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Tian, Yudong, Christa D. Peters-Lidard, Bhaskar J. Choudhury, and Matthew Garcia. "Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications." Journal of Hydrometeorology 8, no. 6 (2007): 1165–83. http://dx.doi.org/10.1175/2007jhm859.1.
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Abstract In this study, the recent work of Gottschalck et al. and Ebert et al. is extended by assessing the suitability of two Tropical Rainfall Measuring Mission (TRMM)-based precipitation products for hydrological land data assimilation applications. The two products are NASA’s gauge-corrected TRMM 3B42 Version 6 (3B42), and the satellite-only NOAA Climate Prediction Center (CPC) morphing technique (CMORPH). The two products were evaluated against ground-based rain gauge–only and gauge-corrected Doppler radar measurements. The analyses were performed at multiple time scales, ranging from annual to diurnal, for the period March 2003 through February 2006. The analyses show that at annual or seasonal time scales, TRMM 3B42 has much lower biases and RMS errors than CMORPH. CMORPH shows season-dependent biases, with overestimation in summer and underestimation in winter. This leads to 50% higher RMS errors in CMORPH’s area-averaged daily precipitation than TRMM 3B42. At shorter time scales (5 days or less), CMORPH has slightly less uncertainty, and about 10%–20% higher probability of detection of rain events than TRMM 3B42. In addition, the satellite estimates detect more high-intensity events, causing a remarkable shift in precipitation spectrum. Summertime diurnal cycles in the United States are well captured by both products, although the 8-km CMORPH seems to capture more diurnal features than the 0.25° CMORPH or 3B42 products. CMORPH tends to overestimate the amplitude of the diurnal cycles, particularly in the central United States. Possible causes for the discrepancies between these products are discussed.
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Li, Xianghu, Zhen Li, and Yaling Lin. "Suitability of TRMM Products with Different Temporal Resolution (3-Hourly, Daily, and Monthly) for Rainfall Erosivity Estimation." Remote Sensing 12, no. 23 (2020): 3924. http://dx.doi.org/10.3390/rs12233924.
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Rainfall erosivity (RE) is a significant indicator of erosion capacity. The application of Tropical Rainfall Measuring Mission (TRMM) rainfall products to deal with RE estimation has not received much attention. It is not clear which temporal resolution of TRMM data is most suitable. This study quantified the RE in the Poyang Lake basin, China, based on TRMM 3B42 3-hourly, daily, and 3B43 monthly rainfall data, and investigated their suitability for estimating RE. The results showed that TRMM 3-hourly product had a significant systematic underestimation of monthly RE, especially during the period of April–June for the large values. The TRMM 3B42 daily product seems to have better performance with the relative bias of 3.0% in summer. At the annual scale, TRMM 3B42 daily and 3B43 monthly data had acceptable accuracy, with mean error of 1858 and −85 MJ∙mm/ha∙h and relative bias of 18.3% and −0.85%, respectively. A spatial performance analysis showed that all three TRMM products generally captured the overall spatial patterns of RE, while the TRMM 3B43 product was more suitable in depicting the spatial characteristics of annual RE. This study provides valuable information for the application of TRMM products in mapping RE and risk assessment of soil erosion.
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Liu, Junzhi, Zheng Duan, Jingchao Jiang, and A.-Xing Zhu. "Evaluation of Three Satellite Precipitation Products TRMM 3B42, CMORPH, and PERSIANN over a Subtropical Watershed in China." Advances in Meteorology 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/151239.
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This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. All the three products were compared and evaluated during a 9-year period at different spatial (grid and watershed) and temporal (daily, monthly, and annual) scales. The results showed that at daily scale, CMORPH had the best performance with coefficients of determination (R2) of 0.61 at grid scale and 0.74 at watershed scale. For precipitation intensities larger than or equal to 25 mm, RMSE% of CMORPH and TRMM 3B42 were less than 50%, indicating CMORPH and TRMM 3B42 might be useful for hydrological applications at daily scale. At monthly and annual temporal scales, TRMM 3B42 had the best performances, with highR2ranging from 0.93 to 0.99, and thus was deemed to be reliable and had good potential for hydrological applications at monthly and annual scales. PERSIANN had the worst performance among the three products at all cases.
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Kneis, D., C. Chatterjee, and R. Singh. "Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)." Hydrology and Earth System Sciences 18, no. 7 (2014): 2493–502. http://dx.doi.org/10.5194/hess-18-2493-2014.
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Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.
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Kneis, D., C. Chatterjee, and R. Singh. "Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi)." Hydrology and Earth System Sciences Discussions 11, no. 1 (2014): 1169–201. http://dx.doi.org/10.5194/hessd-11-1169-2014.
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Abstract. The paper examines the quality of satellite-based precipitation estimates for the Lower Mahanadi River Basin (Eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gage-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gage data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analyzing their performance in the context of rainfall-runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gage-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high intensity levels. The rainfall depth derived from rain gage data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day−1). At the same time, the remotely sensed rainfall rates frequently exceed the gage-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalization of rain gage data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gage data were used as model input (Nash–Sutcliffe Index of 0.76–0.88 at gages not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gage-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.
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Li, Yun, Bin Guo, Kaicun Wang, Guocan Wu, and Chunming Shi. "Performance of TRMM Product in Quantifying Frequency and Intensity of Precipitation during Daytime and Nighttime across China." Remote Sensing 12, no. 4 (2020): 740. http://dx.doi.org/10.3390/rs12040740.
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The Tropical Rainfall Measurement Mission (TRMM) satellite is the first to be designed to measure precipitation, and its precipitation products have been assessed in a variety of ways. Data for its post-real-time level 2 product (3B42) performed well in terms of the precipitation amount at the monthly scale because they were corrected by a precipitation dataset that was gauged every month. However, the performance of this dataset in terms of precipitation frequency and intensity is still not ideal. To this end, TRMM 3B42 products were evaluated using precipitation data from 747 meteorological stations over mainland China in this study. The Pearson’s correlation coefficient (CC), relative bias (RB), and relative error (RE) were used to assess the capability of TRMM products in terms of estimating the frequency, intensity, and amount of precipitation for different categories of precipitation during nighttime and daytime in a multiscale analysis (including interannual variation, seasonal cycles, and spatial distribution). Our results showed the following: (1) The 3B42 products reproduced interannual trends of the frequency and amount of precipitation (except for trace precipitation) with an average correlation coefficient of 0.84. (2) 3B42 performed well at calculating the annual and monthly precipitation amount, but performed poorly for frequency and even worse for intensity. The biases in these two properties canceled out, however, which led to a better estimate of the amount. (3) 3B42 represented the distribution of the subdaily amount of precipitation over a majority of the regions in the east, but did not perform well on the Tibetan Plateau or in northwest China. The performance of 3B42, as detailed in this study, can serve as valuable guidance to data users and algorithm developers.
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Venkatesh, Kolluru, N. Y. Krakauer, E. Sharifi, and H. Ramesh. "Evaluating the Performance of Secondary Precipitation Products through Statistical and Hydrological Modeling in a Mountainous Tropical Basin of India." Advances in Meteorology 2020 (November 16, 2020): 1–23. http://dx.doi.org/10.1155/2020/8859185.
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This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.
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Worqlul, A. W., B. Maathuis, A. A. Adem, S. S. Demissie, S. Langan, and T. S. Steenhuis. "Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground-observed data for the Lake Tana basin in Ethiopia." Hydrology and Earth System Sciences 18, no. 12 (2014): 4871–81. http://dx.doi.org/10.5194/hess-18-4871-2014.
