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Journal articles on the topic 'Satellite rainfall estimation'
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Mohamad, Z., M. Z. A. Bakar, and M. Norman. "Evaluation of Satellite Based Rainfall Estimation." IOP Conference Series: Earth and Environmental Science 620 (January 9, 2021): 012011. http://dx.doi.org/10.1088/1755-1315/620/1/012011.
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Shih, Sun F. "GOES Satellite Data in Rainfall Estimation." Journal of Irrigation and Drainage Engineering 115, no. 5 (October 1989): 839–52. http://dx.doi.org/10.1061/(asce)0733-9437(1989)115:5(839).
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Tapiador, F. J., C. Kidd, K. L. Hsu, and F. Marzano. "Neural networks in satellite rainfall estimation." Meteorological Applications 11, no. 1 (March 2004): 83–91. http://dx.doi.org/10.1017/s1350482704001173.
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Kuntoro, A. A., R. K. Hapsari, M. B. Adityawan, M. Farid, Widyaningtias, and Radhika. "Estimation of Extreme Rainfall over Kalimantan Island based on GPM IMERG Daily Data." IOP Conference Series: Earth and Environmental Science 1065, no. 1 (July 1, 2022): 012036. http://dx.doi.org/10.1088/1755-1315/1065/1/012036.
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Abstract Rainfall is one of the critical data for water resources infrastructure planning. In many cases in developing countries such as Indonesia, rainfall stations are not evenly distributed. In many cases, regional development occurs much faster than the improvement of hydrological measurement instruments. The plan to move the capital city of Indonesia to Kalimantan is one example. Satellites rainfall products can be utilized, especially for areas with a limited number of rainfall stations. This study examines the potential use of Global Precipitation Measurement (GPM) satellite products to estimate the spatial distribution of rainfall in the Kalimantan region. Twenty years data of daily maximum rainfall from GPM satellite rainfall products in 2001-2020 were compared to twenty years data of daily maximum rainfall from 16 rainfall stations under the Meteorology, Climatology, and Geophysical Agency (BMKG), with data time spanning from the 1970s to 2020. The analysis results show a significant difference between extreme rainfall analysis computed by using station data and the satellite. The use of the correction function can increase the accuracy of the GPM rainfall product. It can be used as an alternative data source for a region with limited rainfall stations.
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Nikolopoulos, E. I., E. Destro, V. Maggioni, F. Marra, and M. Borga. "Satellite Rainfall Estimates for Debris Flow Prediction: An Evaluation Based on Rainfall Accumulation–Duration Thresholds." Journal of Hydrometeorology 18, no. 8 (August 1, 2017): 2207–14. http://dx.doi.org/10.1175/jhm-d-17-0052.1.
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Abstract Rainfall thresholds are often used in early warning systems to identify rainfall conditions that, when reached or exceeded, are likely to result in debris flows. Rain gauges are typically used for the definition of these thresholds. However, in mountainous areas in situ observations are often sparse or nonexistent. Satellite-based rainfall estimates offer a solution to overcome the coverage problem at the global scale but are associated with significant estimation uncertainty. Evaluating satellite-based rainfall thresholds is thus necessary to understand their potential and limitations. In this work, an intercomparison among satellite-based precipitation products is presented in the context of estimating rainfall thresholds for debris flow prediction. The study is performed for the upper Adige River basin in the eastern Italian Alps during 2000–10. Large differences are observed between event-based characteristics (event duration and magnitude) derived from rain gauge and satellite-based estimates, revealing considerable interproduct variability in the debris flow–triggering rainfall characteristics. The parameters of the satellite-based thresholds differ less than 30% from the corresponding rain gauge–based parameters. Results further suggest that the adjustment of satellite-based estimates (either gauge based or by applying an error model) together with spatial resolution has an important impact on the estimation of the accumulation–duration thresholds.
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Goodarzi, Mohammad Reza, Roxana Pooladi, and Majid Niazkar. "Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran." Sustainability 14, no. 20 (October 12, 2022): 13051. http://dx.doi.org/10.3390/su142013051.
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Evaluating satellite-based products is vital for precipitation estimation for sustainable water resources management. The current study evaluates the accuracy of predicting precipitation using four remotely sensed rainfall datasets—Tropical Rainfall Measuring Mission products (TRMM-3B42V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN-CDR), Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), and National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR)—over the Haraz-Gharehsoo basin during 2008–2016. The benchmark values for the assessment are gauge-observed data gathered without missing precipitation data at nine ground-based measuring stations over the basin. The results indicate that the TRMM and CCS-CDR satellites provide more robust precipitation estimations in 75% of high-altitude stations at daily, monthly, and annual time scales. Furthermore, the comparative analysis reveals some precipitation underestimations for each satellite. The underestimation values obtained by TRMM CDR, CCS-CDR, and CFSR are 8.93 mm, 20.34 mm, 9.77 mm, and 17.23 mm annually, respectively. The results obtained are compared to previous studies conducted over other basins. It is concluded that considering the accuracy of each satellite product for estimating remotely sensed precipitation is valuable and essential for sustainable hydrological modelling.
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Manz, Bastian, Sebastián Páez-Bimos, Natalia Horna, Wouter Buytaert, Boris Ochoa-Tocachi, Waldo Lavado-Casimiro, and Bram Willems. "Comparative Ground Validation of IMERG and TMPA at Variable Spatiotemporal Scales in the Tropical Andes." Journal of Hydrometeorology 18, no. 9 (September 1, 2017): 2469–89. http://dx.doi.org/10.1175/jhm-d-16-0277.1.
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Abstract An initial ground validation of the Integrated Multisatellite Retrievals for GPM (IMERG) Day-1 product from March 2014 to August 2015 is presented for the tropical Andes. IMERG was evaluated along with the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) against 302 quality-controlled rain gauges across Ecuador and Peru. Detection, quantitative estimation statistics, and probability distribution functions are calculated at different spatial (0.1°, 0.25°) and temporal (1 h, 3 h, daily) scales. Precipitation products are analyzed for hydrometeorologically distinct subregions. Results show that IMERG has a superior detection and quantitative rainfall intensity estimation ability than TMPA, particularly in the high Andes. Despite slightly weaker agreement of mean rainfall fields, IMERG shows better characterization of gauge observations when separating rainfall detection and rainfall rate estimation. At corresponding space–time scales, IMERG shows better estimation of gauge rainfall probability distributions than TMPA. However, IMERG shows no improvement in both rainfall detection and rainfall rate estimation along the dry Peruvian coastline, where major random and systematic errors persist. Further research is required to identify which rainfall intensities are missed or falsely detected and how errors can be attributed to specific satellite sensor retrievals. The satellite–gauge difference was associated with the point-area difference in spatial support between gauges and satellite precipitation products, particularly in areas with low and irregular gauge network coverage. Future satellite–gauge evaluations need to identify such locations and investigate more closely interpixel point-area differences before attributing uncertainties to satellite products.
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Hong, Yang, Kuo-Lin Hsu, Soroosh Sorooshian, and Xiaogang Gao. "Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System." Journal of Applied Meteorology 43, no. 12 (December 1, 2004): 1834–53. http://dx.doi.org/10.1175/jam2173.1.
