Relevant bibliographies by topics / Satellite rainfall estimation

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Academic literature on the topic 'Satellite rainfall estimation'

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Published: 6 September 2023

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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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Dissertations / Theses on the topic "Satellite rainfall estimation"

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D'Souza, G. "Rainfall estimation over Africa using satellite data." Thesis, University of Bristol, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384497.

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Hsu, Kuo-Lin, Soroosh Sorooshian, Xiaogang Gao, and Hoshin Vijai Gupta. "Rainfall estimation from satellite infrared imagery using artificial neural networks." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1997. http://hdl.handle.net/10150/615703.

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Infrared (IR) imagery collected by geostationary satellites provides useful information about the dirunal evolution of cloud systems. These IR images can be analyzed to indicate the location of clouds as well as the pattern of cloud top temperatures (Tbs). During the past several decades, a number of different approaches for estimation of rainfall rate (RR) from Tb have been explored and concluded that the Tb-RR relationship is (1) highly nonlinear, and (2) seasonally and regionally dependent. Therefore, to properly model the relationship, the model must be able to: (1) detect and identify a non-linear mapping of the Tb-RR relationship; (2) Incorporate information about various cloud properties extracted from IR image; (3) Use feedback obtained from RR observations to adaptively adjust to seasonal and regional variations; and (4) Effectively and efficiently process large amounts of satellite image data in real -time. In this study, a kind of artificial neural network (ANN), called Modified Counter Propagation Network (MCPN), that incorporates these features, has been developed. The model was calibrated using the data around the Japanese Islands provided by the Global Precipitation Climatology Project (GPCP) First Algorithm Intercomparison Project (AIP-I). Validation results over the Japanese Islands and Florida peninsula show that by providing limited ground-truth observation, the MCPN model is effective in monthly and hourly rainfall estimation. Comparison of results from MCPN model and GOES Precipitation Index (GPI) approach is also provided in the study.
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Hsu, Kuo-lin 1961. "Rainfall estimation from satellite infrared imagery using artificial neural networks." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/191209.

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Infrared (IR) imagery collected by geostationary satellites provides useful information about the dirunal evolution of cloud systems. These JR images can be analyzed to indicate the location of clouds as well as the pattern of cloud top temperatures (Tbs). During the past several decades, a number of different approaches for estimation of rainfall rate (RR) from Tb have been explored and concluded that the Tb-RR relationship is (1) highly nonlinear, and (2) seasonally and regionally dependent. Therefore, to properly model the relationship, the model must be able to: (1) detect and identify a non-linear mapping of the Tb-RR relationship; (2) Incorporate information about various cloud properties extracted from IR image; (3) Use feedback obtained from RR observations to adaptively adjust to seasonal and regional variations; and (4) Effectively and efficiently process large amounts of satellite image data in real-time. In this study, a kind of artificial neural network (ANN), called Modified Counter Propagation Network (MCPN), that incorporates these features, has been developed. The model was calibrated using the data around the Japanese Islands provided by the Global Precipitation Climatology Project (GPCP) First Algorithm Intercompari son Project (AIP-I). Validation results over the Japanese Islands and Florida peninsula show that by providing limited ground-truth observation, the MCPN model is effective in monthly and hourly rainfall estimation. Comparison of results from MCPN model and GOES Precipitation Index (GPI) approach is also provided in the study.
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Morland, June Christine. "Observations of surface microwave emission in the context of satellite rainfall estimation." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245823.

