GPM IMERG Research Articles

Evaluation of GPM IMERG and error sources for tropical cyclone precipitation over eastern China

Long-term precipitation data with high temporal-spatial resolution and high precision is crucial for the monitoring of tropical cyclone (TC) precipitation and the understanding of its response to global climate change. The performance of the Final run of Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) V06B product to characterize the TC precipitation is evaluated during the TC season (May to September) from 2014 to 2019 against a dense network of gauges over eastern China, considering the impact of land cover types (LCTs), different techniques (morphing) and instruments, i.e., passive microwave (PMW) and infrared (IR) sensors. Overall, IMERG captures well the spatial pattern and temporal variations of mean TC rainfall rate (RR) over eastern China but shows underestimations of mean TC RR compared to gauge observations, with the mean error (ME) being −0.78 mm/h, which are mainly contributed by the underestimations of heavy TC RR (ME: −12.1 mm/h). Moreover, the IMERG performance shows dependence on the LCTs and IMERG components (i.e., morph, IR + morph, and PMW sensors). The overestimation/underestimation of light/heavy TC precipitation events is most obvious over water/evergreen broadleaf forests, with the bias being 1.2/0.5. Differently, IMERG even slightly underestimates light TC precipitation events over grasslands (bias = 0.88). Among four IMERG components, Microwave Humidity Sounder (MHS) performs the best in characterizing all TC precipitation events, followed by morph, Special Sensor Microwave Imager/Sounder (SSMIS), and finally IR + morph, indicating that IR algorithms might need to be further improved. The mean TC RR is slightly overestimated by MHS over most regions of eastern China with positive ME exceeding 2.8 mm/h, while is largely underestimated by IR + morph among four components with most MEs lower than −1.2 mm/h. This study may provide an observational reference for the continued development and future improvement of the IMERG product.

High-Resolution Rainfall Maps from Commercial Microwave Links for a Data-Scarce Region in West Africa

Abstract We present high-resolution rainfall maps from commercial microwave link (CML) data in the city of Ouagadougou, Burkina Faso. Rainfall was quantified based on data from 100 CMLs along unique paths and interpolated to achieve rainfall maps with a 5-min temporal and 0.55-km spatial resolution for the monsoon season of 2020. Established processing methods were combined with newly developed filtering methods, minimizing the loss of data availability. The rainfall maps were analyzed qualitatively both at a 5-min and aggregated daily scales. We observed high spatiotemporal variability on the 5-min scale that cannot be captured with any existing measurement infrastructure in West Africa. For the quantitative evaluation, only one rain gauge with a daily resolution was available. Comparing the gauge data with the corresponding CML rainfall map pixel showed a high agreement, with a Pearson correlation coefficient > 0.95 and an underestimation of the CML rainfall maps of ∼10%. Because the CMLs closest to the gauge have the largest influence on the map pixel at the gauge location, we thinned out the CML network around the rain gauge synthetically in several steps and repeated the interpolation. The performance of these rainfall maps dropped only when a radius of 5 km was reached and approximately one-half of all CMLs were removed. We further compared ERA5 and GPM IMERG data with the rain gauge and found that they had much lower correlation than data from the CML rainfall maps. This clearly highlights the large benefit that CML data can provide in the data-scarce but densely populated African cities. Significance Statement In this study, we investigate the possibility of deriving accurate high-resolution rainfall maps from commercial microwave link (CML) data in West Africa. The main challenges are the lack of reference data in this area and the adoption of existing processing tools without reference data. We show CML rainfall maps for Ouagadougou, Burkina Faso, with a resolution of 5 min and 0.55 km, which is unprecedented in this region. The comparison with the only available rain gauge, which provides data only at a daily resolution, yields a Pearson correlation of >0.95. An analysis of synthetically thinned-out networks shows that this accuracy is valid for the whole domain. Comparing reanalysis and satellite data with the rain gauge and CML data showed a poor performance of these gridded reference datasets. Also, a high coincidence of temporal dynamics between CML rainfall maps and satellite products was observed. Overall, these findings support the potential of CMLs for future hydrometeorological applications in West Africa.

Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG Data and Terrain Decomposition Using the BEMD Algorithm

Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG Data and Terrain Decomposition Using the BEMD Algorithm

Performance Evaluation of GPM IMERG Precipitation Products over the Tropical Oceans Using Buoys

Abstract The majority of global precipitation falls in tropical oceans. Nonetheless, due to the lack of in situ precipitation measurements, the number of studies over the tropical oceans remains limited. Similarly, the performance of IMERG products over the tropical oceans is unknown. In this context, this study quantitatively evaluates the 20 years (2001–20) of IMERG V06 Early, Late, and Final products against the in situ buoys’ estimates using the pixel–point approach at a daily scale across the tropical oceans. Results show that IMERG represents well the mean spatial pattern and spatial variation of precipitation, though significant differences exist in the magnitude of precipitation amount. Overall, IMERG notably overestimates precipitation across the tropical ocean, with maxima over the western Pacific and Indian Oceans, while it performs better over the eastern Pacific and Atlantic Oceans. Moreover, irrespective of the region, IMERG sufficiently detects precipitation events (i.e., >0.1 mm day−1) for high-precipitation regions, though it significantly overestimates the magnitude. Despite IMERG’s detection issues of precipitation events over the regions with lower precipitation, it is in good agreement with the buoys in total precipitation estimation. The positive hit bias and false alarm bias are the major contributions to the overall total positive bias. Furthermore, the detection capability of IMERG tends to decline with increasing precipitation rates. In terms of IMERG runs, the IMERG Final product performs slightly better than the Early and Late runs. More detailed studies over the tropical oceans are required to better characterize the biases and their sources.

Applicability Assessment of GPM IMERG Satellite Heavy-Rainfall-Informed Reservoir Short-Term Inflow Forecast and Optimal Operation: A Case Study of Wan’an Reservoir in China

Satellite precipitation estimate (SPE) dedicated to reservoir inflow forecasting is very attractive as it can provide near-real-time information for reservoir monitoring. However, the potential of SPE retrievals with fine temporal resolution in supporting the high-quality pluvial flood inflow forecast and robust short-term operation of a reservoir remains unclear. In this study, the hydrological applicability of half-hourly Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM IMERG) heavy rainfall data was explored using a synthetic experiment of flood inflow forecast at sub-daily to daily lead times and resultant reservoir short-term operation. The event-based flood forecast was implemented via the rainfall–runoff model GR4H driven by the forecasted IMERG. Then, inflow forecast-informed reservoir multi-objective optimal operation was conducted via a numerical reservoir system and assessed by the risk-based robustness indices encompassing reliability, resilience, vulnerability for water supply, and flood risk ratio for flood prevention. Selecting the Wan’an reservoir located in eastern China as the test case, the results show that the flood forecast forced with IMERG exhibits slightly lower accuracy than that driven by the gauge rainfall across varying lead times. For a specific robustness index, its trends between IMERG and gauge rainfall inputs are comparable, while its magnitude depends on varying lead times and scale ratios (i.e., the reservoir scale). The pattern that the forecast errors in IMERG increase with the lead time is changed in the resultant inflow forecast series and dynamics in the robustness indices for the optimal operation decision. This indicates that the flood forecast model coupled with reservoir operation system could partly compensate the original SPE errors. Our study highlights the acceptable hydrological applicability of IMERG rainfall towards reservoir inflow forecast for robust operation, despite the intrinsic error in SPE.

Thunderstorm detection from GPM IMERG rainfall: Climatology of dynamical and thermodynamical processes over India

AbstractThis study aims to (i) prepare a premonsoon thunderstorms database, (ii) understand the thunderstorm frequency, duration and intensity and (iii) composite analysis of dynamic and thermodynamic processes related to thunderstorms over India. The thunderstorm associated rainfall varies across India. Hence, a percentile‐based approach is implemented with the integrated multi‐satellite retrievals for global precipitation measurement (IMERG) dataset at 0.1° resolution to identify thunderstorms for 2001–2021. The 93rd percentile appears to be better for thunderstorm detection, with a success ratio of 82% (642 events are confirmed out of 786 detected) in eastern India. Further analysis indicated that 84% of the detected thunderstorms in eastern and northeastern India are associated with lightning activity. Based on this long‐term (2001–2021) thunderstorm data, the highest frequency of thunderstorms (40–45 events·year−1) is observed over the western foothills of the Himalayas, the northeast region, and the west coast of Kerala. The thunderstorm duration in the eastern and northeastern regions and the southwest coast of India is mostly 0.5–2.5 h, producing heavy rainfall (>7 mm·h−1) due to more moisture content and stronger updrafts. The composite structure of thermodynamic indices exhibits significant spatial variations over India and can be used to differentiate the regions of high thunderstorm activity. The minimum (maximum) convective available potential energy (convective inhibition) value required for thunderstorm development is not uniform throughout the country. However, the composites of K index and total totals index during thunderstorms are mostly uniform. This study highlights the benefits of IMERG rainfall in thunderstorm detection over India and helps to understand the local forcings and the effect of thunderstorm activity on different sectors like aviation, agriculture and so forth.

