Tropical Rainfall Measuring Mission 3B42V7 Research Articles

The extreme precipitation and added value of East Asia regional reanalysis system

AbstractIn the study, we evaluate extreme precipitation of the East Asia regional reanalysis system (EARS‐CMA), ERA‐Interim and ERA5 applying Tropical Rainfall Measuring Mission 3B42V7 (TRMM), Climate Prediction Center Morphing Method satellite‐gauge blended product (CMORPH) and CN05.1 over land‐only East Asia in 2008–2017. The added value of EARS‐CMA is also calculated to identify the small‐scale processes at different timescales and spatial resolutions. Compared to TRMM, CMORPH has a better agreement with CN05.1 for the annual, summer and winter spatial pattern and amplitude of most extreme precipitation indices. EARS‐CMA is capable to reproduce amplitude and locations of most extreme precipitation indices for annual and summertime precipitation over China mainland against CN05.1. For land‐only East Asia, ERA5 can reproduce the closest spatial features of various extreme precipitation indices to CMORPH for annual, summer and winter precipitation, followed by EARS‐CMA. All reanalysis datasets show worse skill on the spatial characteristics of duration indicators than on other extreme precipitation indices against CN05.1, but perform better than TRMM and CMORPH. The real added value, potential added value and relative added value of EARS‐CMA show that EARS‐CMA can capture the mesoscale processes over south China, Yangtze River basin, Tibetan Plateau and Indo‐China efficiently, especially the daily and subseasonal timescales over the Tibetan Plateau subregion, which benefits from fine‐scale topographical detail over the complex terrain region in the regional climate model. Also, higher extreme precipitation of aggregating EARS‐CMA than ERA‐Interim can be obtained at the same resolution, which implies that the added value obtained with EARS‐CMA is not merely the contribution of a higher resolution, but also the physical processes resolved by the dynamical downscaling technique.

Comparison of forecasting accuracy for Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) using Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim precipitation forecast data for Indonesia

Tropical Rainfall Measuring Mission (TRMM) and ERA-Interim forecast data analyzed using second-order autoregressive AR(2) and space-time-spectra analysis methods (respectively) revealed contrasting results for predicting Madden Julian Oscillation (MJO) and Convectively Coupled Equatorial Waves (CCEW) phenomena over Indonesia. This research used the same 13-year series of daily TRMM 3B42 V7 derived datasets and ERA-Interim reanalysis model datasets from the European Center for Medium-Range Weather Forecasts (ECMWF) for precipitation forecasts. Three years (2016 to 2018) of the filtered 3B42 and ERA-Interim forecast data was then used to evaluate forecast accuracy by looking at correlation coefficients for forecast leads from day +1 through day +7. The results revealed that rainfall estimation data from 3B42 provides better results for the shorter forecast leads, particularly for MJO, equatorial Rossby (ER), mixed Rossby-gravity (MRG), and inertia-gravity phenomena in zonal wavenumber 1 (IG1), but gives poor correlation for Kelvin waves for all forecast leads. A consistent correlation for all waves was achieved from the filtered ERA-Interim precipitation forecast model, and although this was quite weak for the first forecast leads it did not reach a negative correlation in the later forecast leads except for IG1. Furthermore, Root Mean Square Error (RMSE) was also calculated to complement forecasting skills for both data sources, with the result that residual RMSE for the filtered ERA-Interim precipitation forecast was quite small during all forecast leads and for all wave types. These findings prove that the ERA-Interim precipitation forecast model remains an adequate precipitation model in the tropics for MJO and CCEW forecasting, specifically for Indonesia.

Study on applicability of remote sensing precipitation products in hilly-plain-wetland complex area of northeast China