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Famennian (Upper Devonian) ammonoids and their biostratigraphy are reviewed with particular reference to the Sauerland and Oberfranken, West Germany. Host european species of the Order Clymeniida are described. The Famennian ammonoid zonal scheme is rationalised and within it 23 faunal levels are proposed. Ammonoid Zones and conodont zones are correlated, and the rhomboidea (conodont) Zone is newly recognised to be coeval with the curvispina Zone. The fOllowing genera and subgenera are dealt with in detail: Progonioclyrnenia, Endosiphonites, Sellaclymenia, Biloclymenia, Gonioclymenia (Gon.), Gonioclymenia (Kalloclymenia), Sphenocl~enia, Platycl~enia (Plat.), Plat. (Pleuroclymenia), Plat. (TrigonocIYmenia), Sulcoclymenia, Piricclymenia, Ornatoclymenia, Cyrto- clyymenia, Protactoclymenia, Carinoclymenia, Clymenia, protoxyclymenia , Kosmoclymenia, Genuclymenia, fymaclymenia, Genn. Nov. D, E, and F. In most cases the types of the type species are illustrated photographically for the first time. The following generic names are recognised to have been wrongly interpreted in the past, and, where necessary new names have been proposed. Kalloclymenia, Biloclymenia, Rectoclymenia and Falciclmenia. Two new subgenera and one new genus are proposed, and two generic names, Protactoclymenia and Endosiphonites, have been revived. Kosmoclymenia is split into four species groups by its ornament and Cymaclymenia bas been split into three species groups. Two widely used specific names are recognised to have been placed in the wrong genus; sedgwicki Munster is a pseudoclymenia (a goniatite), and ~pentina MUnster is a Protoxyclymenia.
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Chadwick, Robin. "Multi-spectral satellite rainfall estimation over Africa using meteosat second generation data." Thesis, University of Reading, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542062.

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Faridhosseini, Alireza. "Evaluation of Summer Rainfall Estimation by Satellite Data using the ANN Model for the GCM Subgrid Distribution." Thesis, The University of Arizona, 1998. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_etd_hy0021_m_sip1_w.pdf&type=application/pdf.

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Diop, Mariane. "The influence of weather systems on satellite rainfall estimation with application to river flow." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.397102.

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Ahmad, Khalil Ali. "ESTIMATION OF OCEANIC RAINFALL USING PASSIVE AND ACTIVE MEASUREMENTS FROM SEAWINDS SPACEBORNE MICROWAVE SENSOR." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3015.

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The Ku band microwave remote sensor, SeaWinds, was developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Two identical SeaWinds instruments were launched into space. The first was flown onboard NASA QuikSCAT satellite which has been orbiting the Earth since June 1999, and the second instrument flew onboard the Japanese Advanced Earth Observing Satellite II (ADEOS-II) from December 2002 till October 2003 when an irrecoverable solar panel failure caused a premature end to the ADEOS-II satellite mission. SeaWinds operates at a frequency of 13.4 GHz, and was originally designed to measure the speed and direction of the ocean surface wind vector by relating the normalized radar backscatter measurements to the near surface wind vector through a geophysical model function (GMF). In addition to the backscatter measurement capability, SeaWinds simultaneously measures the polarized radiometric emission from the surface and atmosphere, utilizing a ground signal processing algorithm known as the QuikSCAT / SeaWinds Radiometer (QRad / SRad). This dissertation presents the development and validation of a mathematical inversion algorithm that combines the simultaneous active radar backscatter and the passive microwave brightness temperatures observed by the SeaWinds sensor to retrieve the oceanic rainfall. The retrieval algorithm is statistically based, and has been developed using collocated measurements from SeaWinds, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, and Numerical Weather Prediction (NWP) wind fields from the National Centers for Environmental Prediction (NCEP). The oceanic rain is retrieved on a spacecraft wind vector cell (WVC) measurement grid that has a spatial resolution of 25 km. To evaluate the accuracy of the retrievals, examples of the passive-only, as well as the combined active / passive rain estimates from SeaWinds are presented, and comparisons are made with the standard TRMM rain data products. Results demonstrate that SeaWinds rain measurements are in good agreement with the independent microwave rain observations obtained from TMI. Further, by applying a threshold on the retrieved rain rates, SeaWinds rain estimates can be utilized as a rain flag. In order to evaluate the performance of the SeaWinds flag, comparisons are made with the Impact based Multidimensional Histogram (IMUDH) rain flag developed by JPL. Results emphasize the powerful rain detection capabilities of the SeaWinds retrieval algorithm. Due to its broad swath coverage, SeaWinds affords additional independent sampling of the oceanic rainfall, which may contribute to the future NASA's Precipitation Measurement Mission (PMM) objectives of improving the global sampling of oceanic rain within 3 hour windows. Also, since SeaWinds is the only sensor onboard QuikSCAT, the SeaWinds rain estimates can be used to improve the flagging of rain-contaminated oceanic wind vector retrievals. The passive-only rainfall retrieval algorithm (QRad / SRad) has been implemented by JPL as part of the level 2B (L2B) science data product, and can be obtained from the Physical Oceanography Distributed Data Archive (PO.DAAC).
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
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Ringard, Justine. "Estimation des précipitations sur le plateau des Guyanes par l'apport de la télédétection satellite." Thesis, Guyane, 2017. http://www.theses.fr/2017YANE0010/document.

