time period 1958-2019. This will be done in the context of the interdisciplinary project Snow2Rain which focusses on understanding how the transition from snow to rain is influencing quality of life in and around Tasiilaq (Southeast Greenland). Here, in a broader geographical context, a combination of results from the Regional Atmospheric Climate Model (RACMO2.3p2) and meteorological observations from weather stations along the coast of East Greenland between 65° N and 70° N will be used to assess changes in the ratio of liquid to solid precipitation. The station data will serve to cross-check the output from the regional climate model. A simple partitioning scheme based on near-surface temperature will be used. The combination of model data and weather observations can increase our understanding of trends in the relative fraction of precipitation that falls as snow or rain along the data sparse Greenlandic East coast.

How to cite: van der Schot, J.: Past trends in precipitation and in the ratio of snow to rain in East Greenland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11632, https://doi.org/10.5194/egusphere-egu21-11632, 2021.

Easterly waves (EWs) are an important feature of the intertropical convergence zone, they often serve as precursors to tropical cyclones, and, during boreal summers, are one of the main contributors to rainfall in various countries in Central America. Given the land-sea configuration that features the region, a better understanding of the EWs impact on regional rainfall would leverage the comprehension of regional interactions processes. EWs were also one of the foci of OTREC (Organization of Tropical East Pacific Convection), an observational campaign that took place in Costa Rica and Colombia from 5 August to 9 October 2019. Here, we will present some results obtained with high-resolution numerical simulations conducted with the System for Atmospheric Modeling (SAM), which are based on data collected during OTREC. We will begin by presenting a series of simulations forced with high-frequency radiosonde data collected in Santa Cruz, Costa Rica, for a weeklong period during OTREC, highlighting model performance in reproducing the data. We will then discuss more idealized SAM simulations designed to investigate convective initiation and convective organization at various stages of EW passage. Finally, using sensitivity experiments with SAM in which we override soil moisture conditions, we will address the role of surface moisture in modulating the interaction between EWs and deep convection over land. This work aims to improve current knowledge on the role of EWs for regional rainfall, influence on the initiation of deep convection and further surface-atmosphere feedbacks.

How to cite: Torri, G., Lintner, B., Durán-Quesada, A. M., and Serra, Y.: Modeling the Interaction between Easterly Waves and Deep Convection in Costa Rica, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6535, https://doi.org/10.5194/egusphere-egu21-6535, 2021.

This study focuses on the considerable spatial variability of precipitation along the western coast of a continent at mid–high latitude and investigates the precipitation climatology and mechanism along the south coast of Alaska, using datasets of spaceborne radars onboard two satellites, namely, the Dual-frequency Precipitation Radar (DPR) KuPR onboard the Global Precipitation Measurement (GPM) core satellite and the Cloud Profiling Radar (CPR) onboard CloudSat. At higher latitudes, differentiating the phase of precipitation particles falling on the ground is crucial in evaluating precipitation. Classification of satellite precipitation products according to the distance from the coastline shows that precipitation characteristics differ greatly on opposite sides of the coastline. Above coastal waters, relatively heavy precipitation with CPR reflectivity larger than 7 dBZ from orographically enhanced nimbostratus clouds, which can be detected by KuPR, is frequently captured. Meanwhile, along coastal mountains, light-to-moderate snowfall events with CPR reflectivity lower than 11 dBZ, which are well detected by the CPR but rarely detected by KuPR, frequently occur, and they are mainly brought by nimbostratus clouds advected from the coast and orographically enhanced shallow cumuliform clouds. There is no clear diurnal variation of precipitation except in summer, and the amplitude of the variation during summer is still low compared with total precipitation especially over the ocean, suggesting that the transport of synoptic-scale water vapor brings much precipitation throughout the year. Case studies and seasonal analysis indicate that frontal systems and moisture flows associated with extratropical cyclones that arrive from the Gulf of Alaska are blocked by terrain and stagnate along the coast to yield long-lasting precipitation along the coastline. The results of this study illustrate the importance of using complementary information provided by these radars to evaluate the precipitation climatology in a region in which both rainfall and snowfall occur.

How to cite: Aoki, S. and Shige, S.: Large Precipitation Gradients along the South Coast of Alaska Revealed by Spaceborne Radars, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13714, https://doi.org/10.5194/egusphere-egu21-13714, 2021.

