Дисертації з теми "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.
Повний текст джерелаHsu, 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.
Повний текст джерелаHsu, 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.
Повний текст джерелаMorland, 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.
Повний текст джерелаChadwick, 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.
Повний текст джерелаFaridhosseini, 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.
Повний текст джерелаDiop, 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.
Повний текст джерелаAhmad, 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.
Повний текст джерелаPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
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.
Повний текст джерелаThe 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
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.
Повний текст джерелаSince 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
Xu, Liming 1958. "Estimating rainfall from satellite infrared imagery: Cloud patch analysis." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/282573.
Повний текст джерелаSiyyid, Alward N. "The use of Meteosat satellite data for spatial rainfall estimations and hydrological simulations." Thesis, Aston University, 1993. http://publications.aston.ac.uk/14308/.
Повний текст джерелаAngerer, Jay Peter. "Examination of high resolution rainfall products and satellite greenness indices for estimating patch and landscape forage biomass." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2827.
Повний текст джерелаde, Luque Söllheim Ángel Luis. "Two satellite-based rainfall algorithms, calibration methods and post-processing corrections applied to Mediterranean flood cases." Doctoral thesis, Universitat de les Illes Balears, 2008. http://hdl.handle.net/10803/9434.
Повний текст джерелаThis Thesis work explores the precision of two methods to estimate rainfall called Auto-Estimator and CRR (Convective Rainfall Rate). They are obtained by using infrared and visible images from Meteosat. Both Algorithms within a set of correction factors are applied and verified in two severe flood cases that took place in Mediterranean regions. The first case has occurred in Albania from 21 to 23 September 2002 and the second, known as the Montserrat case, has occurred in Catalonia the night from the 9 to 10 of June 2000. On the other hand it is explored new methods to perform calibrations to both satellite algorithms using direct rain rates from rain gauges. These kinds of adjustments are usually done using rain rates from meteorological radars. In addition it is proposed changes on some correction factors that seem to improve the results on estimations and it is defined an efficient correction factor that employ electrical discharges to detect the most convective and rainy areas in cloud systems.
Quiroz, Jiménez Karena. "Modelagem hidrológica com uso da estimativa de chuva por sensoriamento remoto." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/49176.
Повний текст джерелаCurrently, satellite rainfall estimates using remote sensing are a potential source of information for hydrological and climatological applications. It applies mainly for regions where conventional measurements are scarce such as the Amazon Basin. In this work, the satellite rainfall estimates were analyzed as input variables to the hydrological model MGBIPH (Collischonn, 2001). This model simulates the hydrological cycle through physical and conceptual relationships where products 3B42, 3B42RT and CMORPH are evaluated. The first evaluation case corresponds to the Huallaga basin located in Peru, being one of the current Amazon highlands characterized by a complex topography. The second evaluation case corresponds to the Amazon basin characterized by a great climatological variability at different altitudes, different hydrological regimes and poor distributions of raingauges. In the case of the Huallaga River basin, comparisons were made between the estimated average satellite rainfall and the observed rainfall for different intervals of time (daily, monthly, seasonal and annual). These results show that the products 3B42 and CMORPH underestimate the basin average rainfall when compared with the weighted average of raingauge measurements. During the Huallaga basin simulation, calibrations of some parameters for each rainfall data were realized. Obtaining the best and worst fitting results with the CMORPH and 3B42 products for the case of maximum discharges, respectively. This rainfall fitting improves for the CMORPH product when raingauge corrections are included. On the other hand, the annual average rainfall value was obtained for each satellite product (3B42, 3B42RT e CMORPH) for the analysis of the Amazon basin. In this calculation, the greater results for the annual average rainfall values are obtained in the following order CMORPH, 3B42RT and 3B42. Moreover, this simulation seems to yield best Nash-Sutcliffe coefficients for the 3B42 product for various Brazilian stations. For stations located in the main stream of the Amazon River the Nash-Sutcliffe coefficients obtained with the 3B42RT product are the best. The CMORPH product yield the best coefficients for the stations located in Tapajós (Brazil) and Urubamba (Peru) basin.
