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Journal articles on the topic 'Satellite rainfall estimates'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Shige, Shoichi, Satoshi Kida, Hiroki Ashiwake, Takuji Kubota, and Kazumasa Aonashi. "Improvement of TMI Rain Retrievals in Mountainous Areas." Journal of Applied Meteorology and Climatology 52, no. 1 (2013): 242–54. http://dx.doi.org/10.1175/jamc-d-12-074.1.

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AbstractHeavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical m

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Itkin, M., and A. Loew. "Multi-satellite rainfall sampling error estimates – a comparative study." Hydrology and Earth System Sciences Discussions 9, no. 10 (2012): 11677–706. http://dx.doi.org/10.5194/hessd-9-11677-2012.

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Abstract. This study focus is set on quantifying sampling related uncertainty in the satellite rainfall estimates. We conduct observing system simulation experiment to estimate sampling error for various constellations of Low-Earth orbiting and geostationary satellites. There are two types of microwave instruments currently available: cross track sounders and conical scanners. We evaluate the differences in sampling uncertainty for various satellite constellations that carry instruments of the common type as well as in combination with geostationary observations. A precise orbital model is use

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3

Harsa, Hastuadi, Agus Buono, Rahmat Hidayat, et al. "Fine-tuning satellite-based rainfall estimates." IOP Conference Series: Earth and Environmental Science 149 (May 2018): 012047. http://dx.doi.org/10.1088/1755-1315/149/1/012047.

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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

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Dietrich, S., D. Casella, F. Di Paola, M. Formenton, A. Mugnai, and P. Sanò. "Lightning-based propagation of convective rain fields." Natural Hazards and Earth System Sciences 11, no. 5 (2011): 1571–81. http://dx.doi.org/10.5194/nhess-11-1571-2011.

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Abstract. This paper describes a new multi-sensor approach for continuously monitoring convective rain cells. It exploits lightning data from surface networks to propagate rain fields estimated from multi-frequency brightness temperature measurements taken by the AMSU/MHS microwave radiometers onboard NOAA/EUMETSAT low Earth orbiting operational satellites. Specifically, the method allows inferring the development (movement, morphology and intensity) of convective rain cells from the spatial and temporal distribution of lightning strokes following any observation by a satellite-borne microwave

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Chua, Zhi-Weng, Yuriy Kuleshov, and Andrew Watkins. "Evaluation of Satellite Precipitation Estimates over Australia." Remote Sensing 12, no. 4 (2020): 678. http://dx.doi.org/10.3390/rs12040678.

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This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yie

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Sahlu, Dejene, Semu A. Moges, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Dereje Hailu. "Evaluation of High-Resolution Multisatellite and Reanalysis Rainfall Products over East Africa." Advances in Meteorology 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/4957960.

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The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Amon

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Areerachakul, Nathaporn, Jaya Kandasamy, Saravanamuthu Vigneswaran, and Kittitanapat Bandhonopparat. "Refining Rainfall Derived from Satellite Radar for Estimating Inflows at Lam Pao Dam, Thailand." Hydrology 12, no. 7 (2025): 163. https://doi.org/10.3390/hydrology12070163.

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This project aimed to evaluate the use of meteorological satellite-derived rainfall data to estimate water inflows to dams. In this study, the Lam Pao Dam in the Chi Basin, Thailand, was used as a case study. Rainfall data were obtained using the PERSIANN technique. To improve accuracy, satellite-derived rainfall estimates were adjusted using ground-based rainfall measurements from stations located near and within the catchment area, applying the 1-DVAR method. The Kriging method was employed to estimate the spatial distribution of rainfall over the catchment area. This approach resulted in a

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9

Le Coz, Camille, Arnold Heemink, Martin Verlaan, Marie-claire ten Veldhuis, and Nick van de Giesen. "Correcting Position Error in Precipitation Data Using Image Morphing." Remote Sensing 11, no. 21 (2019): 2557. http://dx.doi.org/10.3390/rs11212557.