The Global Precipitation Climatology Project (GPCP) is currently providing a next-generation Version 3.1 Monthly product, which covers the period 1983-2019.  This modernized product includes higher spatial resolution (0.5°x0.5°); a wider coverage (60°N-S) by geosynchronous IR estimates, now based on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) algorithm, with monthly recalibration using Goddard Profiling (GPROF) algorithm retrievals from selected passive microwave sensors; and improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation, used outside 60ºN-S.  The merged satellite estimate is adjusted to the Tropical Combined Climatology (TCC) at lower latitudes, and the Merged CloudSat, TRMM, and GPM (MCTG) climatology at higher latitudes.  Finally, V3.1 provides a merger of the satellite-only estimates with the Global Precipitation Climatology Centre (GPCC) monthly 1°x1° gauge analyses. 

As well, the GPCP team is advancing a companion global Version 3 Daily product, in which the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates are used where available (initially restricted to 60°N-S), and rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate.  Since IMERG currently extends back to June 2000, daily PERSIANN-CDR data will be used for the period January 1983–May 2000 to complete the record.

This presentation will provide early results for, and the latest status of, the Monthly and Daily GPCP products as a function of time and region.  Key points include examining homogeneity over time and across time and space boundaries between input datasets.  One key activity is to refine the V3 products while we continue to produce the Version 2 GPCP products for on-going use.

How to cite: Huffman, G. J., Behrangi, A., Adler, R. F., Bolvin, D. T., Nelkin, E. J., Song, Y., and Wang, J.-J.: The Global Precipitation Climatology Project Version 3 Products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8468, https://doi.org/10.5194/egusphere-egu21-8468, 2021.

The profile classification module in GPM DPR level-2 algorithm outputs various products  such as rain type classification, melting layer  detection and  identification of  surface snowfall , as well as presence of graupel and hail. Extensive evaluation and validation activities have been performed on these products and have illustrated excellent performance. The latest version of these products is 6X.  With increasing interests  on severe weather  such as hail and  extreme precipitation, in  the next version (version 7), we development a flag to identify hail along the vertical profile using  precipitation type index (PTI).

Precipitation type index (PTI) plays an important role in a couple of algorithms in the profile classification module. PTI is a value calculated for each dual-frequency profile with precipitation observed by GPM DPR.   DFRm slope, the maximum value of the Zm(Ku) , and  storm top height  are used in calculating PTI. PTI is effective in separating snow and Graupel/Hail  profiles. In version 7, we zoom in further into PTI for  Graupel/ hail profiles and separate  them into graupel and hail profiles with different PTI thresholds. A new Boolean product of “flagHail” is a hail only identifier for each vertical profile.  This hail product will be validated with ground radar products and other DPR products from Trigger module of DPR level-2 algorithm.   In version 7, we make improvements of the surface snowfall algorithm. An adjustment is made accounting for global variability of storm top profiles.. A storm top normalization is introduced to obtain a smooth transition of surface snowfall identification algorithm along varying latitudes globally.

How to cite: Chandra, C. V. and Le, M.: GPM DPR Profile Classification Algorithm: Enhancement from V6X to V7, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13770, https://doi.org/10.5194/egusphere-egu21-13770, 2021.

The potential of global high-resolution near-realtime multi-sensor merged satellite precipitation products such as NASA’s 30-minute, 0.1° Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) to monitor, characterize and model the water cycle has been widely recognized. Despite continuing improvements in the coverage, accuracy, and resolution of these products, their usefulness in real-world applications is still limited by the lack of insight into errors in estimated precipitation and the ability to properly quantify errors in ways that benefit various end users. A fundamental limitation is the lack of reliable “ground truth” data (e.g., rain gauges or ground weather radars)—such reference observations are lacking in precisely the places (complex terrain, ungauged areas, and developing countries) that could benefit most from satellite products. Moreover, error characterization of satellite precipitation products poses a unique challenge due to the “mixed” discrete and continuous distribution of errors, a challenge that is increasingly important to address as satellite precipitation products advance to higher resolutions.

In this work, we propose to use the instantaneous swath-based data products from the Dual-frequency Precipitation Radar (DPR) aboard the GPM core observatory as an alternative reference to replace ground observations—which could facilitate IMERG global error estimation at its native resolution. We compare two DPR-based products, 2ADPR and 2BCMB, against the Multi-Radar/Multi-Sensor (MRMS) data over the contiguous United States (CONUS). We then select 2BCMB to train a mixed discrete-continuous error model based on the Censored Shifted Gamma Distribution (CSGD) to estimate IMERG errors. This error model is evaluated and compared against an alternative CSGD model trained on MRMS data in the CONUS during 2014-2019. Using NASA’s MERRA-2 reanalysis products, we also demonstrate how IMERG errors can be further constrained by including ancillary information as covariates within the error model. This error modeling framework will be further examined at several ground validation sites around the globe (e.g., WegenerNet, AMMA-CATCH among others) to evaluate its robustness under different climatic, land cover, and DPR sampling conditions.