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Abstract. Planning for drought relief and floods in developing countries is greatly hampered by the lack of a sufficiently dense network of weather stations measuring precipitation. In this paper, we test the utility of three satellite products to augment the ground-based precipitation measurement to provide improved spatial estimates of rainfall. The three products are the Tropical Rainfall Measuring Mission (TRMM) product (3B42), Multi-Sensor Precipitation Estimate–Geostationary (MPEG) and the Climate Forecast System Reanalysis (CFSR). The accuracy of the three products is tested in the Lake Tana basin in Ethiopia, where 38 weather stations were available in 2010 with a full record of daily precipitation amounts. Daily gridded satellite-based rainfall estimates were compared to (1) point-observed ground rainfall and (2) areal rainfall in the major river sub-basins of Lake Tana. The result shows that the MPEG and CFSR satellites provided the most accurate rainfall estimates. On average, for 38 stations, 78 and 86% of the observed rainfall variation is explained by MPEG and CFSR data, respectively, while TRMM explained only 17% of the variation. Similarly, the areal comparison indicated a better performance for both MPEG and CFSR data in capturing the pattern and amount of rainfall. MPEG and CFSR also have a lower root mean square error (RMSE) compared to the TRMM 3B42 satellite rainfall. The bias indicated that TRMM 3B42 was, on average, unbiased, whereas MPEG consistently underestimated the observed rainfall. CFSR often produced large overestimates.
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AL-Falahi, Ali Hamoud, Naeem Saddique, Uwe Spank, Solomon H. Gebrechorkos, and Christian Bernhofer. "Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management." Remote Sensing 12, no. 18 (2020): 2984. http://dx.doi.org/10.3390/rs12182984.
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Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.
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Kamahori, Hirotaka. "Mean Features of Tropical Cyclone Precipitation from TRMM/3B42." SOLA 8 (2012): 17–20. http://dx.doi.org/10.2151/sola.2012-005.
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Bajracharya, Sagar Ratna, Wahid Palash, Mandira Singh Shrestha, et al. "Systematic Evaluation of Satellite-Based Rainfall Products over the Brahmaputra Basin for Hydrological Applications." Advances in Meteorology 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/398687.
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Estimation of the flow generated in the Brahmaputra river basin is important for establishing an effective flood prediction and warning services as well as for water resources assessment and management. But this is a data scarce region with few and unevenly distributed hydrometeorological stations. Five high-resolution satellite rainfall products (CPC RFE2.0, RFE2.0-Modified, CMORPH, GSMaP, and TRMM 3B42) were evaluated at different spatial and temporal resolutions (daily, dekadal, monthly, and seasonal) with observed rain gauge data from 2004 to 2006 to determine their ability to fill the data gap and suitability for use in hydrological and water resources management applications. Grid-to-grid (G-G) and catchment-to-catchment (C-C) comparisons were performed using the verification methods developed by the International Precipitation Working Group (IPWG). Comparing different products, RFE2.0-Modified, TRMM 3B42, and CMORPH performed best; they all detected heavy, moderate, and low rainfall but still significantly underestimated magnitude of rainfall, particularly in orographically influenced areas. Overall, RFE2.0-Modified performed best showing a high correlation coefficient with observed data and low mean absolute error, root mean square error, and multiple bias and is reasonably good at detecting the occurrence of rainfall. TRMM 3B42 showed the second best performance. The study demonstrates that there is a potential use of satellite rainfall in a data scarce region.
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Zhang, Yueyuan, Yungang Li, Xuan Ji, Xian Luo, and Xue Li. "Evaluation and Hydrologic Validation of Three Satellite-Based Precipitation Products in the Upper Catchment of the Red River Basin, China." Remote Sensing 10, no. 12 (2018): 1881. http://dx.doi.org/10.3390/rs10121881.
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Satellite-based precipitation products (SPPs) provide alternative precipitation estimates that are especially useful for sparsely gauged and ungauged basins. However, high climate variability and extreme topography pose a challenge. In such regions, rigorous validation is necessary when using SPPs for hydrological applications. We evaluated the accuracy of three recent SPPs over the upper catchment of the Red River Basin, which is a mountain gorge region of southwest China that experiences a subtropical monsoon climate. The SPPs included the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 product, the Climate Prediction Center (CPC) Morphing Algorithm (CMORPH), the Bias-corrected product (CMORPH_CRT), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (PERSIANN_CDR) products. SPPs were compared with gauge rainfall from 1998 to 2010 at multiple temporal (daily, monthly) and spatial scales (grid, basin). The TRMM 3B42 product showed the best consistency with gauge observations, followed by CMORPH_CRT, and then PERSIANN_CDR. All three SPPs performed poorly when detecting the frequency of non-rain and light rain events (<1 mm); furthermore, they tended to overestimate moderate rainfall (1–25 mm) and underestimate heavy and hard rainfall (>25 mm). GR (Génie Rural) hydrological models were used to evaluate the utility of the three SPPs for daily and monthly streamflow simulation. Under Scenario I (gauge-calibrated parameters), CMORPH_CRT presented the best consistency with observed daily (Nash–Sutcliffe efficiency coefficient, or NSE = 0.73) and monthly (NSE = 0.82) streamflow. Under Scenario II (individual-calibrated parameters), SPP-driven simulations yielded satisfactory performances (NSE >0.63 for daily, NSE >0.79 for monthly); among them, TRMM 3B42 and CMORPH_CRT performed better than PERSIANN_CDR. SPP-forced simulations underestimated high flow (18.1–28.0%) and overestimated low flow (18.9–49.4%). TRMM 3B42 and CMORPH_CRT show potential for use in hydrological applications over poorly gauged and inaccessible transboundary river basins of Southwest China, particularly for monthly time intervals suitable for water resource management.
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Dezfooli, Donya, Banafsheh Abdollahi, Seyed-Mohammad Hosseini-Moghari, and Kumars Ebrahimi. "A comparison between high-resolution satellite precipitation estimates and gauge measured data: case study of Gorganrood basin, Iran." Journal of Water Supply: Research and Technology-Aqua 67, no. 3 (2018): 236–51. http://dx.doi.org/10.2166/aqua.2018.062.
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Abstract The aim of this paper is to evaluate the accuracy of the precipitation data gathered from satellites including PERSIANN, TRMM-3B42V7, TRMM-3B42RTV7, and CMORPH, over Gorganrood basin, Iran. The data collected from these satellites (2003–2007) were then compared with precipitation gauge observations at six stations, namely, Tamar, Ramiyan, Bahlakeh-Dashli, Sadegorgan, Fazel-Abad, and Ghaffar-Haji. To compare these two groups, mean absolute error (MAE), bias, root mean square error (RMSE), and Pearson correlation coefficient criteria were calculated on daily, monthly, and seasonal basis. Furthermore, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were calculated for these datasets. Results indicate that, on a monthly scale, the highest correlation between observed and satellite-gathered data calculated is 0.404 for TRMM-3B42 at Bahlakeh-Dashli station. At a seasonal scale, the highest correlation is calculated for winter data and using PERSIANN data, while for the other seasons, TRMM-3B42 data showed the best correlation with observed data. The high values of RMSE and MAE for winter data showed that the satellites provided poor estimations at this season. The best and the worst values of RMSE for studied satellites belonged to Sadegorgan and Ramiyan stations, respectively. Furthermore, the PERSIANN gains a better CSI and POD while TRMM-3B42V7 showed a better FAR.
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Shrivastava, R., S. K. Dash, M. N. Hegde, K. S. Pradeepkumar, and D. N. Sharma. "Validation of the TRMM Multi Satellite Rainfall Product 3B42 and estimation of scavenging coefficients for 131I and 137Cs using TRMM 3B42 rainfall data." Journal of Environmental Radioactivity 138 (December 2014): 132–36. http://dx.doi.org/10.1016/j.jenvrad.2014.08.011.
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Chokngamwong, Roongroj, and Long S. Chiu. "Thailand Daily Rainfall and Comparison with TRMM Products." Journal of Hydrometeorology 9, no. 2 (2008): 256–66. http://dx.doi.org/10.1175/2007jhm876.1.