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Abstract A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared (10.7 μm) geostationary satellite imagery in estimating finescale (0.04° × 0.04° every 30 min) rainfall distribution. This algorithm processes satellite cloud images into pixel rain rates by 1) separating cloud images into distinctive cloud patches; 2) extracting cloud features, including coldness, geometry, and texture; 3) clustering cloud patches into well-organized subgroups; and 4) calibrating cloud-top temperature and rainfall (Tb–R) relationships for the classified cloud groups using gauge-corrected radar hourly rainfall data. Several cloud-patch categories with unique cloud-patch features and Tb–R curves were identified and explained. Radar and gauge rainfall measurements were both used to evaluate the PERSIANN CCS rainfall estimates at a range of temporal (hourly and daily) and spatial (0.04°, 0.12°, and 0.25°) scales. Hourly evaluation shows that the correlation coefficient (CC) is 0.45 (0.59) at a 0.04° (0.25°) grid scale. The averaged CC of daily rainfall is 0.57 (0.63) for the winter (summer) season.
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Diem, Jeremy E., Joel Hartter, Sadie J. Ryan, and Michael W. Palace. "Validation of Satellite Rainfall Products for Western Uganda." Journal of Hydrometeorology 15, no. 5 (September 25, 2014): 2030–38. http://dx.doi.org/10.1175/jhm-d-13-0193.1.
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Abstract Central equatorial Africa is deficient in long-term, ground-based measurements of rainfall; therefore, the aim of this study is to assess the accuracy of three high-resolution, satellite-based rainfall products in western Uganda for the 2001–10 period. The three products are African Rainfall Climatology, version 2 (ARC2); African Rainfall Estimation Algorithm, version 2 (RFE2); and 3B42 from the Tropical Rainfall Measuring Mission, version 7 (i.e., 3B42v7). Daily rainfall totals from six gauges were used to assess the accuracy of satellite-based rainfall estimates of rainfall days, daily rainfall totals, 10-day rainfall totals, monthly rainfall totals, and seasonal rainfall totals. The northern stations had a mean annual rainfall total of 1390 mm, while the southern stations had a mean annual rainfall total of 900 mm. 3B42v7 was the only product that did not underestimate boreal-summer rainfall at the northern stations, which had ~3 times as much rainfall during boreal summer than did the southern stations. The three products tended to overestimate rainfall days at all stations and were borderline satisfactory at identifying rainfall days at the northern stations; the products did not perform satisfactorily at the southern stations. At the northern stations, 3B42v7 performed satisfactorily at estimating monthly and seasonal rainfall totals, ARC2 was only satisfactory at estimating seasonal rainfall totals, and RFE2 did not perform satisfactorily at any time step. The satellite products performed worst at the two stations located in rain shadows, and 3B42v7 had substantial overestimates at those stations.
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Bellerby, T. J. "Satellite Rainfall Uncertainty Estimation Using an Artificial Neural Network." Journal of Hydrometeorology 8, no. 6 (December 1, 2007): 1397–412. http://dx.doi.org/10.1175/2007jhm846.1.
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Abstract This paper describes a neural network–based approach to estimate the conditional distribution function (cdf) of rainfall with respect to multidimensional satellite-derived input data. The methodology [Conditional Histogram of Precipitation (CHIP)] employs a histogram-based approximation of the cdf. In addition to the conditional expected rainfall rate, it provides conditional probabilities for that rate falling within each of a fixed set of intervals or bins. A test algorithm based on the CHIP approach was calibrated against Goddard profiling algorithm (GPROF) rainfall data for June–August 2002 and then used to produce a 30-min, 0.5° rainfall product from global (60°N–60°S) composite geostationary thermal infrared imagery for June–August 2003. Estimated rainfall rates and conditional probabilities were validated against 2003 GPROF data. The CHIP methodology provides the means to extend existing probabilistic and ensemble satellite rainfall error models, conditioned on a single, scalar, satellite rainfall predictor or upon scalar rainfall-rate outputs, to make full use of multidimensional input data.
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Tajudin, Noraisyah, Norsuzila Ya’acob, Darmawaty Mohd Ali, and Nor Aizam Adnan. "Estimation of TRMM rainfall for landslide occurrences based on rainfall threshold analysis." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 3208. http://dx.doi.org/10.11591/ijece.v10i3.pp3208-3215.
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Landslide can be triggered by intense or prolonged rainfall. Precipitation data obtained from ground-based observation is very accurate and commonly used to do analysis and landslide prediction. However, this approach is costly with its own limitation due to lack of density of ground station, especially in mountain area. As an alternative, satellite derived rainfall techniques have become more favorable to overcome these limitations. Moreover, the satellite derived rainfall estimation needs to be validated on its accuracy and its capability to predict landslide which presumably triggered by rainfall. This paper presents the investigation of using the TRMM-3B42V7 data in comparison to the available rain-gauge data in Ulu Kelang, Selangor. The monthly average rainfall, cumulative rainfall and rainfall threshold analysis from 1998 to 2011 is compared using quantitative statistical criteria (Pearson correlation, bias, root mean square error, mean different and mean). The results from analysis showed that there is a significant and strong positive correlation between the TRMM 3B42V7 and rain gauge data. The threshold derivative from the satellite products is lower than the rain gauge measurement. The findings indicated that the proposed method can be applied using TRMM satellite estimates products to derive rainfall threshold for the possible landslide occurrence.
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Kumah, Kingsley K., Joost C. B. Hoedjes, Noam David, Ben H. P. Maathuis, H. Oliver Gao, and Bob Z. Su. "Combining MWL and MSG SEVIRI Satellite Signals for Rainfall Detection and Estimation." Atmosphere 11, no. 9 (August 19, 2020): 884. http://dx.doi.org/10.3390/atmos11090884.
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Accurate rainfall detection and estimation are essential for many research and operational applications. Traditional rainfall detection and estimation techniques have achieved considerable success but with limitations. Thus, in this study, the relationships between the gauge (point measurement) and the microwave links (MWL) rainfall (line measurement), and the MWL to the satellite observations (area-wide measurement) are investigated for (area-wide) rainfall detection and rain rate retrieval. More precisely, we investigate if the combination of MWL with Meteosat Second Generation (MSG) satellite signals could improve rainfall detection and rainfall rate estimates. The investigated procedure includes an initial evaluation of the MWL rainfall estimates using gauge measurements, followed by a joint analysis of the rainfall estimates with the satellite signals by means of a conceptual model in which clouds with high cloud top optical thickness and large particle sizes have high rainfall probabilities and intensities. The analysis produced empirical thresholds that were used to test the capability of the MSG satellite data to detect rainfall on the MWL. The results from Kenya, during the “long rains” of 2013, 2014, and 2018 show convincing performance and reveal the potential of MWL and MSG data for area-wide rainfall detection.
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Shen, Fei, Mingming Sui, Yifan Zhu, Xinyun Cao, Yulong Ge, and Haohan Wei. "Using BDS MEO and IGSO Satellite SNR Observations to Measure Soil Moisture Fluctuations Based on the Satellite Repeat Period." Remote Sensing 13, no. 19 (October 3, 2021): 3967. http://dx.doi.org/10.3390/rs13193967.
