EVALUATION OF DAILY SATELLITE AND REANALYSIS OF RAINFALL DATA OVER SOUTH SUMATRA REGION

ABSTRACTData from the Tropical Rainfall Measuring Mission (TRMM) rainfall estimations have been evaluated at different time scales in the previous research, in particular, sub-daily, monthly, seasonally and annually. However, in arid and semi-arid regions water balance may be reached several days after a rainfall event. Hence, it becomes of crucial importance to investigate sub-monthly time periods (i.e. multi-day periods). For this reason, TRMM precipitation data version 3B42 (3B42) were evaluated and calibrated for 1, 2, 3, 5, 7, 10, 15, 20 days and monthly time scales using rain gauges data in Fars province, Islamic Republic of Iran, 1 January 2000 to 31 December 2014. Pearson’s correlation coefficient (r), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Bias (MB), Prediction of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI) were used for the purpose of evaluation. The results showed that with a logarithmic trend, r, NRMSE, and FAR values decreased while RMSE, CSI, and POD values increased with increasing time scales. Moreover, the spatial average MB was almost constant for various time scales, although the percentage of grid cells with over-estimated rainfall increased from 1 day to 1 month. By fitting logarithmic functions over the values of r, RMSE, NRMSE, POD, FAR, and CSI at 1, 10 days, and monthly time scales, the corresponding values of these measures were predicted for other time scales with the relative error (NRMSE) of less than 0.1, which indicates the accurate performance of these functions. Through linear regression analysis, the slope (M) and interception (B) of the equations for calibrating 3B42 precipitation estimates at various time scales were obtained. Furthermore, the results showed that the obtained values of M and B in 1, 10 days, and monthly time scales can be estimated with a high accuracy at 2, 3, 5, 7, 15, and 20 days.

In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

ABSTRACTAccurate estimation of precipitation is crucial for crop yield assessment, flood and drought monitoring, and water structures management. Precipitation is subject to both temporal and spatial variability. While recording rain gauges support temporal resolution, they measure point rainfall and require dense network and application of interpolation techniques to provide spatial dimension. On the other hand, remote-sensing products cover regional and global spatial scales. Building upon the Tropical Rainfall Measuring Mission (TRMM) heritage, the Global Precipitation Measurement (GPM) mission is an international net of satellites that present the next-generation global observations of rain and snow at a spatial resolution of 0.1° × 0.1° with a half-hour temporal resolution. In this study, March–December 2014 3-hourly TRMM data (3B42V7) and half-hourly Integrated Multi-satellite Retrievals for GPM (IMERG) data are compared with the 3-hourly rain gauges data in Khorasan Razavi province, located in northwest of Iran. Coefficient of determination (R2), Bias, MBias, RBias, mean absolute error (MAE), root mean square error (RMSE) as well as probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics were measured for validation purposes. The result showed that correlation between IMERG data and rain gauge rainfall data is higher than those of 3B42V7 data. In addition, the values of MBias, Bias, and RBias confirmed that both of 3B42V7 and IMERG underestimated rainfall over the study area, whereas MBias of IMERG was higher than 3B42V7. Furthermore, MAE and RMSE values of 3B42V7 and IMERG were similar while IMERG evaluation turned out a better correlation coefficient (r) and POD than 3B42V7. This study showed that IMERG generally had reasonable agreement with the gauge data.

