Global Precipitation Measurement Research Articles

Hydrometeor Identification Using GMI Passive Microwave Brightness Temperatures

Abstract Using passive microwave brightness temperatures (Tbs) from the Global Precipitation Measurement (GPM) mission Microwave Imager (GMI) and hydrometeor identification (HID) data from dual-polarization ground radars, empirical lookup tables are developed for a multifrequency estimation of the likelihood a precipitation column includes certain hydrometeor types, as a function of Tb. Eight years of co-located Tbs and HID data from the GPM Validation Network are used for development and testing of the GMI-based HID retrieval, with 2015-2020 used for training and 2021-2022 used for testing the GMI-based HID retrieval. The occurrence of profiles with hail and graupel are both slightly underpredicted by the lookup tables, but the percentage of profiles predicted is highly correlated with the percentage observed (0.98 correlation coefficient for hail, and 0.99 for graupel). By having snow appear before rain in the hierarchy, the sample size for rain, without ice aloft, is fairly small, and the percentage of rain profiles is less than snow for all Tbs.

Prediction of Tropical Pacific Rain Rates with Over-parameterized Neural Networks

Abstract The prediction of tropical rain rates from atmospheric profiles poses significant challenges, mainly due to the heavy-tailed distribution exhibited by tropical rainfall. This study introduces over-parameterized neural networks not only to forecast tropical rain rates, but also to explain their heavy-tailed distribution. The investigation is separately conducted for three rain types (stratiform, deep convective, and shallow convective) observed by the Global Precipitation Measurement satellite radar over the West and East Pacific regions. Atmospheric profiles of humidity, temperature, and zonal and meridional winds from the MERRA-2 reanalysis are considered as features. Although over-parameterized neural networks are well-known for their “double descent phenomenon,” little has been explored about their applicability to climate data and capability of capturing the tail behavior of data. In our results, over-parameterized neural networks accurately estimate the rain rate distributions and outperform other machine learning methods. Spatial maps show that over-parameterized neural networks also successfully describe spatial patterns of each rain type across the tropical Pacific. In addition, we assess the feature importance for each over-parameterized neural network to provide insight into the key factors driving the predictions, with low-level humidity and temperature variables being the overall most important. These findings highlight the capability of over-parameterized neural networks in predicting the distribution of the rain rate and explaining extreme values.

Can IMERG QPE product capture the heavy rain on urban flood scale?

Urban areas are increasingly vulnerable to sudden flooding disasters caused by intense rainfall and high imperviousness degree, resulting in great economic losses and human casualties. Interactions between rainfall data and urban catchment characteristics highlight the urgent need of accurate and effective precipitation data to apply in reliable hydrological simulations. However, it remains a challenge to obtain accurate rainfall datasets on such small scales in urban areas. As satellite remote sensing is the only method that can achieve global observation, it is important to evaluate satellite precipitation products in their ability to accurately capture intense precipitation on urban flood scales. This study evaluates the performance of the latest version 06B (V06B) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) in North China Plain, with using the Radar-Gauge merged precipitation estimates as reference data. First, it could be concluded that IMERG fails to accurately estimate precipitation in the whole study area, having the problem of overestimating light precipitation and underestimating heavy precipitation. Second, results show that IMERG has poor ability to capture heavy precipitation on small scales, with the percentage of Hit nearly 0 and the percentage of Miss higher than 40 % for all the precipitation cases. Third, with the expansion of heavy precipitation centers' coverage, the problem of IMERG not to detect heavy precipitation gets mitigated, with the percentage of Miss decreasing by 14 % (19 %). However, the ability to capture both spatial location and precipitation intensity is still not good, the percentage of Hit ranging from 0.05 % to 7 %, without obvious improvement. When IMERG is able to capture the center of strong precipitation, it also tends to overestimate the weak precipitation around the center of strong precipitation. Results of this study provide an improved understanding of how well the V06B IMERG products capture the heavy precipitation center at small scales in urban areas, which will be useful for both developers and users of IMERG.