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Soil moisture is an important geophysical parameter for studying terrestrial water and energy cycles. It has been proven that Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) can be applied to monitor soil moisture. Unlike the Global Positioning System (GPS) that has only medium earth orbit (MEO) satellites, the Beidou Navigation Satellite System (BDS) also has geosynchronous earth orbit (GEO) satellites and inclined geosynchronous satellite orbit (IGSO) satellites. Benefiting from the distribution of three different orbits, the BDS has better coverage in Asia than other satellite systems. Previous retrieval methods that have been confirmed on GPS cannot be directly applied to BDS MEO satellites due to different satellite orbits. The contribution of this study is a proposed multi-satellite soil moisture retrieval method for BDS MEO and IGSO satellites based on signal-to-noise ratio (SNR) observations. The method weakened the influence of environmental differences in different directions by considering satellite repeat period. A 30-day observation experiment was conducted in Fengqiu County, China and was used for verification. The satellite data collected were divided according to the satellite repeat period, and ensured the response data moved in the same direction. The experimental results showed that the BDS IGSO and MEO soil moisture estimation results had good correlations with the in situ soil moisture fluctuations. The BDS MEO B1I estimation results had the best performance; the estimation accuracy in terms of correlation coefficient was 0.9824, root mean square error (RMSE) was 0.0056 cm3cm−3, and mean absolute error (MAE) was 0.0040 cm3cm−3. The estimations of the BDS MEO B1I, MEO B2I, and IGSO B2I performed better than the GPS L1 and L2 estimations. For the BDS IGSO satellites, the B1I signal was more suitable for soil moisture retrieval than the B2I signal; the correlation coefficient was increased by 19.84%, RMSE was decreased by 42.64%, and MAE was decreased by 43.93%. In addition, the BDS MEO satellites could effectively capture sudden rainfall events.
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Ayasha, Nadine. "A COMPARISON OF RAINFALL ESTIMATION USING HIMAWARI-8 SATELLITE DATA IN DIFFERENT INDONESIAN TOPOGRAPHIES." International Journal of Remote Sensing and Earth Sciences (IJReSES) 17, no. 2 (March 24, 2021): 189. http://dx.doi.org/10.30536/j.ijreses.2020.v17.a3441.
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The Himawari-8 satellite can be used to derive precipitation data for rainfall estimation. This study aims to test several methods for suchestimation employing the Himawari-8 satellite. The methods are compared in three regions with different topographies, namely Bukittinggi, Pontianak and Ambon. The rainfall estimation methods that are tested are auto estimator, IMSRA, non-linear relation and non-linear inversion approaches. Based on the determination of the statistical verification(RMSE, standard deviation and correlation coefficient) of the amount of rainfall, the best method in Bukittinggi and Pontianak was shown to be IMSRA, while for the Ambon region was the non-linear relations. The best methods from each research area were mapped using the Google Maps Application Programming Interface (API).
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Zhuge, Xiao-Yong, Fan Yu, and Cheng-Wei Zhang. "Rainfall Retrieval and Nowcasting Based on Multispectral Satellite Images. Part I: Retrieval Study on Daytime 10-Minute Rain Rate." Journal of Hydrometeorology 12, no. 6 (December 1, 2011): 1255–70. http://dx.doi.org/10.1175/2011jhm1373.1.
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Abstract This study develops a method for both precipitation area and intensity retrievals based on multispectral geostationary satellite images. This method can be applied to continuous observation of large-scale precipitation so as to solve the problem from the measurements of rainfall radar and rain gauge. Satellite observation is instantaneous, whereas the rain gauge records accumulative data during a time interval. For this reason, collocated 10-min rain gauge measurements and infrared (IR) and visible (VIS) data from the FengYun-2C (FY-2C) geostationary satellite are employed to improve the accuracy of satellite rainfall retrieval. First of all, the rainfall probability identification matrix (RPIM) is used to distinguish rainfall clouds from nonrainfall clouds. This RPIM is more efficient in improving the retrieval accuracy of rainfall area than previous threshold combination screening methods. Second, the multispectral segmented curve-fitting rainfall algorithm (MSCFRA) is proposed and tested to estimate the 10-min rain rates. Rainfall samples taken from June to August 2008 are used to assess the performance of the rainfall algorithm. Assessment results show that the MSCFRA improves the accuracy of rainfall estimation for both stratiform cloud rainfall and convective cloud rainfall. These results are practically consistent with rain gauge measurements in both rainfall area division and rainfall intensity grade estimation. Furthermore, this study demonstrates that the temporal resolution of satellite detection is important and necessary in improving the precision of satellite rainfall retrieval.
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Giro, Riccardo Angelo, Lorenzo Luini, and Carlo Giuseppe Riva. "Rainfall Estimation from Tropospheric Attenuation Affecting Satellite Links." Information 11, no. 1 (December 23, 2019): 11. http://dx.doi.org/10.3390/info11010011.
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A novel methodology for estimating rainfall rate from satellite signals is presented. The proposed inversion algorithm yields rain rate estimates by making opportunistic use of the downlink signal and exploiting local ancillary meteorological information (0 °C isotherm height and monthly convectivity index), which can be extracted on a Global basis from Numerical Weather Prediction (NWP) products. The methodology includes different expressions to take the different impact of stratiform and convective rain events on the link into due account. The model accuracy in predicting the rain rate is assessed (and compared to the one of other models), both on a statistical and on an instantaneous basis, by exploiting a full year of data collected in Milan, in the framework of the Alphasat Aldo Paraboni propagation experiment.
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Tsonis, A. A., G. N. Triantafyllou, and K. P. Georgakakos. "Hydrological applications of satellite data: 1. Rainfall estimation." Journal of Geophysical Research: Atmospheres 101, no. D21 (November 1, 1996): 26517–25. http://dx.doi.org/10.1029/96jd01654.
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Vrieling, Anton, Geert Sterk, and Steven M. de Jong. "Satellite-based estimation of rainfall erosivity for Africa." Journal of Hydrology 395, no. 3-4 (December 2010): 235–41. http://dx.doi.org/10.1016/j.jhydrol.2010.10.035.
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Nashwan, Mohamed Salem, Shamsuddin Shahid, and Xiaojun Wang. "Assessment of Satellite-Based Precipitation Measurement Products over the Hot Desert Climate of Egypt." Remote Sensing 11, no. 5 (March 7, 2019): 555. http://dx.doi.org/10.3390/rs11050555.
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The performance of three satellite-based high-resolution gridded rainfall datasets, namely the gauge corrected Global Satellite Mapping of Precipitation (GSMaP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) in the hot desert climate of Egypt were assessed. Seven statistical indices including four categorical indices were used to assess the capability of the products in estimating the daily rainfall amounts and detecting the occurrences of rainfall under different intensity classes from March 2014 to May 2018. Although the products were gauge-corrected, none of them showed a consistent performance, and thus could not be titled as the best or worst performing product over Egypt. The CHIRPS was found to be the best product in estimating rainfall amounts when all rainfall events were considered and IMERG was found as the worst. However, IMERG was better at detecting the occurrence of rainfall than CHIRPS. For heavy rainfall events, IMERG was better at the majority of the stations in terms of the Kling–Gupta efficiency index (−0.34) and skill-score (0.33). The IMERG was able to show the spatial variability of rainfall during the recent big flash flood event that hit Northern Egypt. The study indicates that accurate estimation of rainfall in the hot desert climate using satellite sensors remains a challenge.