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Le plateau des Guyanes est une région qui est caractérisée à 90% d’une forêt tropicale primaire et compte pour environ 20% des réserves mondiales d’eau douce. Ce territoire naturel, au vaste réseau hydrographique, montre des intensités pluviométriques annuelles atteignant 4000 mm/an ; ce qui fait de ce plateau une des régions les plus arrosées du monde. De plus les précipitations tropicales sont caractérisées par une variabilité spatiale et temporelle importante. Outre les aspects liés au climat, l’impact des précipitations dans cette région du globe est important en termes d’alimentation énergétique (barrages hydroélectriques). Il est donc important de développer des outils permettant d’estimer quantitativement et qualitativement et à haute résolution spatiale et temporelle les précipitations dans cette zone. Cependant ce vaste espace géographique est caractérisé par un réseau de stations pluviométriques peu développé et hétérogène, ce qui a pour conséquence une méconnaissance de la répartition spatio-temporelle précise des précipitations et de leurs dynamiques.Les travaux réalisées dans cette thèse visent à améliorer la connaissance des précipitations sur le plateau des Guyanes grâce à l’utilisation des données de précipitations satellites (Satellite Precipitation Product : SPP) qui offrent dans cette zone une meilleure résolution spatiale et temporelle que les mesures in situ, au prix d’une qualité moindre en terme de précision.Cette thèse se divise en 3 parties. La première partie compare les performances de quatre produits d’estimations satellitaires sur la zone d’étude et tente de répondre à la question : quelle est la qualité de ces produits au Nord de l’Amazone et sur la Guyane française dans les dimensions spatiales et temporelles ? La seconde partie propose une nouvelle technique de correction de biais des SPP qui procède en trois étapes : i) utiliser les mesures in situ de précipitations pour décomposer la zone étudiée en aires hydro-climatiques ii) paramétrer une méthode de correction de biais appelée quantile mapping sur chacune de ces aires iii) appliquer la méthode de correction aux données satellitaires relatives à chaque aire hydro-climatique. On cherche alors à répondre à la question suivante : est-ce que le paramétrage de la méthode quantile mapping sur différentes aires hydro-climatiques permet de corriger les données satellitaires de précipitations sur la zone d’étude ? Après avoir montré l’intérêt de prendre en compte les différents régimes pluviométriques pour mettre en œuvre la méthode de correction QM sur des données SPP, la troisième partie analyse l’impact de la résolution temporelle des données de précipitations utilisées sur la qualité de la correction et sur l’étendue spatiale des données SPP potentiellement corrigeables (données SPP sur lesquelles la méthode de correction peut s’appliquer avec efficacité). Concrètement l’objectif de cette partie est d’évaluer la capacité de notre méthode à corriger sur une large échelle spatiale le biais des données TRMM-TMPA 3B42V7 en vue de rendre pertinente l’exploitation de ce produit pour différentes applications hydrologiques.Ce travail a permis de corriger les séries satellites journalières à haute résolution spatiale et temporelle sur le plateau des Guyanes selon une approche nouvelle qui utilise la définition de zones hydro-climatiques. Les résultats positifs en terme de réduction du biais et du RMSE obtenus grâce à cette nouvelle approche, rendent possible la généralisation de cette nouvelle méthode dans des zones peu équipées en pluviomètres