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Rainfall estimates based on satellite data are subject to errors in the position of the rainfall events in addition to errors in their intensity. This is especially true for localized rainfall events such as the convective rainstorms that occur during the monsoon season in sub-Saharan Africa. Many satellite-based estimates use gauge information for bias correction. However, bias adjustment methods do not correct the position errors explicitly. We propose to gauge-adjust satellite-based estimates with respect to the position using a morphing method. Image morphing transforms an image, in our ca

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10

GAUTAM, NARAYAN PRASAD, SUSUMU FUJIOKA, and KAZUHIKO FUKAMI. "Analyzing the heavy rainfall event of July 2011 in Niigata using ground, satellite and radar rainfalls." MAUSAM 75, no. 4 (2024): 1117–24. http://dx.doi.org/10.54302/mausam.v75i4.3566.

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In recent years the application of radar and satellite precipitation observations has become increasingly useful in poorly gauged areas. However, understanding the relationships between these data sources and ground observations is vital to correct the datasets and improve their application in hydrological studies. In this study we analyzed the spatial and temporal relationships between Global Satellite Mapping of Precipitation-Near Real Time (GSMaP_NRT) data set with radar and ground observations at downstream of Shinano River, Japan. GSMap_NRT observation showed better relationship with grou

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Pandey, Ashish, S. K. Mishra, and Amar Kant Gautam. "Soil Erosion Modeling Using Satellite Rainfall Estimates." Journal of Water Resource and Hydraulic Engineering 4, no. 4 (2015): 318–25. http://dx.doi.org/10.5963/jwrhe0404002.

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Mganu Manyatsi, Absalom. "Evaluation of Satellite Rainfall Estimates for Swaziland." American Journal of Agriculture and Forestry 3, no. 3 (2015): 93. http://dx.doi.org/10.11648/j.ajaf.20150303.15.

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13

Young, Matthew P., Charles J. R. Williams, J. Christine Chiu, Ross I. Maidment, and Shu-Hua Chen. "Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia." Journal of Hydrometeorology 15, no. 6 (2014): 2347–69. http://dx.doi.org/10.1175/jhm-d-13-0111.1.

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Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mount

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Utomo, Joko Budi, Eko Yuli Handoko, Muhammad Aldila Syariz, and Ardhasena Sopaheluwakan. "Developing Surface Rainfall Data Based on Blending of Satellite-Based Products and Rain Gauge Observations in the Ngawi Region, East Java." JURNAL ILMU FISIKA | UNIVERSITAS ANDALAS 17, no. 2 (2025): 110–24. https://doi.org/10.25077/jif.17.2.110-124.2025.

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Rainfall estimation can be performed using various methods, including direct satellite observations (RR-Satellite). However, these estimates show discrepancies when compared to actual observations in-situ rain gauges (RR-Obs). To address this challenge, one potential solution is integrating RR-Satellite with RR-Obs. The Kriging with External Drift (KED) interpolation method is a blending technique that incorporates RR-Satellite as external drift. This study utilized four satellite dataset, namely CHIRP, CMORPH, GSMAP_V8, and IMERG as auxiliary information to generate monthly rainfall estimates

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Ozturk, U., H. Saito, Y. Matsushi, I. Crisologo, and W. Schwanghart. "Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting?" Landslides 18, no. 9 (2021): 3119–33. http://dx.doi.org/10.1007/s10346-021-01689-3.

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AbstractPredicting rainfall-induced landslides hinges on the quality of the rainfall product. Satellite rainfall estimates or rainfall reanalyses aid in studying landslide occurrences especially in ungauged areas, or in the absence of ground-based rainfall radars. Quality of these rainfall estimates is critical; hence, they are commonly crosschecked with their ground-based counterparts. Beyond their temporal precision compared to ground-based observations, we investigate whether these rainfall estimates are adequate for hindcasting landslides, which particularly requires accurate representatio

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Fatkhuroyan, Fatkhuroyan, Trinah Wati, Alfan Sukmana, and Roni Kurniawan. "Validation of Satellite Daily Rainfall Estimates Over Indonesia." Forum Geografi 32, no. 2 (2018): 170–80. http://dx.doi.org/10.23917/forgeo.v32i2.6288.