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Yeh, Nan-Ching, Yao-Chung Chuang, Hsin-Shuo Peng, and Kuo-Lin Hsu. "Bias Adjustment of Satellite Precipitation Estimation Using Ground-Based Observation: Mei-Yu Front Case Studies in Taiwan." Asia-Pacific Journal of Atmospheric Sciences 56, no. 3 (November 29, 2019): 485–92. http://dx.doi.org/10.1007/s13143-019-00152-7.
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AbstractThe Global Satellite Mapping of Precipitation (GSMaP) was used to estimate the accumulated rainfall in May from the Mei-Yu front in Taiwan. Rainfall estimation from GSMaP during 2002–2017 were evaluated using more than 400 local gauge observations, collected from the Taiwan Central Weather Bureau (CWB). Studies have demonstrated that the GSMaP rainfall estimation estimates can be biased, depending on the target region, elevation, and season. In this experiment, we have evaluated GSMaP over three elevation ranges. The GSMaP systemic errors for each elevation range were identified and corrected using regression analysis. The results indicated that GSMaP estimation can be improved significantly through adjustment over three elevation ranges (elevation less than 50 m, elevation of 50–100 m, and elevation higher than 100 m). For these three elevation ranges, the correlation coefficient between the GSMaP estimations and CWB rainfall data was 0.76, 0.78, and 0.59, respectively. This indicated that the GSMaP estimation was more accurate for low-elevation regions than high-elevation regions. After the proposed approaches were employed to correct the errors, the bias errors were respectively improved by 5.64(13.7%), 7.33(38.4%) and 10.52(31.2%) mm for low-, mid- and high-elevation regions. This study demonstrated that the local correction approaches can be used to improve GSMaP estimation of Mei-Yu rainfall in Taiwan.
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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 (November 22, 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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Sadeghi, Mojtaba, Ata Akbari Asanjan, Mohammad Faridzad, Phu Nguyen, Kuolin Hsu, Soroosh Sorooshian, and Dan Braithwaite. "PERSIANN-CNN: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Convolutional Neural Networks." Journal of Hydrometeorology 20, no. 12 (November 27, 2019): 2273–89. http://dx.doi.org/10.1175/jhm-d-19-0110.1.
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Abstract Accurate and timely precipitation estimates are critical for monitoring and forecasting natural disasters such as floods. Despite having high-resolution satellite information, precipitation estimation from remotely sensed data still suffers from methodological limitations. State-of-the-art deep learning algorithms, renowned for their skill in learning accurate patterns within large and complex datasets, appear well suited to the task of precipitation estimation, given the ample amount of high-resolution satellite data. In this study, the effectiveness of applying convolutional neural networks (CNNs) together with the infrared (IR) and water vapor (WV) channels from geostationary satellites for estimating precipitation rate is explored. The proposed model performances are evaluated during summer 2012 and 2013 over central CONUS at the spatial resolution of 0.08° and at an hourly time scale. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)–Cloud Classification System (CCS), which is an operational satellite-based product, and PERSIANN–Stacked Denoising Autoencoder (PERSIANN-SDAE) are employed as baseline models. Results demonstrate that the proposed model (PERSIANN-CNN) provides more accurate rainfall estimates compared to the baseline models at various temporal and spatial scales. Specifically, PERSIANN-CNN outperforms PERSIANN-CCS (and PERSIANN-SDAE) by 54% (and 23%) in the critical success index (CSI), demonstrating the detection skills of the model. Furthermore, the root-mean-square error (RMSE) of the rainfall estimates with respect to the National Centers for Environmental Prediction (NCEP) Stage IV gauge–radar data, for PERSIANN-CNN was lower than that of PERSIANN-CCS (PERSIANN-SDAE) by 37% (14%), showing the estimation accuracy of the proposed model.
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Tesfagiorgis, K., S. E. Mahani, and R. Khanbilvardi. "Bias correction of satellite rainfall estimation using a radar-gauge product." Hydrology and Earth System Sciences Discussions 7, no. 6 (November 16, 2010): 8913–45. http://dx.doi.org/10.5194/hessd-7-8913-2010.
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Abstract. Satellite rainfall estimates can be used in operational hydrologic prediction, but are prone to systematic errors. The goal of this study is to seamlessly blend a radar-gauge product with a corrected satellite product that fills gaps in radar coverage. To blend different rainfall products, they should have similar bias features. The paper presents a pixel by pixel method, which aims to correct biases in hourly satellite rainfall products using a radar-gauge rainfall product. Bias factors are calculated for corresponding rainy pixels, and a desired number of them are randomly selected for the analysis. Bias fields are generated using the selected bias factors. The method takes into account spatial variation and random errors in biases. Bias field parameters were determined on a daily basis using the Shuffled Complex Evolution optimization algorithm. To include more sources of errors, ensembles of bias factors were generated and applied before bias field generation. The procedure of the method was demonstrated using a satellite and a radar-gauge rainfall data for several rainy events in 2006 for the Oklahoma region. The method was compared with bias corrections using interpolation without ensembles, the ratio of mean and maximum ratio. Results show the method outperformed the other techniques such as mean ratio, maximum ratio and bias field generation by interpolation.
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Maggioni, Viviana, Humberto J. Vergara, Emmanouil N. Anagnostou, Jonathan J. Gourley, Yang Hong, and Dimitrios Stampoulis. "Investigating the Applicability of Error Correction Ensembles of Satellite Rainfall Products in River Flow Simulations." Journal of Hydrometeorology 14, no. 4 (August 1, 2013): 1194–211. http://dx.doi.org/10.1175/jhm-d-12-074.1.
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Abstract This study uses a stochastic ensemble-based representation of satellite rainfall error to predict the propagation in flood simulation of three quasi-global-scale satellite rainfall products across a range of basin scales. The study is conducted on the Tar-Pamlico River basin in the southeastern United States based on 2 years of data (2004 and 2006). The NWS Multisensor Precipitation Estimator (MPE) dataset is used as the reference for evaluating three satellite rainfall products: the Tropical Rainfall Measuring Mission (TRMM) real-time 3B42 product (3B42RT), the Climate Prediction Center morphing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Imagery Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS). Both ground-measured runoff and streamflow simulations, derived from the NWS Research Distributed Hydrologic Model forced with the MPE dataset, are used as benchmarks to evaluate ensemble streamflow simulations obtained by forcing the model with satellite rainfall corrected using stochastic error simulations from a two-dimensional satellite rainfall error model (SREM2D). The ability of the SREM2D ensemble error corrections to improve satellite rainfall-driven runoff simulations and to characterize the error variability of those simulations is evaluated. It is shown that by applying the SREM2D error ensemble to satellite rainfall, the simulated runoff ensemble is able to envelope both the reference runoff simulation and observed streamflow. The best (uncorrected) product is 3B42RT, but after applying SREM2D, CMORPH becomes the most accurate of the three products in the prediction of runoff variability. The impact of spatial resolution on the rainfall-to-runoff error propagation is also evaluated for a cascade of basin scales (500–5000 km2). Results show a doubling in the bias from rainfall to runoff at all basin scales. Significant dependency to catchment area is exhibited for the random error propagation component.
