Academic literature on the topic 'TRMM-3B42'

Abstract In this study, the recent work of Gottschalck et al. and Ebert et al. is extended by assessing the suitability of two Tropical Rainfall Measuring Mission (TRMM)-based precipitation products for hydrological land data assimilation applications. The two products are NASA’s gauge-corrected TRMM 3B42 Version 6 (3B42), and the satellite-only NOAA Climate Prediction Center (CPC) morphing technique (CMORPH). The two products were evaluated against ground-based rain gauge–only and gauge-corrected Doppler radar measurements. The analyses were performed at multiple time scales, ranging from annual to diurnal, for the period March 2003 through February 2006. The analyses show that at annual or seasonal time scales, TRMM 3B42 has much lower biases and RMS errors than CMORPH. CMORPH shows season-dependent biases, with overestimation in summer and underestimation in winter. This leads to 50% higher RMS errors in CMORPH’s area-averaged daily precipitation than TRMM 3B42. At shorter time scales (5 days or less), CMORPH has slightly less uncertainty, and about 10%–20% higher probability of detection of rain events than TRMM 3B42. In addition, the satellite estimates detect more high-intensity events, causing a remarkable shift in precipitation spectrum. Summertime diurnal cycles in the United States are well captured by both products, although the 8-km CMORPH seems to capture more diurnal features than the 0.25° CMORPH or 3B42 products. CMORPH tends to overestimate the amplitude of the diurnal cycles, particularly in the central United States. Possible causes for the discrepancies between these products are discussed.

Rainfall erosivity (RE) is a significant indicator of erosion capacity. The application of Tropical Rainfall Measuring Mission (TRMM) rainfall products to deal with RE estimation has not received much attention. It is not clear which temporal resolution of TRMM data is most suitable. This study quantified the RE in the Poyang Lake basin, China, based on TRMM 3B42 3-hourly, daily, and 3B43 monthly rainfall data, and investigated their suitability for estimating RE. The results showed that TRMM 3-hourly product had a significant systematic underestimation of monthly RE, especially during the period of April–June for the large values. The TRMM 3B42 daily product seems to have better performance with the relative bias of 3.0% in summer. At the annual scale, TRMM 3B42 daily and 3B43 monthly data had acceptable accuracy, with mean error of 1858 and −85 MJ∙mm/ha∙h and relative bias of 18.3% and −0.85%, respectively. A spatial performance analysis showed that all three TRMM products generally captured the overall spatial patterns of RE, while the TRMM 3B43 product was more suitable in depicting the spatial characteristics of annual RE. This study provides valuable information for the application of TRMM products in mapping RE and risk assessment of soil erosion.

This study conducted a comprehensive evaluation of three satellite precipitation products (TRMM (Tropical Rainfall Measuring Mission) 3B42, CMORPH (the Climate Prediction Center (CPC) Morphing algorithm), and PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)) using data from 52 rain gauge stations over the Meichuan watershed, which is a representative watershed of the Poyang Lake Basin in China. All the three products were compared and evaluated during a 9-year period at different spatial (grid and watershed) and temporal (daily, monthly, and annual) scales. The results showed that at daily scale, CMORPH had the best performance with coefficients of determination (R2) of 0.61 at grid scale and 0.74 at watershed scale. For precipitation intensities larger than or equal to 25 mm, RMSE% of CMORPH and TRMM 3B42 were less than 50%, indicating CMORPH and TRMM 3B42 might be useful for hydrological applications at daily scale. At monthly and annual temporal scales, TRMM 3B42 had the best performances, with highR2ranging from 0.93 to 0.99, and thus was deemed to be reliable and had good potential for hydrological applications at monthly and annual scales. PERSIANN had the worst performance among the three products at all cases.