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Rainfall is the most important factor in the Earth’s water and energy cycles. The aim of this research is to evaluate the accuracy of Global Satellite Mapping of Rainfall (GSMaP) data by referencing daily rain-gauged rainfall measurements across the Indonesian Maritime Continent. We compare the daily rainfall data from GSMaP Moving Kalman Filter (MVK) to readings from 152 rain-gauge observation stations across Indonesia from March 2014 to December 2017. The results show that the correlation coefficient (CC) provides better validation in the rainy season while root mean square error (RMSE) is m

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Molina-Aguilar, Juan Pablo, Bruno Paz-Aviña, Josué Elizondo-Gómez, and Miguel Ángel Sánchez Quijano. "Acoplamiento de estimaciones de precipitación basadas en imágenes satelitales, con registros pluviométricos." Aqua-LAC 11, no. 1 (2019): 77–92. http://dx.doi.org/10.29104/phi-aqualac/2019-v11-1-06.

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La estimación de precipitación en tiempo real a partir de imágenes satelitales digitales (ISD) es una metodología ampliamente utilizada por meteorólogos e hidrólogos, su aplicación sobre una región superficial es indirecta, en la cual las resoluciones temporal y espacial de la información definen la precisión, los resultados obtenidos deben validarse empleando registros de redes pluviométricas. La finalidad del presente trabajo es presentar una metodología de acoplamiento temporal y espacial (ATE), para información con resolución de 15 minutos. La información empleada corresponde a valores del

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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 (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

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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 (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 Eth

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20

Mohsan, Muhammad, Ralph Allen Acierto, Akiyuki Kawasaki, and Win Win Zin. "Preliminary Assessment of GPM Satellite Rainfall over Myanmar." Journal of Disaster Research 13, no. 1 (2018): 22–30. http://dx.doi.org/10.20965/jdr.2018.p0022.

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Intensive and long-term rainfall in Myanmar causes floods and landslides that affect thousands of people every year. However, the rainfall observation network is still limited in number and extent, so satellite rainfall products have been shown to supplement observations over the ungauged areas. One example is the estimates from Global Precipitation Measurement (GPM) called Integrated Multi-satellite Retrievals for GPM (IMERG), which has high spatial (0.1 × 0.1 degree) and temporal (30 min) resolution. This has potential to be used for modeling streamflow, early warnings, and forecasting syste

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Nikolopoulos, Efthymios I., Emmanouil N. Anagnostou, and Marco Borga. "Using High-Resolution Satellite Rainfall Products to Simulate a Major Flash Flood Event in Northern Italy." Journal of Hydrometeorology 14, no. 1 (2013): 171–85. http://dx.doi.org/10.1175/jhm-d-12-09.1.

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Abstract Effective flash flood warning procedures are usually hampered by observational limitations of precipitation over mountainous basins where flash floods occur. Satellite rainfall estimates are available over complex terrain regions, offering a potentially viable solution to the observational coverage problem. However, satellite estimates of heavy rainfall rates are associated with significant biases and random errors that nonlinearly propagate in hydrologic modeling, imposing severe limitations on the use of these products in flood forecasting. In this study, the use of three quasi-glob

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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 (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 So

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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 (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 limitation

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Emberson, Robert A. "Dynamic rainfall erosivity estimates derived from IMERG data." Hydrology and Earth System Sciences 27, no. 19 (2023): 3547–63. http://dx.doi.org/10.5194/hess-27-3547-2023.

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Abstract. Soil degradation is a critical threat to agriculture and food security around the world. Understanding the processes that drive soil erosion is necessary to support sustainable management practices and to reduce eutrophication of water systems from fertilizer runoff. The erosivity of precipitation is a primary control on the rate of soil erosion, but to calculate erosivity high-frequency precipitation data are required. Prior global-scale analysis has almost exclusively used ground-based rainfall gauges to calculate erosivity, but the advent of high-frequency satellite rainfall data

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Noraisyah, Tajudin, Ya'acob Norsuzila, Mohd Ali Darmawaty, and Aizam Adnan Nor. "Estimation of TRMM rainfall for landslide occurrences based on rainfall threshold analysis." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 3208–15. https://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 ra

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Dinku, Tufa, Pietro Ceccato, Keith Cressman, and Stephen J. Connor. "Evaluating Detection Skills of Satellite Rainfall Estimates over Desert Locust Recession Regions." Journal of Applied Meteorology and Climatology 49, no. 6 (2010): 1322–32. http://dx.doi.org/10.1175/2010jamc2281.1.