How to cite: Li, Z., Wright, D., Hartke, S., Kirschbaum, D., Khan, S., Maggioni, V., and Kirstetter, P.-E.: A Prototype IMERG Error Modeling Framework based on GPM DPR Observations and its Global Validation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6479, https://doi.org/10.5194/egusphere-egu21-6479, 2021.

Using a two-year dataset (2016–17) from 17 one-minute rain gauges located in the moist forest region of Ghana, the performance of the Integrated Multisatellite Retrievals for GPM, version 6b (IMERG), is evaluated based on a subdaily time scale, down to the level of the underlying passive microwave (PMW) and infrared (IR) sources. Additionally, the spaceborne cloud product Cloud Property Dataset Using SEVIRI, edition 2 (CLAAS-2), available every 15 minutes, is used to link IMERG rainfall to cloud-top properties. Several important issues are identified: 1) IMERG’s proneness to low-intensity false alarms, accounting for more than a fifth of total rainfall; 2) IMERG’s overestimation of the rainfall amount from frequently occurring weak convective events, while that of relatively rare but strong mesoscale convective systems is underestimated, resulting in an error compensation; and 3) a decrease of skill during the little dry season in July and August, known to feature enhanced low-level cloudiness and warm rain. These findings are related to 1) a general oversensitivity for clouds with low ice and liquid water path and a particular oversensitivity for low cloud optical thickness, a problem which is slightly reduced for direct PMW overpasses; 2) a pronounced negative bias for high rain intensities, strongest when IR data are included; and 3) a large fraction of missed events linked with rainfall out of warm clouds, which are inherently misinterpreted by IMERG and its sources. This paper emphasizes the potential of validating spaceborne rainfall products with high-resolution rain gauges on a subdaily time scale, particularly for the understudied West African region.

How to cite: Maranan, M., Fink, A. H., Knippertz, P., Amekudzi, L. K., Atiah, W. A., and Stengel, M.: A Process-Based Validation of GPM IMERG and Its Sources Using a Mesoscale Rain Gauge Network in the West African Forest Zone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10649, https://doi.org/10.5194/egusphere-egu21-10649, 2021.

The Global Precipitation Measurement mission (GPM) is one of the recent efforts to provide satellite-based global precipitation estimates. The GPM Profiling Algorithm (GPROF) converts microwave radiation measured by passive microwave (PMW) sensors onboard constellation satellites into precipitation. Over land, precipitation estimates are obtained from high frequency PMW-channels that measure the radiance scattered by ice particles in rain clouds. However, due to the limited scattering related to shallow and light precipitation, it is challenging to distinguish these signals from background radiation that is naturally emitted from the Earth’s surface.

Increased understanding of the physical processes during precipitation events can be used to improve PMW-based precipitation retrievals. This study couples overpasses of GPM radiometers over the Netherlands to two dual-polarization radars from the Royal Netherlands Meteorological Institute (KNMI). The Netherlands is an ideal setting for this study due to the availability of high-quality ground-based measurements, the frequent occurrence of shallow events, the absence of ground-clutter related to mountains, and the varying background emission related to its coastal location.

The coupling of overpasses with ground-based precipitation radars provides the opportunity to relate GPROFs performance to physical characteristics of precipitation events, such as the vertical reflectivity profile and dual-polarization information on the melting layer. Furthermore, simultaneous radiometer estimates and space-based reflectivity profiles from the dual-frequency precipitation radar (DPR) onboard the GPM core satellite are coupled to the ground-based reflectivity profiles for selected case studies. Because the a-priori database implemented in the GPROF algorithm is based on observations from the DPR, the comparison of the reflectivity profiles further unravels discrepancies between GPROF and ground-based estimates.

How to cite: Bogerd, L., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Understanding performance of GPROF precipitation retrievals over the Netherlands in relation to precipitation characteristics as derived from ground-based dual-polarization radars, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6141, https://doi.org/10.5194/egusphere-egu21-6141, 2021.