Evaluation of the RF-MEP Method for Merging Multiple Gridded Precipitation Products in the Chongqing City, China

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products.

Reliability of GPM IMERG Satellite Precipitation Data for Modelling Flash Flood Events in Selected Watersheds in the UAE

Arid regions are prone to unprecedented extreme rainfall events that often result in severe flash floods. Using near-real-time precipitation data in hydrological modelling can aid in flood preparedness. This study analyzed rainfall data obtained from Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG V. 06) since 2001 to highlight recent trends of extreme rainfall indices for three selected watersheds in the UAE. Additionally, to validate the trends, the present study incorporated CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) into the analysis. Furthermore, for the first time, this study assessed the performance of the three products of IMERG in modelling flash flood events in the selected watersheds of UAE. A physical-based, fully distributed model was used to simulate the heaviest storm event. Also, a sensitivity analysis of the model’s output to variations in the input parameters was conducted using the one-factor-at-a-time method. The result of the trend analysis indicated that IMERG and CHIRPS show similar trends in both datasets, indicating agreement and reliability in their observations. However, there are a few instances where IMERG and CHIRPS show slight discrepancies in the nature of the trend. In general, the trend analysis results indicated an increasing trend of total precipitation (mm) and consecutive wet days, which suggests a rise in the risk of flash floods. The simulation of the flash flood event showed that the IMERG final product outperformed the other two products, closely matching the model output of the gauge rainfall data with mean absolute error (MAE) of 1.5, 2.37, and 0.5 for Wadi Ham, Wadi Taween, and Wadi Maidaq, respectively. The model’s performance was positively correlated with the size of the watershed. The sensitivity analysis results demonstrated that the model’s output was most sensitive to infiltration parameters. The study’s outcomes provide a good opportunity to improve near-real-time impact evaluation of flash flood events in the watersheds of the UAE.

The Evaluation of GPM IMERG v.06 Rainfall Product over the Lau Simeme Watershed in Indonesia

In Indonesia, rainfall is still significant spatially and temporally. In order to gain optimal results from utilising water resources, we have to ensure that the precipitation data is provided in good quality and quantity. Several spatial rainfall measurement sources have become available in recent years, such as GPM data (Global Precipitation Measure). In this study, the GPM IMERG V.06 product was evaluated using rain gauge measurements in the Lau Simeme watershed in North Sumatra Province, Indonesia. The relevance of the GPM IMERG was tested by direct comparison with observations at different time scales (daily, monthly, annual and seasonal) between 2005 and 2019. Results show that the satellite product provides poor rainfall estimations at the daily and annual time scales. However, the accuracy of GPM IMERG Final datasets is improved when temporally average to monthly timescale (R2 of 0.728, RMSE of 68.318 mm and NSE of 0.725), wet seasonal time scale (R2 of 0.673, RMSE of 79.287 mm and NSE of 0.658) and dry seasonal time scale (R2 of 0.947, RMSE of 20.356 mm and NSE of 0.924).

How well does the IMERG satellite precipitation product capture the timing of precipitation events?