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Abstract This paper evaluates rainfall detection capabilities of seven satellite rainfall estimates over the desert locust recession regions of the world. The region of interest covers the arid and semiarid region from northwestern Africa to northwestern India. The evaluated satellite rainfall products are the African rainfall climatology (ARC), rainfall estimation algorithm (RFE), Tropical Rainfall Measuring Mission 3B42 and its real-time version (3B42RT), NOAA/Climate Prediction Center morphing technique (CMORPH), and two versions of the Global Satellite Mapping of Precipitation moving vecto

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du Plessis, J. A., and J. K. Kibii. "Applicability of CHIRPS-based satellite rainfall estimates for South Africa." Journal of the South African Institution of Civil Engineering 63, no. 3 (2021): 1–12. http://dx.doi.org/10.17159/2309-8775/2021/v63n3a4.

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Long-term rainfall data with good spatial and temporal distribution is essential for all climate-related analyses. The availability of observed rainfall data has become increasingly problematic over the years due to a limited and deteriorating rainfall station network, occasioned by limited reporting and/or quality control of rainfall and, in some cases, closure of these stations. Remotely sensed satellite-based rainfall data sets offer an alternative source of information. In this study, daily and monthly rainfall data derived from Climate Hazards Group InfraRed Precipitation (CHIRPS) is comp

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Kumah, Kingsley K., Joost C. B. Hoedjes, Noam David, Ben H. P. Maathuis, H. Oliver Gao, and Bob Z. Su. "The MSG Technique: Improving Commercial Microwave Link Rainfall Intensity by Using Rain Area Detection from Meteosat Second Generation." Remote Sensing 13, no. 16 (2021): 3274. http://dx.doi.org/10.3390/rs13163274.

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Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation (MSG) satellite data to improve MWL rainfall estimates. The investigation, conducted during daytime, used MSG optical (VIS0.6) and near IR (NIR1.6) data to estimate rain areas along a 15 GHz, 9.88 km MWL for classifying the MWL signal into wet–dry periods and estimate the baseline level. Additiona

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Palharini, Rayana Santos Araujo, Daniel Alejandro Vila, Daniele Tôrres Rodrigues, et al. "Assessment of the Extreme Precipitation by Satellite Estimates over South America." Remote Sensing 12, no. 13 (2020): 2085. http://dx.doi.org/10.3390/rs12132085.

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In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These

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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 (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) satelli

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Romilly, T. G., and M. Gebremichael. "Evaluation of satellite rainfall estimates over Ethiopian river basins." Hydrology and Earth System Sciences 15, no. 5 (2011): 1505–14. http://dx.doi.org/10.5194/hess-15-1505-2011.

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Abstract. High resolution satellite-based rainfall estimates (SREs) have enormous potential for use in hydrological applications, particularly in the developing world as an alternative to conventional rain gauges which are typically sparse. In this study, three SREs have been evaluated against collocated rain gauge measurements in Ethiopia across six river basins that represent different rainfall regimes and topography. The comparison is made using five-year (2003–2007) averages, and results are stratified by river basin, elevation and season. The SREs considered are: the Climate Prediction Ce

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Chang, Alfred T. C., and Long S. Chiu. "Uncertainty in satellite rainfall estimates: Time series comparison." Advances in Space Research 19, no. 3 (1997): 469–72. http://dx.doi.org/10.1016/s0273-1177(97)00056-2.

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Bezak, Nejc, Pasquale Borrelli, and Panos Panagos. "Exploring the possible role of satellite-based rainfall data in estimating inter- and intra-annual global rainfall erosivity." Hydrology and Earth System Sciences 26, no. 7 (2022): 1907–24. http://dx.doi.org/10.5194/hess-26-1907-2022.

