High-resolution Satellite Precipitation Products Research Articles

Spatiotemporal performance evaluation of high-resolution multiple satellite and reanalysis precipitation products over the semiarid region of India.

The present investigation evaluates three satellite precipitation products (SPPs), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Centre (GPCC), Climate Hazard Infrared Precipitation with Station Data (CHIRPS), and two reanalysis datasets, namely, the ERA5 atmosphere reanalysis dataset (ERA5) and Indian Monsoon Data Assimilation and Analysis (IMDAA), against the good quality gridded reference dataset (1991-2022) developed by the India Meteorological Department (IMD). The evaluation was carried out in terms of the rainfall detection ability and estimation accuracy of the products using metrics such as the false alarm ratio (FAR), probability of detection (POD), misses, root mean square error (RMSE), and percent bias (PBIAS). Among all the rainfall products, ERA5 had the best ability to capture rainfall events with a higher POD, followed by MSWEP. Both MSWEP and ERA5 had PODs of 70-100% in more than 90% of the grids and less than 35% of missing rainfall events in the entire Tamil Nadu. In the case of the rainfall estimation accuracy evaluation, the MSWEP exhibited superior performance, with lower RMSEs and biases ranging from - 25 to 25% at the annual and seasonal scales. In northeast monsoon (NEM), CHIRPS demonstrated a comparable performance to that of MSWEP in terms of the RMSE and PBIAS. These findings will help product users select the best reliable rainfall dataset for improved research, diversified applications in various sectors, and policy-making decisions.

Validation of high-resolution satellite precipitation products over West Africa for rainfall monitoring and early warning

Satellite rainfall estimation products (SRPs) can help overcome the absence of rain gauge data to monitor rainfall-related risks and provide early warning. However, SRPs can be subject to several sources of errors and need to be validated before specific uses. In this study, a comprehensive validation of nine high spatial resolution SRPs (less than 10 km) was performed on monthly and dekadal time scales for the period 2001–2015 in West Africa. Both SRPs and reference data were remapped to a spatial resolution of 0.1 ° and the validation process was carried out on a grid scale, with 1,202 grids having at least one rain gauge throughout West Africa. Unconditional statistical metrics, such as mean absolute error, Pearson correlation, Kling-Gupta efficiency and relative bias, as well as the reproducibility of rainfall seasonality, were used to describe the agreement between SRPs and reference data. The PROMETHEE II multi-criteria decision analysis (MCDA) method was employed to rank SRPs by considering criteria encompassing both their intrinsic characteristics and performance metrics. Overall, IMERGv6-Final, MSWEPv2.2, RFE2, ARC2, and TAMSATv3.1, performed reasonably well, regardless of West African climate zones and rainy season period. Given the performances displayed by each of these SRPs, RFE2, ARC2, and MSWEPv2.2 would be suitable for drought monitoring. TAMSATv3.1, IMERGv6-Final, RFE2, ARC2, and MSWEPv2.2 are recommended for comprehensive basin water resources assessments. TAMSATv3.1 and MSWEPv2.2 would be of interest for the characterization of variability and long-term changes in precipitation. Finally, TAMSATv3.1, ARC2, and MSWEPv2.2, could be good alternatives to observed data as predictants in West African Regional Climate Outlook Forum (RCOF) process.

Qualitative Evaluation of the High Resolution Satellite Precipitation Products with Dense Surface Rain Gauge Observations in Odisha Over a Time Period

The study aims to the Hydro-Estimator Method (HEM) has been used as the noble approach for analysing the heavy rainfall episodes over entire Odisha using the INSAT-3D satellite- derived rainfall estimates. The findings demonstrate that, in terms of the frequency of rainfall occurrences, INSAT-3D satellite rainfall products clearly illustrated both the spatial and temporal variability in rainfall pattern of Odisha. The performance statistics with IMERG and daily merged satellite retrieved rainfall show that both the dataset corelated well with the HEM with a small deviation. For heavy rainfall events, HEM shows good skill and correlation in detecting heavy rainfall with an accuracy of 20 mm and good pattern matching with actual rainfall. Entire Odisha is considered as study area, which is located in the eastern part of India. It comprises of 30 districts and of 314 blocks spreading over an area of 155707 km2. The state has 30 districts, which are further divided into 314 revenue blocks. The rainfall data (1 year; 2016) for all the blocks of Odisha has been obtained from Special Relief Commissioner (SRC), Government of Odisha and subjected to further analysis process. The satellite derived rainfall estimates viz., HEM, were evaluated with rain gauge based gridded data viz., IMD-GRIDDED Dataset for the year 2016 over the Odisha region at the summer monsoon period. The satellite derived rainfall estimates HE have exhibited some good results with the IMD-GRIDDED Dataset its average R2 on a daily basis for different blocks is 0.34 and on a monthly basis it is 0.44. RMSE has also been determined to different block for JJAS month. The average RMSE on daily basis is 17.7 and on monthly basis is 94.6 as shown in bias maps.