Abstract. The paper examines the quality of satellite-based precipitation estimates for the lower Mahanadi River basin (eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gauge-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gauge data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analysing their performance in the context of rainfall–runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gauge-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high-intensity levels. The rainfall depth derived from rain gauge data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day-1). At the same time, the remotely sensed rainfall rates frequently exceed the gauge-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real-time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalisation of rain gauge data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gauge data were used as model input (Nash–Sutcliffe index of 0.76–0.88 at gauges not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gauge-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

Abstract. The paper examines the quality of satellite-based precipitation estimates for the Lower Mahanadi River Basin (Eastern India). The considered data sets known as 3B42 and 3B42-RT (version 7/7A) are routinely produced by the tropical rainfall measuring mission (TRMM) from passive microwave and infrared recordings. While the 3B42-RT data are disseminated in real time, the gage-adjusted 3B42 data set is published with a delay of some months. The quality of the two products was assessed in a two-step procedure. First, the correspondence between the remotely sensed precipitation rates and rain gage data was evaluated at the sub-basin scale. Second, the quality of the rainfall estimates was assessed by analyzing their performance in the context of rainfall-runoff simulation. At sub-basin level (4000 to 16 000 km2) the satellite-based areal precipitation estimates were found to be moderately correlated with the gage-based counterparts (R2 of 0.64–0.74 for 3B42 and 0.59–0.72 for 3B42-RT). Significant discrepancies between TRMM data and ground observations were identified at high intensity levels. The rainfall depth derived from rain gage data is often not reflected by the TRMM estimates (hit rate < 0.6 for ground-based intensities > 80 mm day−1). At the same time, the remotely sensed rainfall rates frequently exceed the gage-based equivalents (false alarm ratios of 0.2–0.6). In addition, the real time product 3B42-RT was found to suffer from a spatially consistent negative bias. Since the regionalization of rain gage data is potentially associated with a number of errors, the above results are subject to uncertainty. Hence, a validation against independent information, such as stream flow, was essential. In this case study, the outcome of rainfall–runoff simulation experiments was consistent with the above-mentioned findings. The best fit between observed and simulated stream flow was obtained if rain gage data were used as model input (Nash–Sutcliffe Index of 0.76–0.88 at gages not affected by reservoir operation). This compares to the values of 0.71–0.78 for the gage-adjusted TRMM 3B42 data and 0.65–0.77 for the 3B42-RT real-time data. Whether the 3B42-RT data are useful in the context of operational runoff prediction in spite of the identified problems remains a question for further research.

The Tropical Rainfall Measurement Mission (TRMM) satellite is the first to be designed to measure precipitation, and its precipitation products have been assessed in a variety of ways. Data for its post-real-time level 2 product (3B42) performed well in terms of the precipitation amount at the monthly scale because they were corrected by a precipitation dataset that was gauged every month. However, the performance of this dataset in terms of precipitation frequency and intensity is still not ideal. To this end, TRMM 3B42 products were evaluated using precipitation data from 747 meteorological stations over mainland China in this study. The Pearson’s correlation coefficient (CC), relative bias (RB), and relative error (RE) were used to assess the capability of TRMM products in terms of estimating the frequency, intensity, and amount of precipitation for different categories of precipitation during nighttime and daytime in a multiscale analysis (including interannual variation, seasonal cycles, and spatial distribution). Our results showed the following: (1) The 3B42 products reproduced interannual trends of the frequency and amount of precipitation (except for trace precipitation) with an average correlation coefficient of 0.84. (2) 3B42 performed well at calculating the annual and monthly precipitation amount, but performed poorly for frequency and even worse for intensity. The biases in these two properties canceled out, however, which led to a better estimate of the amount. (3) 3B42 represented the distribution of the subdaily amount of precipitation over a majority of the regions in the east, but did not perform well on the Tibetan Plateau or in northwest China. The performance of 3B42, as detailed in this study, can serve as valuable guidance to data users and algorithm developers.

This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.