Wei, Chiang, and 衛強. "Study on Rainfall Estimation Using Weather Satellite Imagery." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/88305097412364275517.
Повний текст джерела國立臺灣大學
生物環境系統工程學系暨研究所
91
In recent years, rainfall estimation using weather satellite imagery has gained increasing attention. GMS imagery and hourly rainfall data of eleven typhoons during 1997-2000 are used in this study. The correlation between cloud top temperature (CCT) derived from weather satellite imagery and corresponding ground measurements of hourly rainfall are analyzed at different spatial and temporal scales. Two approaches are used to estimate rainfall in this study: (i) image classification method and (ii) spatial and temporal rainfall apportionment scheme (STRAS). After the models are established, the cross validation scheme is used to validate the stability and feasibility of the model. Finally, real-time forecasting of 5-km and watershed scale is accomplished by the kalman filtering algorithm. Preliminary results show the relationship between CTT and rain rate is dependent on individual typhoon event magnitude and total rainfall depth. The correlation between CCT and rain rate at the same time becomes better as the spatial scale is larger. The correlation of the average CCT and next three to six hour accumulative rainfall in basin scale also becomes better as the time frame is longer. In image classification approach, the maximum likelihood and bayesian classification method are used to analyze the data. The total accuracy of two methods using 14 factor scores as classification features are 87.068% and 88.321%, respectively. In STRAS approach, the correlation between raingauge point measurements and rainfall estimates by spatial convolution using IR1 ,IR1 and IR3 , and all three infrared images are 0.742, 0.858, and 0.932, respectively. Correlation coefficients increase to 0.874, 0.940, and 0.970 respectively, when pixel-average rainfall estimates are considered by block kriging. The correlations also become better when the spatial and temporal scale increase. Results of a cross validation scheme reveal that the correlation coefficients are over 0.7 in most raingauges. The result suggests a great potential in real-time rainfall forecasting using satellite images. The testing result of the rainfall forecasting by the kalman filtering algorithm demonstrates that the proposed model can master the trend of rainfall variation. However, the statistical characteristics of some parameters in kalman filtering algorithm are yet to be verified.
Wei, Shiao-Ping, and 魏曉萍. "Study on Mesoscale Rainfall Estimation by Combing Satellite Data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/54376497170321815473.
Повний текст джерелаLu, Pei-Wen, and 呂珮雯. "Watershed Rainfall Estimation from Satellite Imagery Using Neural Networks." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/37570032073912064870.
Повний текст джерела淡江大學
水資源及環境工程學系碩士班
95
The main purpose of this study is to explore the influence of satellite imagery information on rainfall estimation using artificial neural networks. However, it is often difficult to extract interpretable information from satellite images, as data dimensions are large and nonlinear. We proposed the self-organizing map (SOM), one of artificial neural network adept at pattern cognition. In this study, watershed rainfall estimation models are constructed to forecast the rainfall summation of future six hours during typhoon events. The models are based on SOM or linear regression to investigate the characteristics of satellite imagery information and its influence on rainfall estimation. The available data are hourly rainfall data of sixteen rainfall gauge stations in the Shihmen watershed from 25 typhoon events and GMS-5/MTSAT remotely sensed data are collected from 2000 to 2004 and 2006. In order to investigate the characteristics and compare the performance among the different models, we designed three cases with different sizes or amount of rainfall in training data, then constructed six different models, multivariate linear regression model (MLR), back-propagation neural network (BP), self-organizing map linking with BP (SOMBP), self-organizing map linking with linear regression (SOMMLR), SOMBP linking with BP (SOMBP+BP) and SOMMLR linking with BP linear regression (SOMMLR+BP), to estimate the future six-hour rainfall summation. The input variables have two types: the past three six-hour rainfall summations and satellite images. The results show that (1) the MLR models have nice performances when the input variable only include the past rainfall summations, (2) SOM indeed has the ability to extract patterns from satellite data, (3) SOMBP and SOMMLR can get better results when the input variables are the past rainfall summations and satellite images. The satellite imagery information is indeed helpful to improve the accurate of rainfall estimation.