Abstract For the hilly-plain-wetland complex ecosystem in the cold region of Northeast China, in order to solve the problems, which include the scarcity of surface rainfall stations and the inability to provide accurate surface precipitation for hydrological process simulation, based on the observed precipitation of rainfall stations, three remote sensing precipitation products are taken as objects of evaluation. They include TRMM (Tropical Rainfall Measuring Mission) 3B42V7,3B42RT and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data,CHIP). In this paper, the observation data of rainfall stations and IDWP precipitation data interpolated by IDW (Inverse Distance Weighted) are used as true value of precision comparison, and the detection accuracy of remote sensing precipitation products from 2001 to 2010 is evaluated on the time scale (day, month and quarter) and spatial scale in Naoli River Basin.The results of the study indicated that 3B42V7 and CHIP have a high detection accuracy for precipitation, and their CC (correlation coefficient) values are 0.47 and 0.49 respectively in daily time scale. The accuracy of their observation for monthly precipitation is better than that of daily precipitation, and the CC are 0.85 and 0.87 respectively. The multi-year average precipitation at different grid positions in the basin is overestimated by 3B42RT, and its evaluation results are poor at different time scales. For the precipitation intensity range of (0,20], the observed results of 3B42V7 and rainfall station are close to each other. For the precipitation intensity ranges of (0,1) and (50,+∞), 3B42RT and CHIP have overestimated or underestimated the precipitation in different degrees. Based on the above analysis results, 3B42RT can be considered as data that can detect whether precipitation occurs on different spatial positions in the basin. 3B42V7 and CHIP can be applied to flood forecasting and non-point source pollution control in cold regions.

Satellite estimates and subpixel variability of rainfall in a semi-arid grassland

Uncertainties in satellite rainfall estimation may derive from both the local rainfall characteristics and its subpixel variability. To study this issue, Micro Rain Radars and a rain gauge network were deployed within a 9-km satellite pixel in the semi-arid Xilingol grassland of China in summer 2009. The authors characterized the subpixel variability with the coefficient of variation (CV) and evaluated the satellite rainfall estimation for this semi-arid area. The results showed that rainfall events with a high CV were mostly convective with a small amount of rainfall. Spatially inhomogeneous rainfall was most likely to occur at the edges of small clouds producing rain. The performance of the TRMM (Tropical Rainfall Measuring Mission) 3B42V7 product for daily rainfall was better than that of the CMORPH (Climate Prediction Center morphing technique) and PERSIANN (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks) products, although the TRMM product tended to overestimate rainfall in a lake area of the semi-arid grassland.摘要卫星估雨精度的不确定性受到当地降雨类型和像元内降雨非均匀性影响, 而结合这两个关键因素开展半干旱草原卫星估雨的研究有限. 2009年夏, 我们在中国锡林郭勒半干旱草原用多部微雨雷达和雨量计构建了9 km卫星像元降雨观测网, 观测了像元内降雨非均匀性(空间变异系数CV), 并评估了卫星估雨精度. 结果表明: (1) CV值受像元内平均降雨量, 降雨类型, 降雨云面积及移向等影响, 如高CV值的降雨过程大多为平均降雨量小, 对流性降雨过程, 降雨云边缘像元CV值较高; (2) TRMM 3B42V7 卫星估雨产品适用性较好, CMORPH和PERSIANN次之, 但TRMM 3B42V7易在半干旱草原湖泊处高估降雨.

Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

Using High-Density Rain Gauges to Validate the Accuracy of Satellite Precipitation Products over Complex Terrains

Topography and precipitation intensity are important factors that affect the quality of satellite precipitation products (SPPs). A clear understanding of the accuracy performance of SPPs over complex terrains and its relationship with topography is valuable for further improvement of product algorithms. The objective of this study is to evaluate three SPPs—the Climate Prediction Center morphing method bias corrected product (CMORPH CRT), Global Precipitation Measurement Integrated MultisatellitE Retrievals (IMERG), and Tropical Rainfall Measuring Mission 3B42V7 (TRMM 3B42V7) against a high-density network of 104 rain gauges over the Taihang Mountains from 1 January 2016 to 31 December 2017, with special focus on the reliability of products’ performance at different elevation and precipitation intensity. The results show that three SPPs slightly overestimate daily precipitation, compared to rain gauge observations, with bias ratios (β) from 1.02 to 1.06 over the entire regions. In terms of accuracy, 3B42 slightly outperforms CRT and IMERG over the Taihang Mountains. As for different elevation ranges, three SPPs show better performance in terms of accuracy in low and moderate elevation (0–500 m) regions. Similar performances of precipitation detection capability can be found for three products over the whole areas, with detection scores ranging from 0.53 to 0.58. Better precipitation detecting performance of three SPPs was discovered in high-elevation (>1000 m) regions. We adopted a linear regression (LR) model and Locally Weighted Regression (LWR) model in an attempt to discover the linear/non-linear relationships between SPPs’ performances and topographic variations. In the accuracy statistical metrics, the errors of 3B42 and CRT showed significantly positive correlations (p < 0.01) with elevation variations. The critical success index for three products gradually increased with elevation variation based on the LR model. The correlation coefficient and probability of detection for three products showed significant non-linear trends in the LWR model. The probability distribution function for the three products in different elevation regions is similar to that over the entire regions. Three SPPs slightly overestimated the frequency of heavy rain events (6.9 < precipitation intensity (PI) ≤ 19.6 mm/d); CRT and 3B42 tended to underestimate the frequency of no rain events (PI < 0.1 mm/d), while IMERG generally overestimated the frequency of no rain events. Our results not only give a detailed assessment of mainly current SPPs over the Taihang Mountains, but also recommend that further improvement on retrieval algorithm is needed by considering topographical impacts for SPPs in the future.