Precipitation occurs in the form of discrete “events” and the event characteristics (event duration, depth, peak intensity, start/end time) significantly influence the hydrologic response of a basin. Despite this importance, event-based performance of satellite precipitation products has still not been fully investigated to assess limitations in the retrieval algorithms, guide future improvements, and inform hydrologic applications. In this study, we evaluate the precipitation event performance of the GPM IMERG product using as reference the high-resolution ground gauge–radar dataset (GV-MRMS) in the Continental United States (CONUS) at the native IMERG resolution (0.1°×0.1°, 0.5 h), with a primary focus on the detectability and timing of events. Our results show that IMERG generally overestimates the event duration but underestimates the mean event precipitation intensity in the summer, while the opposite is true for winter. This discrepancy is mostly attributed to the under-representation of short-duration intense events in the summer and long-duration moderate events in the winter in IMERG. In terms of the detection of individual events, about 50% of the reference events are properly detected by IMERG, and conversely, 50% of IMERG events do not match a reference event. However, nearly 40% of the missed or false events result from temporal mismatching of less than 3 h between the retrieved and the reference event. The remaining 60% comes from IMERG not detecting an existing event or inventing a nonexistent event. When IMERG successfully detects an event, the average temporal overlap with the reference event is about 70% of its total duration, which mostly stems from the mistiming of IMERG-derived events. IMERG events tend to start, peak, and end earlier than GV-MRMS events, with national mean shifts of −26 min, −17 min, and −7min, respectively. For about only 20% of all situations the starting time of the event is correctly reproduced by IMERG, and the same applies to the peak time and end time. Our results provide guidance for applications of IMERG at sub-daily scales, as well as new insights for the improvement of satellite retrieval algorithms.

Aerosol-heavy precipitation relationship within monsoonal regimes in the Western Himalayas

Studies reported increased heavy precipitation and aerosol loading in Western Himalayas (WH) due to changes in large-scale dynamics and anthropogenic emissions. In this study, we investigate the long-term relationship between aerosols and heavy precipitation from 2001 to 2020 by incorporating the changes in dynamics and eliminating the influence of meteorological covariates. We analyzed composites of aerosol optical depth (AOD) from MERRA2 reanalysis and precipitation from GPM IMERG products within monsoonal regimes – M1 (westerly), M2 (westerly + easterly) and M3 (easterly) with distinct large-scale dynamics. A significant positive correlation is found between aerosols and heavy precipitation in M1 (0.27) and M2 (0.15) but not M3. We eliminated the influence of three covariates, specifically the moisture flux convergence (MFC), Webster Yang Index (WYI), and relative humidity (RH) at 850 hPa, on the aerosol-heavy precipitation relationship. We found MFC to be the strongest influencer using partial correlation analysis, whereas WYI and RH only weakly affected this relationship. The overall partial correlation coefficient between aerosols and heavy precipitation is 0.17 in both M1 and M2 but insignificant in M3. Further, we studied the evolution of cloud properties during heavy precipitation under distinct dynamics. We found smaller cloud droplets with a narrow size distribution in M1, but larger droplets and broad distribution for M3. We did not find a significant correlation between AOD and cloud properties in the ice phase in any of the regimes. Our findings imply that dynamical changes result in distinct aerosol-heavy precipitation relations and microphysical processes causing heavy precipitation.

SPATIAL DOWNSCALING OF GPM IMERG V06 GRIDDED PRECIPITATION USING MACHINE LEARNING ALGORITHMS

Abstract. According to recent studies, Remote sensing data plays a significant role in filling gaps in the poor gauge station, particularly at high elevations and with complex underlying surface features. In order to provide high-resolution precipitation estimates over the poor gauge and with complex terrain areas, downscaling low-resolution satellite precipitation estimates using various environmental variables. In this paper, we tried to downscale the GPM IMERG V06 with a resolution of (0.1° × 0.1°) nearly 10km to (1km × 1 km) using four machine learning algorithms namely, Decision Trees, Multiple Linear Regression, Support Vector Regressor and random forest. Vegetation indices Normalized difference in vegetation index (NDVI), Topography, Land Surface Temperature (LST), and latitude and longitude. This framework can downscale the 0.1° resolution of the GPM IMERG precipitation product to 1 km, by determining the importance of features, and automatically optimizing the model parameters. Additionally, ground recorded data from rain gauge stations have validated downscaled precipitation products. Spatial downscaling can generally increase the accuracy of GPM IMERG gridded precipitation data and results reveal that spatial downscaling is an acceptable way of investigating the precipitation over Taiwan.