UAV based comprehensive modelling approach for flood hazard assessment and mitigation planning

The assessment of flood hazard in semi-arid region under the scare data situation is a challenging task under the changing climatic situation. Therefore, the present research is carry out for Rel river catchment, Gujrat, India as case study and presented the comprehensive approach to identify the possible flood extend and flood mitigation measures for better flood assessment. The UAV based high resolution (3.6 cm × 3.6 cm) DEM is generated for most flood vulnerable area (i.e. Dhanera city), whereas scare precipitation data under ungauged condition is generated using GEE and National Aeronautics and Space Administration (NASA) Global Precipitation Measurement Mission (GPM) system. The HEC-HMS based hydrologic model is utilized for simulation of stream flow under the observed data, however, the 2D HEC-RAS based open-source model is used for flood assessment in 2D under extreme flood, dam break and levees condition. The flood decision making maps i.e water surface elevation, flood inundation, flood arrival time, and flood velocity are prepared for flood event 2017. The six flood hazard classes were selected from H1 (Low) to H6 (High) categories. The hazard maps of flood for different scenario i.e., 2D hydrodynamic analysis, 2D dam breach, 2D analysis with extended levees, and Dam break analysis with extended levees are derived. The model was validated with observed river gauge (water level) and discharge data at Dhanera City of flood 2017. The developed UAV based comprehensive modelling approach for flood hazard and mitigation planning provides the systematic approach for flood hazard assessment and would be adopted as a system framework for flood risk assessment for any similar case worldwide.

Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China

Abstract. Accurate flood event simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatiotemporal resolutions of precipitation on flood event simulation in a large-scale catchment of China. We use high-spatiotemporal-resolution Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcing for hydrologic simulation. Three hydrological models (HBV, SWAT and DHSVM) and a data-driven model (long short-term memory (LSTM) network) are utilized for flood event simulation. Two calibration strategies are carried out, one of which targets matching of the flood events, with peak discharge exceeding 8600 m3 s−1 between January 2015 and December 2017, and the other one is the conventional strategy for matching the entire streamflow time series. The results indicate that the event-based calibration strategy improves the performance of flood event simulation compared with a conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood event simulation at a finer temporal resolution, especially in flood peak simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains improvement when degrading the spatial resolution of IMERG-L. Generally, LSTM outperforms the hydrological models in most flood events, which implies the usefulness of the deep learning algorithms for flood event simulation.

An integrated modeling approach for estimating monthly global rainfall erosivity

Modeling monthly rainfall erosivity is vital to the optimization of measures to control soil erosion. Rain gauge data combined with satellite observations can aid in enhancing rainfall erosivity estimations. Here, we presented a framework which utilized Geographically Weighted Regression approach to model global monthly rainfall erosivity. The framework integrates long-term (2001–2020) mean annual rainfall erosivity estimates from IMERG (Global Precipitation Measurement (GPM) mission’s Integrated Multi-satellitE Retrievals for GPM) with station data from GloREDa (Global Rainfall Erosivity Database, n = 3,286 stations). The merged mean annual rainfall erosivity was disaggregated into mean monthly values based on monthly rainfall erosivity fractions derived from the original IMERG data. Global mean monthly rainfall erosivity was distinctly seasonal; erosivity peaked at ~ 200 MJ mm ha−1 h−1 month−1 in June–August over the Northern Hemisphere and ~ 700 MJ mm ha−1 h−1 month−1 in December–February over the Southern Hemisphere, contributing to over 60% of the annual rainfall erosivity over large areas in each hemisphere. Rainfall erosivity was ~ 4 times higher during the most erosive months than the least erosive months (December–February and June–August in the Northern and Southern Hemisphere, respectively). The latitudinal distributions of monthly and seasonal rainfall erosivity were highly heterogeneous, with the tropics showing the greatest erosivity. The intra-annual variability of monthly rainfall erosivity was particularly high within 10–30° latitude in both hemispheres. The monthly rainfall erosivity maps can be used for improving spatiotemporal modeling of soil erosion and planning of soil conservation measures.