The Guiana Shield is a region that is characterized by 90% of a primary rainforest and about 20% of the world’s freshwater reserves. This natural territory, with its vast hydrographic network, shows annual rainfall intensities up to 4000 mm/year; making this plateau one of the most watered regions in the world. In addition, tropical rainfall is characterized by significant spatial and temporal variability. In addition to climate-related aspects, the impact of rainfall in this region of the world is significant in terms of energy supply (hydroelectric dams). It is therefore important to develop tools to estimate quantitatively and qualitatively and at high spatial and temporal resolution the precipitation in this area. However, this vast geographical area is characterized by a network of poorly developed and heterogeneous rain gauges, which results in a lack of knowledge of the precise spatio-temporal distribution of precipitation and their dynamics.The work carried out in this thesis aims to improve the knowledge of precipitation on the Guiana Shield by using Satellite Precipitation Product (SPP) data that offer better spatial and temporal resolution in this area than the in situ measurements, at the cost of poor quality in terms of precision.This thesis is divided into 3 parts. The first part compares the performance of four products of satellite estimates on the study area and attempts to answer the question : what is the quality of these products in the Northern Amazon and French Guiana in spatial and time dimensions ? The second part proposes a new SPP bias correction technique that proceeds in three steps: i) using rain gauges measurements to decompose the studied area into hydro climatic areas ii) parameterizing a bias correction method called quantile mapping on each of these areas iii) apply the correction method to the satellite data for each hydro-climatic area. We then try to answer the following question : does the parameterization of the quantile mapping method on different hydro-climatic areas make it possible to correct the precipitation satellite data on the study area ? After showing the interest of taking into account the different rainfall regimes to implement the QM correction method on SPP data, the third part analyzes the impact of the temporal resolution of the precipitation data used on the quality of the correction and the spatial extent of potentially correctable SPP data (SPP data on which the correction method can be applied effectively). In summary, the objective of this section is to evaluate the ability of our method to correct on a large spatial scale the bias of the TRMM-TMPA 3B42V7 data in order to make the exploitation of this product relevant for different hydrological applications.This work made it possible to correct the daily satellite series with high spatial and temporal resolution on the Guiana Shield using a new approach that uses the definition of hydro-climatic areas. The positive results in terms of reduction of the bias and the RMSE obtained, thanks to this new approach, makes possible the generalization of this new method in sparselygauged areas
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Cassé, Claire. "Impact du forçage pluviométrique sur les inondations du fleuve Niger à Niamey : Etude à partir de données satellitaires et in-situ." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30236/document.