One of the major characteristics of dual-frequency precipitation radar (DPR) onboard Global Precipitation Measurement (GPM) core satellite, is estimation of cloud physical properties of precipitation such as drop size distribution (DSD), existence of hail/graupel particles and possibly the mixed phase region above freezing height.  In this study, ground-based X-band radar network data are utilized for evaluate the cloud physical products from GPM/DPR.  The X-band radar network, composed of 39 X-band dual polarimetric radars developed by the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) of Japan, called XRAIN[1] is utilized for the evaluation.  The XRAIN radar completes volume scan up to the elevation angle of 20 degrees in 5 minutes.  By using multiple radars, three dimensional wind field is estimated by using the dual-Doppler analysis technique.　In this analysis DSD parameter from DPR (which is called epsilon in DPR product) and dual frequency ratio (DFR) that correlate well median diameter of DSD are compared with ZDR and KDP from XRAIN data.  The vertical wind data from XRAIN is utilized to characterize the Z of DPR. The case on August 27, 2018, on which GPM satellite flew over a hail producing convective storm around Tokyo, is analyzed.  Comparison of three dimensional structure of the storm between KuPR (Ku-band radar of DPR) and XRAIN from multiple radar observations shows that both observations are quite similar each other except for the KuPR observation show rather larger volume because of the larger footprint size.  At the rain region (below freezing height), the DSD parameter of DPR (epsilon) and DFR correlate well with ZDR and KDP from XRAIN, respectively.  This result indicates the DPR algorithm works well to estimate the DSD information of rain.  The comparison of Z with vertical wind speed indicates that the higher Z is characterized as higher variance of vertical wind speed. Above the freezing height, the relationship between both observations are complicated.  This result indicates that the various types of precipitation particles not only solid particles but also liquid/mixed phase particle can exist in the severe convective storm.  The hydrometeor type classification from XRAIN by using the method by Kouketsu et al. (2015) [2] confirms that the various types of precipitation exist in this case.

References

[1] Tsuchiya, S., M. Kawasaki, H. Godo, 2015: Improvement of the radar rainfall accuracy of XRAIN by modifying of rainfall attenuation correction and compositing radar rainfall, Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 2015, Volume 71, Issue 4, pp. I_457-I_462 (in Japanese with English abstract).

[2] Kouketsu, T., Uyeda, H., Ohigashi, T., Oue, M., Takeuchi, H., Shinoda, T., Tsuboki, K., Kubo, M., and Muramoto, K., 2015: A Hydrometeor Classification Method for X-Band Polarimetric Radar: Construction and Validation Focusing on Solid Hydrometeors under Moist Environments, Journal of Atmospheric and Oceanic Technology, 32(11), 2052-2074.

How to cite: Takahashi, N. and Kouketsu, T.: Evaluation of micro physical products of GPM/DPR with X-band radar network data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13934, https://doi.org/10.5194/egusphere-egu21-13934, 2021.

The Asian summer monsoon system is the strongest monsoon circulation on the Earth. A huge reversal of meridional temperature gradient develops over the area covering the hemispheric region due to strong diabatic heating associated with convective activities. Vigorous conventions reach the upper troposphere providing a great amount of high potential temperature airmass. This high potential temperature air mass originates from the high equivalent potential temperature airmass accumulated in the lower troposphere over the Asian monsoon region. The highest potential temperature tropospheric air mass is observed only over the Asian summer monsoon region. To get a total view of the Asian summer monsoon circulation system, we focused on the mass budget of the upper-tropospheric air mass with a potential temperature between 355K to 370K. The non-conservative change of the air mass corresponds with the diabatic heating due to the convective activities, and the diabatic cooling due to the radiative process. To analyze the radiative cooling process that takes place in the upper troposphere, we utilized hourly GSMaP pixel values to detect rain-free pixels of the ERA5 dataset. We calculated the non-conservative air mass tendency over the rain-free pixels on a daily and 0.5 degrees spatio-temporal scale. We found the radiative equilibrium amount of high potential temperature air mass and the Newtonian cooling process with a relaxation time scale of 6 to 7 days. We will show the quantitative estimates of the total convective process of the Asian summer monsoon system associated with the convective clouds and radiative processes, through the mass budget of 355K-370K potential temperature air mass. We will further show results of the evaluation of the accuracy of TRMM and GPM products using our high-resolution tipping bucket raingauge network distributed over the Northeastern Indian subcontinent.

How to cite: Terao, T., Murata, F., Yamane, Y., Kiguchi, M., Fukushima, A., Masahiro, T., Kamimera, H., and Hayashi, T.: Application of GSMaP for the analysis of upper tropospheric radiative cooling over the Asian summer monsoon region, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14545, https://doi.org/10.5194/egusphere-egu21-14545, 2021.