Precipitation Characteristics of Easterly Waves Across the Global Tropics

AbstractTropical easterly waves (TEWs) are a recurrent mode of low‐latitude weather that are often convectively coupled and impact precipitation extremes. Previous work has examined the development of TEWs and their associated precipitation for individual seasons or regional domains, but no studies exist that document the importance of TEW precipitation globally. This study quantifies the precipitation associated with TEWs across the entire tropics using satellite (Integrated Multi‐satellitE Retrievals for the Global Precipitation Measurement [IMERG]) and reanalysis (Modern‐Era Retrospective analysis for Research and Applications, Version 2 [MERRA‐2]) data. Traditional space‐time filtering of precipitation reveals a mostly similar climatological power distribution for westward traveling, synoptic period disturbances corresponding to TEWs within all data sets. Using objective tracking, we find that areas with maximum TEW frequency such as the North Atlantic, Equatorial Pacific, and Indian Ocean have the highest accumulation of TEW‐associated precipitation. TEWs account for at most 30% of total annual precipitation in regions where they commonly occur and 1%–5% over much of the tropics. Vertically collocated storms, where the 850 and 700 hPa tracks correspond with each other, have higher conditional rain rates and indicate that waves with vertical development produce stronger and more organized convection. We find similar regional patterns using MERRA‐2 precipitation and latent heating, although the importance and contribution of TEWs to the background are reduced compared to IMERG. While the broad pattern of TEW associated precipitation in MERRA‐2 is like observations, the underestimation of rainfall contributions from TEWs, coupled with occasional false alarms in reanalysis data, suggests that MERRA‐2 does not capture organized convection within TEWs correctly.

Sensitivity analysis of cumulus and microphysics schemes in the WRF model in simulating Extreme Rainfall Events over the hilly terrain of Nagaland

A comprehensive study is carried out to investigate the sensitivity of various combinations between cumulus (CU) and microphysics (MP) schemes using the Weather Research and Forecasting (WRF) model for simulating Extreme Rainfall Events (EREs) over the hilly terrain of Nagaland (25.1 oN – 27.2 oN; 93.2 oE– 95.3 °E). The study explores fifty-four distinct simulation combinations resulting from six CU and nine MP schemes. The simulated rainfall is compared with ground-based rain gauge network and Global Precipitation Measurement (GPM) satellite observations. The investigation focuses primarily on finding the optimal CU-MP scheme combination based on spatiotemporal precision achieved by the identified schemes. The C14 (New Simplified Arakawa Schubert) cumulus scheme is found to be the optimum individual CU scheme. Similarly, the M14 (WDM5) microphysics scheme performs as the optimum individual MP scheme. The study highlights the significance of evaluating the performance of combined schemes over individual ones. Among the fifty-four combinations, sensitivity analysis identified six top-performing combinations, out of which the C11-M8 (Multiscale Kain Fritsch - New Thompson) combination stands out to be optimum with a robust correlation coefficient (CC) of 0.75, lower standard deviation (STD) of 0.76 mm, and RMSD of 0.51 mm. A variation in lead/lag time is found to vary from (−) 3.2 to (+) 5.0 h, with a median value of (+) 1.3 h, suggesting that, on average, the model tends to predict the occurrence of peak rainfall events slightly earlier than observed one. The absolute difference in latitudes (longitudes) of peak rainfall variation ranges from 0.42o - 0.8o (0 o - 0.51o) with a median value of 0.66o (0.37o), indicating that the model could better represent the longitudinal peak rainfall positions with notable accuracy. The spatial correlation coefficient (SCC) ranges between 0.31 and 0.68 with a median value of 0.50. From the overall analysis, it is also found that double moment MP schemes (New Thompson, Morrison (2 M), WDM5, WDM6) consistently perform better than the single moment MP schemes (Kessler, Lin et al., WSM3, WSM5, WSM6), whether in combination with CU schemes or individually. Spatiotemporal variations observed across different cases underscore the pivotal role of accurate topography representation in complex terrains, emphasizing the significance of terrain data in shaping rainfall patterns.

Role of remote-sensing techniques in unveiling the spatiotemporal response of vegetation to climate change in the western Makkah Province of Saudi Arabia

Climate change is a global problem that dramatically affects natural resources, resulting in significant changes in temperature, precipitation, and humidity, which affect vegetation cover. Under this light, this study aimed to identify the potential of remote-sensing techniques to reveal the spatiotemporal response of vegetation cover to climate change in the western Makkah Province using Landsat-5 Thematic Mapper, Landsat-8 operational land imager, Global Land Data Assimilation System model, Global Precipitation Measurement, and Famine Early Warning Systems Network Land Data Assimilation System model data from 2000 to 2023. Optimised Soil-Adjusted Vegetation Index (OSAVI), classification, overlay, change detection, and correlation analysis were utilized to process data. Time series analysis of data revealed climate-related changes which were particularly intense in recent years. Specifically, temperature, precipitation, and specific humidity were found to differ depending on the landforms and season. Temperature was higher during the dry season compared to the wet season. A decrease was observed in the overall precipitation rate, which did not exceed 81.39 mm during the wet season and approximately 11.46 mm during the dry season. Additionally, precipitation increased in 2023 but decreased in 2018. Moreover, the study area was located on semi-arid lands for all years except for the wet season of 2023. OSAVI analysis, which is sensitive to climate change, revealed that vegetation coverage can be both positively and negatively affected by climate change. The most profound vegetation coverage in the study region was observed in 2023. A strong correlation was also observed between precipitation and vegetation in the study area, which showed less high-greenness in the dry season and more widespread grasses. The implications of these findings for the development of strategies for biodiversity conservation in semi-arid regions are significant.