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Castro, Lina Mabel, Marcelo Miranda, and Bonifacio Fernández. "Evaluation of TRMM multi-satellite precipitation analysis (TMPA) in a mountainous region of the central Andes range with a Mediterranean climate." Hydrology Research 46, no. 1 (November 8, 2013): 89–105. http://dx.doi.org/10.2166/nh.2013.096.
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Estimating the spatial variability of precipitation for hydrological purposes is a challenge, especially in mountainous regions with sparse rain gauges. This study assessed the use of the satellite product Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) for the rainfall estimation at the local and regional level, on a daily and monthly basis. The evaluation was carried out in a mountainous region of the central Andes Range with a Mediterranean climate. The performance of the satellite estimation was carried out using categorical metrics, residual methods, and correlation and efficiency measures. The local analysis showed that TMPA product performance was better for rainfall events of medium magnitude. Regional analysis results suggest that TMPA products are able to capture the mean spatial pattern for flat areas on a monthly basis. However, the intercomparison in the mountains is likely not reliable, because there are not enough rain gauges to enable a spatial comparison in this area. The satellite estimates also tend to miss precipitation that is enhanced by flow lifting over the mountains. Moreover, the low performance is because the precipitation in the study site is predominantly produced by frontal mechanisms, where the ice content is also lower than that from convective origin.
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Sarachi, Sepideh, Kuo-lin Hsu, and Soroosh Sorooshian. "A Statistical Model for the Uncertainty Analysis of Satellite Precipitation Products." Journal of Hydrometeorology 16, no. 5 (October 1, 2015): 2101–17. http://dx.doi.org/10.1175/jhm-d-15-0028.1.
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Abstract Earth-observing satellites provide a method to measure precipitation from space with good spatial and temporal coverage, but these estimates have a high degree of uncertainty associated with them. Understanding and quantifying the uncertainty of the satellite estimates can be very beneficial when using these precipitation products in hydrological applications. In this study, the generalized normal distribution (GND) model is used to model the uncertainty of the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) precipitation product. The stage IV Multisensor Precipitation Estimator (radar-based product) was used as the reference measurement. The distribution parameters of the GND model are further extended across various rainfall rates and spatial and temporal resolutions. The GND model is calibrated for an area of 5° × 5° over the southeastern United States for both summer and winter seasons from 2004 to 2009. The GND model is used to represent the joint probability distribution of satellite (PERSIANN) and radar (stage IV) rainfall. The method is further investigated for the period of 2006–08 over the Illinois watershed south of Siloam Springs, Arkansas. Results show that, using the proposed method, the estimation of the precipitation is improved in terms of percent bias and root-mean-square error.
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Zhang, Xinxuan, Emmanouil N. Anagnostou, Maria Frediani, Stavros Solomos, and George Kallos. "Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas." Journal of Hydrometeorology 14, no. 6 (November 22, 2013): 1844–58. http://dx.doi.org/10.1175/jhm-d-12-0174.1.
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Abstract In this study, the authors investigate the use of high-resolution simulations from the Weather Research and Forecasting Model (WRF) for evaluating satellite rainfall biases of flood-inducing storms in mountainous areas. A probability matching approach is applied to evaluate a power-law relationship between satellite-retrieved and WRF-simulated rain rates over the storm domain. Satellite rainfall in this study is from the NOAA Climate Prediction Center morphing technique (CMORPH). Results are presented based on analyses of five heavy precipitation events that induced flash floods in northern Italy and southern France complex terrain basins. The WRF-based adjusted CMORPH rain rates exhibited improved error statistics against independent radar rainfall estimates. The authors show that the adjustment procedure reduces the underestimation of high rain rates, thus moderating the magnitude dependence of CMORPH rainfall bias. The Heidke skill score for the WRF-based adjusted CMORPH was consistently higher for a range of rain rate thresholds. This is an indication that the adjustment procedure ameliorates the satellite rain rates to provide a better estimation. Results also indicate that the low rain detection of CMORPH technique is also identifiable in the WRF–CMORPH comparison; however, the adjustment procedure herein does not incorporate this effect on the satellite rainfall bias adjustment.
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D'souza, G., E. C. Barrett, and C. H. Power. "Satellite rainfall estimation techniques using visible and infrared imagery." Remote Sensing Reviews 4, no. 2 (January 1990): 379–414. http://dx.doi.org/10.1080/02757259009532111.
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Shih, Sun F. "Satellite Data and Geographic Information System for Rainfall Estimation." Journal of Irrigation and Drainage Engineering 116, no. 3 (May 1990): 319–31. http://dx.doi.org/10.1061/(asce)0733-9437(1990)116:3(319).
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Ebert, Elizabeth E., and Michael J. Manton. "Performance of Satellite Rainfall Estimation Algorithms during TOGA COARE." Journal of the Atmospheric Sciences 55, no. 9 (May 1998): 1537–57. http://dx.doi.org/10.1175/1520-0469(1998)055<1537:posrea>2.0.co;2.
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Grimes, D. I. F., E. Pardo-Igúzquiza, and R. Bonifacio. "Optimal areal rainfall estimation using raingauges and satellite data." Journal of Hydrology 222, no. 1-4 (September 1999): 93–108. http://dx.doi.org/10.1016/s0022-1694(99)00092-x.
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Petty, Grant W. "The status of satellite-based rainfall estimation over land." Remote Sensing of Environment 51, no. 1 (January 1995): 125–37. http://dx.doi.org/10.1016/0034-4257(94)00070-4.
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Adane, Girma Berhe, Birtukan Abebe Hirpa, Belay Manjur Gebru, Cholho Song, and Woo-Kyun Lee. "Integrating Satellite Rainfall Estimates with Hydrological Water Balance Model: Rainfall-Runoff Modeling in Awash River Basin, Ethiopia." Water 13, no. 6 (March 15, 2021): 800. http://dx.doi.org/10.3390/w13060800.
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Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.
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Karbalaee, Negar, Kuolin Hsu, Soroosh Sorooshian, and Dan Braithwaite. "Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data." Journal of Geophysical Research: Atmospheres 122, no. 7 (April 12, 2017): 3859–76. http://dx.doi.org/10.1002/2016jd026037.
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Vergara, Humberto, Yang Hong, Jonathan J. Gourley, Emmanouil N. Anagnostou, Viviana Maggioni, Dimitrios Stampoulis, and Pierre-Emmanuel Kirstetter. "Effects of Resolution of Satellite-Based Rainfall Estimates on Hydrologic Modeling Skill at Different Scales." Journal of Hydrometeorology 15, no. 2 (April 1, 2014): 593–613. http://dx.doi.org/10.1175/jhm-d-12-0113.1.