Comprehensive Evaluation of High-Resolution Satellite Precipitation Products over the Qinghai–Tibetan Plateau Using the New Ground Observation Network

Satellite precipitation products (SPPs) have been widely evaluated at regional scales. However, there have been few quantitative comprehensive evaluations of SPPs using multiple indices. Ten high-resolution SPPs were quantitatively and comprehensively evaluated from precipitation occurrence and series indices using an improved rank score (RS) method in the data-scarce Qinghai–Tibetan Plateau (QTP). The new observation network, along with a number of national basic stations, was applied for SPP evaluation to obtain more reliable results. The results showed that the GPM and MSWEP showed the strongest overall performance, with an RS value of 0.75. CHIRPS and GPM had the strongest performance at measuring precipitation occurrence (RS = 0.92) and series (RS = 0.75), respectively. The optimal SPPs varied in evaluation indices, but also concentrated in the MSWEP, GPM, and CHIRPS. The bias of SPPs was markedly in the QTP, with relative error generally between −80% and 80%. In general, most SPPs showed the ability to detect precipitation occurrence. However, the SPPs showed relatively weak performance at measuring precipitation series. The mean Kling–Gupta efficiency of all stations was <0.50 for each SPP. The SPPs showed better performance in monsoon-affected regions, which mainly include the Yangtze, Yellow, Nu–Salween, Lancang–Mekong, Yarlung Zangbo–Bramaputra, and Ganges river basins. Performance was relatively poor in the westerly circulation areas, which mainly include the Tarim, Indus, and QTP inland river basins. The performance of SPPs showed a seasonal pattern during the year for most occurrence indices. The performance of SPPs in different periods was opposite in different indices. Therefore, multiple indices representing different characteristics are recommended for the evaluation of SPPs to obtain a comprehensive evaluation result. Overall, SPP measurement over the QTP needs further improvement, especially with regard to measuring precipitation series. The proposed improved RS method can also potentially be applied for comprehensive evaluation of other products and models.

Evaluation of Satellite Precipitation Products for Estimation of Floods in Data-Scarce Environment

Utilization of satellite precipitation products (SPPs) for reliable flood modeling has become a necessity due to the scarcity of conventional gauging systems. Three high-resolution SPPs, i.e., Integrated Multi-satellite Retrieval for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), data were assessed statistically and hydrologically in the sparsely gauged Chenab River basin of Pakistan. The consistency of rain gauge data was assessed by the double mass curve (DMC). The statistical metrics applied were probability of detection (POD), critical success index (CSI), false alarm ratio (FAR), correlation coefficient (CC), root mean square error (RMSE), and bias (B). The hydrologic evaluation was conducted with calibration and validation scenarios for the monsoon flooding season using the Integrated Flood Analysis System (IFAS) and flow duration curve (FDC). Sensitivity analysis was conducted using ±20% calibrating parameters. The rain gauge data have been found to be consistent with the higher coefficient of determination (R2). The mean skill scores of GSMaP were superior to those of CHIRPS and IMERG. More bias was observed during the monsoon than during western disturbances. The most sensitive parameter was the base flow coefficient (AGD), with a high mean absolute sensitivity index value. During model calibration, good values of performance indicators, i.e., R2, Nash−Sutcliffe efficiency (NSE), and percentage bias (PBIAS), were found for the used SPPs. For validation, GSMaP performed better with comparatively higher values of R2 and NSE and a lower value of PBIAS. The FDC exhibited SPPs’ excellent performance during 20% to 40% exceedance time.