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Abstract Daily rainfall data collected from more than 100 gauges over Thailand for the period 1993–2002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data binned at 1° × 1° with the Global Precipitation Climatology Centre (GPCC) monitoring product shows a small bias (1.11%), and the differences can be reconciled in terms of the increased number of stations in the TG dataset. Comparison of daily TG with Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 rain estimates shows improvements over version 5 (V5) in terms of bias and mean absolute difference (MAD). The V5 is computed from the adjusted Geostationary Operational Environmental Satellite (GOES) precipitation index (AGPI) and V6 is computed using the TRMM Multisatellite Precipitation Analysis (TMPA) algorithm. The V6 histogram is much closer to that of TG than V5 in terms of rain fraction and conditional rain rates. Scatterplots show that both versions of the satellite products are deficient in capturing heavy rain events. In terms of detecting rain events, a critical success index (CSI) shows no difference between V6 and V5 3B42. The CSI for V6 is higher for the rainy season than the dry season. These results are generally insensitive to rain-rate threshold and averaging periods. The temporal and spatial autocorrelation of daily rain rates for TG, V6, and V5 3B42 are computed. Autocorrelation function analyses show improved temporal and spatial autocorrelations for V6 compared to TG over V5 with e-folding times of 1, 1, and 2 days, and isotropic spatial decorrelation distances of 1.14°, 1.87°, and 3.61° for TG, V6, and V5, respectively. Rain event statistics show that the V6 3B42 overestimates the rain event durations and underestimates the rain event separations and the event conditional rain rates when compared to TG. This study points to the need to further improve the 3B42 algorithm to lower the false detection rate and improve the estimation of heavy rainfall events.
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Sekaranom, Andung Bayu, Emilya Nurjani, M. Pramono Hadi, and Muh Aris Marfai. "Comparsion of TRMM Precipitation Satellite Data over Central Java Region – Indonesia." Quaestiones Geographicae 37, no. 3 (2018): 97–114. http://dx.doi.org/10.2478/quageo-2018-0028.
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Abstract This research aims to compare precipitation data derived from satellite observation and ground measurements through a dense station network over Central Java, Indonesia. A precipitation estimate from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 Version 7 are compared with precipitation data from interpolated rain gauge stations. Correlation analysis, mean bias error (MBE), and root mean square error (RMSE) were utilized in the analysis for each thee-monthly seasonal statistics. The result shows that the 3B42 products often estimate lower rainfall than observed from weather stations in the peak of the rainy season (DJF). Further, it is revealed that the 3B42 product are less robust in estimating rainfall at high elevation, especially when humid environment, which is typical during the rainy season peak, are involved.
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He, Zhihua, Long Yang, Fuqiang Tian, Guangheng Ni, Aizhong Hou, and Hui Lu. "Intercomparisons of Rainfall Estimates from TRMM and GPM Multisatellite Products over the Upper Mekong River Basin." Journal of Hydrometeorology 18, no. 2 (2017): 413–30. http://dx.doi.org/10.1175/jhm-d-16-0198.1.
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Abstract The aim of this study is to evaluate the accuracy of daily rainfall estimates based on the GPM level-3 final product derived from the IMERG algorithm (abbreviated as IMERG) and TRMM 3B42, version 7 (abbreviated as 3B42), in the upper Mekong River basin, a mountainous region in southwestern China. High-density rain gauges provide exceptional resources for ground validation of satellite rainfall estimates over this region. The performance of the two satellite rainfall products is evaluated during two rainy seasons (May–October) over the period 2014–15, as well as their applications in hydrological simulations. Results indicate that 1) IMERG systematically reduces the bias value in rainfall estimates at the gridbox scale and presents a greater ability to capture rainfall variability at the local domain scale compared with 3B42; 2) IMERG improves the ability to capture rain events with moderate intensities and presents higher capability in detecting occurrences of extreme rain events, but significantly overestimates the amounts of these extreme events; and 3) IMERG generally produces comparable daily streamflow simulations to 3B42 and tends to outperform 3B42 in driving hydrological simulations when calibrating model parameters using each rainfall input. This study provides an early evaluation of the IMERG rainfall product over a mountainous region. The findings indicate the potential of the IMERG product in overestimating extreme rain events, which could serve as the basis for further improvement of IMERG rainfall retrieval algorithms. The hydrological evaluations described here could shed light on the emerging application of retrospectively generated IMERG products back to the TRMM era.
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Cohen Liechti, T., J. P. Matos, J. L. Boillat, and A. J. Schleiss. "Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin." Hydrology and Earth System Sciences 16, no. 2 (2012): 489–500. http://dx.doi.org/10.5194/hess-16-489-2012.
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Abstract. In the framework of the African DAms ProjecT (ADAPT), an integrated water resource management study in the Zambezi Basin is currently under development. In view of the sparse gauging network for rainfall monitoring, the observations from spaceborne instrumentation currently produce the only available rainfall data for a large part of the basin. Three operational and acknowledged high resolution satellite derived estimates: the Tropical Rainfall Measuring Mission product 3B42 (TRMM 3B42), the Famine Early Warning System product 2.0 (FEWS RFE2.0) and the National Oceanic and Atmospheric Administration/Climate Prediction Centre (NOAA/CPC) morphing technique (CMORPH) are analyzed in terms of spatial and temporal repartition of the precipitations. They are compared to ground data for the wet seasons of the years 2003 to 2009 on a point to pixel basis at daily, 10-daily and monthly time steps and on a pixel to pixel basis for the wet seasons of the years 2003 to 2007 at monthly time steps. The general North-South gradient of precipitation is captured by all the analyzed products. Regarding the spatial heterogeneity, FEWS pixels are much more inter-correlated than TRMM and CMORPH pixels. For a rainfall homogeneity threshold criterion of 0.5 global mean correlation coefficient, the area of each sub-basin should not exceed a circle of 2.5° latitude/longitude radius for FEWS and a circle of 0.75° latitude/longitude radius for TRMM and CMORPH considering rectangular meshes. In terms of reliability, the correspondence of all estimates with ground data increases with the time step chosen for the analysis. The volume ratio computation indicates that CMORPH is overestimating the rainfall by nearly 50%. The statistics of TRMM and FEWS estimates show quite similar results. Due to its lower inter-correlation and longer data set, the TRMM 3B42 product is chosen as input for the hydraulic-hydrologic model of the basin. Further work will focus on the calibration of the hydraulic-hydrological model of the basin, including the major existing hydraulic structures with their operation rules.
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Cohen Liechti, T., J. P. Matos, J. L. Boillat, and A. J. Schleiss. "Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin." Hydrology and Earth System Sciences Discussions 8, no. 4 (2011): 8173–201. http://dx.doi.org/10.5194/hessd-8-8173-2011.
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Abstract. In the framework of the African Dams ProjecT (ADAPT), an integrated water resource management study in the Zambezi Basin is currently under development. In view of the sparse gauging network for rainfall monitoring, the observations from spaceborne instrumentation currently produce the only available rainfall data for a large part of the basin. Three operational and acknowledged high resolution satellite derived estimates: the Tropical Rainfall Measuring Mission product 3B42 (TRMM 3B42), the Famine Early Warning System product 2.0 (FEWS RFE2.0) and the National Oceanic and Atmospheric Administration/Climate Prediction Centre (NOAA/CPC) morphing technique (CMORPH) are analyzed in terms of spatial and temporal repartition of the precipitations. They are compared to ground data for the wet seasons of the years 2003 to 2009 on a point to pixel basis at daily, 10-daily and monthly time steps and on a pixel to pixel basis for the wet seasons of the years 2003 to 2007 at monthly time steps. The general North-South gradient of precipitation is captured by all the analyzed products. Regarding the spatial heterogeneity, FEWS pixels are much more inter-correlated than TRMM and CMORPH pixels. For a rainfall homogeneity threshold criterion of 0.5 global mean correlation coefficient, the area of each subbasin should not exceed a circle of 2.5° latitude/longitude radius for FEWS and a circle of 0.75° latitude/longitude radius for TRMM and CMORPH considering rectangular mesh. In terms of reliability, the correspondence of all estimates with ground data increases with the time step chosen for the analysis. The volume ratio computation indicates that CMORPH is overestimating by nearly 1.5 times the rainfall. The statistics of TRMM and FEWS estimates show quite similar results. Due to the its lower inter-correlation and longer data set, the TRMM 3B42 product is chosen as input for the hydraulic-hydrologic model of the basin. Further work will focus on the calibration of the hydraulic-hydrological model of the basin, including the major existing hydraulic structures with their operation rules.