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Abstract Uncertainty due to resolution of current satellite-based rainfall products is believed to be an important source of error in applications of hydrologic modeling and forecasting systems. A method to account for the input’s resolution and to accurately evaluate the hydrologic utility of satellite rainfall estimates is devised and analyzed herein. A radar-based Multisensor Precipitation Estimator (MPE) rainfall product (4 km, 1 h) was utilized to assess the impact of resolution of precipitation products on the estimation of rainfall and subsequent simulation of streamflow on a cascade of basins ranging from approximately 500 to 5000 km2. MPE data were resampled to match the Tropical Rainfall Measuring Mission’s (TRMM) 3B42RT satellite rainfall product resolution (25 km, 3 h) and compared with its native resolution data to estimate errors in rainfall fields. It was found that resolution degradation considerably modifies the spatial structure of rainfall fields. Additionally, a sensitivity analysis was designed to effectively isolate the error on hydrologic simulations due to rainfall resolution using a distributed hydrologic model. These analyses revealed that resolution degradation introduces a significant amount of error in rainfall fields, which propagated to the streamflow simulations as magnified bias and dampened aggregated error (RMSEs). Furthermore, the scale dependency of errors due to resolution degradation was found to intensify with increasing streamflow magnitudes. The hydrologic model was calibrated with satellite- and original-resolution MPE using a multiscale approach. The resulting simulations had virtually the same skill, suggesting that the effects of rainfall resolution can be accounted for during calibration of hydrologic models, which was further demonstrated with 3B42RT.
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Bitew, Menberu M., Mekonnen Gebremichael, Lula T. Ghebremichael, and Yared A. Bayissa. "Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia." Journal of Hydrometeorology 13, no. 1 (February 1, 2012): 338–50. http://dx.doi.org/10.1175/2011jhm1292.1.
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Abstract This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.
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Tadesse, Alemu, and Emmanouil N. Anagnostou. "The Effect of Storm Life Cycle on Satellite Rainfall Estimation Error." Journal of Atmospheric and Oceanic Technology 26, no. 4 (April 1, 2009): 769–77. http://dx.doi.org/10.1175/2008jtecha1129.1.
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Abstract The study uses storm tracking information to evaluate error statistics of satellite rain estimation at different maturity stages of storm life cycles. Two satellite rain retrieval products are used for this purpose: (i) NASA’s Multisatellite Precipitation Analysis–Real Time product available at 25-km/hourly resolution (3B41-RT) and (ii) the University of California (Irvine) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product available at 4-km–hourly resolution. Both algorithms use geostationary satellite infrared (IR) observations calibrated to an array of passive microwave (PM) earth-orbiting satellite sensor rain retrievals. The techniques differ in terms of algorithmic structure and in the way they use the PM rainfall to calibrate the IR rain algorithms. The satellite retrievals are evaluated against rain gauge–calibrated radar rainfall estimates over the continental United States. Error statistics of hourly rain volumes are determined separately for thunderstorm and shower-type convective systems and for different storm life durations and stages of maturity. The authors show distinct differences between the two satellite retrieval error characteristics. The most notable difference is the strong storm life cycle dependence of 3B41-RT relative to the nearly independent PERSIANN behavior. Another is in the algorithm performance between thunderstorms and showers; 3B41-RT exhibits significant bias increase at longer storm life durations. PERSIANN exhibits consistently improved correlations relative to the 3B41-RT for all storm life durations and maturity stages. The findings of this study support the hypothesis that incorporating cloud type information into the retrieval (done by the PERSIANN algorithm) can help improve the satellite retrieval accuracy.
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Thiemig, Vera, Rodrigo Rojas, Mauricio Zambrano-Bigiarini, Vincenzo Levizzani, and Ad De Roo. "Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins." Journal of Hydrometeorology 13, no. 6 (December 1, 2012): 1760–83. http://dx.doi.org/10.1175/jhm-d-12-032.1.
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Abstract Six satellite-based rainfall estimates (SRFE)—namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]—were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba–Shabelle, and Baro–Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-to-pixel, subcatchment, and river basin for the period 2003–06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs.
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Shukla, A. K., C. S. P. Ojha, and R. D. Garg. "Satellite-based estimation and validation of monthly rainfall distribution over Upper Ganga river basin." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 399–404. http://dx.doi.org/10.5194/isprsarchives-xl-8-399-2014.
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Water is one of the most precious natural resources for all living flora and fauna. 97.5% of water on the Earth is sea water, the remaining 2.5 % is fresh water of which slightly over two thirds is frozen in glaciers and polar ice caps. The unfrozen fresh water is mainly found as groundwater, with only a small fraction present above ground or in the air. Since one of the main source of water is rainfall. Therefore, proper information on rainfall and its variability in space and time is required for better watershed planning and management and other applications. In Himalayan basin, the rain gauge network is relatively sparse with uneven distribution. Hence, there is lack of proper information on rainfall patterns of this region. The main advantage of satellite derived rainfall estimation over rain gauge derived rain data is that these provide homogenous spatio-temporal rainfall information over a large area e.g. Upper Ganga river basin region. Therefore, a better understanding of the rainfall patterns of this region is required for better disaster mitigation. The objectives of this study are to evaluate the reliability of Tropical Rainfall Measuring Mission (TRMM) 3B43 V7 derived high resolution satellite product to study the rainfall distribution over the Upper Ganga river basin. TRMM 3B43 V7 derived monthly rainfall data is analyzed and the monthly rainfall product is validated and correlated with IMD (Indian Meteorological Department) gauge station's rainfall data. The monthly rainfall data of 15 years i.e. from 1998 to 2012 is used in the study. Statistical indices can be used to evaluate, compare and validate satellite rainfall data with respect to gauge rainfall data. Statistical indices used in this study are Correlation Coefficient (r), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Average Percentage Error (Avg. % Error). Most of the rainfall in the study area occurs in the months of June, July, August, September and October. The isohyets were prepared using gauge rainfall data and are matched with the spatially distributed rainfall surface prepared from TRMM satellite data for all the months of the rainy season of the study area. Kriging spatial interpolation method was used to generate the spatially distributed rainfall surface. From the results it was observe d that they matched fairly well with each other showing high spatial correlation. The monthly rainfall result showed that TRMM data is underestimated with low accuracy, though TRMM data and rain gauge data have positive correlation.
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Hu, Q., H. Yang, X. Meng, Y. Wang, and P. Deng. "Satellite and gauge rainfall merging using geographically weighted regression." Proceedings of the International Association of Hydrological Sciences 368 (May 6, 2015): 132–37. http://dx.doi.org/10.5194/piahs-368-132-2015.
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Abstract. A residual-based rainfall merging scheme using geographically weighted regression (GWR) has been proposed. This method is capable of simultaneously blending various satellite rainfall data with gauge measurements and could describe the non-stationary influences of geographical and terrain factors on rainfall spatial distribution. Using this new method, an experimental study on merging daily rainfall from the Climate Prediction Center Morphing dataset (CMOROH) and gauge measurements was conducted for the Ganjiang River basin, in Southeast China. We investigated the capability of the merging scheme for daily rainfall estimation under different gauge density. Results showed that under the condition of sparse gauge density the merging rainfall scheme is remarkably superior to the interpolation using just gauge data.