Abstract. Planning for drought relief and floods in developing countries is greatly hampered by the lack of a sufficiently dense network of weather stations measuring precipitation. In this paper, we test the utility of three satellite products to augment the ground-based precipitation measurement to provide improved spatial estimates of rainfall. The three products are the Tropical Rainfall Measuring Mission (TRMM) product (3B42), Multi-Sensor Precipitation Estimate–Geostationary (MPEG) and the Climate Forecast System Reanalysis (CFSR). The accuracy of the three products is tested in the Lake Tana basin in Ethiopia, where 38 weather stations were available in 2010 with a full record of daily precipitation amounts. Daily gridded satellite-based rainfall estimates were compared to (1) point-observed ground rainfall and (2) areal rainfall in the major river sub-basins of Lake Tana. The result shows that the MPEG and CFSR satellites provided the most accurate rainfall estimates. On average, for 38 stations, 78 and 86% of the observed rainfall variation is explained by MPEG and CFSR data, respectively, while TRMM explained only 17% of the variation. Similarly, the areal comparison indicated a better performance for both MPEG and CFSR data in capturing the pattern and amount of rainfall. MPEG and CFSR also have a lower root mean square error (RMSE) compared to the TRMM 3B42 satellite rainfall. The bias indicated that TRMM 3B42 was, on average, unbiased, whereas MPEG consistently underestimated the observed rainfall. CFSR often produced large overestimates.

Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

Fundação de Amparo a Pesquisa no Estado do Rio Grande do Sul<br>Understanding the spatial and temporal rainfall occurrence, improves the water resources management, both in order to prevent losses related to the occurrence of floods and droughts events, as in relation to the supply of the various sectors. Thus, satellite rainfall estimates are an alternative to obtain representative data of large areas, since the gauge data from meteorological stations are scarce, frequently due the low density of stations per area. However, these satellite products contain uncertainties when compared to gauge data. In this way, this study aims to evaluate the representativeness of rainfall estimates derived from satellites in the Rio Grande do Sul state. To this, were used satellite TRMM (3B42 V7) products, which were compared with gauge data in the State provided by the Agência Nacional de Águas and by the Instituto Nacional de Meteorologia, considering the period from 1998 to 2013. This paper compared rainfall estimates and gauge data was accomplished through a set statistics like skill scores, such as event detection percentage (PC), hit rate (H), false alerts ratios (FAR and F), critical success index (CSI), the ratio of planned events and observed (B), and the indexes of Heidke (HSS) and Pierce (PSS). Some equations were applied too: correlation coefficient (r) mean absolute error (MPE), root mean square error (RMSE), the Nash-Sutcliffe efficiency coefficient (NS) and bias. The data were compared in daily and accumulated series of 15 and 30 days, through the following methods: Pixel to Point, Point to Point, Pixel to Pixel, from Sub-pixels and aggregate analysis. The 3B42 products were also evaluated for their skill to determine heavy rainfall, using as reference intensity-duration-frequency equations (IDF) derived from gauge data. The results obtained by the methods, except for the analysis of heavy rainfall, not differ much from each other. Spatial analysis showed the relationship of assessments estimates has to the density of stations and the regions of Rio Grande do Sul, while specific analyzes indicated the good performance of TRMM even in Pixel to Point comparison. The results improved in steps that the daily series were accumulated in 15 and 30 days. It was evident the decrease of the quality of the estimates in the eastern RS region, where the ocean effects generates overestimates.<br>A compreensão da ocorrência espacial e temporal da precipitação pluviométrica permite melhorar a gestão dos recursos hídricos, tanto no sentido de prevenir prejuízos relacionados à ocorrência de eventos de enchentes e estiagens, quanto em relação ao suprimento dos diversos setores. Assim, estimativas de precipitação de satélites são uma alternativa para obtenção de dados representativos de extensas áreas, tendo em vista que os dados observados em estações meteorológicas são escassos muitas vezes. Todavia, estes produtos de satélite contêm incertezas quando comparados aos dados medidos. O estudo procura avaliar a representatividade das estimativas de chuva oriundas de satélites no estado do Rio Grande do Sul. Para tal utilizaramse produtos do satélite TRMM (3B42 V7), que foram comparados com observados no Estado, disponibilizados pela Agência Nacional de Águas e pelo Instituto Nacional de Meteorologia, no período de 1998 a 2013. O trabalho consistiu em comparar dados de precipitações estimadas e observadas por meio de um conjunto de índices de desempenho, tais como o percentual de detecção de eventos (PC), percentual de acertos (H), percentual de falsos alertas (FAR e F), índice de sucesso crítico (CSI), a razão entre eventos previstos e observados (B), bem como os índices de Heidke (HSS), e Peirce (PSS). Além de outras equações como: coeficiente de correlação (r) erro médio absoluto (EMA), erro médio quadrático (EQM), o coeficiente de eficiência de Nash-Sutcliffe (NS) e viés. Os dados foram comparados em séries diárias e acumulados de 15 e 30 dias, por meio dos seguintes métodos: Pixel a Ponto, Ponto a Ponto, Pixel a Pixel, a partir de Sub-pixels e Análise agregada. Os produtos 3B42 também foram avaliados em relação a capacidade de determinar chuvas intensas, usando como referência equações de intensidade-duração-frequência derivadas de dados observados. Os resultados obtidos pelas metodologias, com exceção da análise de precipitações intensas, não diferenciaram muito entre si. As análises espaciais mostraram a intimidade das avaliações das estimativas tem com a densidade de postos e com as regiões do Rio Grande do Sul, enquanto as análises pontuais indicaram a boa performance do TRMM mesmo na comparação Pixel a Ponto. A medida que as séries diárias foram acumuladas em 15 e 30 dias, os resultados melhoraram. Ficou evidente o decréscimo da qualidade das estimativas na região Leste do RS, onde os efeitos da maritimidade acabam gerando superestimativas.