Hydrological and meteorological studies demand accurate, continuous, long-term, reliable, and uniformly distributed precipitation data. Considering low density rain gauges with incomplete data in developing nations, a plethora of gridded precipitation products (GPPs) have made their place as an alternative to rain gauge records. However, GPPs house inherent errors depending on the type of data, gauge density, gridding algorithm, etc. Hence, it is crucial to evaluate them prior to their application. This study evaluated monthly products of eight GPPs over 17 years (1998-2014) – Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation data (APHRODITE), Climate Prediction Center (CPC), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Southeast Asian Observed dataset (SA-OBS), Climate Prediction Center Morphing Technique (CMORPH), The Tropical Rainfall Measuring Mission (TRMM)-daily products, Climate Research Unit (CRU), and Global Precipitation Climatology Center (GPCC). An entropy-based weight calculation for each statistical index and compromise programming was employed to rank the GPPs in the selected sub-basins (Nam Ngum River Basin, NRB, and Vietnam Mekong Delta, VMD) of the Lower Mekong Region (LMR) for mean and six extreme precipitation indices. The correlation coefficient (r), root mean square error (RMSE), skilled score (SS), and bias were the continuous statistical indices and probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI) were the categorical indices used in this study. In terms of capturing mean monthly precipitation, GPCC outweighed all other products for both the studied basins. However, APHRODITE ranked first for daily precipitation products based on compromise programming algorithm for NRB. APHRODITE consistently recorded r between 0.85 and 0.95, RMSE between 50 and 100 mm/month, and SS between 0.72 and 0.90 for the 5 observed stations. Similarly, in case of VMD, TRMM ranked first for the daily precipitation products with r between 0.8 and 0.95, RMSE between 50 and 70 mm/month, and SS between 0.56 and 0.9 when evaluated with 11 observed stations. The APHRODITE for NRB and TRMM for VMD can be used as alternate to gauge data for hydrological and meteorological studies.

Research has been conducted to compare daily, monthly and seasonal rain rates derived from Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis (TMPA) using rain gauge analysis from 1998 to 2002. Three rain gauges in the Bali islands were employed. Statistical analysis was used to analyse the relationship of the TMPA product with the rain gauge data. Resulting statistical measures consisted of the linear correlation coefficient (r), the mean bias error (MBE), the root mean square error (RMSE) and the mean absolute error (MAE). The results of these analyses indicate that satellite data have lower values than the gauge estimation values. The validation analysis showed a very good relationship with the gauge data on monthly timescales. However, a poor relationship was found between the gauge data and the daily data analysis from the TMPA. The 3B42 and 3B43 products showed the same levels of relationship during the wet season and dry season. The correlation in the dry season was better than during the wet season. Statistical error levels during the wet season were better than in the dry season. The 3B43 showed slight improvement in these values when compared with the 3B42 (both the random error measurement and the scatter of the estimates were reduced). In general, the data from TMPA are potentially usable to replace rain gauge data, especially to replace the monthly data, if inconsistencies and errors are taken into account.

Satellite-based and reanalysis products are precipitation data sources with high potential, which may exhibit high uncertainties over areas with a complex climate and terrain. This study aimed to evaluate the accuracy of the latest versions of six precipitation products (i.e., Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) V2.0, gauge-satellite blended (BLD) Climate Prediction Center Morphing technique (CMORPH) V1.0, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) 5-Land, Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) V6 Final, Global Satellite Mapping of Precipitation (GSMaP) near-real-time product (NRT) V6, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-CDR) over the Yellow River Basin, China. The daily precipitation amounts determined by these products were evaluated against gauge observations using continuous and categorical indices to reflect their quantitative accuracy and capability to detect rainfall events, respectively. The evaluation was first performed at different time scales (i.e., daily, monthly, and seasonal scales), and indices were then calculated at different precipitation grades and elevation levels. The results show that CMORPH outperforms the other products in terms of the quantitative accuracy and rainfall detection capability, while CHIRPS performs the worst. The mean absolute error (MAE), root mean square error (RMSE), probability of detection (POD), and equitable threat score (ETS) increase from northwest to southeast, which is similar to the spatial pattern of precipitation amount. The correlation coefficient (CC) exhibits a decreasing trend with increasing precipitation, and the mean error (ME), MAE, RMSE, POD and BIAS reveal an increasing trend. CHIRPS demonstrates the highest capability to detect no-rain events and the lowest capability to detect rain events, while ERA5 has the opposite performance. This study suggests that CMORPH is the most reliable among the six precipitation products over the Yellow River Basin considering both the quantitative accuracy and rainfall detection capability. ME, MAE, RMSE, POD (except for ERA5) and BIAS (except for ERA5) increase with the daily precipitation grade, and CC, RMSE, POD, false alarm ratio (FAR), BIAS, and ETS exhibit a negative correlation with elevation. The results of this study could be beneficial for both developers and users of satellite and reanalysis precipitation products in regions with a complex climate and terrain.