Evaluating precipitation products for hydrologic modeling over a large river basin in the Midwestern USA

ABSTRACT We evaluated precipitation estimates, TRMM (Tropical Rainfall Measuring Mission 3B42V7), CFSR (Climate Forecast System Reanalysis), GHCN-D (Global Historical Climatology Network-Daily Version 3.24), and Daymet, using the Soil and Water Assessment Tool (SWAT). The suitability and quality of TRMM, CFSR and Daymet in forcing the SWAT-based hydrological model was examined by means of model calibration. A calibrated TRMM-driven model slightly overestimated streamflow, while a calibrated CFSR-driven model performed worst. The Daymet-driven model performance was as good as the GHCN-D-driven model in reproducing observations. In addition, the temperature was far less sensitive compared with precipitation in driving SWAT. TRMM 3B42V7 showed great potential in streamflow simulation. The results and findings from this study provide new insights into the suitability of precipitation products for hydrological and climate impact studies in large basins, particularly those in typical climates and physiographic settings similar to the Midwestern USA.

Merging Satellite and Gauge Rainfalls for Flood Forecasting of Two Catchments Under Different Climate Conditions

As satellite rainfall data has the advantages of wide spatial coverage and high spatial and temporal resolution, it is an important means to solve the problem of flood forecasting in ungauged basins (PUB). In this paper, two catchments under different conditions, Xin’an River Basin and Wuding River Basin, were selected as the representatives of humid and arid regions, respectively, and four kinds of satellite rainfall data of TRMM 3B42RT, TRMM 3B42V7, GPM IMERG Early, and GPM IMERG Late were selected to evaluate the monitoring accuracy of rainfall processes in the two catchments on hourly scale. Then, these satellite rainfall data were respectively integrated with the gauged data. HEC-HMS (The Hydrologic Engineering Center's-Hydrologic Modeling System) model was calibrated and validated to simulate flood events in the two catchments. Then, improvement effect of the rainfall merging on flood forecasting was evaluated. According to the research results, in most cases, the Nash–Sutcliffe efficiency coefficients of the simulated streamflow from initial TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) satellite rainfall data were negative at the two catchments. By merging gauge and TRMM rainfall, the Nash–Sutcliffe efficiency coefficient is mostly around 0.7, and the correlation coefficient is as high as 0.9 for streamflow simulation in the Xin'an River basin. For the streamflow simulated by merging gauge and GPM rainfall in Wuding River basin, the Nash–Sutcliffe efficiency coefficient is about 0.8, and the correlation coefficient is more than 0.9, which indicate good flood forecasting accuracy. Generally, higher performance statistics were obtained in the Xin'an River Basin than the Wuding River Basin. Compared with the streamflow simulated by the initial satellite rainfalls, significant improvement was obtained by the merged rainfall data, which indicates a good prospect for application of satellite rainfall in hydrological forecasting. In the future, it is necessary to further improve the monitoring accuracy of satellite rainfall products and to develop the method of merging multi-source rainfall data, so as to better applications in PUB and other hydrological researches.