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Mambu, Joe Yuan. "GIS-Based Rainfall Estimator Evaluation and Interpolation Analysis Using ArcGIS." CogITo Smart Journal 4, no. 1 (June 28, 2018): 230. http://dx.doi.org/10.31154/cogito.v4i1.118.230-242.
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We have been always trying to predict the weather to minimize risk, produce strategy, and other decision making situation. To achieve this, monitoring method need to be used to gather data. Rainfall monitoring is one of the area that widely used and one of the mostly used method is satellite-based rainfall monitoring. However, there are limitation to apply to the Satellite Rainfall (SR) estimation specifically on its accuracy and lack of certainty. Thus, study that directed to measure the inaccuracies of SR measurements is needed by using data of distribution of Actual Rainfall (AR). The SR data is taken from the National Oceanic and Atmospheric Administration (NOAA)’s Hydro—Estimator while the AR data was from the National Institute of Water and Atmospheric Research (NIWA). Through the statistics and interpolation analysis using ArcGIS, the study shows a prominent result of SR estimation accuracy in the sample area and thus may opens up more similar implementation as well as stands as a good benchmark for future improvement of the method. This study also shows how interpolation method through a GIS software could provide a significant result on a geographical related studies. Keywords : GIS, Geospatial Analysis, Interpolation Analysis, Arcgis
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SINGH, CHARAN, SUNIT DAS, R. B. VERMA, B. L. VERMA, and B. K. BANDYOPADHYAY. "Rainfall estimation of landfalling tropical cyclones over Indian coasts through satellite imagery." MAUSAM 63, no. 2 (December 16, 2021): 193–202. http://dx.doi.org/10.54302/mausam.v63i2.1377.
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One of the most significant impacts of landfalling tropical cyclones is caused by the copiousrainfall associated with it. The main emphasis of present study is to provide some guidance to the operational forecastersfor indicating the possible rainfall over the areas likely to be affected by the cyclones after landfall. Study of 14 pastlandfalling cyclones reveals that the maximum rainfall occurred in the first forward quadrant of tropical cyclonemovement, followed by the second quadrant and the areas near the track of the cyclones. Isohyetal analysis of 24 hoursrainfall for each cyclone reveals that occurrence of heavy rainfall is generally confined up to 150 kms radius from thestorm centre and rainfall is found to generally extend up to 300 kms with gradual decrease in amount. The rainfallreceiving areas are mostly covered with convective clouds with cloud top temperatures of -80 to -60 ºC, prior to and afterthe landfall of the systems. In 93% of tropical cyclones out of the 14 cases studied, 70 % convection lay to the right of thetrack. To examine the rainfall asymmetry due to asymmetry in distribution of convection, cloud top temperatures derivedfrom satellite infrared imagery data have been taken as the proxy of strong convection. It is also revealed in the study thatthe slow moving tropical cyclones cause heavy rain rather than fast moving tropical cyclones. The Bay of Bengalcyclones which crossed coast as cyclonic storm and very severe cyclonic storm caused 71.4% rainfall within the range 0-10 cm, 22.8% rainfall in the range 11-20 cm and 4.3% rainfall within the range 21-30 cm in the area of radius of 300 kmsfrom the centre of the cyclonic storms. For the Arabian Sea tropical cyclones, in general, about 70% rainfall occurredwithin the range 16-25 cm in 24 hours.
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Worqlul, A. W., B. Maathuis, A. A. Adem, S. S. Demissie, S. Langan, and T. S. Steenhuis. "Comparison of TRMM, MPEG and CFSR rainfall estimation with the ground observed data for the Lake Tana Basin, Ethiopia." Hydrology and Earth System Sciences Discussions 11, no. 7 (July 14, 2014): 8013–38. http://dx.doi.org/10.5194/hessd-11-8013-2014.
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Abstract. Planning of drought relief and floods in developing countries is greatly hampered by lack of a sufficiently dense network of weather station 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: Tropical Rainfall Measuring Mission (TRMM) product (3B42), Multi-Sensor Precipitation Estimate-Geostationary (MPEG) and Climate Forecast System Reanalysis (CFSR). The accuracy of three products is tested in the Lake Tana Basin in Ethiopia where in 2010 38 weather stations were available with a full record of daily precipitation amounts. Daily grid satellite based rainfall estimates were compared to: (1) point observed ground rainfall (2) areal rainfall in the major river sub-basins of Lake Tana. The result shows that, the MPEG and CFSR satellite provided most accurate rainfall estimates. On the average for 38 stations 78 and 86% of the observed rainfall variation is explained by MPEG and CFSR data respectively while TRIMM 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 have also a lower RMSE compared to the TRMM satellite rainfall. The Bias indicated that, the MPEG is consistent in underestimating the observed rainfall while the TRMM and CFSR were not consistent; they overestimated for some and underestimated for the others.
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Monsieurs, Elise, Olivier Dewitte, and Alain Demoulin. "A susceptibility-based rainfall threshold approach for landslide occurrence." Natural Hazards and Earth System Sciences 19, no. 4 (April 15, 2019): 775–89. http://dx.doi.org/10.5194/nhess-19-775-2019.
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Abstract. Rainfall threshold determination is a pressing issue in the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering conditions for landsliding, the now well-established rainfall intensity or event-duration thresholds for landsliding suffer from several limitations. Here, we propose a new approach of the frequentist method for threshold definition based on satellite-derived antecedent rainfall estimates directly coupled with landslide susceptibility data. Adopting a bootstrap statistical technique for the identification of threshold uncertainties at different exceedance probability levels, it results in thresholds expressed as AR = (α±Δα)⋅S(β±Δβ), where AR is antecedent rainfall (mm), S is landslide susceptibility, α and β are scaling parameters, and Δα and Δβ are their uncertainties. The main improvements of this approach consist in (1) using spatially continuous satellite rainfall data, (2) giving equal weight to rainfall characteristics and ground susceptibility factors in the definition of spatially varying rainfall thresholds, (3) proposing an exponential antecedent rainfall function that involves past daily rainfall in the exponent to account for the different lasting effect of large versus small rainfall, (4) quantitatively exploiting the lower parts of the cloud of data points, most meaningful for threshold estimation, and (5) merging the uncertainty on landslide date with the fit uncertainty in a single error estimation. We apply our approach in the western branch of the East African Rift based on landslides that occurred between 2001 and 2018, satellite rainfall estimates from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TMPA 3B42 RT), and the continental-scale map of landslide susceptibility of Broeckx et al. (2018) and provide the first regional rainfall thresholds for landsliding in tropical Africa.
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Tamrakar, Bijaya, and Knut Alfredsen. "Satellite-Based Precipitation Estimation for Hydropower Development." Hydro Nepal: Journal of Water, Energy and Environment 12 (October 29, 2013): 52–58. http://dx.doi.org/10.3126/hn.v12i0.9033.