The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of a high temporal resolution and large spatial coverage over oceans and land. This is particularly the case in tropical regions in Asia. The objective of this study is therefore to analyze the performance of rainfall estimation from TRMM 3B42-V7 (henceforth TRMM) using rain gauge data in Malaysia, specifically from the Pahang river basin as a case study, and using a set of performance indicators/scores. The results suggest that the altitude of the region affects the performances of the scores. Root Mean Squared Error (RMSE) is lower mostly at a higher altitude and mid-altitude. The correlation coefficient (CC) generally shows a positive but weak relationship between the rain gauge measurements and TRMM (0 < CC < 0.4), while the Nash-Sutcliffe Efficiency (NSE) scores are low (NSE < 0.1). The Percent Bias (PBIAS) shows that TRMM tends to overestimate the rainfall measurement by 26.95% on average. The Probability of Detection (POD) and Threat Score (TS) demonstrate that more than half of the pixel-point pairs have values smaller than 0.7. However, the Probability of False Detection (POFD) and False Alarm Rate (FAR) show that most of the pixel-point gauges have values lower than 0.55. The seasonal analysis shows that TRMM overestimates during the wet season and underestimates during the dry season. The bias adjustment shows that Mean Bias Correction (MBC) improved the scores better than Double-Kernel Residual Smoothing (DS) and Residual Inverse Distance Weighting (RIDW). The large errors imply that TRMM may not be suitable for applications in environmental, water resources, and ecological studies without prior correction.

This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain <1 mm/day) and extremely heavy rain (rain >50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level.

A validation of passive microwave–adjusted rainfall analyses of tropical cyclones using spaceborne radar data is presented. This effort is part of the one-dimensional plus four-dimensional variational (1D+4D-Var) rain assimilation project that is being carried out at the European Centre for Medium-Range Weather Forecasts (ECMWF). Brightness temperatures or surface rain rates from the Tropical Rainfall Measuring Mission (TRMM) satellite are processed through a 1D-Var retrieval to derive values of total column water vapor that can be ingested into the operational ECMWF 4D-Var. As an indirect validation, the precipitation fields produced at the end of the 1D-Var minimization process are converted into equivalent radar reflectivity at the frequency of the TRMM precipitation radar (13.8 GHz) and are compared with the observations averaged at model resolution. The averaging process is validated using a sophisticated downscaling/upscaling approach that is based on wavelet decomposition. The precipitation radar measurements are ideal for this validation exercise, being approximately collocated with but completely independent of the TRMM Microwave Imager (TMI) radiometer measurements. Qualitative and statistical comparisons between radar observations and retrievals from the TMI-derived surface rain rates and from TMI radiances are made using 17 well-documented tropical cyclone occurrences between January and April of 2003. Several statistical measures, such as bias, root-mean-square error, and Heidke skill score, are introduced to assess the 1D-Var skill as well as the model background skill in producing a realistic rain distribution. Results show a good degree of skill in the retrievals, especially near the surface and for medium–heavy rain. The model background produces precipitation in the domain that is sometimes in excess with respect to the observations, and it often shows an error in the location of precipitation maxima. Differences between the two 1D-Var approaches are not large enough to make final conclusions regarding the advantages of one method over the other. Both methods are capable of redistributing the rain patterns according to the observations. It appears, however, that the brightness temperature approach is in general more effective in increasing precipitation amounts at moderate-to-high rainfall rates.