Hydrological Model Evaluation of Ground, GPM IMERG, and CHIRPS precipitation data for Shabelle Basin in Ethiopia

Consistent and accurate precipitation measurement is a fundamental input component of the hydrological model. However, most developing countries, including Ethiopia, lack consistent and precise precipitation measurements. Nevertheless, satellitebased precipitation data may play a crucial role in bridging the data gap and providing precipitation inputs for rainfall-runoff models in regions with limited ground data. The study compares the ground precipitation data from the Ethiopian meteorological agency and SWALIM to the satellite-based GPM_IMERG and CHIRPS precipitation products. HECHMS software was used to model rainfall-runoff simulation, and the study area’s spatial characteristics and model development were processed with ArcGIS and HEC- GeoHMS. Since the data on hand is enough and fit for continuous event assessment, the deficit and constant loss method, a continuous event-based loss method, was tested, and found good model performance results. The onhand 14 years of precipitation and discharge data were divided into seven years for calibration and seven years for validation. Using Nash–Sutcliffe efficiency (ENS), coefficient of determination (R2), Root Mean Square Error (RMSE), and percentage bias objective functions, the performance of the satellite precipitation was evaluated. During calibration periods (Jan 2013 to Dec 2009), the model performance showed ENS values of 0.65, R2 values of 0.68, RMSE values of 0.6, a percent bias of 0.58% for the metrological stations, ENS values of 0.61, R2 values of 0.64, RMSE values of 0.6, a percent bias of 5.38% for CHIRPS, and ENS values of 0.63, R2 values of 0.66, RMSE values of 0.7, and a percent bias of 18.9% for GMP_IMERG satellite precipitation products. During validation periods (Jan 2010 to Dec 2016), the model performance showed ENS values of 0.75, R2 values of 0.78, RMSE values of 0.6, a percent bias of -16.9% for the metrological stations, ENS values of 0.71, R2 values of 0.74, RMSE values of 0.6, a percent bias of -22.12% for CHIRPS2, and ENS values of 0.76, R2 values of 0.76, RMSE values of 0.5, and a percent bias of -0.03% for GPM_IMERG satellite precipitation products. The study showed that the HEC-HMS model performed well and gave very good results for the hydrological model for the CHIRPS and GPM_IMERG rainfall products. The study also indicates that the model outperformed well during the validation period. Overall, the study found that the simulated GPM_IMERG product gave better results than the simulated CHIRPS product.

Evaluation of CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6 Satellite Precipitation Datasets in Arabian Arid Regions

Rainfall depth is a crucial parameter in water resources and hydrological studies. Rain gauges provide the most reliable point-based rainfall estimates. However, they do not have a proper density/distribution to provide sufficient rainfall measurements in many areas, especially in arid regions. To evaluate the adequacy of satellite datasets as an alternative to the rain gauges, the Kingdom of Saudi Arabia (KSA) is selected for the current study as a representative of the arid regions. KSA occupies most of the Arabian Peninsula and is characterized by high variability in topographic and climatic conditions. Five satellite precipitation datasets (SPDSs)—CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6—are evaluated versus 324 conventional rain-gauges’ daily precipitation measures. The evaluation is conducted based on nine quantitative and categorical metrics. The evaluation analysis is carried out for daily, monthly, yearly, and maximum yearly records. The daily analysis revealed a low correlation for all SPDSs (<0.31), slightly improved in the yearly and maximum yearly analysis and reached its highest value (0.58) in the monthly analysis. The GPM IMERG V6 and PERSIANN-CDR have the highest probability of detection (0.55) but with a high false alarm ratio (>0.8). Accordingly, in arid regions, the use of daily SPDSs in rainfall estimation will lead to high uncertainty in the obtained results. The best performance for all statistical metrics was found at 500–750 m altitudes in the central and northern parts of the study area for all satellites except minor anomalies. CMORPH dataset has the lowest centered root mean square error (RMSEc) for all analysis periods with the best results in the monthly analyses.

Performance of Seven Gridded Precipitation Products over Arid Central Asia and Subregions