Drought Monitoring Utility using Satellite-Based Precipitation Products over the Xiang River Basin in China

Drought is a natural hazard disaster that can deeply affect environments, economies, and societies around the world. Therefore, accurate monitoring of patterns in drought is important. Precipitation is the key variable to define the drought index. However, the spare and uneven distribution of rain gauges limit the access of long-term and reliable in situ observations. Remote sensing techniques enrich the precipitation data at different temporal–spatial resolutions. In this study, the climate prediction center morphing (CMORPH) technique (CMORPH-CRT), the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TRMM 3B42V7), and the integrated multi-satellite retrievals for global precipitation measurement (IMERG V05) were evaluated and compared with in situ observations for the drought monitoring in the Xiang River Basin, a humid region in China. A widely-used drought index, the standardized precipitation index (SPI), was chosen to evaluate the drought monitoring utility. The atmospheric water deficit (AWD) was used for comparison of the drought estimation with SPI. The results were as follows: (1) IMERG V05 precipitation products showed the highest accuracy against grid-based precipitation, followed by CMORPH-CRT, which performed better than TRMM 3B42V7; (2) IMERG V05 showed the best performance in SPI-1 (one-month SPI) estimations compared with CMORPH-CRT and TRMM 3B42V7; (3) SPI-1 was more suitable for drought monitoring than AWD in the Xiang River Basin, because its high R-values and low root mean square error (RMSE) compared with the corresponding index based on in situ observations; (4) drought conditions in 2015 were apparently more severe than that in 2016 and 2017, with the driest area mainly distributed in the southwest part of the Xiang River Basin.

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 > 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 (>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.

English

Satellite rainfall estimates (SRE) with high spatial and temporal resolution and large areal coverage provide a potential alternative source to force hydrological models within regions where ground-based measurements are not readily available. The Gambia Basin in West Africa provides a good example of a case where the use of satellite precipitation estimates could be beneficial. This study aims to compare three SRE over a 12-year periods (1998-2010), before and after their integration into the GR4J hydrological model over the Gambia Basin. The inter -compared products are Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and TRMM 3B42v7 (Tropical Rainfall Measuring Mission). The calibration and validation of the GR4J model over the Gambia basin using a reference rainfall product (RRP) pointed out a very good performance. The correlation coefficient between simulated and observed daily discharge is higher than 0.8 both for calibration and validation. The inter-comparison of SRE against RRP and using them as forcing data into the calibrated GR4J hydrological model presented some coherence in the product performance. PERSIANN-CDR performs better both when comparing against RRP and when used in GR4J. The low performance of CHIRPS is surprising because it is supposed to be a product that includes ground-base station. This result may also indicate that in areas without ground stations, the CHIRPS is less accurate than other rainfall products that are based only on satellite images. Finally, a bias correction is applied to the SRE using the RRP. The bias correction had significantly improved the product performance. On average, the bias fell from 100 to 1.5% compared to the RRP, but the impact on the error is less significant. When using the corrected SRE in the hydrological model, the impact is very significant both on the bias and error. The overall performance of the different biases that corrected SRE is comparable. Key words: Gambia, precipitation, satellite, evaluation, modeling, bias correction, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission (TRMM).

Evaluation of 3B42V7 and IMERG daily-precipitation products for a very high-precipitation region in northwestern South America