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Abstract. Despite recent developments in modeling global soil erosion by water, to date, no substantial progress has been made towards more dynamic inter- and intra-annual assessments. In this regard, the main challenge is still represented by the limited availability of high temporal resolution rainfall data needed to estimate rainfall erosivity. As the availability of high temporal resolution rainfall data will most likely not increase in future decades since the monitoring networks have been declining since the 1980s, the suitability of alternative approaches to estimate global rainfall ero

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Fisher, Brad L. "Statistical Error Decomposition of Regional-Scale Climatological Precipitation Estimates from the Tropical Rainfall Measuring Mission (TRMM)." Journal of Applied Meteorology and Climatology 46, no. 6 (2007): 791–813. http://dx.doi.org/10.1175/jam2497.1.

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Abstract Monthly rainfall estimates inferred from the NASA Tropical Rainfall Measuring Mission (TRMM) satellite contain errors due to discrete temporal sampling and remote spaceborne rain retrievals. This paper develops a regional-scale error model that uses the rain information in the ground data to disentangle the sampling and retrieval errors in the satellite estimate statistically. The proposed method computes a mean rain rate from monthly rainfall statistics for each TRMM rain sensor by subsampling high-resolution ground-based rain data at satellite overpass times. This additional rain-su

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Santos, Luiz Octavio Fabricio dos, Carlos Alexandre Santos Querino, Juliane Kayse Albuquerque da Silva Querino, et al. "Validation of rainfall data estimated by GPM satellite on Southern Amazon region." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 14, no. 1 (2019): 1. http://dx.doi.org/10.4136/ambi-agua.2249.

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Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The sate

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Ponukumati, Padmini, Azharuddin Mohammed, and Satish Regonda. "Insights on Satellite-Based IMERG Precipitation Estimates at Multiple Space and Time Scales for a Developing Urban Region in India." Journal of Hydrometeorology 24, no. 6 (2023): 977–96. http://dx.doi.org/10.1175/jhm-d-22-0160.1.

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Abstract Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall eve

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Crow, Wade T., George J. Huffman, Rajat Bindlish, and Thomas J. Jackson. "Improving Satellite-Based Rainfall Accumulation Estimates Using Spaceborne Surface Soil Moisture Retrievals." Journal of Hydrometeorology 10, no. 1 (2009): 199–212. http://dx.doi.org/10.1175/2008jhm986.1.

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Abstract Over land, remotely sensed surface soil moisture and rainfall accumulation retrievals contain complementary information that can be exploited for the mutual benefit of both product types. Here, a Kalman filtering–based tool is developed that utilizes a time series of spaceborne surface soil moisture retrievals to enhance short-term (2- to 10-day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated

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Maggioni, Viviana, Rolf H. Reichle, and Emmanouil N. Anagnostou. "The Efficiency of Assimilating Satellite Soil Moisture Retrievals in a Land Data Assimilation System Using Different Rainfall Error Models." Journal of Hydrometeorology 14, no. 1 (2013): 368–74. http://dx.doi.org/10.1175/jhm-d-12-0105.1.

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Abstract The efficiency of assimilating near-surface soil moisture retrievals from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) observations in a Land Data Assimilation System (LDAS) is assessed using satellite rainfall forcing and two different satellite rainfall error models: a complex, multidimensional satellite rainfall error model (SREM2D) and the simpler (control) model (CTRL) used in the NASA Goddard Earth Observing System Model, version 5 LDAS. For the study domain of Oklahoma, LDAS soil moisture estimates improve over the satellite retrievals and the open

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Ageet, Simon, Andreas H. Fink, Marlon Maranan, et al. "Validation of Satellite Rainfall Estimates over Equatorial East Africa." Journal of Hydrometeorology 23, no. 2 (2022): 129–51. http://dx.doi.org/10.1175/jhm-d-21-0145.1.