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Depuis le développement des mesures satellites de nombreuses missions spatiales sont dédiées au suivi de l'atmosphère et de la surface terrestre. Ces travaux de thèse s'inscrivent dans le cadre de la mission Megha-Tropiques dédiée au cycle de l'eau et de l'énergie en zone tropicale. L'objectif est d'évaluer le potentiel des estimations de précipitation par satellite pour des applications hydrologiques en zone tropicale. Les Tropiques réunissent les plus grands fleuves du globe, mais ne bénéficient pas de réseaux d'observation in-situ denses et continus permettant une gestion intégrée efficace de la ressource et des systèmes d'alertes. Les estimations des précipitations issues des systèmes d'observation satellite offrent une alternative pour ces bassins peu ou pas instrumentés et souvent exposés aux extrêmes climatiques. C'est le cas du fleuve Niger, qui a subi une grande variabilité climatique depuis les années 1950, mais aussi d'importants changements environnementaux et hydrologiques. Depuis les années 2000, le Niger moyen connaît une recrudescence des inondations pendant la période de crue Rouge (engendrée par ses affluents sahéliens pendant la mousson). A Niamey, des niveaux record de hauteur d'eau et de période d'inondation ont été enregistrés en 2003, 2010, 2012 et 2013, engendrant de nombreuses pertes humaines et matérielles. Ces travaux analysent l'influence du forçage pluviométrique sur les inondations liées à la crue Rouge à Niamey. Une gamme de produits pluviométriques (in situ et satellite) et la modélisation hydrologique (ISBA-TRIP) sont combinés pour étudier : (i) l'apport des produits satellite pour diagnostiquer la crue Rouge récente, (ii) l'impact des caractéristiques des produits et de leurs incertitudes sur les simulations et enfin (iii) l'évaluation du rôle des précipitations, face aux changements de conditions de surface, dans l'évolution de la crue Rouge à Niamey depuis les années 1950. L'étude a mis en évidence l'impact des caractéristiques des estimations des précipitations (cumul, intensité et distribution spatio-temporelle) sur la modélisation hydrologique et le potentiel des produits satellites pour le suivi des inondations. Les caractéristiques des précipitations se propageant dans la modélisation, la détection des inondations est plus efficace avec une approche relative à chaque produit plutôt qu'avec un seuil absolu. Ainsi des produits présentant des biais peuvent être envisagés pour la simulation hydrologique et la détection des inondations. Le nouveau produit TAPEER de la mission MT présente un fort potentiel hydrologique, en 2012 et pour la zone d'étude. D'autre part, l'étude de la propagation de l'erreur associée à ces précipitations a mis en évidence, la nécessité de déterminer la structure du champ d'erreur pour l'utilisation d'une telle information en hydrologie. Enfin la modélisation a été utilisée comme levier pour décomposer les sensibilités de la crue Rouge aux variations des précipitations et des conditions de surface. Pour simuler les changements hydrologiques entre les périodes 1953-1982 et 1983-2012, les changements d'occupation du sol et d'aire de drainage doivent être pris en compte. Puis les variations des précipitations peuvent expliquer les changements majeurs décennaux et annuels entre les années 1983 et 2012
Since the development of satellite based remote sensing in the 1970s, many missions have been dedicated to monitoring the terrestrial atmosphere and surfaces. Some of these satellites are dedicated to the Tropics with specific orbits. Megha-Tropiques (MT) is devoted to the water and energy cycle in the tropical atmosphere and provides an enhanced sampling for rainfall estimation in the tropical region. This PhD work was initiated within MT hydro-meteorological activities, with the objective of assessing the hydrological potential of satellite rainfall products in the Tropics. The world most important rivers lay in tropical areas where the in situ observation networks are deficient. Alternative information is therefore needed for water resource management and alert systems. The present work focuses on the Niger River a basin which has undergone drastic climatic variations leading to disasters such as droughts and floods. Since 1950, the Niger has been through 3 main climatic periods: a wet period (1950-1960), a long and intense drought period (1970-1980) and since 1990 a partial recovery of the rainfall. These climatic variations and the anthropic pressure, have modified the hydrological behaviour of the basin. Since 2000, the middle Niger River has been hit by an increase of floods hazards during the so-called Red flood period. In Niamey city, the highest river levels and the longest flooded period were recorded in 2003, 2010, 2012 and 2013, leading to heavy casualties and property damage. This study combines hydrological modelling and a variety of rainfall estimation products (satellite and in-situ) to meet several objectives: (i) the simulation of the Niamey Red flood and the detection of floods (during the recent period 2000-2013) (ii) the study of the propagation of satellite rainfall errors in hydrological modelling (iii) the evaluation of the role of rainfall variability, and surface conditions, in the changes of the Red flood in Niamey since the 50s. The global model ISBA-TRIP, is run with a resolution of 0.5° and 3h, and several rainfall products were used as forcing. Products derived from gauges (KRIG, CPC), pure satellite products (TAPEER, 3B42RT, CMORPH, PERSIANN) and mixed satellite products adjusted by rain gauges (3B42v7, RFE2, PERSIANN-CDR). This work confirms the hydrological potential of satellite rainfall products and proposes an original approach to overcome their biases. It highlights the need for documenting the errors associated with the rainfall products and the error structure. Finally, the hydrological modelling results since the 1950s have given a new understanding of the relative role of rainfall and surface conditions in the drastic increase of flood risk in Niamey
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Books on the topic "Satellite rainfall estimation"