The availability of water is critical in determining the distribution of species by favoring or limiting their development, and leading to the formation of different ecosystems. Thus, analyzing the trends and fluctuations of the precipitation is a key factor to understanding our planet's biodiversity. Different physical conditions exist in northwestern South America that cause extreme climate conditions, especially on the west coast of the continent. This region, known as Biogeographic Chocó, has high annual precipitation caused by interactions between the humid currents of the Pacific Ocean and the Andes Mountains. A limited network of hydro-meteorological stations are available in the region to monitor precipitation. Satellite precipitation products can provide valuable information in the absence of field stations, complementing the network of field stations in remote areas, and completing time series for stations that have stopped working. However, remote sensing data must be validated before being used. The goal of this study is thus to evaluate the accuracy of the products of the Tropical Rainfall Measuring Mission 3B42V7 and the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), under very high precipitation conditions, and evaluate their strengths and shortcomings. These programs provide daily-precipitation information with a spatial resolution of 0.25° for the former and 0.1° for the latter. The validation was done by using a time series of daily-precipitation data obtained from 185 hydro-meteorological stations distributed over the Biogeographic Chocó. Different statistic metrics were used in the evaluation and comparison: error metrics (mean difference MD, relative mean difference RMD, and root mean square error RMSE), a correlation metric (Pearson correlation CP), contingency metrics (probability of detection POD, false-alarm ratio FAR, and critical success index CSI). We also evaluate the grid and areal scale. The results show that (i) the 3B42V7 and IMERG daily-precipitation products represent well the spatial and temporal distribution of mean daily precipitation over the Biographic Chocó, and both products are accurate for detecting precipitation events. (ii) Mean daily precipitation tends to be overestimated in areas with relative low precipitation and medium-to-high altitude whereas, on the contrary, mean daily precipitation tends to be underestimated in areas with very high precipitation and medium-to-low altitude. (iii) Finally, copious precipitation (i.e., an annual accumulated precipitation over 5000 mm, which is common for over 55% of the study area) strongly affects the accuracy of the satellite products, leading to significant errors in estimates of daily precipitation for some regions. This study constitutes one of the first exhaustive validation of the IMERG daily precipitation product over the Biogeographic Chocó, and the results provide important information about the potential for using this product in the study area and over regions with high precipitation.

Validation of TRMM 3B42V7 Rainfall Product under Complex Topographic and Climatic Conditions over Hexi Region in the Northwest Arid Region of China

Continuous and accurate spatiotemporal precipitation data plays an important role in regional climate and hydrology research, particularly in the arid inland regions where rain gauges are sparse and unevenly distributed. The main objective of this study is to evaluate and bias-correct the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 rainfall product under complex topographic and climatic conditions over the Hexi region in the northwest arid region of China with the reference of rain gauge observation data during 2009–2015. A series of statistical indicators were adopted to quantitatively evaluate the error of 3B42V7 and its ability in detecting precipitation events. Overall, the 3B42V7 overestimates the precipitation with Bias of 11.16%, and its performance generally becomes better with the increasing of time scale. The agreements between the rain gauge data and 3B42V7 are very low in cold season, and moderate in warm season. The 3B42V7 shows better correlation with rain gauges located in the southern mountainous and central oasis areas than in the northern extreme arid regions, and is more likely to underestimate the precipitation in high-altitude mountainous areas and overestimate the precipitation in low-elevation regions. The distribution of the error on the daily scale is more related to the elevation and rainfall than in monthly and annual scale. The 3B42V7 significantly overestimates the precipitation events, and the overestimation mainly focuses on tiny amounts of rainfall (0–1 mm/d), which is also the range of false alarm concentration. Bias correction for 3B42V7 was carried out based on the deviation of the average monthly precipitation data during 2009–2015. The bias-corrected 3B42V7 was significantly improved compared with the original product. Results suggest that regional assessment and bias correction of 3B42V7 rainfall product are of vital importance and will provide substantive reference for regional hydrological studies.

Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia

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.

Applicability of TRMM Precipitation for Hydrologic Modeling in a Basin in the Northeast Brazilian Agreste

Abstract Determining precipitation using remote sensing is gaining space in hydrologic studies, helping make up for the lack of data in many regions of Brazil. The products from satellite TRMM (Tropical Rainfall Measuring Mission) are widely applied in studies in Brazil, but there are still few results about their applicability for hydrologic modeling in the Northeast Region, which is characterized by an irregular precipitation regime. The objective of this study is to evaluate the feasibility of using the TRMM 3B42 V7 data for hydrologic modeling in the Japaratuba river basin in Sergipe at three timescales: daily, every ten days, and monthly. The comparative analysis between the rainfall data from rain gauges and TRMM did not indicate satisfactory adequacy at these studied scales, since the TRMM data underestimated the total rainfall for all stations used in the study. However, for the hydrologic modeling, acceptable values were obtained for the efficiency coefficients evaluated only for the ten-day and monthly scales.