Satellite-based rainfall products have extensive applications in global change studies, but they are known to contain deviations that require comprehensive verification at different scales. In this paper, we evaluated the accuracies of two high-resolution satellite-based rainfall products: the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7 and the Climate Prediction Center morphing (CMORPH) technique from January 2010 to December 2011 in Shanghai, by using categorical metrics (Probability of Detection, False Alarm Ratio, and Critical Success Index) and statistical indicators (Mean Absolute Error, Root Mean Square Error, Relative Bias, and Correlation Coefficient). Our findings show that, firstly, CMORPH data has a higher accuracy than 3B42V7 at the daily scale, but it underestimates the occurrence frequency of daily rainfall for some intensity ranges. Most errors of the two products are distributed between −10 and 10 mm/day. Second, at the monthly scale, the total accuracy of 3B42V7 is higher than CMORPH in terms of the value of the Correlation Coefficient (CC) and Relative Bias (RB). Finally, CMORPH brings about daily rainfall detection results from categorical metrics computation better than 3B42V7. Generally, the two satellite-based rainfall products show a high correlation with rain gauge data in Shanghai, particularly in spring and winter. Unfortunately, in summer, both of them do not perform well in detecting the short-duration heavy rainfall events. Overall, the relatively poor data accuracy has limited their further applications in Shanghai and similar urban areas.

Based on the complex topography and climate conditions over the Tianshan Mountains (TSM) in Xinjiang, China, the new precipitation product, the Global Precipitation Measurement (GPM) (IMERG), and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) 3B42 (TMPA), were evaluated and compared. The evaluation was based on daily-scale data from April 2014 to March 2015 and analyses at annual, seasonal and daily scales were performed. The results indicated that, overall, the annual precipitation in the Tianshan area tends to be greater in the north than in the south and greater in the west than in the east. Compared with the ground reference dataset, GPM and TRMM datasets represent the spatial variation of annual and seasonal precipitation over the TSM well; however, both measurements underestimate the annual precipitation. Seasonal analysis found that the spatial variability of seasonal precipitation has been underestimated. For the daily assessment, the coefficient of variation (CV), correlation coefficient (R) and relative bias (RB) were calculated. It was found that the GPM and TRMM data underestimated the larger CV. The TRMM data performed better on the daily variability of precipitation in the TSM. The R and RB data indicate that the performance of GPM is generally better than that of TRMM. The R value of GPM is generally greater than that of TRMM, and the RB value is closer to 0, indicating that it is closer to the measured value. As for the ability to detect precipitation events, the GPM products have significantly improved the probability of detection (POD) (POD values are all above 0.8, the highest is 0.979, increased by nearly 17%), and the critical success index (CSI) (increased by nearly 9% in the TSM) is also better than TRMM, although it is only slightly weaker than TRMM in terms of the false alarm ratio (FAR) and frequency bias index (FBI). Overall, GPM underestimates the low rainfall rate by 6.4% and high rainfall rate by 22.8% and overestimates middle rain rates by 29.1%. However, GPM is better than TRMM in capturing all types of rainfall events. Based on these results, GPM-IMERG presents significant improvement over its predecessor TRMM 3B42. Considering the performance of GPM in different subregions, a lot of work still needs to be done to improve the performance of the satellite before being used for research.

This study explores the application of the fully convolutional network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG), thunderstorms, and short-term heavy rain (STHR) in Gansu. The training data come from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations. The performance of the proposed FCN model, based on 2017 to 2021 training datasets, demonstrated a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when tested against the 2022 dataset. Operational deployment in 2023 yielded an average critical success index (CSI) of 24.3%, a probability of detection (POD) of 62.6%, and a false alarm ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model’s optimal performances in terms of hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model’s ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future.