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Fensterseifer, Cesar, Daniel G. Allasia, and Adriano R. Paz. "Assessment of the TRMM 3B42 Precipitation Product in Southern Brazil." JAWRA Journal of the American Water Resources Association 52, no. 2 (2016): 367–75. http://dx.doi.org/10.1111/1752-1688.12398.
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Michot, Véronique, Daniel Vila, Damien Arvor, et al. "Performance of TRMM TMPA 3B42 V7 in Replicating Daily Rainfall and Regional Rainfall Regimes in the Amazon Basin (1998–2013)." Remote Sensing 10, no. 12 (2018): 1879. http://dx.doi.org/10.3390/rs10121879.
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Knowledge and studies on precipitation in the Amazon Basin (AB) are determinant for environmental aspects such as hydrology, ecology, as well as for social aspects like agriculture, food security, or health issues. Availability of rainfall data at high spatio-temporal resolution is thus crucial for these purposes. Remote sensing techniques provide extensive spatial coverage compared to ground-based rainfall data but it is imperative to assess the quality of the estimates. Previous studies underline at regional scale in the AB, and for some years, the efficiency of the Tropical Rainfall Measurement Mission (TRMM) 3B42 Version 7 (V7) (hereafter 3B42) daily product data, to provide a good view of the rainfall time variability which is important to understand the impacts of El Nino Southern Oscilation. Then our study aims to enhance the knowledge about the quality of this product on the entire AB and provide a useful understanding about his capacity to reproduce the annual rainfall regimes. For that purpose we compared 3B42 against 205 quality-controlled rain gauge measurements for the period from March 1998 to July 2013, with the aim to know whether 3B42 is reliable for climate studies. Analysis of quantitative (Bias, Relative RMSE) and categorical statistics (POD, FAR) for the whole period show a more accurate spatial distribution of mean daily rainfall estimations in the lowlands than in the Andean regions. In the latter, the location of a rain gauge and its exposure seem to be more relevant to explain mismatches with 3B42 rather than its elevation. In general, a good agreement is observed between rain gauge derived regimes and those from 3B42; however, performance is better in the rainy period. Finally, an original way to validate the estimations is by taking into account the interannual variability of rainfall regimes (i.e., the presence of sub-regimes): four sub-regimes in the northeast AB defined from rain gauges and 3B42 were found to be in good agreement. Furthermore, this work examined whether TRMM 3B42 V7 rainfall estimates for all the grid points in the AB, outgoing longwave radiation (OLR) and water vapor flux patterns are consistent in the northeast of AB.
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Wei, Guanghua, Haishen Lü, Wade T. Crow, Yonghua Zhu, Jianqun Wang, and Jianbin Su. "Comprehensive Evaluation of GPM-IMERG, CMORPH, and TMPA Precipitation Products with Gauged Rainfall over Mainland China." Advances in Meteorology 2018 (December 4, 2018): 1–18. http://dx.doi.org/10.1155/2018/3024190.
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The comprehensive assessment of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) V05B is important for benchmarking the product’s continued improvement and future development. The performance of IMERG V05B precipitation products was systematically evaluated using 542 precipitation gauges at multiple spatiotemporal scales from March 2014 to February 2017 over China. Moreover, IMERG V05B was compared with IMERG V04A, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Climate Prediction Center Morphing technique (CMORPH)-CRT in this study. Categorical verification techniques and statistical methods are used to quantify their performance. Results illustrate the following. (1) Except for IMERG V04A’s severe underestimation over the Tibetan Plateau (TP) and Xinjiang (XJ) with high negative relative biases (RBs) and CMORPH-CRT’s overestimation over XJ with high positive RB, the four satellite-based precipitation products generally capture the same spatial patterns of precipitation over China. (2) At the annual scale over China, the IMERG products do not show an advantage over its predecessor (TRMM 3B42) in terms of RMSEs, RRMSEs, and Rs; meanwhile, the performance of IMERG products is worse than TRMM 3B42 in spring and summer according to the RMSE, RRMSE, and R metrics. Between the two IMERG products, IMERG V05B shows the anticipated improvement (over IMERG V04A) with a decrease in RMSE from 0.4496 to 0.4097 mm/day, a decrease of RRMSE from 16.95% to 15.44%, and an increase of R from 0.9689 to 0.9759 during the whole study period. Similar results are obtained at the seasonal scale. Among the four satellite products, CMORPH-CRT shows the worst seasonal performance with the highest RMSE (0.6247 mm/day), RRMSE (23.55%), and lowest R (0.9343) over China. (3) Over XJ and TP, IMERG V05B clearly improves the strong underestimation of precipitation in IMERG V04A with the RBs of 5.2% vs. −21.8% over XJ, and 2.78% vs. −46% over TP. Results at the annual scale are similar to those obtained at the seasonal scale, except for summer results over XJ. While, over the remaining subregions, the two IMERG products have a close performance; meanwhile, IMERG V04A slightly improves IMERG V05B’s overestimation according to RBs (except for HN) at the annual scale. However, all four products are unreliable over XJ at both an annual and seasonal scale. (4) Across all products, TRMM 3B42 best reproduces the probability density function (PDF) of daily precipitation intensity. (5) According to the categorical verification technique in this study, both IMERG products yield better results for the detection of precipitation events on the basis of probability of detection (POD) and critical success index (CSI) categorical evaluations compared to TRMM 3B42 and CMORPH-CRT over China and across most of the subregions. However, all four products have room for further improvement, especially in high-latitude and dry climate regions. These findings provide valuable feedback for both IMERG algorithm developers and data set users.
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Munzimi, Yolande A., Matthew C. Hansen, Bernard Adusei, and Gabriel B. Senay. "Characterizing Congo Basin Rainfall and Climate Using Tropical Rainfall Measuring Mission (TRMM) Satellite Data and Limited Rain Gauge Ground Observations." Journal of Applied Meteorology and Climatology 54, no. 3 (2015): 541–55. http://dx.doi.org/10.1175/jamc-d-14-0052.1.
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AbstractQuantitative understanding of Congo River basin hydrological behavior is poor because of the basin’s limited hydrometeorological observation network. In cases such as the Congo basin where ground data are scarce, satellite-based estimates of rainfall, such as those from the joint NASA/JAXA Tropical Rainfall Measuring Mission (TRMM), can be used to quantify rainfall patterns. This study tests and reports the use of limited rainfall gauge data within the Democratic Republic of Congo (DRC) to recalibrate a TRMM science product (TRMM 3B42, version 6) in characterizing precipitation and climate in the Congo basin. Rainfall estimates from TRMM 3B42, version 6, are compared and adjusted using ground precipitation data from 12 DRC meteorological stations from 1998 to 2007. Adjustment is achieved on a monthly scale by using a regression-tree algorithm. The output is a new, basin-specific estimate of monthly and annual rainfall and climate types across the Congo basin. This new product and the latest version-7 TRMM 3B43 science product are validated by using an independent long-term dataset of historical isohyets. Standard errors of the estimate, root-mean-square errors, and regression coefficients r were slightly and uniformly better with the recalibration from this study when compared with the 3B43 product (mean monthly standard errors of 31 and 40 mm of precipitation and mean r2 of 0.85 and 0.82, respectively), but the 3B43 product was slightly better in terms of bias estimation (1.02 and 1.00). Despite reasonable doubts that have been expressed in studies of other tropical regions, within the Congo basin the TRMM science product (3B43) performed in a manner that is comparable to the performance of the recalibrated product that is described in this study.
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Zhang, Man, Sheng Chen, You Cun Qi, and Yi Yang. "Evaluation of TRMM Summer Precipitation over Huai-River Basin in China." Advanced Materials Research 726-731 (August 2013): 3401–6. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.3401.