1

Shrestha, Mandira. Satellite rainfall estimation in the Hindu Kush-Himalayan Region. Kathmandu: International Centre for Integrated Mountain Development, 2008.

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Griffith, Cecilia Girz. The estimation from satellite imagery of summertime rainfall over varied space and time scales. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.

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A, Vila Daniel, ed. Satellite rainfall estimation over South America: Evaluation of two major events. Washington, DC: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 2003.

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United States. National Aeronautics and Space Administration., ed. Application of lightning data to satellite-based rainfall estimation: A final report to the National Aeronautics and Space Administration, grant NAGW-1767 : for the period of 1 January 1989 through 31 December 1991. Madison, Wis: Space Sciences and Engineering Center at the University of Wisconsin-Madison, 1991.

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Siyyid, Alward Nawazish. The use of Meteosat satellite data for spatial rainfall estimations and hydrological simulations. Birmingham: Aston University. Department of Civil Engineering, 1993.

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Johnson, Michelle L. Estimating precipitation over the Amazon Basin from satellite and in-situ measurements. Middleton, Del: Legates Consulting, 2003.

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Satellite rainfall estimation in the Hindu Kush-Himalayan region. Kathmandu: International Centre for Integrated Mountain Development, 2008.

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Rain volume estimation over areas using satellite and radar data: Semiannual report on grant no. NAG 5-396, period covered: 1 January 1985 - 30 June 1985. [Washington, D.C.?: National Aeronautics and Space Administration?, 1985.

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Book chapters on the topic "Satellite rainfall estimation"

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Hsu, K. L., H. V. Gupta, X. Gao, and S. Sorooshian. "Rainfall Estimation from Satellite Imagery." In Water Science and Technology Library, 209–34. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9341-0_12.

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Hsu, Kuo-Lin, Ali Behrangi, Bisher Imam, and Soroosh Sorooshian. "Extreme Precipitation Estimation Using Satellite-Based PERSIANN-CCS Algorithm." In Satellite Rainfall Applications for Surface Hydrology, 49–67. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_4.

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Tapiador, Francisco J., Chris Kidd, Vincenzo Levizzani, and Frank S. Marzano. "Neural Network tools for Satellite Rainfall Estimation." In Measuring Precipitation From Space, 149–61. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_12.

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Hong, Yang, Robert F. Adler, George J. Huffman, and Harold Pierce. "Applications of TRMM-Based Multi-Satellite Precipitation Estimation for Global Runoff Prediction: Prototyping a Global Flood Modeling System." In Satellite Rainfall Applications for Surface Hydrology, 245–65. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_15.

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Jobard, Isabelle, and Michel Desbois. "Combination of Satellite Microwave and Infrared Measurements for Rainfall Estimation." In Global Precipitations and Climate Change, 265–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79268-7_16.

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Ouallouche, Fethi, Karim Labadi, Yacine Mohia, Mourad Lazri, and Soltane Ameur. "Artificial Intelligence for Satellite Image Processing: Application to Rainfall Estimation." In Lecture Notes in Electrical Engineering, 165–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6581-4_14.

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Anagnostou, Emmanouil N., and Themis G. Chronis. "The Worth of Long-Range Lightning Observations on Overland Satellite Rainfall Estimation." In Measuring Precipitation From Space, 135–48. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_11.

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Rientjes, Tom H. M., Alemseged T. Haile, Ambro S. M. Gieske, Ben H. P. Maathuis, and Emad Habib. "Satellite Based Cloud Detection and Rainfall Estimation in the Upper Blue Nile Basin." In Nile River Basin, 93–107. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0689-7_4.

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Lasmono, Farid, Risyanto, Fadli Nauval, Elfira Saufina, Trismidianto, and Teguh Harjana. "Satellite Rainfall Estimation from Himawari-8 Multi Channels Observation Based on AWS Data Trained Machine Learning Methods." In Springer Proceedings in Physics, 495–506. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0308-3_39.