Comparison of High-Resolution Satellite Precipitation Products in Sub-Saharan Morocco

Precipitation is a crucial source of data in hydrological applications for water resources management. However, several regions suffer from limited data from a ground measurement network. Remotely sensed data may provide a viable alternative for these regions. This study aimed to evaluate six satellite products (GPM-F, CHIRPS, PERSIANN-CCS-CDR, GPM-L, GPM-E and PDIR-Now), with high spatio-temporal resolution, in the sub-Saharan regions of Morocco. Precipitation observation data from 33 rain-gauge stations were collected and used over the period from September 2000 to August 2020. The assessment was performed on three temporal scales (daily, monthly and annually) and two spatial scales (pixel and basin scales), using different quantitative and qualitative statistical indices. The results showed that the GPM-F product performed the best, according to the different evaluation metrics, up to events with 40 mm/day, while the GPM near real-time products (GPM-E and GPM-L) were better at detecting more intense rainfall events. At the daily time scale, GPM-E and GPM-L and, on monthly and annual scales, CHIRPS and PERSIANN-CCS-CDR, provided satisfactory precipitation estimates. Moreover, the altitude-based analysis revealed a bias increasing from low to high altitudes. The continental and mountainous basins showed the lowest performance compared to the other locations closer to the Atlantic Ocean. The evaluation based on the latitudes of rain gauges showed a decrease of bias towards the most arid zones. These results provide valuable information in a scarcely gauged and arid region, showing that GPM-F could be a valuable alternative to rain gauges.

The Warming and Wetting Ecological Environment Changes over the Qinghai-Tibetan Plateau and the Driving Effect of the Asian Summer Monsoon

The impact of warming and wetting on the ecological environment of the Qinghai-Tibet Plateau (TP) underthe background of climate change has been a concern of the global scientific community. In this paper, the optimizedinterpolation variational correction approach is adopted for the analysis of monthly high-resolution satellite precipitationproducts and observations from meteorological stations during the past 20 years. As a result, the corrected precipitationproducts can not only supplement the“blank area”of precipitation observation stations on the TP, but also improve theaccuracy of the original satellite precipitation products. The precipitation over the TP shows different spatial changes inthe vegetation growing season, known as the time from May to September. The precipitation in the vegetation growingseason and leaf area index (LAI) in the following month show a similar change pattern, indicating a“one-month lag”response of LAI to precipitation on the TP. Further analysis illustrates the influence of water vapor transport driven bythe Asian summer monsoon. Water vapor derived from trans-equatorial air flows across the Indian Ocean and ArabianSea is strengthened, leading to the increase of precipitation in the central and northern TP, where the trend of warmingand wetting and the increase of vegetation tend to be more obvious. By contrast, as a result of the weakening trend ofwater vapor transport in the middle and low levels in southern TP, the precipitation decreases, and the LAI shows adowntrend, which inhibits the warming and wetting ecological environment in this area.

Risk Assessment Method of Non-point Source Pollution Output for Watershed Using High Resolution Data

In recent years, non-point source pollution has become the main cause of water quality deterioration in some reservoirs in China. Taking the Panjiakou Reservoir as an example, the classical output risk model was improved by introducing a precipitation factor and terrain factor. Combined with high-resolution satellite precipitation products (GPM) and GF-6 satellite images, a high-resolution data-driven risk assessment method for non-point source pollution output was established to study the temporal and spatial distribution characteristics of non-point source pollution output risk in the Panjiakou Reservoir basin. The results showed that the non-point source pollution output risk was high in the study area in 2018. The areas with higher and highest risk of nitrogen pollution output accounted for approximately 70.6% of the total watershed area, whereas the higher risk of phosphorus pollution output accounted for approximately 21.9%. The temporal and spatial distribution characteristics of non-point source pollution output risk in the Panjiakou Reservoir basin were analyzed. It was found that the non-point source pollution output risk in the Panjiakou Reservoir basin increased first and then decreased from April to September. This was consistent with the spatial and temporal distribution of precipitation in the basin. Combined with the analysis of land use distribution characteristics, the upstream area of the basin was mainly cultivated land, whereas cities were concentrated in the downstream portion of the basin. Affected by agricultural production and human activities, the risk of non-point source pollution output was higher in these regions. In view of the temporal and spatial distribution characteristics of non-point source pollution output risk, it is necessary to formulate a reasonable agricultural fertilization method, plan the landscape layout of source-sinks, and construct vegetation buffer zones.