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Abstract Rain gauge data sparsity over Africa is known to impede the assessments of hydrometeorological risks and of the skill of numerical weather prediction models. Satellite rainfall estimates (SREs) have been used as surrogate fields for a long time and are continuously replaced by more advanced algorithms and new sensors. Using a unique daily rainfall dataset from 36 stations across equatorial East Africa for the period 2001–18, this study performs a multiscale evaluation of gauge-calibrated SREs, namely, IMERG, TMPA, CHIRPS, and MSWEP (v2.2 and v2.8). Skills were assessed from daily to a

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Bellerby, T. J. "Ensemble Representation of Uncertainty in Lagrangian Satellite Rainfall Estimates." Journal of Hydrometeorology 14, no. 5 (2013): 1483–99. http://dx.doi.org/10.1175/jhm-d-12-0121.1.

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Abstract A new algorithm called Lagrangian Simulation (LSIM) has been developed that enables the interpolation uncertainty present in Lagrangian satellite rainfall algorithms such as the Climate Prediction Center (CPC) morphing technique (CMORPH) to be characterized using an ensemble product. The new algorithm generates ensemble sequences of rainfall fields conditioned on multiplatform multisensor microwave satellite data, demonstrating a conditional simulation approach that overcomes the problem of discontinuous uncertainty fields inherent in this type of product. Each ensemble member is cons

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Wiwoho, Bagus Setiabudi, Ike Sari Astuti, Imam Abdul Gani Alfarizi, and Hetty Rahmawati Sucahyo. "Validation of Three Daily Satellite Rainfall Products in a Humid Tropic Watershed, Brantas, Indonesia: Implications to Land Characteristics and Hydrological Modelling." Hydrology 8, no. 4 (2021): 154. http://dx.doi.org/10.3390/hydrology8040154.

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A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and qualitative accuracy indicators, as well as R2 and NSE from hydrological estimates, were used as the performance measures. The results show that all of the satellite estimates are unsatisfactory, giving the NRMSE ranging from 6 to 30% at a daily level, with CC only 0.21–0.36. Limited number of gauges, coarse spatial data resolution, and physic

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Zhou, Yuanyuan, Nianxiu Qin, Qiuhong Tang, Huabin Shi, and Liang Gao. "Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework." Remote Sensing 13, no. 6 (2021): 1057. http://dx.doi.org/10.3390/rs13061057.

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The accuracy of the rain distribution could be enhanced by assimilating the remotely sensed and gauge-based precipitation data. In this study, a new nonparametric general regression (NGR) framework was proposed to assimilate satellite- and gauge-based rainfall data over southeast China (SEC). The assimilated rainfall data in Meiyu and Typhoon seasons, in different months, as well as during rainfall events with various rainfall intensities were evaluated to assess the performance of this proposed framework. In rainy season (Meiyu and Typhoon seasons), the proposed method obtained the estimates

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Dinku, Tufa, Franklyn Ruiz, Stephen J. Connor, and Pietro Ceccato. "Validation and Intercomparison of Satellite Rainfall Estimates over Colombia." Journal of Applied Meteorology and Climatology 49, no. 5 (2010): 1004–14. http://dx.doi.org/10.1175/2009jamc2260.1.

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Abstract Seven different satellite rainfall estimates are evaluated at daily and 10-daily time scales and a spatial resolution of 0.25° latitude/longitude. The reference data come from a relatively dense station network of about 600 rain gauges over Colombia. This region of South America has a very complex terrain with mountain ranges that form the northern tip of the Andes Mountains, valleys between the mountain ranges, and a vast plain that is part of the Amazon. The climate is very diverse with an extremely wet Pacific coast, a dry region in the north, and different rainfall regimes between

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Shi, Tingting, Xiaomei Yang, George Christakos, Jinfeng Wang, and Li Liu. "Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates." Atmosphere 6, no. 9 (2015): 1307–26. http://dx.doi.org/10.3390/atmos6091307.

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Yu, Zifeng, Hui Yu, Peiyan Chen, Chuanhai Qian, and Caijun Yue. "Verification of Tropical Cyclone–Related Satellite Precipitation Estimates in Mainland China." Journal of Applied Meteorology and Climatology 48, no. 11 (2009): 2227–41. http://dx.doi.org/10.1175/2009jamc2143.1.