The evaluation of gridded precipitation products is important for the region where meteorological stations are scarce. To find out the applicable gridded precipitation products in arid Central Asia (ACA) for better follow-up research, this paper evaluated the accuracy of five long-term gridded precipitation products (GPCC, CRU, MERRA-2, ERA5-Land, and PREC/L) and two short-term products (PERSIANN-CDR and GPM IMERG) on different time scales for the whole ACA and two subregions, Central Asia (CA) and Xinjiang of China (XJ). Seven evaluation indices were used to evaluate the consistency between the seven gridded precipitation products and observations at 328 meteorological stations for 40 years from 1980 to 2019. The main findings were as follows: (1) Each product can correctly reflect the trend of decreasing annual precipitation in CA and increasing annual precipitation in XJ, except for ERA5-Land. (2) GPCC captured extreme events by 75.9% for heavy rainfall and 67.9% for drought events, and GPM IMERG outperformed PERSIANN-CDR with a capture probability of 61% for heavy rainfall and 50% for drought events. (3) Annually, except for GPCC and CRU without significant deviations (BIAS < 2%), ERA5-Land, GPM IMERG, and PERSIANN-CDR generally overestimated precipitation (20% < BIAS < 60%). MERRA-2 and PREC/L underestimated precipitation, with approximately −5% for PREC/L and −20% for MERRA-2. (4) Seasonally, GPCC outperformed the other four long-term products in all seasons with the lowest BIAS (<0.93%), and GPM IMERG (BIAS < 30.88%) outperformed PERSIANN-CDR. (5) Monthly, the areas with large deviations (BIAS > 60%) for the seven products were near the Tianshan Mountains; comparatively, they performed better in CA than in XJ, with BIAS approximately 20% for CA and 40% for XJ. Despite regional differences, GPCC performed the best across the five long-term products overall, followed by CRU, MERRA-2, PREC/L, and ERA5-Land. For the two short-term products, GPM IMERG outperformed PERSIANN-CDR.

Reliability of satellite-based precipitation products in capturing extreme precipitation indices over Iran

The goal of this study is to assess the performance of four widely-used satellite precipitation products in capturing extreme precipitation indices across Iran over the period 2001–2018; these products include GPM IMERG (Integrated Multi-Satellite Retrievals for Global Precipitation Measurement), TRMM 3B42 (Tropical Rainfall Measuring Mission), CHIRPS (Climate Hazards Center InfraRed Precipitation with Station data), and PERSIANN-CDR (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). For this aim, a national gridded precipitation dataset was developed using a dense network of rain gauges as a reference dataset. The results suggest that the IMERG product outperforms the other three precipitation products in capturing extreme precipitation indices both temporally and spatially. TRMM 3B42 data show promising results in identifying many extreme indices, while the CHIRPS and PERSIANN-CDR products show less performance in accurately generating many of the extreme precipitation indices.

Spatial Patterns of Errors in GPM IMERG Summer Precipitation Estimates and Their Connections to Geographical Features in Complex Topographical Area

Error evaluation is essential for the improvement and application of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) Version 06 daily precipitation estimates, including early-, late-, and final-run products (IMERG-DE, IMERG-DL, and IMERG-DF, respectively), especially for summer precipitation in complex topographical areas. However, many existing works mainly focus on comparing the error statistical metrics of precipitation estimates, but few further analyze the internal relationships between these error statistics and geographical features. Therefore, taking Sichuan Province of China as a case study of the complex topographic and mountainous area, we adopt statistical metrics, error decomposition schemes, systematic and random error separation models, and regression methods to analyze the relationships between the spatial distribution of IMERG summer precipitation error metrics and geographical features. These features include longitude, latitude, distance from Sichuan Basin edge (DFBE), digital elevation model (DEM), normalized difference vegetation index (NDVI), slope, aspect, and topographic position index (TPI). The results show that: (1) DEM and DFBE are the two most important geographical features affecting the spatial distribution of error metrics, while both aspect and TPI have negligible effects on these metrics; (2) the variations in DEM, DFBE, and latitude have the negative relationships with error metrics; (3) longitude and DFBE do not have a direct impact on the errors, but indirectly affect the precipitation errors through the changing DEM; (4) slope shows a strong negative correlation with hit bias, and its increase significantly amplifies the sensitivity of systematic errors of hit bias from IMERG-DE and DL; and (5) the high detection probability and small missed precipitation error of the three IMERG estimates are virtually unaffected by changes in geographical features.