Classifying microwave radiometer observations over The Netherlands into dry, shallow-, and non-shallow precipitation using a random forest model

Abstract Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures, Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using space-borne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest model (RF) to classify microwave radiometer observations as dry, shallow, or non-shallow over the Netherlands - a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF is trained on five years of data (2016-2020) and tested with two independent years (2015, 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM’s Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA-5 2-meter temperature and freezing level reanalysis and/or Dual Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb-values as non-shallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb-values, likely resulting from the presence of ice particles in non-precipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles.

Hydrologic Evaluation of the Global Precipitation Measurement Mission over the U.S.: Effect of Spatial and Temporal Scales

This research focuses on the effects of spatio-temporal resolutions of global satellite precipitation on simulated flood events characteristics. The analysis is carried out by spatially, temporally, and spatiotemporally upscaling fine-scale precipitation forcings from the ground-radar-based Multi-Radar Multi-Sensor system (MRMS) to the resolution of the Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite precipitation product. These upscaled products were then run through the Ensemble Framework for Flash Flood Forecasting hydrologic modeling framework over the U.S. to assess how the different spatial and temporal resolutions impact the simulated outputs of flood event magnitude, duration, and timing. It was found that the quantitative uncertainties generated by the IMERG Early (IMERG-E) simulations overpowered those associated with resolution across all flood characteristics. When compared to native MRMS simulations, however, the upscaled precipitation estimates tended to underestimate peak discharges and event durations, associated with distinct negative water balance errors. It was also found that the upscaled precipitation simulations exhibited a scaling effect with regards to error contribution of peak discharge, with higher contributions at smaller basin sizes and decreasing contributions as basin sizes increased. Looking at how upscaling can affect the modeled output desired allows for improved understanding of which resolutions lead to the greatest changes in simulation accuracy, increasing the potential global use and utility of current or future satellite products, especially in regions where high-resolution ground radars are sparse.

Assessing precipitation event characteristics throughout North Carolina derived from GPM IMERG data products

The Global Precipitation Measurement (GPM) mission provides near-real time precipitation estimates that can be used for monitoring water supply infrastructure. To better understand the potential use of GPM data products for use in relevant applications, this study examines the performance of the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation products throughout North Carolina to capture rainfall events. Event characteristics derived from IMERG precipitation products: early (4 h latency), late (14 h latency), and final (3.5 month latency) are compared to characteristics derived from gauges (N = 282) for the period 2000–2021. Results show that the IMERG data products identify events reasonably well (mean event errors of 6.0, 6.8, and 1.7 mm for early, late, and final products; probability of detection: 0.83, 0.83, 0.86; false alarm ratio: 0.20, 0.18, 0.18, respectively). While the final product performs best, the early and late products perform similarly. While additional research is needed to better understand factors leading to under- and over-estimates of event magnitudes, the findings from this study support the potential use of early/late IMERG data products in water supply monitoring or flood warning systems, where rapid estimates of event precipitation characteristics are needed.

Modeling run-off flow hydrographs using remote sensing data: an application to the Bashar basin, Iran

Abstract Precipitation is hard to access in countries like Iran, due to inadequate number of rain gauge stations. Remote sensing provides an alternative source of rainfall estimation. In this study, the effectiveness of the HEC-HMS model was evaluated using GPM (Global Precipitation Measurement Mission) satellite and rain gauge station data. The model was calibrated and validated using 5 flood events' data of a hydrometric station at the outlet of Bashar basin. Important flood parameters including peak discharge (QP), flood volume (V) and time of concentration (TC) were used to evaluate and compare the application of satellite and ground station data in the model, using various statistical indices. The accuracy of QP and V estimations using rain gauge data was higher than those obtained by satellite data. However, the difference between mean relative error (MRE) in QP estimation was about 1% (9.9% and 10.6% for rain gauge and satellite data, respectively). Conversely, higher accuracies were met for TC estimation using satellite (with MRE 9.1% and 10.2% for GPM and rain gauge data, respectively). Such results imply the sole utilization of satellite precipitation data is reliable for modeling hydrological key parameters, which can be helpful in areas with limited ground station coverage.