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Abstract To evaluate the abilities of satellite retrievals in reflecting precipitation features related to tropical cyclones (TCs) affecting mainland China, four years of 6- and 24-h precipitation retrievals from three datasets, namely the Tropical Rainfall Measuring Mission satellite algorithm 3B42, version 6 (3B42), Climate Prediction Center morphed (CMORPH) product, and one based on the Geostationary Meteorological Satellite-5 infrared brightness temperature (GMS5-TBB), are compared statistically with direct measurements from surface gauge rainfall data during the periods affected by TCs. T

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Nikolopoulos, E. I., N. S. Bartsotas, E. N. Anagnostou, and G. Kallos. "Using High-Resolution Numerical Weather Forecasts to Improve Remotely Sensed Rainfall Estimates: The Case of the 2013 Colorado Flash Flood." Journal of Hydrometeorology 16, no. 4 (2015): 1742–51. http://dx.doi.org/10.1175/jhm-d-14-0207.1.

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Abstract The September 2013 flash flood–triggering rainfall event in Colorado highlighted the strong underestimation of remote sensing techniques over mountainous terrain. In this work, the use of high-resolution rainfall forecasts for adjusting weather radar– [Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (Q3)] and satellite-based [CPC morphing technique (CMORPH) and TRMM 3B42RT] rainfall estimates is examined. Evaluation of the adjustment procedures is based on the NCEP Stage IV product. Results show that 1-km-grid-resolution rainfall forecasts provided by a numerical

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Vergara, Humberto, Yang Hong, Jonathan J. Gourley, et al. "Effects of Resolution of Satellite-Based Rainfall Estimates on Hydrologic Modeling Skill at Different Scales." Journal of Hydrometeorology 15, no. 2 (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

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Brasil Neto, Reginaldo Moura, Celso Augusto Guimarães Santos, Thiago Victor Medeiros do Nascimento, Richarde Marques da Silva, and Carlos Antonio Costa dos Santos. "Evaluation of the TRMM Product for Monitoring Drought over Paraíba State, Northeastern Brazil: A Statistical Analysis." Remote Sensing 12, no. 14 (2020): 2184. http://dx.doi.org/10.3390/rs12142184.

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Drought is a natural phenomenon that originates from the absence of precipitation over a certain period and is capable of causing damage to societal development. With the advent of orbital remote sensing, rainfall estimates from satellites have appeared as viable alternatives to monitor natural hazards in ungauged basins and complex areas of the world; however, the accuracies of these orbital products still need to be verified. Thus, this work aims to evaluate the performance of Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates in monitoring the spatiotemporal behavior of

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Abdulrazzaq, Zaidoon, Nadia Aziz, and Abdulkareem Mohammed. "Flood modelling using satellite-based precipitation estimates and digital elevation model in eastern Iraq." International Journal of Advanced Geosciences 6, no. 1 (2018): 72. http://dx.doi.org/10.14419/ijag.v6i1.8946.

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Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. The study conducted in Wasit province/Eastern Iraq when a flood occurs due to heavy rainfall in May 2013. In this study the capability of Tropical Rainfall Measuring Mission (TRMM) rainfall daily data have been used to estimate the relationship between measured precipitation and the Digital Elevation Model (DEM), also to study the relationship between rainfall intensity and flood waters are

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Wolff, David B., D. A. Marks, E. Amitai, et al. "Ground Validation for the Tropical Rainfall Measuring Mission (TRMM)." Journal of Atmospheric and Oceanic Technology 22, no. 4 (2005): 365–80. http://dx.doi.org/10.1175/jtech1700.1.

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Abstract An overview of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) Program is presented. This ground validation (GV) program is based at NASA Goddard Space Flight Center in Greenbelt, Maryland, and is responsible for processing several TRMM science products for validating space-based rain estimates from the TRMM satellite. These products include gauge rain rates, and radar-estimated rain intensities, type, and accumulations, from four primary validation sites (Kwajalein Atoll, Republic of the Marshall Islands; Melbourne, Florida; Houston, Texas; and Darwin, Australia

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