Effects of temporal scales and space mismatches on the TRMM 3B42 v7 precipitation product in a remote mountainous area

AbstractThe accurate measurement of precipitation is essential to understanding regional hydrological processes and hydrological cycling. Quantification of precipitation over remote regions such as the Tibetan Plateau is highly unreliable because of the scarcity of rain gauges. The objective of this study is to evaluate the performance of the satellite precipitation product of tropical rainfall measuring mission (TRMM) 3B42 v7 at daily, weekly, monthly, and seasonal scales. Comparison between TRMM grid precipitation and point‐based rain gauge precipitation was conducted using nearest neighbour and bilinear weighted interpolation methods. The results showed that the TRMM product could not capture daily precipitation well due to some rainfall events being missed at short time scales but provided reasonably good precipitation data at weekly, monthly, and seasonal scales. TRMM tended to underestimate the precipitation of small rainfall events (less than 1 mm/day), while it overestimated the precipitation of large rainfall events (greater than 20 mm/day). Consequently, TRMM showed better performance in the summer monsoon season than in the winter season. Through comparison, it was also found that the bilinear weighted interpolation method performs better than the nearest neighbour method in TRMM precipitation extraction.

Evaluation of extreme precipitation based on satellite retrievals over China

The objective of this study is to evaluate satellite precipitation extremes of the Tropical Rainfall Measuring Mission (TRMM) 3B42 Version 7 product over China during the period of 2009–2013. Eight extreme indices are used to characterize precipitation extremes: monthly maximum 1-day precipitation (RX1day), monthly maximum consecutive 2-day precipitation (RX2day), monthly maximum 5-day consecutive precipitation (RX5day), simple daily intensity index (SDII), annual total precipitation amount for the wet days (PRCPTOT), annual wet days (R1), consecutive dry days (CDD), and consecutive wet days (CWD). The precipitation amount for indices RX1day, RX2day, RX5day, and PRCPTOT is well captured by TRMM 3B42-V7, as verified by lower mean relative bias and normalized root mean square error and the high spatial correlation coefficient. In contrast, the performance of TRMM 3B42-V7 in depicting the indices on intensity and duration (i.e., SDII, R1, CDD, and CWD) is not as good as its performance in depicting the precipitation amount indices. TRMM 3B42-V7 can reproduce extreme indices better in eastern China than in western China, and better in summer than in winter. Probability density function is also calculated better for RX1day, RX2day, RX5day, and PRCPTOT than for SDII, R1, CDD, and CWD. Investigation on the monthly time series of RX1day, RX2day, and RX5day at different spatial scales indicates that TRMM 3B42-V7 performs better at the large spatial scale than at the grid cell scale. Caution should be observed when the satellite-based extreme indices are used.

Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high‐density rain gauge network

AbstractThe objective of this study is to evaluate two satellite rainfall products Global Precipitation Measurement Integrated MultisatellitE Retrievals and Tropical Rainfall Measuring Mission 3B42V7 (GPM IMERG and TRMM 3B42V7) in southern Tibetan Plateau region, with special focus on the dependence of products' performance on topography and rainfall intensity. Over 500 in situ rain gauges constitute an unprecedentedly dense rain gauge network over this region and provide an exceptional resource for ground validation of satellite rainfall estimates. Our evaluation centers on the rainy season from May to October in 2014. Results indicate that (1) GPM product outperforms TRMM at all spatial scales and elevation ranges in detecting daily rainfall accumulation; (2) rainfall accumulation over the entire rainy season is negatively correlated with mean elevation for rain gauges and the two satellite rainfall products, while the performance of TRMM also significantly correlates with topographic variations; (3) in terms of the ability of rainfall detection, false alarming ratio of TRMM (21%) is larger than that of GPM (14%), while missing ratio of GPM (13%) is larger than that of TRMM (9%). GPM tends to underestimate the amount of light rain events of 0–1 mm/d, while the opposite (overestimation) is true for TRMM. GPM shows better detecting ability for light rainfall (0–5 mm/d) events but there is no detection skill for both GPM and TRMM at high‐elevation (>4500 m) regions. Our results not only highlight the superiority of GPM to TRMM in southern Tibetan Plateau region but also recommend that further improvement on the rainfall retrieval algorithm is needed by considering topographical influences for both GPM and TRMM rainfall products.