Downscaling Correction and Hydrological Applicability of the Three Latest High-Resolution Satellite Precipitation Products (GPM, GSMAP, and MSWEP) in the Pingtang Catchment, China

The emergence of various high-resolution satellite precipitation products (SPPs) solves the problem of precipitation data sources for areas with a lack of precipitation data and is recognized as a reliable supplement to rain gauge observations in hydrometeorological applications. However, there still exists a shortcoming of coarse spatial resolution when applying these products to small and microscale river basins. In this study, a typical karst watershed in Southwest China—the Pingtang River Basin (PTRB)—was selected, and based on the relationship between precipitation and normalized difference vegetation index (NDVI), aspect, slope, and elevation, we used the geographically weighted regression (GWR) to downscale three SPPs, namely, global precipitation measurement (GPM), global satellite mapping of precipitation (GSMAP), and multisource weighted-ensemble precipitation (MSWEP), to 1 km × 1 km, respectively. Combined with rain gauge stations, the geographical differential analysis (GDA) was used to carry out error corrections to obtain three downscaling correction satellite precipitation products (DC-SPPs) with a 1 km spatial resolution, including DC-GPM, DC -GSMAP, and DC-MSWEP. Several statistical indices were used to perform error evaluation and precipitation capture ability analysis on SPPs and DC-SPPs, and the Grid-Xin’anjiang (the Grid-XAJ) model was used to compare their hydrological utility. The results show the following: (1) The downscaling correction method is effective. GWR can effectively improve the spatial resolution of SPPs, while GDA can reduce errors and further improve the accuracy of precipitation estimation. In addition, (2) the precipitation event characterization capabilities of GPM and GSMAP have been improved after downscaling correction, while the ability to capture precipitation events before and after the MSWEP correction is poor, showing a high hit rate and a high false alarm rate, which is unreliable to monitor precipitation events in the PTRB. Finally, (3) compared with SPPs, the hydrological performances of the three kinds of DC-SPPs have been significantly improved, and the NSE are all above 0.75 with low error. In general, the overall performance of DC-GSMAP is satisfactory. The accuracy of different SPPs after downscaling correction is different, but the applicability has been improved to different degrees.

Satellite-based monitoring of meteorological drought over different regions of Iran: application of the CHIRPS precipitation product.

In the present study, the spatiotemporal evaluation of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product is performed in capturing meteorological drought over different climatic regions of Iran. The performance of the product as a high spatial resolution dataset in monitoring drought is evaluated against the 68 meteorological stations from short to long scale (i.e., SPI1, SPI3, SPI6, SPI9, and SPI12) in the period of 1987 to 2017. Besides, the capability of the CHIRPS in detecting drought events is assessed in different drought classes. The results suggest that the climate type, the time scale, and the drought class affect the quality of the CHIRPS performance. The CHIRPS offers the best performance in the detection of all drought events with SPI < - 1 over the SPI1 (0.69 < POD < 0.85). However, the product provides the worst performance for SPI12 (0.50 < POD < 0.70). At the country level, the highest agreement between the CHIRPS- and observation data-based SPI is found over the SPI6 (CC = 0.56), while the lowest is observed over the SPI12 (CC = 0.47). Based on the temporal evaluation, the G6 (0.18 < CC < 0.44, 1.06 < RMSE < 1.28) and G8 (0.17 < CC < 0.43, 1.06 < RMSE < 1.29) regions located in the southern coast of the Caspian Sea have an inadequate performance. However, the southern parts (G4 region) (0.38 < CC < 0.65, 0.83 < RMSE < 1.27) and the northwestern area (G3 region) (0.53 < CC < 0.62, 0.87 < RMSE < 0.97) of the country offer the best performance. The spatial evaluation describes the high accuracy (CC > 0.7, RMSE < 0.5) in some regions, including the western parts of G1, the northern area of G3, and the southern parts of G4. The research findings provided an important opportunity to advance the understanding of drought monitoring over the different climatic regions based on the high-resolution satellite precipitation products.

Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

Inter-comparison of high-resolution satellite precipitation products over India during the summer monsoon season

Inter-comparison of high-resolution satellite precipitation products over India during the summer monsoon season

Evaluation of different precipitation inputs on streamflow simulation in Himalayan River basin

This study evaluates four high-resolution Satellite Precipitation Products (SPPs) and gauge-based precipitation (Gauge) in a well-performed J2000 hydrological model across the Himalayan River basin originated from the Tibetan Plateau. The four high-resolution SPPs include Tropical Rainfall Measuring Mission 3B-42 (TRMM), Climate Hazards Group Infrared Precipitation version 2 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 1 (MSWEP), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR). Two methods were deployed to evaluate hydrological response on a daily and monthly scale. The first method (Method I) consists of calibration based on gauge and re-running the model for SPPs with gauge-based calibration parameters whereas input-specific calibration of SPPs was adopted in the second method (Method II). The model-driven by TRMM demonstrated superior result followed by CHIRPS, PERSIANN-CDR, and MSWEP respectively both on the daily and monthly scale under method I. Comparing the performance metric with the method I, the substantial improvement of Nash Sutcliffe efficiency (NSE) and Percentage bias error (Pbias) were observed in MSWEP whereas improvement of Pbias was found in PERSIANN-CDR for method II. In the context of method I, MSWEP could not predict hydrological behavior properly in the Himalayan River basin while it performed best in method II on both a daily and monthly scale. SPPs could be used in the future to predict hydrological responses in the Himalayan River basin with the application of the hydrological model but it contains error and bias which should be concisely examined and corrected before practical application for planning and management of water resources project.

Comprehensive evaluation of latest GPM era IMERG and GSMaP precipitation products over mainland China

Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the latest GPM era high-resolution satellite precipitation products (SPPs), namely Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP), have been released. A critical evaluation of these newly released SPPs is very important for both end users and data developers. This study aims to evaluate the performance of the IMERG and GSMaP products over mainland China from 1 January 2015 to 31 December 2017, using daily precipitation data from 778 meteorological stations. The SPPs are validated adopting three widely used statistical metrics (CC, RMSE and RB). The assessment also considers the precipitation detection capability (POD, FAR and CSI), probability density function (PDF) of precipitation intensities, and elevation factor. Results show that GSMaP_G performs better in daily, monthly and seasonal scales than other SPPs. In terms of CC and RMSE, all SPPs share similar spatial patterns, with high values in southeastern China and low values in northwestern China. In addition, all SPPs underestimate the precipitation amount in southern China, but overestimate that in northern China, especially in northwestern China. Obviously, the precipitation detection ability of all products is good in southern China, except the Sichuan Basin. All SPPs can better reproduce the PDF in terms of precipitation intensity. Compared with other SPPs, the performance of GSMaP_G is better at all elevations. However, significant elevation-dependent errors exist in the GSMaP_N. Overall, the gauge-corrected GSMaP_G and IMERG_F perform better than other SPPs in reducing various errors, especially the former. This study will be valuable for both algorithm developers and users, as well as for associated products, and it will also better support disaster risk reduction and hydrological applications, especially in areas with a sparse rain gauge network.

Spatio-temporal accuracy evaluation of three high-resolution satellite precipitation products in China area

Evaluating the accuracy of the spatial and temporal dimensions of satellite precipitation products (SPPs) can effectively grasp product performance and potential application. Three high spatial resolution SPPs, CHIRPS, GPM-IMERG, and PERSIANN-CCS, were collected from 2015 to 2018 in this study. Observation data rain gauges were used to analyze and evaluate the spatio-temporal accuracy features of these SPPs. First, based on statistical and categorical indices, the accuracy of the SPPs temporal dimension in different months and seasons was evaluated, and the accuracy of the SPPs' spatial dimension in different basins was analyzed. Then, targeting the precipitation and process of the rainstorm events, observation capabilities of these SPPs were explored. In addition, effects of rain gauge irregular distribution, autocorrelation in SPPs, and elevation gradients on the performance of products in detecting precipitation were discussed. The main conclusions are: (1) the performance of SPPs in summer is relatively good, and the bias (relative bias, frequency bias) in spring is large. The performance of SPPs in winter is slightly weak, the probability of detection is low and the false alarm rate is high. (2) there are performance differences in the precipitation spatial expression of SPPs in different basins. The gradient features are obvious from the southeast coast to the northwest inland. (3) the products have certain capabilities to express extreme precipitation events, but the performance is slightly lower compared with rain gauges. Among the three SPPs, GPM-IMERG shows best expression of precipitation with correlation in 0.79 and probability of detection in 0.70. (4) in the range of 200–2000 m, the detection performance of SPPs is closely affected by elevation. Autocorrelation can cause overestimation of evaluation indices at product calibration rain gauges. This study clarifies the spatial and temporal features and accuracy performance of three high-resolution SPPs in China area, which can provide references for its application in the fields of hydrological process, meteorology and climate.