Dans la zone intertropicale, les précipitations sont le principal marqueur climatique saisonnier et déterminent très largement l’hydrologie de surface et de nombreuses activités anthropiques. Le bassin amazonien est caractérisé par divers régimes régionaux de précipitations, dont la variabilité spatiale et temporelle est forte. De nombreux travaux ont montré que cette variabilité est liée à des forçages externes de large échelle, comme les températures de surface de l’océan. L’étude des précipitations dans cette région porte le plus souvent sur les tendances ou les extrêmes pluviométriques. En revanche, la détection d’années similaires constituant des sous-régimes régionaux et leur lien avec une configuration océano-atmosphérique particulière a été, jusqu’à présent, peu abordée. L’objectif principal de cette thèse est ainsi de créer une typologie des sous-régimes de précipitations régionaux dans le bassin amazonien et de les mettre en relation avec le contexte océano-atmosphérique pouvant en partie les expliquer. Dans ce but, des données issues de 205 pluviomètres répartis sur 5 pays du Bassin Amazonien ont été sélectionnées et soumises à une série de tests statistiques et de reconstruction. Cette thèse utilise également des données de nébulosité (Outgoing Longwave Radiation), de flux d’humidité et de température de surface de l’océan ainsi que des données satellitaires (TRMM3B42 version 7) qui permettent de compléter les informations sur la variabilité spatiale des pluies.Au sein de chacune des sept régions amazoniennes déterminées dans ce travail, deux à quatre sous-régimes de précipitations ont été détectés. Parmi les vingt-six sous-régimes, vingt sont associés à des anomalies de circulation des flux d’humidité et de température de surface des océans. Les sous-régimes de pluies de la moitié nord et les Andes de l’ouest du bassin sont le plus liés à des anomalies océaniques. De plus, comme cela est régulièrement décrit, des déficits ou excédents correspondent souvent à des phases El Niño ou La Niña, mais cette thèse met également en évidence le rôle important de l’Atlantique, en particulier sud, sur le déplacement de la ZCIT et sur les flux d’humidité ; et elle souligne également le lien entre la temporalité des événements océaniques et celle des anomalies de pluies.Le produit TRMM 3B42 V7 permet d’aller plus loin dans l’analyse de la variabilité spatiale intra-régionale des pluies de la région Nord-est du bassin amazonien et de relativiser la cohérence spatiale des sous-régimes de précipitations de cette région<br>Precipitations are the main seasonal climate marker between the tropics and largely determine surface hydrolosy as well as many anthropogenic activities. The Amazon Basin is characterized by various regional rainfall patterns, whose spatial and temporal variability is high. Numerous studies have shown that this variability is related to large scale external forcing, such as sea surface temperatures. The analysis of precipitation in this region is generally related to trends or extreme of rainfall. However, the detection of similar years associated with regional sub-regimes and the analysis of their links with a specific ocean-atmosphere configuration has only been fewly addressed until now. The main objective of this thesis is to create a typology of regional precipitation sub-régimes in the Amazon Basin and to link them to ocean-atmosphere areas able to partly explain them. For that purpose data from 205 raingauges in 5 countries of the Amazon Basin were selected and submitted to a series of statistical tests and reconstruction. Outgoing longwave radiation, specific humidity, sea surface temperature, as well as satellite data (TRMM 3B42 version 7) were also used with the aim of improving the understanding of the spatial rainfall variability.Within each of the seven Amazon regions identified in this work, two to four precipitation sub-regimes were detected. Among the twenty six sub-regimes, twenty are associated with specific humidity and sea surface temperature anomalies. The precipitation sub-regimes of the northern half and the westernmost Andes of the Amazon Basin are most closely related to oceanic anomalies. Moreover, as previously described in the literature, reduction or surplus of rain often correspond to El Niño or La Niña phases, but this thesis also highlights the important role of the Atlantic, more specifically the southern part, on the move of the ITZC and on specific humidity. This work also stresses the link between the temporality of ocean events anomalies and rainfall anomalies.The TRMM 3B42 v7 product allows to enhance the analysis of the spatial variability of rainfall at the intra-regional scale of the North region of the Amazon Basin and to relativize the spatial coherence of its precipitation sub-regimes