Abstract. This study provides a comprehensive evaluation of eight high-spatial-resolution gridded precipitation products in semi-arid regions of Tamil Nadu, India, focusing specifically on Coimbatore, Madurai, Tiruchirappalli, and Tuticorin, where both irrigated and rainfed agriculture is prevalent. The study regions lack sufficiently long-term and spatially representative observed precipitation data, which are essential for agro-hydrological studies and better understanding and managing the nexus between food production and water and soil management. Hence, the present study evaluates the accuracy of five remote-sensing-based precipitation products, namely the Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN CDR), the CPC MORPHing technique (CMORPH), the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), and three reanalysis-based precipitation products, namely the National Centers for Environmental Prediction Reanalysis 2 (NCEP2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 Land (ERA5-Land), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), against the station data. Linearly interpolated precipitation products were statistically evaluated at two spatial (grid and district-wise) and three temporal (daily, monthly, and yearly) resolutions for the period 2003–2014. Based on overall statistical metrics, ERA5-Land was the best-performing precipitation product in Coimbatore, Madurai, and Tiruchirappalli, with MSWEP following closely behind. In Tuticorin, however, MSWEP outperformed the others. On the other hand, MERRA2 and NCEP2 performed the worst in all the study regions, as indicated by their higher root mean square error (RMSE) and lower correlation values. Except in Coimbatore, most of the precipitation products underestimated the monthly monsoon precipitation, which highlights the need for a better algorithm for capturing convective precipitation events. Moreover, the percent mean absolute error (%MAE) was higher in non-monsoon months, indicating that product-based agro-hydrological modelling, like irrigation scheduling for water-scarce periods, may be less reliable. The ability of the precipitation products to capture extreme-precipitation intensity differed from the overall statistical metrics, where MSWEP performed the best in Coimbatore and Madurai, PERSIANN CDR in Tiruchirappalli, and ERA5-Land in Tuticorin. This study offers crucial guidance for managing water resources in agricultural areas, especially in regions with scarce precipitation data, by helping to select suitable precipitation products and bias correction methods for agro-hydrological research.

Deep convective clouds, such as towering cumulus and Cumulonimbus, can endanger lives and property, also being a major hazard to aviation. This study presents the convective index (IndexCON) used operationally at the Portuguese Meteorological Watch Office. Moreover, IndexCON is evaluated against lightning and precipitation data for two years, between January 2022 and December 2023, over mainland Portugal and its surrounding areas. This index combines several European Center for Medium-Range Weather Forecasts (ECMWF) prognostic variables, such as stability indices, cloud water content, relative humidity and vertical velocity, using a fuzzy-logic approach. IndexCON performs well in the warm season (May–October), with a probability of detection (POD) of 70%, a false alarm ratio (FAR) of 30% and a probability of false detection (POFD) less than 5%, leading to a Critical Success Index (CSI) above 0.55. However, IndexCON performs worse in the cold season (November–April), when dynamical drivers are more relevant, mainly due to overestimating the convective activity, resulting in CSI and Heidke Skill Score (HSS) values below 0.3. Optimizing the membership functions partially reduces this overestimation. Finally, the added value of IndexCON was illustrated in detail for a thunderstorm episode, using satellite products, lightning and precipitation data.

Rain gauge and satellite-retrieved data have been widely used in basin-scale hydrological applications. While rain gauges provide accurate measurements that are generally unevenly distributed in space, satellites offer spatially regular observations and common error prone retrieval. Comparative evaluation of gauge-based and satellite-based data is necessary in hydrological studies, as precipitation is the most important input in basin-scale water balance. This study uses quality-controlled rain gauge data and prevailing satellite products (Tropical Rainfall Measuring Mission (TRMM) 3B43, 3B42 and 3B42RT) to examine the consistency and discrepancies between them at different scales. Rain gauges and TRMM products were available in the Poyang Lake Basin, China, from 1998 to 2007 (3B42RT: 2000–2007). Our results show that the performance of TRMM products generally increases with increasing spatial scale. At both the monthly and annual scales, the accuracy is highest for TRMM 3B43, with 3B42 second and 3B42RT third. TRMM products generally overestimate precipitation because of a high frequency and degree of overestimation in light and moderate rain cases. At the daily scale, the accuracy is relatively low between TRMM 3B42 and 3B42RT. Meanwhile, the performances of TRMM 3B42 and 3B42RT are highly variable in different seasons. At both the basin and pixel scales, TRMM 3B43 and 3B42 exhibit significant discrepancies from July to September, performing worst in September. For TRMM 3B42RT, all statistical indices fluctuate and are low throughout the year, performing worst in July at the pixel scale and January at the basin scale. Furthermore, the spatial distributions of the statistical indices of TRMM 3B43 and 3B42 performed well, while TRMM 3B42RT displayed a poor performance.