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Colli, M., M. Stagnaro, A. Caridi, L. G. Lanza, A. Randazzo, M. Pastorino, D. D. Caviglia, and A. Delucchi. "A Field Experiment of Rainfall Intensity Estimation Based on the Analysis of Satellite-to-Earth Microwave Link Attenuation." In Lecture Notes in Electrical Engineering, 137–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11973-7_17.

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Conference papers on the topic "Satellite rainfall estimation"

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Nirala, M. L., and A. P. Cracknell. "Rainfall estimation using TRMM satellite data." In IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174). IEEE, 1998. http://dx.doi.org/10.1109/igarss.1998.702824.

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Martins de Freitas, Greice, Ana Maria Heuminski de Avila, and João Paulo Papa. "Satellite-Based Rainfall Estimation through Semi-supervised Learning." In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.1103.

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Vasavi, S., P. V. Sai Krishna, P. D. L. Nikhita Sri, M. Navena, and C. HariKiran. "Rainfall Estimation From Satellite Images Using Cloud Classifications." In 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon). IEEE, 2022. http://dx.doi.org/10.1109/nkcon56289.2022.10126540.

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Gharanjik, Ahmad, Kumar Vijay Mishra, Bhavani Shankar M.R., and Bjorn Ottersten. "Learning-Based Rainfall Estimation via Communication Satellite Links." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450726.

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Putra, Maulana, Mohammad Syamsu Rosid, and Djati Handoko. "Rainfall Estimation Using Machine Learning Approaches with Raingauge, Radar, and Satellite Data." In 2022 International Conference on Electrical Engineering and Informatics (ICELTICs). IEEE, 2022. http://dx.doi.org/10.1109/iceltics56128.2022.9932109.

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Sulistio, Emmilia Monica Andrianni, Anisa Budi Lestari, Rizaldi Ramadhan, Raden Mochammad Dwi Mulya, Chray Fanly Jovini Tambengi, Eko Wardoyo, and Imma Redha Nugraheni`. "Verification of the effect of quality control implementation to increase accuracy of rainfall estimation in Lombok areas." In Sixth International Symposium on LAPAN-IPB Satellite, edited by Tien Dat Pham, Kasturi D. Kanniah, Kohei Arai, Gay Jane P. Perez, Yudi Setiawan, Lilik B. Prasetyo, and Yuji Murayama. SPIE, 2019. http://dx.doi.org/10.1117/12.2541852.

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Mugnai, Clio, Francesco Sermi, Fabrizio Cuccoli, and Luca Facheris. "Rainfall estimation with a commercial tool for satellite internet in KA band: Model evolution and results." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7325908.

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Pasaribu, Octo Mario, Aris Poniman, Andrian Andaya Lestari, Yosef Prihanto, Asep Adang Supriyadi, and Yahya Darmawan. "Exploration of CHIRPS Satellite Data as Rainfall Estimation Data in Medan City and Deli Serdang Regency." In 2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS). IEEE, 2022. http://dx.doi.org/10.1109/agers56232.2022.10093448.

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Mugnai, Clio, Fabrizio Cuccoli, and Francesco Sermi. "Rainfall estimation with a commercial tool for satellite internet in Ka band: concept and preliminary data analysis." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2014. http://dx.doi.org/10.1117/12.2067263.

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Saggese, Fabio, Filippo Giannetti, and Vincenzo Lottici. "A Novel Approach to Rainfall Rate Estimation based on Fusing Measurements from Terrestrial Microwave and Satellite Links." In 2020 XXXIIIrd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS). IEEE, 2020. http://dx.doi.org/10.23919/ursigass49373.2020.9232257.

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Reports on the topic "Satellite rainfall estimation"

1

Shrestha, M., P. K. Mool, and S. R. Bajracharya. Satellite Rainfall Estimation in the Hindu Kush-Himalayan Region. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2008. http://dx.doi.org/10.53055/icimod.492.

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Shrestha, M., P. K. Mool, and S. R. Bajracharya. Satellite Rainfall Estimation in the Hindu Kush-Himalayan Region. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2008. http://dx.doi.org/10.53055/icimod.492.

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