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In this study, the biases and uncertainty of TRMM 3B42 estimates are investigated over the Huai-River Basin during the summer season in 2010. TRMM products of 3B42RT, 3B42V6 and 3B42V7 are cross-compared to Chinese Precipitation Analyses Products (CPAP) as the reference. It is found that the distribution of bias is closely depend on the terrain with the dry bias locates at hills/mountains and wet bias lies on the plains area. It is concluded that the bias may be caused by the defect of TRMM algorithm which cannot discern different types of precipitation. 3B42V7 product shows the best improvement in reducing both wet and dry bias; it also appears small uncertainty on summer season precipitation estimate.
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Wu, Yifan, Zengxin Zhang, Yuhan Huang, Qiu Jin, Xi Chen, and Juan Chang. "Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products in the Yangtze River Basin, China." Water 11, no. 7 (2019): 1459. http://dx.doi.org/10.3390/w11071459.
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The purpose of this study is to quantitatively evaluate the accuracy of the GPM IMERG v5 and the TRMM 3B42 v7, with the reference of 224 rain gauge stations over the Yangtze River basin in China from April 2014 to December 2017. The results showed that: (1) The changing pattern of IMERG v5 was similar to the 3B42 v7, and higher correlations can be found between the satellite-based precipitation products (SPPs) and observed precipitation for the monthly and annual time scale; (2) the IMERG v5 tended to overestimate the distribution range of the main rain band while the 3B42 v7 underestimated the precipitation in Sichuan basin, and the largest differences were found for the precipitation less than 1 mm/d for two SPPs; (3) both of the IMERG v5 and 3B42 v7 overestimated the precipitation in the lower elevation areas (<3000 m), while the opposite was true for areas ≥ 3000 m (RBIMERG v5 = −5.42%, RB3B42 v7 = −1.87%), and the retrieved results of PPDFc index and average precipitation at different altitudes for IMERG v5 were better than 3B42 v7. This study highlighted that IMERG v5 performed generally better than 3B42 v7 in detecting precipitation, especially light precipitation in the Yangtze River basin, indicating the great potential utility in hydrological applications. However, its poor skills when retrieving data for high precipitation events and for detecting complex terrain environments remains, leaving room for IMERG v5 to improve its inversion algorithm.
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De Jesús, Aurea, Jose Breña-Naranjo, Adrián Pedrozo-Acuña, and Victor Alcocer Yamanaka. "The Use of TRMM 3B42 Product for Drought Monitoring in Mexico." Water 8, no. 8 (2016): 325. http://dx.doi.org/10.3390/w8080325.
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Venugopal, V., and J. M. Wallace. "Climatology of contribution-weighted tropical rain rates based on TRMM 3B42." Geophysical Research Letters 43, no. 19 (2016): 10,439–10,447. http://dx.doi.org/10.1002/2016gl069909.
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Arshad, Muhammad, Xieyao Ma, Jun Yin, et al. "Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan." Atmospheric Research 249 (February 2021): 105341. http://dx.doi.org/10.1016/j.atmosres.2020.105341.
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Jiang, Shanshan, Zengxin Zhang, Yuhan Huang, Xi Chen, and Sheng Chen. "Evaluating the TRMM Multisatellite Precipitation Analysis for Extreme Precipitation and Streamflow in Ganjiang River Basin, China." Advances in Meteorology 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2902493.
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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.
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Stampoulis, Dimitrios, and Emmanouil N. Anagnostou. "Evaluation of Global Satellite Rainfall Products over Continental Europe." Journal of Hydrometeorology 13, no. 2 (2012): 588–603. http://dx.doi.org/10.1175/jhm-d-11-086.1.
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Abstract An extensive evaluation of two global-scale high-resolution satellite rainfall products is performed using 8 yr (2003–10) of reference rainfall data derived from a network of rain gauges over Europe. The comparisons are performed at a daily temporal scale and 0.25° spatial grid resolution. The satellite rainfall techniques investigated in this study are the Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 (gauge-calibrated version) and the Climate Prediction Center morphing technique (CMORPH). The intercomparison and validation of these satellite products is performed both qualitatively and quantitatively. In the qualitative part of the analysis, error maps of various validation statistics are shown, whereas the quantitative analysis provides information about the performance of the satellite products relative to the rainfall magnitude or ground elevation. Moreover, a time series analysis of certain error statistics is used to depict the temporal variations of the accuracy of the two satellite techniques. The topographical and seasonal influences on the performance of the two satellite products over the European domain are also investigated. The error statistics presented herein indicate that both orography and seasonal variability affect the efficiency of the satellite rainfall retrieval techniques. Specifically, both satellite techniques underestimate rainfall over higher elevations, especially during the cold season, and their performance is subject to seasonal changes. A significant difference between the two satellite products is that TRMM 3B42 V6 generally overestimates rainfall, while CMORPH underestimates it. CMORPH’s mean error is shown to be of higher magnitude than that of 3B42 V6, while in terms of random error variance, CMORPH exhibits lower (higher) values than those of 3B42 V6 in the winter (summer) months.
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Osei-Kwarteng, Josephine, Qiong Fang Li, and Kwaku Amaning Adjei. "Comparison of TRMM Data with Rain Gauge Observations in the Upper Huaihe River Basin of China." Advanced Materials Research 726-731 (August 2013): 3385–90. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.3385.
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In this study, the Tropical Rainfall Measuring Mission (TRMM) version 7 satellite rainfall product, TRMM 3B42 (V7), was validated using rain gauge measurements in the Upper Huaihe Basin, China. This validation was carried out at monthly and annual temporal scales for an 11-year period using four selected grids with six, four, two and one rain gauge station (s) located within the TRMM grid respectively; the rain gage measurements for grids with more than one rain gauge were averaged. This study found that the validation of the TRMM dataset in grids where there were adequate rain gauge were present to capture the distributed and stochastic nature of rainfall with very good correlation (0.87-0.94) and with very little relative bias when the rain gage accumulations were compared with the TRMM estimates. From the study we found that the TRMM dataset can be used as precipitation input for hydrological modeling at monthly and annual scales for sustainable water resources management in the Upper Huaihe River and even in un-gaged or sparsely gaged basins in other parts of the world.
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Rana, Sapna, James McGregor, and James Renwick. "Precipitation Seasonality over the Indian Subcontinent: An Evaluation of Gauge, Reanalyses, and Satellite Retrievals." Journal of Hydrometeorology 16, no. 2 (2015): 631–51. http://dx.doi.org/10.1175/jhm-d-14-0106.1.
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Abstract This paper evaluates the seasonal (winter, premonsoon, monsoon, and postmonsoon) performance of seven precipitation products from three different sources: gridded station data, satellite-derived data, and reanalyses products over the Indian subcontinent for a period of 10 years (1997/98–2006/07). The evaluated precipitation products are the Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE), the Climate Prediction Center unified (CPC-uni), the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM) post-real-time research products (3B42-V6 and 3B42-V7), the Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim). Several verification measures are employed to assess the accuracy of the data. All datasets capture the large-scale characteristics of the seasonal mean precipitation distribution, albeit with pronounced seasonal and/or regional differences. Compared to APHRODITE, the gauge-only (CPC-uni) and the satellite-derived precipitation products (GPCP, 3B42-V6, and 3B42-V7) capture the summer monsoon rainfall variability better than CFSR and ERA-Interim. Similar conclusions are drawn for the postmonsoon season, with the exception of 3B42-V7, which underestimates postmonsoon precipitation. Over mountainous regions, 3B42-V7 shows an appreciable improvement over 3B42-V6 and other gauge-based precipitation products. Significantly large biases/errors occur during the winter months, which are likely related to the uncertainty in observations that artificially inflate the existing error in reanalyses and satellite retrievals.
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Zhang, Yin, Gulimire Hanati, Sulitan Danierhan, Qianqian Liu, and Zhiyuan Xu. "Evaluation and Comparison of Daily GPM/TRMM Precipitation Products over the Tianshan Mountains in China." Water 12, no. 11 (2020): 3088. http://dx.doi.org/10.3390/w12113088.
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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.
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Bhati, Dinesh Singh, Swatantra Kumar Dubey, and Devesh Sharma. "Application of Satellite-Based and Observed Precipitation Datasets for Hydrological Simulation in the Upper Mahi River Basin of Rajasthan, India." Sustainability 13, no. 14 (2021): 7560. http://dx.doi.org/10.3390/su13147560.