Enhancing consistency of microphysical properties of precipitation across the melting layer in dual-frequency precipitation radar data

Abstract. Stratiform rain and the overlying ice play crucial roles in Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles' concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a spaceborne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a large increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold-rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and the precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling.

Interannual Variation of Half-Hourly Extreme Precipitation in Africa: The Assessment of the Warmest Decade (2011–2020) Since the Beginning of Observations

AbstractAfrica is experiencing widespread extreme precipitation. However, the continent remains understudied regarding extreme precipitation variation, particularly at high temporal resolution. This study assessed the changes in spatial distribution, frequency, and intensity of half-hourly violent precipitation (≥ 50 mm/h) in Africa during 2011–2020. The data were sourced from the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) program. The results reflect that the 50–60 mm/h precipitation category was the most densely spatially distributed with less-discernable interannual changes. There was a strong interannual variation in the spatial distribution of the 60–70 mm/h and ≥ 70 mm/h precipitation categories. There was a general decrease in the frequency of occurrence of violent precipitation in all regions, except in North Africa (NA). Among the study regions, NA had the broadest precipitation intensity distribution extended to high values and consequently, on average, the most intense violent precipitation, followed by Southern Africa (SA) and Southwest Indian Ocean (SWIO). The regions in the tropics, i.e., West Africa (WA), Central Africa (CA), and East Africa (EA), had a relatively narrow intense violent precipitation distribution. The results further illustrate that the strength of climate variability modes has a strong influence in the distribution, frequency, and intensity of violent precipitation over the continent particularly the ≥ 70 mm/h precipitation category. This study underscores a need for a strengthened capacity in forecasting climate variability modes impacting on Africa, augmentation of continental precipitation observation network, and improved extreme precipitation adaptation infrastructure.

Reconstruction of 3D DPR Observations Using GMI Radiances

AbstractThree‐dimensional global precipitation observation is crucial for understanding climate and weather dynamics. While spaceborne precipitation radars provide precise but limited observations, passive microwave imagers are available much more frequently. In this study, we propose a deep learning approach to reconstruct active radar observations using passive microwave radiances. We introduce the Hybrid Deep Neural Network (HDNN) model, which utilizes reflectivity profiles from the Dual‐frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) Core Observatory Satellite as the “target” and combines radiances from the GPM Microwave Imager (GMI) with supplementary reanalysis data to serve as the “features.” Results underscore the HDNN's exemplary performance, with a root mean square error below 4 dBZ across all altitude levels, and a consistent accuracy across different precipitation types. Its efficacy is further illustrated when applied to typhoon cases of Haishen and Khanun, emerging as a superior tool for capturing 3D structures of expansive precipitation systems.

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.

The performance of IMERG near-real-time estimations during the record-breaking Meiyu season in 2020

The extreme Meiyu season, characterized by continuous heavy precipitation, is often leads to numerous flood-related disasters. Accurate and timely monitoring of extreme Meiyu precipitation is crucial for the effective prevention and mitigation of these disasters. The near-real-time (NRT) products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), specifically IMERG-E and IMERG-L products, have high potential utility to acquire timely precipitation data. Therefore, it is important to quantify their errors and uncertainties during the extreme Meiyu season. In this study, we assess and compare the performance of IMERG NRT products with Global Satellite Mapping of Precipitation Motion Vector Kalman product (GSMaP-MVK) using hourly gauge observations during the recor-breaking Meiyu event in 2020. The main conclusions are as follows: all three satellite products generally capture the spatial–temporal evolution of Meiyu precipitation, with IMERG-L showing the closest agreement with gauge observations, particularly for the precipitation center. In terms of the statistical and volumetric categorical metrics, IMERG-L performs best among the three products, while IMERG-E does not exhibit superior performance compared to GSMaP-MVK. Regarding the diurnal cycle, IMERG-L generally reproduces the diurnal cycle of observed precipitation amount with a peak time occurring two hours earlier. In contrast, both IMERG-E and GSMaP-MVK exhibit notably poorer performance. When it comes to the diurnal cycle of precipitation frequency and intensity, both IMERG products display bad performance in reproducing their diurnal variations. Furthermore, all three satellite products show a wet biases for light precipitation and a dry biases for heavy precipitation, which is consistent with previous evaluation studies. However, a novel phenomenon is noticed that theses biases tend to worsen significantly in the afternoon compared to the morning. Although this study is limited to a case study, the findings can serve as a valuable reference for the NRT applications of IMERG products during the extreme Meiyu seasons and provide insights for the improvement of IMERG algorithm.