Data Assimilation of High-Resolution Satellite Rainfall Product Improves Rainfall Simulation Associated with Landfalling Tropical Cyclones in the Yangtze River Delta

Floods caused by heavy rainfall events associated with landfalling tropical cyclones (TCs) represent a major risk for the Yangtze River Delta (YRD) region of China. Accurate extreme precipitation forecasting, at long lead times, is crucial for the improvement of flood prevention and warning. However, accurate prediction of timing, location, and intensity of the heavy rainfall events is a major challenge for the Numerical Weather Prediction (NWP). In this study, high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) are evaluated at the hourly timescale, and the optimal Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product is selected and applied to directly assimilate into the Weather Research and Forecasting (WRF) model via the four-dimensional variational (4D-Var) method. The TC Jondari and Rumbia events of August 2018 are evaluated to analyze the performance of the WRF model with the 4D-Var method assimilated IMERG precipitation product (DA-IMERG) and the conventional observation (DA-CONV) for real-time heavy rainfall forecasting. The results indicate that (1) IMERG precipitation products were larger and wetter than the observed precipitation values over YRD. By comparison, the performance of “late” run precipitation product (IMERG-L) was the closest to the observation data with lower deviation and higher detection capability; (2) DA-IMERG experiment substantially affected the magnitude of the WRF model primary variables, which changed the precipitation pattern of the TC heavy rain. (3) DA-IMERG experiment further improved the forecast of heavy rainbands and relatively reduced erroneous detection rate than CTL and DA-CONV experiments at the grid scale. Meanwhile, the DA-IMERG experiment has a better fractions skill score (FSS) value (especially in the threshold of 10 mm/h) than DA-CONV for TC Jondari and Rumbia at the spatial scale, while it shows a lower performance than CTL and DA-CONV experiments when the threshold is lower than the 5 mm/h for the TC Rumbia.

Evaluation of Tropical Rainfall Measuring Mission (TRMM) satellite precipitation products for drought monitoring over the middle and lower reaches of the Yangtze River Basin, China

Drought is one of the most frequent and widespread natural disasters and has tremendous agricultural, ecological, societal, and economic impacts. Among the many drought indices, the standardized precipitation index (SPI) based on monthly precipitation data is simple to calculate and has multiscale characteristics. To evaluate the applicability of high spatiotemporal resolution satellite precipitation products for drought monitoring, based on the Tropical Rainfall Measuring Mission (TRMM) products and station-based meteorological data, the SPI values at different time scales (1, 3, 6, and 12 months) were calculated for the period of 1998–2016 in the middle and lower reaches of the Yangtze River Basin (MLRYRB). The temporal correlations show that there is a high degree of consistency between calculations at the different time scales (1, 3, 6 and 12 months) based on the two data sources and that the amplitude of fluctuations decreases with increasing time scale. In addition, the Mann-Kendall (MK) test method was applied to analyze the trends from 1998 to 2016, and the results suggest that wetting trends clearly prevailed over drying trends. Moreover, a correlation analysis of the two data sources based on 60 meteorological stations was performed with the SPI values at different time scales. The correlation coefficients at the short time scales (1, 3, and 6 months) are all greater than 0.7, and the correlation coefficient at the long time scale (12 months) is greater than 0.5. In summary, the results demonstrate that the TRMM 3B43 precipitation product provides a new data source that can be used for reliable drought monitoring in the MLRYRB.

Systematical Evaluation of GPM IMERG and TRMM 3B42V7 Precipitation Products in the Huang-Huai-Hai Plain, China