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Hydrological modeling is an important tool used for basin management and studying the impacts of extreme events in a river basin. In streamflow simulations, precipitation plays an essential role in hydrological models. Meteorological satellite precipitation measurement techniques provide highly accurate rainfall information with high spatial and temporal resolution. In this analysis, the tropical rainfall monitoring mission (TRMM) 3B42 V7 precipitation products were employed for simulating streamflow by using the soil water assessment tool (SWAT) model. With India Metrological Department and TRMM data, the SWAT model can be used to predict streamflow discharge and identify sensitive parameters for the Mahi basin. The SWAT model was calibrated for 2 years and then independently validated for 2 years by comparing observed and simulated streamflow. A strong correlation was observed between the calibration and validation results for the Paderdibadi station, with a Nash–Sutcliffe efficiency of >0.34 and coefficient of determination (R2) of >0.77. The SWAT model was used to adequately simulate the streamflow for the Upper Mahi basin with a satisfactory R2 value. The analysis indicated that TRMM 3B42 V7 is useful in SWAT applications for predicting streamflow and performance and for sensitivity analysis. In addition, satellite data may require correction before its utilization in hydrological modeling. This study is helpful for stakeholders in monitoring and managing agricultural, climatic, and environmental changes.
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Ren, Meifang, Zongxue Xu, Bo Pang, et al. "Assessment of Satellite-Derived Precipitation Products for the Beijing Region." Remote Sensing 10, no. 12 (2018): 1914. http://dx.doi.org/10.3390/rs10121914.
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Performance of four satellite precipitation products, namely, the China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing technique (CMORPH), as well as 3B42 calibrated and 3B42-RT dataset, which are derived from the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA), were evaluated from daily to annual temporal scales over Beijing, using observations from 36 ground meteorological stations. Five statistical properties and three categorical metrics were used to test the results. The assessment showed that all four satellite precipitation products captured the temporal variability of precipitation. Although four satellite precipitation products captured the trend of more precipitation in the northeastern regions, all four products showed different distribution from the observations for 2001–2015 over Beijing. All precipitation products tended to overestimate moderate precipitation events and underestimate heavy precipitation events over Beijing, except for 3B42RT, which tended to overestimate most precipitation events. By comparison, the CMFD performed better than the CMORPH, 3B42 calibrated, and 3B42-RT datasets, having the higher correlation coefficient and low root mean squared difference, and mean absolute difference at all temporal scales. The average correlation coefficient of the CMFD, CMORPH, 3B42 calibrated, and 3B42-RT products for all 36 stations were 0.70, 0.60, 0.59, and 0.54 for daily precipitation and 0.78, 0.32, 0.74, and 0.44 for monthly precipitation. Overall, the CMFD was the most reliable for the Beijing region.
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Ahmed, Ehtesham, Firas Al Janabi, Jin Zhang, Wenyu Yang, Naeem Saddique, and Peter Krebs. "Hydrologic Assessment of TRMM and GPM-Based Precipitation Products in Transboundary River Catchment (Chenab River, Pakistan)." Water 12, no. 7 (2020): 1902. http://dx.doi.org/10.3390/w12071902.
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Water resources planning and management depend on the quality of climatic data, particularly rainfall data, for reliable hydrological modeling. This can be very problematic in transboundary rivers with limited disclosing of data among the riparian countries. Satellite precipitation products are recognized as a promising source to substitute the ground-based observations in these conditions. This research aims to assess the feasibility of using a satellite-based precipitation product for better hydrological modeling in an ungauged and riparian river in Pakistan, i.e., the Chenab River. A semidistributed hydrological model of The soil and water assessment tool (SWAT) was set up and two renowned satellite precipitation products, i.e., global precipitation mission (GPM) IMERG-F v6 and tropical rainfall measuring mission (TRMM) 3B42 v7, were selected to assess the runoff pattern in Chenab River. The calibration was done from 2001–2006 with two years of a warmup period. The validation (2007–2010) results exhibit higher correlation between observed and simulated discharges at monthly timescale simulations, IMERG-F (R2 = 0.89, NSE = 0.82), 3B42 (R2 = 0.85, NSE = 0.72), rather than daily timescale simulations, IMERG-F (R2 = 0.66, NSE = 0.61), 3B42 (R2 = 0.64, NSE = 0.54). Moreover, the comparison between IMERG-F and 3B42, shows that IMERG-F is superior to 3B42 by indicating higher R2, NSE and lower percent bias (PBIAS) at both monthly and daily timescale. The results are strengthened by Taylor diagram statistics, which represent a higher correlation (R) and less RMS error between observed and simulated values for IMERG-F. IMERG-F has great potential utility in the Chenab River catchment as it outperformed the 3B42 precipitation in this study. However, its poor skill of capturing peaks at daily timescale remains, leaving a room for IMERG-F to improve its algorithm in the upcoming release.
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Soares Cruz, Marcus Aurélio, Leonardo Teixeira Rocha, Ricardo de Aragão, and André Quintão de Almeida. "Applicability of TRMM Precipitation for Hydrologic Modeling in a Basin in the Northeast Brazilian Agreste." Revista Brasileira de Meteorologia 33, no. 1 (2018): 57–64. http://dx.doi.org/10.1590/0102-7786331013.
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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.
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Wang, Jianxin, Walter A. Petersen, and David B. Wolff. "Validation of Satellite-Based Precipitation Products from TRMM to GPM." Remote Sensing 13, no. 9 (2021): 1745. http://dx.doi.org/10.3390/rs13091745.
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The global precipitation measurement mission (GPM) has been in operation for seven years and continues to provide a vast quantity of global precipitation data at finer temporospatial resolutions with improved accuracy and coverage. GPM’s signature algorithm, the integrated multisatellite retrievals for GPM (IMERG) is a next-generation of precipitation product expected for wide variety of research and operational applications. This study evaluates the latest version (V06B) of IMERG and its predecessor, the tropical rainfall measuring mission (TRMM) multisatellite precipitation (TMPA) 3B42 (V7) using ground-based and gauge-corrected multiradar multisensor system (MRMS) precipitation products over the conterminous United States (CONUS). The spatial distributions of all products are analyzed. The error characteristics are further examined for 3B42 and IMERG in winter and summer by an error decomposition approach, which partitions total bias into hit bias, biases due to missed precipitation and false precipitation. The volumetric and categorical statistical metrics are used to quantitatively evaluate the performance of the two satellite-based products. All products show a similar precipitation climatology with some regional differences. The two satellite-based products perform better in the eastern CONUS than in the mountainous Western CONUS. The evaluation demonstrates the clear improvement in IMERG precipitation product in comparison with its predecessor 3B42, especially in reducing missed precipitation in winter and summer, and hit bias in winter, resulting in better performance in capturing lighter and heavier precipitation.
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Ochoa, A., L. Pineda, P. Crespo, and P. Willems. "Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific–Andean region of Ecuador and Peru." Hydrology and Earth System Sciences 18, no. 8 (2014): 3179–93. http://dx.doi.org/10.5194/hess-18-3179-2014.
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Abstract. The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6° S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6° S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day−1 to 0.2 for rates higher than 20 mm day−1. Compared to its predecessor, 3B42 V7 shows modest region-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.
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Tompkins, Adrian M., and Adeyemi A. Adebiyi. "Using CloudSat Cloud Retrievals to Differentiate Satellite-Derived Rainfall Products over West Africa." Journal of Hydrometeorology 13, no. 6 (2012): 1810–16. http://dx.doi.org/10.1175/jhm-d-12-039.1.