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Runoff is one of the major factors that govern the capacity of a hydropower project. Precipitation data are needed for estimation of runoff through runoff simulation using a hydrological model. Dense setup of rain gauge network in a mountainous topography is difficult and expensive. An alternative for this problem is the use of Satellite precipitation data with high spatial and temporal resolution. They have an additional advantage that they represent areal precipitation. But, these data should be duly evaluated before using them. In this study, Tropical Rainfall Measuring Mission (TRMM 3B42) precipitation data are evaluated using ground based precipitation stations over Nepal and fed in a rainfall-runoff model to estimate monthly discharge through four of the major basins of Nepal. A simple water balance model has been used, initially developed by Thornthwaite. Statistical parameters showed significant under-estimation of precipitation over major areas of Nepal. The results from the water balance model presented quiet a good estimation of discharge through basins with an average Nash Sutcliffe Efficiency (R²) value of 0.8. This implies that TRMM data can be used for runoff simulations over Nepal. The TRMM satellite data can be used during the planning stage of hydropower projects as well as on ungauged catchments. Hydro Nepal: Journal of Water, Energy and Environment Vol. 12, 2013, January Page: 52-58DOI: http://dx.doi.org/10.3126/hn.v12i0.9033 Uploaded Date : 10/29/2013
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Delgado, G., Luiz A. T. Machado, Carlos F. Angelis, Marcus J. Bottino, Á. Redaño, J. Lorente, L. Gimeno, and R. Nieto. "Basis for a Rainfall Estimation Technique Using IR–VIS Cloud Classification and Parameters over the Life Cycle of Mesoscale Convective Systems." Journal of Applied Meteorology and Climatology 47, no. 5 (May 1, 2008): 1500–1517. http://dx.doi.org/10.1175/2007jamc1684.1.
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Abstract This paper discusses the basis for a new rainfall estimation method using geostationary infrared and visible data. The precipitation radar on board the Tropical Rainfall Measuring Mission satellite is used to train the algorithm presented (which is the basis of the estimation method) and the further intercomparison. The algorithm uses daily Geostationary Operational Environmental Satellite infrared–visible (IR–VIS) cloud classifications together with radiative and evolution properties of clouds over the life cycle of mesoscale convective systems (MCSs) in different brightness temperature (Tb) ranges. Despite recognition of the importance of the relationship between the life cycle of MCSs and the rainfall rate they produce, this relationship has not previously been quantified precisely. An empirical relationship is found between the characteristics that describe the MCSs’ life cycle and the magnitude of rainfall rate they produce. Numerous earlier studies focus on this subject using cloud-patch or pixel-based techniques; this work combines the two techniques. The algorithm performs reasonably well in the case of convective systems and also for stratiform clouds, although it tends to overestimate rainfall rates. Despite only using satellite information to initialize the algorithm, satisfactory results were obtained relative to the hydroestimator technique, which in addition to the IR information uses extra satellite data such as moisture and orographic corrections. This shows that the use of IR–VIS cloud classification and MCS properties provides a robust basis for creating a future estimation method incorporating humidity Eta field outputs for a moisture correction, digital elevation models combined with low-level moisture advection for an orographic correction, and a nighttime cloud classification.
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Meza-Ruiz, Marilu, and Alfonso Gutierrez-Lopez. "Goes-13 IR Images for Rainfall Forecasting in Hurricane Storms." Forecasting 2, no. 2 (April 30, 2020): 85–101. http://dx.doi.org/10.3390/forecast2020005.
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Currently, it is possible to access a large amount of satellite weather information from monitoring and forecasting severe storms. However, there are no methods of employing satellite images that can improve real-time early warning systems in different regions of Mexico. The auto-estimator is the most commonly used technique that was developed for specific locations in the United States of America (32°–49° latitude) for the type of convective storms. However, the estimation of precipitation intensities for meteorological conditions in tropic latitudes, using the auto-estimator technique, needs to be re-adjusted and calibrated. It is necessary to improve this type of technique that allows decision-makers to have hydro-informatic tools capable of improving early warning systems in tropical regions (15°–25° Mexican tropic latitude). The main objective of the work is to estimate rainfall from satellite imagery in the infrared (IR) spectrum from the Geostationary Operational Environmental Satellite (GOES), validating these estimates with a network of surface rain gauges. Using the GOES-13 IR images every 15 min and using the auto-estimator, a downscaling of six hurricanes was performed from which surface precipitation events were measured. The two main difficulties were to match the satellite images taken every 15 min with the surface data measured every 10 min and to develop a program in C+ that would allow the systematic analysis of the images. The results of this work allow us to get a new adjustment of coefficients in a new equation of the auto-estimator, valid for rain produced by hurricanes, something that has not been done until now. Although no universal relationship has been found for hurricane rainfall, it is evident that the original formula of the auto-estimator technique needs to be modified according to geographical latitude.
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Kidd, C., and V. Levizzani. "Status of satellite precipitation retrievals." Hydrology and Earth System Sciences Discussions 7, no. 5 (October 18, 2010): 8157–77. http://dx.doi.org/10.5194/hessd-7-8157-2010.
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Abstract. Satellites offer an unrivalled vantage point to observe and measure Earth system processes and parameters. Precipitation (rain and snow) in particular, benefit from such observations since precipitation is spatially and temporally highly variable and overcomes some of the deficiencies of conventional gauge and radar measurements. This paper provides an overall review of quantitative precipitation estimation, covering the basis of the satellite systems used in the observation of precipitation and the dissemination of this data, the processing of these measurements to generate the rainfall estimates and the availability, verification and validation of these precipitation estimates.
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Meyer, Hanna, Johannes Drönner, and Thomas Nauss. "Satellite-based high-resolution mapping of rainfall over southern Africa." Atmospheric Measurement Techniques 10, no. 6 (June 6, 2017): 2009–19. http://dx.doi.org/10.5194/amt-10-2009-2017.
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Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010–2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.
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Bergès, J. C., I. Jobard, F. Chopin, and R. Roca. "EPSAT-SG: a satellite method for precipitation estimation; its concepts and implementation for the AMMA experiment." Annales Geophysicae 28, no. 1 (January 27, 2010): 289–308. http://dx.doi.org/10.5194/angeo-28-289-2010.
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Abstract. This paper presents a new rainfall estimation method, EPSAT-SG which is a frame for method design. The first implementation has been carried out to meet the requirement of the AMMA database on a West African domain. The rainfall estimation relies on two intermediate products: a rainfall probability and a rainfall potential intensity. The first one is computed from MSG/SEVIRI by a feed forward neural network. First evaluation results show better properties than direct precipitation intensity assessment by geostationary satellite infra-red sensors. The second product can be interpreted as a conditional rainfall intensity and, in the described implementation, it is extracted from GPCP-1dd. Various implementation options are discussed and comparison of this embedded product with 3B42 estimates demonstrates the importance of properly managing the temporal discontinuity. The resulting accumulated rainfall field can be presented as a GPCP downscaling. A validation based on ground data supplied by AGRHYMET (Niamey) indicates that the estimation error has been reduced in this process. The described method could be easily adapted to other geographical area and operational environment.