Accurate estimation of high-resolution satellite precipitation products like Global Precipitation Measurement (GPM) and Tropical Rainfall Measuring Mission (TRMM) is critical for hydrological and meteorological research, providing a benchmark for the continued development and future improvement of these products. This study aims to comprehensively evaluate the Integrated Multi-Satellite Retrievals for GPM (IMERG) and TRMM 3B42V7 products at multiple temporal scales from 1 January 2015 to 31 December 2017 over the Huang-Huai-Hai Plain in China, using daily precipitation data from 59 meteorological stations. Three commonly used statistical metrics (CC, RB, and RMSE) are adopted to quantitatively verify the accuracy of two satellite precipitation products. The assessment also takes into account the precipitation detection capability (POD, FAR, CSI, and ACC) and frequency of different precipitation intensities. The results show that the IMERG and 3B42V7 present strong correlation with meteorological stations observations at annual and monthly scales (CC &gt; 0.90), whereas moderate at the daily scale (CC = 0.76 and 0.69 for IMERG and 3B42V7, respectively). The spatial variability of the annual and seasonal precipitation is well captured by these two satellite products. And spatial patterns of precipitation gradually decrease from south to north over the Huang-Huai-Hai Plain. Both IMERG and 3B42V7 products overestimate precipitation compared with the station observations, of which 3B42V7 has a lower degree of overestimation. Relative to the IMERG, annual precipitation estimates from 3B42V7 show lower RMSE (118.96 mm and 142.67 mm, respectively), but opposite at the daily, monthly, and seasonal scales. IMERG has a better precipitation detection capability than 3B42V7 (POD = 0.83 and 0.67, respectively), especially when detecting trace and solid precipitation. The two precipitation products tend to overestimate moderate (2–10 mm/d) and heavy (10–50 mm/d) precipitation events, but underestimate violent (&gt;50 mm/d) precipitation events. The IMERG is not found capable to detecting precipitation events of different frequencies more precisely. In general, the accuracy of IMERG is better than 3B42V7 product in the Huang-Huai-Hai Plain. The IMERG satellite precipitation product with higher temporal and spatial resolutions can be regarded a reliable data sources in studying hydrological and climatic research.

Statistical evaluation of High Resolution satellite precipitation products in arid and semi‐arid parts of Ethiopia: a note for hydro‐meteorological applications

AbstractThe prime objective of this study was evaluating six satellite precipitation products by using standard statistical techniques to assess its capability to provide reliable rain rate (amount) and detect rainfall event correctly. High‐resolution precipitation products, Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), Tropical Applications of Meteorology using Satellite data and ground‐based observations (TAMSAT), Tropical Rainfall Measuring Mission (TRMM 3B42RT v7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIAN CDR), African Rainfall Climatology (ARC v2) and Climate Prediction Center Morphing technique (CMORPH v1.0) were utilized. Evaluation includes both numerical and categorial metrices. Results indicated that in terms of rainfall amount estimation, TAMSAT has relatively better capability whilst for detecting rain event, ARC v2 was found capable in Eastern Ethiopian landscape. All precipitation products underestimate precipitation amount with profound bias level. These needs a thorough bias correction before utilization of these satellite precipitation products.

Evaluation of latest GPM-Era high-resolution satellite precipitation products during the May 2017 Guangdong extreme rainfall event

This study evaluates the performance of latest version 5B (V5B) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Final Run products during a 60-year return extreme precipitation storm on 7 May 2017 over southern China with gauge observations as the reference dataset. Version 4 (V4) Global Satellite Mapping of Precipitation (GSMaP) products and quantitative precipitation estimates derived from a local ground-based S-band dual polarization weather radar (radar, hereafter) were used for parallel comparisons. The satellite-only products (IMERGUncal and GSMaP_MVK) and gauge-corrected products (IMERGCal, GSMaP_Gauge) were selected for this study. The results showed that: 1) GSMaP_MVK, IMERGUncal, GSMaP_Gauge and IMERGCal generally capture the spatio-temporal patterns of storm-accumulated rainfall with correlation coefficient (CC) values about 0.76, 0.70, 0.68 and 0.72, respectively, while radar was well correlated with gauge measurement (CC about 0.94); 2) The GSMaP_Gauge (−19.38%), IMERGCal (−40.23%), GSMaP_MVK (−57.12%), IMERGUncal (−58.77%) satellite-based precipitation products all underestimated the storm-accumulated precipitation, while ground radar overestimated by 27.48%; 3) Both IMERGCal and IMERGUncal outperformed their GSMaP counterparts in capturing the time-series with much higher CC (0.50 vs. -0.21, 0.51 vs. 0.17); 4) Among the satellite-based QPE products, when the rainfall rates are <5 mm/h, IMERGCal performs best with highest CSI. The V4 GSMaP standard products and latest V5B IMERG final run products still had resolution and accuracy limitations in estimating extreme precipitation for this type of warm sector rainfall storm. These findings will help developers of rainfall retrieval products to understand the errors and also help improve modeling of hydrological extremes.