A new pixel-to-object method for evaluating the capability of the GPM IMERG product to quantify precipitation systems

One of the major advantages of satellite precipitation estimates is that they can provide a complete picture of precipitation systems; a difficult task to achieve using sparsely distributed rain gauges. This advantage greatly enhances our understanding of natural precipitation. Also, for this reason, it is difficult to know satellite-derived precipitation system performance using a direct “object-to-object” method involving ground-truth reference data. To solve this issue, we develop a broadly applicable method, namely, a “pixel-to-object” method, which regards discrete gauges as sampling gridboxes to evaluate a satellite precipitation product from the precipitation system perspective. Approximately 46,000 AWSs recorded hourly data in China are used to evaluate the IMERG precipitation product. The results show that the smaller the precipitation systems are, the higher the false alarm proportion, the lower the miss proportion, and the more underestimated the precipitation rates are. The miss and false alarm proportions near the precipitation system boundaries are much larger than those far away from the boundaries, while the precipitation rate bias presents a negative-positive–negative bias pattern from the boundaries to the centers of precipitation systems. These results indicate the strong influences of the precipitation system scale and position in the systems on satellite product biases. A universal overestimation of precipitation system sizes is revealed, which is more severe for small systems than for large systems and in the afternoon than in the morning. The different data sources in the IMERG product throughout a day evidently influence the delineation accuracy of precipitation system size in a day.

Rainfall in the Greater and Lesser Antilles: Performance of five gridded datasets on a daily timescale

Study regionThe studied region is the Greater Antilles (Cuba, Hispaniola, Jamaica and Puerto Rico) and the Lesser Antilles (Southern part of the Caribbean arc). Study focusThe performance of MSWEP, CHIRPS, PERSIANN-CDR, ERA-5 and GPM IMERG were evaluated to highlight their qualities and shortcomings and to guide researchers in the choice of these rainfall datasets to use for hydro-meteorological applications in this study area. Five quantitative (RMSE, KGE and his three components) and three qualitative (POD, FAR and CSI) statistical metrics are used to evaluate the amount and detection capacity of the rainfall datasets. Heavy rainfall percentiles are calculated to assess the ability of rainfall datasets to estimate rare and extreme rainfall. New hydrological insights for the regionMSWEP performs well for most statistical metrics and is recommended for most hydro-meteorological research. CHIRPS and PERSIANN-CDR performs well in estimating the annual rainfall seasonality and are recommended for research on water resources management (irrigation, energy production, etc.). CHIRPS also performs well in estimating heavy rainfall percentiles and is also recommended for statistical research of heavy rainfall events. ERA-5 and GPM IMERG have a good ability to capture wet and dry days and is recommended for determination of climatic research or atmospheric sciences applications. However, bias reduction methods for these rainfall gridded datasets are advised before applications due to their low KGE and high RMSE.

Systematic assessment of GPM IMERG V06 precipitation products with dense rain gauge observations over Zhejiang Province, China

AbstractThis study systematically assessed the performance of the Integrated Multi‐satellitE Retrievals (IMERG) for Global Precipitation Measurement V06, including the near‐real‐time “Late Run” (IMERG‐L) and the post‐real‐time “Final Run” (IMERG‐F), over Zhejiang Province (ZJP), China. The evaluation was conducted at daily and hourly timescales for a full year and for each season, based on dense rain gauge observations and continuous and categorical validation statistics. For the full year and for each season, IMERG‐F outperformed IMERG‐L in representing the spatial pattern of multiyear mean precipitation. For regional mean of ZJP, IMERG‐F and IMERG‐L overestimated the daily/hourly precipitation for the full year by 6.51 and 4.98%, respectively. Among seasons, the regional mean relative biases for IMERG‐F were between 5.65 and 8.63%; however, for IMERG‐L, they exhibited notable variations with a maximum of 11.09% in fall and a minimum of 0 in spring. Bias composition suggested that the regional mean overestimations were largely due to false bias for the full year and for each season, except in winter, wherein it was due to hit bias. Spatially, the biases for the full year and for each season commonly arose from false and hit biases at daily timescale, and from false and miss biases at hourly timescale. Based on the remaining continuous metrics (i.e., root‐mean‐square‐error [RMSE], correlation coefficient [CC], and Kling–Gupta Efficiency [KGE]) and all categorical metrics, the IMERG daily/hourly performance was acceptable on regional and grid scales throughout the year and in all seasons. From a region‐average perspective, IMERG‐F outperformed IMERG‐L according to CC, RMSE, and KGE, but both products showed the same performance overall based on all categorical metrics; most grids also share these characteristics. This study provides a valuable reference for IMERG developers to improve product accuracy from the perspective of the final postprocessing step and for potential users in ZJP.