Flood peak estimation for the keureuto watershed using the hydrological modeling system HEC-HMS

The Keureuto watershed has been the water resource used mainly for irrigation and municipal use. Extreme rainfall along with changes in the system may trigger rainfall excess causing flood at downstream. The occurrences may impact the usability of the system that supports the water resources supply. Investigating the impact of the floods requires initial research on the rainfall-runoff process of the watershed system. Rainfall-runoff modeling is an approach widely used for estimating the discharge hydrograph of the river as the result of the processes. The HEC-HMS rainfall-runoff model tool is applied in this study to estimate the peak flood discharge for the Keureto watershed located in the North Aceh region. The data used are satellite Global Precipitation Measurement (GPM) data and Automatic Water Level Recorder (AWLR) data at the watershed outlet. The 302 km2 Keureuto Watershed is divided into 23 sub-basins for the HEC-HMS model. The SCS Curve Number is applied to the model. The simulated freturncompared to the observed flow using the Nash Sutcliffe Efficiency criteria which returns the NSE value of 0.38 for seven years simulation, based on data of year 2015 to 2021. At an event based on October 2020 to March 2021, the NSE value returns 0.52, satisfactory for the simulation data range. The highest peak of the event is captured by the model simulation in the timing and the magnitude which value lower 20% from the observed flow.

Isolated deep convections over the Tibetan Plateau in the rainy season during 2001–2020

The Tibetan Plateau (TP) is a prevalent region for convection systems due to its unique thermodynamic forcing. This study investigated isolated deep convections (IDCs), which have a smaller spatial and temporal size than mesoscale convective systems (MCSs), over the TP in the rainy season (June–September) during 2001–2020. The authors used satellite precipitation and brightness temperature observations from the Global Precipitation Measurement mission. Results show that IDCs mainly concentrate over the southern TP. The IDC number per rainy season decreases from around 140 over the southern TP to around 10 over the northern TP, with an average 54.2. The initiation time of IDCs exhibits an obvious diurnal cycle, with the peak at 1400–1500 LST and the valley at 0900–1000 LST. Most IDCs last less than five hours and more than half appear for only one hour. IDCs generally have a cold cloud area of 7422.9 km2, containing a precipitation area of approximately 65%. The larger the IDC, the larger the fraction of intense precipitation it contains. IDCs contribute approximately 20%–30% to total precipitation and approximately 30%–40% to extreme precipitation over the TP, with a larger percentage in July and August than in June and September. In terms of spatial distribution, IDCs contribute more to both total precipitation and extreme precipitation over the TP compared to the surrounding plain regions. IDCs over the TP account for a larger fraction than MCSs, indicating the important role of IDCs over the region.摘要本文利用卫星观测资料, 研究了2001–2020年雨季 (6–9月) 青藏高原上孤立深对流 (Isolated deep convections, IDCs) 的气候特征. IDCs定义为比中尺度对流系统 (Mesoscale convective systems, MCSs) 时空尺度小的对流. 结果显示, 每年雨季青藏高原上平均的IDC数量为54.2个, 主要分布在高原的南部. IDCs的初始时刻呈现明显的日循环, 在下午14–15时为峰值, 在上午9–10时为谷值. 大部分IDCs持续时间在5小时以内, 超过一半的IDCs仅持续1小时. IDCs的冷云平均面积约为7422.9km2, 其中包含65%的降水面积. IDC面积越大, 包含的强降水范围也越大. IDCs对青藏高原总降水的贡献约为20%–30%, 对极端降水贡献约为30%–40%, 在7月和8月的占比大于6月和9月. 在空间分布方面, 青藏高原上IDCs对总降水和极端降水的贡献大于周围平原地区. 青藏高原上IDCs对降水的贡献大于MCSs, 表明IDCs在该地区起着重要作用.