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<br>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

Les travaux réalisés au cours de cette Thèse ont contribué à améliorer l'estimation de l'humidité de surface du sol et de l'humidité racinaire sur la bande sahélienne en Afrique de l'ouest. La première partie a été consacrée à évaluer la capacité de différents algorithmes d'estimations de l'humidité du sol basées sur des mesures satellites dans le domaine spectral de l'infrarouge thermique. Dans un deuxième temps, une méthode a été développée dans le but d'obtenir une cartographie de l'humidité du sol à une résolution temporelle fine (< 3h) en se basant sur deux produits satellites: un produit satellite de précipitation et une cartographie journalière micro-onde sensible à l'humidité du sol. Dans un troisième temps, une évaluation de la fiabilité et de la robustesse de la méthodologie a été proposée. Enfin, une méthode semi-empirique a été utilisée afin de produire une cartographie de l'humidité de la zone racinaire (0-1 m) sur l'ensemble de la bande sahélienne. Les mesures de terrain obtenues sur trois sites situés au Mali, au Niger et au Bénin dans le cadre du programme AMMA ont servi de référence pour évaluer la qualité des estimations de l'humidité superficielle et racinaire à chaque étape de ce travail. Les résultats montrent qu'une estimation de l'humidité superficielle du sol est possible avec une précision de moins de 3 % vol. sur la bande sahélienne. L'erreur est de l'ordre de 5% vol. sur la zone soudanienne (Bénin). La précision est identique sur l'estimation de l'humidité racinaire. Enfin, la méthode développée permet parallèlement de corriger les produits satellites de précipitation et notamment la surestimation du cumul annuel et du nombre d'événements des trois produits satellites utilisés dans ce travail.