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Abstract Daily precipitation retrievals from three algorithms [the Tropical Rainfall Measuring Mission 3B42 rain product (TRMM-3B42), the Climate Prediction Center morphing technique (CMORPH), and the second version (RFEv2) of the Famine Early Warning System (FEWS)] and CloudSat retrievals of cloud liquid water, ice amount, and cloud fraction are used to document the cloud structures associated with rainfall location and intensity in the West African monsoon. The different rainfall retrieval approaches lead to contrasting cloud sensitivities between all three algorithms most apparent in the onset period of June and July. During the monsoon preonset phase, CMORPH produces a precipitation peak at around 12°N associated with upper-level cirrus clouds, while FEWS and TRMM both produce rainfall maxima collocated with the tropospheric–deep convective cloud structures at 4°–6°N. In July similar relative displacements of the rainfall maxima are observed. Conditional sampling of several hundred convection systems proves that, while upper-level cirrus is advected northward relative to the motion of the convective system cores, the reduced cover and water content of lower-tropospheric clouds in the northern zone could be due to signal attenuation as the systems there appear to be more intense, producing higher ice water contents. Thus, while CMORPH may overestimate rainfall in the northern zone due to its reliance on cloud ice, TRMM and FEWS are likely underestimating precipitation in this zone, potentially due to the use of infrared based products in TRMM and FEWS when microwave is not available. Mapping the CloudSat retrievals as a function of rain rate confirms the greater sensitivity of CMORPH to ice cloud and indicates that high-intensity rainfall events are associated with systems that are deeper and of a greater spatial scale.
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Yan, Denghua, Shaohua Liu, Tianling Qin, et al. "Evaluation of TRMM precipitation and its application to distributed hydrological model in Naqu River Basin of the Tibetan Plateau." Hydrology Research 48, no. 3 (2016): 822–39. http://dx.doi.org/10.2166/nh.2016.090.
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The Tibetan Plateau (TP) is the roof of the world and water towers of Asia. However, research on hydrological processes is restricted by the sparse gauge network in the TP. The distributed hydrological model is an efficient tool to explore hydrological processes. Meanwhile, the spatial distribution of precipitation directly affects the precision of distributed hydrological modelling. The latest TRMM 3B42 (V7) precipitation was evaluated compared with gauge precipitation at station and basin scales in the Naqu River Basin of the TP. The results show that Tropical Rainfall Measuring Mission (TRMM) precipitation overestimated the precipitation with BIAS of 0.2; the intensity distributions of daily precipitation are consistent in the two precipitation data. TRMM precipitation was then corrected by the good linear relation between monthly areal TRMM precipitation and gauge precipitation, and applied into the Water and Energy Process model. The results indicate that the simulated streamflow using both precipitation data produce a good fit with observed streamflow, especially at monthly scale. Furthermore, the better relations between average slopes and runoff coefficients of sub-basins from the corrected TRMM precipitation-based model implies that the spatial distribution of TRMM precipitation is closer to the spatial distribution of actual precipitation, and has an advantage in driving distributed hydrological models.
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Yuga Suseno, Dwi Prabowo, and Tomohito J. Yamada. "The Role of GPS Precipitable Water Vapor and Atmosphere Stability Index in the Statistically Based Rainfall Estimation Using MTSAT Data." Journal of Hydrometeorology 14, no. 6 (2013): 1922–32. http://dx.doi.org/10.1175/jhm-d-12-0128.1.
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Abstract A rainfall estimation method was developed based on the statistical relationships between cloud-top temperature and rainfall rates acquired by both the 10.8-μm channel of the Multi-Functional Transport Satellite (MTSAT) series and the Automated Meteorological Data Acquisition System (AMeDAS) C-band radar, respectively. The method focused on cumulonimbus (Cb) clouds and was developed in the period of June–September 2010 and 2011 over the landmass of Japan and its surrounding area. Total precipitable water vapor (PWV) and atmospheric vertical instability were considered to represent the atmospheric environmental conditions during the development of statistical models. Validations were performed by comparing the estimated values with the observed rainfall derived from the AMeDAS rain gauge network and the Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall estimation product. The results demonstrated that the models that considered the combination of total PWV and atmospheric vertical instability were relatively more sensitive to heavy rainfall than were the models that considered no atmospheric environmental conditions. The use of such combined information indicated a reasonable improvement, especially in terms of the correlation between estimated and observed rainfall. Intercomparison results with the TRMM 3B42 confirmed that MTSAT-based rainfall estimations made by considering atmospheric environmental conditions were more accurate for estimating rainfall generated by Cb cloud.
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SANTOS DA SILVA ALVES ALVES, KEYLYANE, LUCIANA SANCHES, NARA LUÍSA REIS DE ANDRADE, GRACYELI SANTOS SOUZA GUARIENTE, and PETER ZEILHOFER. "Estimation of rainfall based on remote sensing and observation fields in Jaru Biological Reserve at the Brazilian Amazonian forest." Theoretical and Applied Engineering 5, no. 2 (2021): 1–12. http://dx.doi.org/10.31422/taae.v5i2.38.
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The Amazon basin, with a drainage area of about 6 million km2, is the largest drainage basin in the world, consequently the accurate measuring the rainfall dynamics at high spatial and temporal resolution is essential for a better understanding of the hydrological cycle. So, we validated rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) satellite using surface precipitation data collected from 2004 to 2012. Rainfall data came from the Jaru Biological Reserve meteorological station, located to the east in the state of Rondônia, Brazil, and was compared with the estimates of the algorithms 3B42 V7 and 3B43 V7 of the product TRMM. Statistical analysis was based on indices of continuous variables such as the Spearman correlation coefficient (ρ), the square root mean square error normalized by the mean of the observed values (NRMSE), the mean square error (RMSE), the error (ERV) and some categorical indices such as probability of detection (POD), False Alarm (FAR) and success rate (CSI) between the daily and monthly precipitation observed data and the estimated precipitation data. The 3B43 V7 precipitation estimates were broadly representative of surface observations, but underestimated precipitation in the wet season and overestimated precipitation in the dry season. The 3B42 V7 product performed poorly and does not generate a robust representation of surface precipitation.
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Arrokhman, Naufal Achmad, Sri Wahyuni, and Ery Suhartanto. "Evaluasi Kesesuaian Data Satelit untuk Curah Hujan dan Evaporasi Terhadap Data Pengukuran di Kawasan Waduk Sutami." Jurnal Teknologi dan Rekayasa Sumber Daya Air 1, no. 2 (2021): 904–16. http://dx.doi.org/10.21776/ub.jtresda.2021.001.02.46.
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Waduk Sutami merupakan waduk multiguna sehingga diperlukan pencatatan data curah hujan maupun data evaporasi yang lengkap sebagai dasar mengatur pola operasi waduk, analisis neraca air, dan lain-lain. Seiring perkembangan zaman, teknologi satelit dapat digunakan sebagai alternatif data hidrologi untuk mengantisipasi ketidaklengkapan dan ketidak-akuratan data saat pengukuran. Tujuan dari penelitian ini untuk melakukan evaluasi data satelit curah hujan dan evaporasi terhadap data pengukuran di Kawasan Waduk Sutami. Dari penelitian ini juga akan menghasilkan satelit yang direkomendasikan untuk dapat diterapkan pada lokasi studi. Dalam penelitian ini, penulis menggunakan data satelit curah hujan TRMM 3B42, CHIRPS, dan GPM V6. Sedangkan satelit evaporasi menggunakan GLDAS-2.1 dan CFS-V2. Masing-masing satelit tersebut mempunyai spesifikasi dan karakteristik yang berbeda-beda. Evaluasi data satelit dilakukan dengan menggunakan simulasi model kalibrasi dan validasi untuk mengetahui performa dari satelit tersebut. Hasil yang terbaik dapat diketahui dari nilai RMSE, NSE, Koefisien Korelasi, dan Kesalahan Relatif. Hasil penelitian menunjukkan bahwa pada intinya, seluruh satelit curah hujan (TRMM 3B42, CHIRPS, GPM V6) maupun satelit evaporasi (GLDAS-2.1, CFSV2) dapat digunakan sebagai alternatif data hidrologi di Kawasan Waduk Sutami. Hanya saja satelit curah hujan GPM V6 dan satelit evaporasi CFS-V2 memiliki tingkat keakurasian yang lebih tinggi dan performa yang lebih baik berdasarkan simulasi kalibrasi dan validasi.