Evaluating the TRMM Multisatellite Precipitation Analysis for Extreme Precipitation and Streamflow in Ganjiang River Basin, China

Based on the observed precipitation data and TRMM (Tropical Rainfall Measuring Mission) 3B42 RTV7 and 3B42 V7 precipitation products from 2003 to 2010, the extreme precipitation and streamflow in the Ganjiang River basin were analyzed. The VIC hydrological model was used to simulate the streamflow driven by RTV7/V7 precipitation products in the Ganjiang River basin. The results show that (1) both of the RTV7 and V7 precipitation products have good applicability in precipitation estimation in the Ganjiang River basin and the correlation between the observed precipitation and RTV7 (V7) was as higher as 0.85 (0.86); (2) the RTV7/V7 precipitation products can well be used to simulate the streamflow by using the VIC hydrological model and the correlation between the observed streamflow and simulated streamflow driven by RTV7 (V7) products was as high as 0.86 (0.89); (3) the extreme precipitation varied greatly in the Ganjiang River basin and both of the RTV7 and V7 can capture the pattern of extreme precipitation in the Ganjiang River basin; however, higher extreme precipitation can be found in the northern Ganjiang River basin; (4) the extreme streamflow simulated driven by RTV7/V7 products agreed well with the observed extreme streamflow in the Ganjiang River basin. This study indicated that the TRMM 3B42 RTV7 and V7 products can be well used in the estimation of extreme precipitation and extreme streamflow.

TRMM卫星降雨数据的精度及径流模拟评估

卫星测雨的精度已成为人们关注的热点，并且在水文研究及应用上有很大的发展前景。本文选取湘江流域为研究区，以地面雨量站点数据为参照，评估了不同时空尺度下热带测雨观测计划(Tropical Rainfall Measuring Mission, TRMM)多卫星降雨分析新产品(TRMM 3B42 V7)的精度，同时分析了精度在不同时期、雨强及高程等因素下的影响规律，并驱动新安江模型展现了径流模拟效果。研究表明：TRMM降水数据与站点数据相关性较好，TRMM数据精度在月尺度明显优于日尺度，流域尺度优于栅格尺度，面雨量精度湿润季节高于干旱季节；雨量估计上TRMM相对站点在流域尺度下整体低估，栅格尺度下受高程影响，在高程较大区域TRMM数据倾向低估，反之倾向高估；各雨强级别下TRMM对小强度降雨稍低估而对强降雨和无雨高估；TRMM数据月径流模拟效果比日径流好，均在可接受范围，但对洪峰、洪量及峰现时间不如站点模拟精准。本研究为TRMM数据的精度订正提供相关思路，并分析其在径流模拟应用中的可行性。 The accuracy of satellite precipitation estimates has become a focus of attention, having a promising prospect in the hydrological researches and applications. In this study, the new precipitation product (3B42 V7) of Tropical Rainfall Measuring Mission (TRMM) was evaluated via comparison with the rain gauge precipitation data in Xiangjiang River Basin. The evaluations were conducted at different spatial and temporal scales. Simultaneously, the laws of data accuracy under different periods, rainfall intensity levels, and elevations were analyzed. These two precipitation data were then adopted to drive the Xinan-jiang Model to perform streamflow simulation. The results showed that TRMM satellite precipitation data had a good correlation with rain gauge precipitation data; it had better accuracy under monthly scale than daily scale and showed better performance at watershed scale than it did at grid scale. The area precipitation of TRMM data showed better accuracy in humid season than in arid season. From an overall estimate on the amount of precipitation, areal precipitation from TRMM data on watershed scale was underestimated in contrast with areal precipitation interpolated from rain gauge data. While at grid scale, TRMM data from higher elevations inclined toward underestimation, while the contrary inclined toward overestimation. The frequency statistical result of different rainfall intensity levels indicated that TRMM tended to underestimate the occurrence of light rainfall intensity but overestimate the occurrence of heavy rainfall intensity and no-rain events. The TRMM-driven streamflow simulation performed better at monthly scale relative to the daily scale, but all were within acceptable range. However, the effects of streamflow simulation driven by TRMM data were not as accurate as those driven by rain gauge data displayed in the aspects of flood peak, flood volume and flood duration. This study assesses the accuracy of the latest product TRMM 3B42 V7, revealing the influential factors regarding accuracy for future reference and analyzing the effectiveness and possibility of streamflow simulation.