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Islam, Md Nazrul, Someshwar Das, and Hiroshi Uyeda. "Calibration of TRMM Derived Rainfall Over Nepal During 1998-2007." Open Atmospheric Science Journal 4, no. 1 (2010): 12–23. http://dx.doi.org/10.2174/1874282301004010012.
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In this study rainfall is calculated from Tropical Rainfall Measuring Mission (TRMM) Version 6 (V6) 3B42 datasets and calibrated with reference to the observed daily rainfall by rain-gauge collected at 15 locations over Nepal during 1998-2007. In monthly, seasonal and annual scales TRMM estimated rainfalls follow the similar distribution of historical patterns obtained from the rain-gauge data. Rainfall is large in the Southern parts of the country, especially in the Central Nepal. Day-to-day rainfall comparison shows that TRMM derived trend is very similar to the observed data but TRMM usually underestimates rainfall on many days with some exceptions of overestimation on some days. The correlation coefficient of rainfalls between TRMM and rain-gauge data is obtained about 0.71. TRMM can measure about 65.39% of surface rainfall in Nepal. After using calibration factors obtained through regression expression the TRMM estimated rainfall over Nepal becomes about 99.91% of observed data. TRMM detection of rainy days is poor over Nepal; it can approximately detect, under-detect and over-detect by 19%, 72% and 9% of stations respectively. False alarm rate, probability of detection, threat score and skill score are calculated as 0.30, 0.68, 0.53 and 0.55 respectively. Finally, TRMM data can be utilized in measuring mountainous rainfall over Nepal but exact amount of rainfall has to be calculated with the help of adjustment factors obtained through calibration procedure. This preliminary work is the preparation of utilization of Global Precipitation Measurement (GPM) data to be commencing in 2013.
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Chen, Yingjun, Elizabeth E. Ebert, Kevin J. E. Walsh, and Noel E. Davidson. "Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAIN data." Journal of Geophysical Research: Atmospheres 118, no. 5 (2013): 2184–96. http://dx.doi.org/10.1002/jgrd.50250.
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Keikhosravi Kiany, Mohammad Sadegh, Seyed Abolfazl Masoodian, Robert C. Balling Jr, and Majid Montazeri. "Evaluation of the TRMM 3B42 product for extreme precipitation analysis over southwestern Iran." Advances in Space Research 66, no. 9 (2020): 2094–112. http://dx.doi.org/10.1016/j.asr.2020.07.036.
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Zhu, Liming, Yu Zhao, Xiaoping Rui, and Qingwei Wei. "Diurnal Variation of Seasonal Precipitation over the CONUS: A Comparison of Gauge Observations with TRMM Data." Advances in Meteorology 2020 (December 24, 2020): 1–13. http://dx.doi.org/10.1155/2020/8859993.
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Diurnal variation of precipitation is a fundamental periodic signal of local climate. Comprehensive study of diurnal variation of precipitation is helpful in studying the formation of local climate and validating satellite precipitation products. In this study, a comparison is drawn between precipitation gauge observations and Tropical Rainfall Measuring Mission (TRMM) 3B42 data on diurnal variation of precipitation. First, using the K-means clustering algorithm, stations with gauge observations and pixels with TRMM data are divided into different groups according to the diurnal variation of precipitation, respectively. In each group, the stations have similar diurnal variation of precipitation. Then maps of diurnal variation of precipitation for gauge observations and TRMM data are obtained. According to these maps, the diurnal variation of precipitation over the contiguous United States (CONUS) presents seasonal variability in both gauge observations and TRMM data. In addition, the diurnal variation of precipitation shows clustered features in space. However, the spatial patterns of the obtained maps do not match, and the TRMM satellite data perform poorly in capturing the hourly precipitation event. Finally, the possible mechanism behind the prevailing nocturnal precipitation over the middle of the CONUS is discussed, with the prevailing nocturnal precipitation judged likely to be strongly related to the mountain-plains solenoid (MPS) circulation.
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Jiang, Haiyan, and Edward J. Zipser. "Contribution of Tropical Cyclones to the Global Precipitation from Eight Seasons of TRMM Data: Regional, Seasonal, and Interannual Variations." Journal of Climate 23, no. 6 (2010): 1526–43. http://dx.doi.org/10.1175/2009jcli3303.1.
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Abstract Based on the University of Utah Tropical Rainfall Measuring Mission (TRMM) precipitation feature (PF) database, tropical cyclone PFs (TCPFs) are identified for over 600 storms that reached tropical storm intensity level or above around the globe during eight TC seasons from the period of 1998–2006. Each TC season includes 6 months yr−1. Six basins are considered: Atlantic (ATL), east-central Pacific (EPA), northwest Pacific (NWP), north Indian Ocean (NIO), south Indian Ocean (SIO), and South Pacific (SPA). TRMM 2A25- (precipitation radar) and 3B42- (multisatellite) derived rainfall amounts are used to assess the impact of tropical cyclone (TC) rainfall in altering the regional, seasonal, and interannual distribution of the global total rainfall during the TC seasons in the six basins. The global, seasonal, and interannual variations of the monthly rainfall inside TCPFs are presented. The fractional rainfall contributions by TCPFs are compared in different basins. The TRMM 2A25 and 3B42 retrievals are compared in terms of the rainfall contribution by TCs. After constraining TC rainfall for being within 500 km from the TC center, 2A25 and 3B42 show similar results: 1) TCs contribute, respectively, 8%–9%, 7%, 11%, 5%, 7%–8%, and 3%–4% of the seasonal rainfall to the entire domain of the ATL, EPA, NWP, NIO, SIO, and SPA basins; 2) both algorithms show that, regionally, the maximum percentage of TC rainfall contribution is located in EPA basin near the Mexico Baja California coast (about 55%), SIO close to the Australia coast (about 55%), and NWP near Taiwan (about 35%–40%); 3) the maximum monthly percentage of TC rainfall contribution is in September for the ATL basin, August and September for EPA, August for NWP, May for NIO, March for SIO, and January and February for SPA; 4) the percentage of rainfall contributed by TCs is higher during El Niño years than La Niña years for EPA and NWP basins. The trend is the reverse for ATL and NIO, and nearly neutral for SIO and SPA. However, this study does not include enough years of data to expect the findings to be representative of long-term statistics of the interannual variations.
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Yang, Xiaoying, Yang Lu, Mou Leong Tan, Xiaogang Li, Guoqing Wang, and Ruimin He. "Nine-Year Systematic Evaluation of the GPM and TRMM Precipitation Products in the Shuaishui River Basin in East-Central China." Remote Sensing 12, no. 6 (2020): 1042. http://dx.doi.org/10.3390/rs12061042.
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Owing to their advantages of wide coverage and high spatiotemporal resolution, satellite precipitation products (SPPs) have been increasingly used as surrogates for traditional ground observations. In this study, we have evaluated the accuracy of the latest five GPM IMERG V6 and TRMM 3B42 V7 precipitation products across the monthly, daily, and hourly scale in the hilly Shuaishui River Basin in East-Central China. For evaluation, a total of four continuous and three categorical metrics have been calculated based on SPP estimates and historical rainfall records at 13 stations over a period of 9 years from 2009 to 2017. One-way analysis of variance (ANOVA) and multiple posterior comparison tests are used to assess the significance of the difference in SPP rainfall estimates. Our evaluation results have revealed a wide-ranging performance among the SPPs in estimating rainfall at different time scales. Firstly, two post-time SPPs (IMERG_F and 3B42) perform considerably better in estimating monthly rainfall. Secondly, with IMERG_F performing the best, the GPM products generally produce better daily rainfall estimates than the TRMM products. Thirdly, with their correlation coefficients all falling below 0.6, neither GPM nor TRMM products could estimate hourly rainfall satisfactorily. In addition, topography tends to impose similar impact on the performance of SPPs across different time scales, with more estimation deviations at high altitude. In general, the post-time IMERG_F product may be considered as a reliable data source of monthly or daily rainfall in the study region. Effective bias-correction algorithms incorporating ground rainfall observations, however, are needed to further improve the hourly rainfall estimates of the SPPs to ensure the validity of their usage in real-world applications.