Hsu, Kuo-lin. "Rainfall estimation from satellite infrared imagery using artificial neural networks." 1996. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_1996_410_sip1_w.pdf&type=application/pdf.
Повний текст джерелаHsu, Huei-Yin, and 許惠茵. "Integrating Satellite Imagery and Meteorological Data for Typhoon Rainfall Estimation Using ANNs." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/81495429796740059902.
Повний текст джерела淡江大學
水資源及環境工程學系碩士班
98
The main purpose of this study is to explore the influence of satellite imagery and meteorological data on typhoon rainfall forecast using artificial neural networks. The self-organizing map (SOM) is adept at recognizing infrared and visible images and can extract some useful information. In this study, six watershed rainfall estimation models are constructed to forecast the amount of rainfall for one, three and six-hour totals during typhoon events. The models are based on SOM, back-propagation neural network (BPNN) or linear regression to investigate the characteristics of satellite imagery information and its influence on rainfall forecast. Twenty-seven typhoon events are collected from 2000 to 2007. The available data are GMS-5/MTSAT remotely sensed data, hourly rainfall data of sixteen rainfall gauge stations of the Shihmen watershed, wind velocity and atmospheric pressure data of three meteorological observation stations. In order to investigate the characteristics and compare the performance among the different models, we design different cases for forecasting the rainfall totals in the daytime and the whole day. Six different models, multivariate linear regression model (MLR), back-propagation neural network (BP), self-organizing map linking with BP (SOMBP), self-organizing map linking with linear regression (SOMMLR), SOMBP linking with BP (SOMBPI+BP) and SOMMLR linking with BP linear regression (SOMMLRI+BP), are constructed to forecast rainfall totals. Seven different combinations of the inputs are used to investigate the effect of rainfall forecast. The results show that (1) the MLR and BP models have nice performances when the input variable only include the past rainfall totals of gauge stations, (2) SOM indeed has the ability to extract patterns from satellite data, (3) SOM can improve results when the rainfall totals are joined, (4) the wind velocity and atmospheric pressure data are helpless for rainfall forecast. The satellite imagery information is indeed helpful to improve the accurate of rainfall forecast.
Filippucci, Paolo. "High-resolution remote sensing for rainfall and river discharge estimation." Doctoral thesis, 2022. http://hdl.handle.net/2158/1275871.
Повний текст джерелаTsai, I.-Chi, and 蔡伊其. "Evaluation of high resolution satellite data in typhoon rainfall estimation and its application." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/9z7zf6.
Повний текст джерела國立中央大學
大氣科學學系
106
The Tropical Rainfall Potential (TRaP) technique presented by Kidder et al. in 2005, shifting rainfall distribution from satellite retrieval, and forecasting rainfall for tropical cyclone. Chen(2010) improved TRaP rainfall forecast practicality by adding orographic effect with historical rainfall distribution(I-TRaP). Since I-TRaP forecast uses rainfall distribution from satellite, how to get better rainfall distribution is an important issue. There is only single satellite rainfall product in past study, limited by temporal resolution. For many study, The performance of multi-satellite rainfall products with high spatial-temporal resolution(0.1°-0.25°, 0.5-3h) are getting better recently but less discussed on heavy rainfall especially for typhoon. This study compares few common multi-satellite products (GSMaP, IMERG, PERSIANN) with typhoon heavy rainfall in the North-West Pacific, GSMaP is better. There are different performance between convective and stratiform rainfall. Indeed, the PMW retrieval fail to classification in rainfall type determination during microwave rainfall retrieving, but not cause rainfall error. In addition, compare liquid water content and rainfall error, the PMW retrieval still cannot estimate liquid water accurately in moderate to heavy rainfall. Apply GSMaP to I-TRaP and calculate typhoon rainfall forecast over Taiwan. In order to highlight satellite rainfall distribution, modify earlier method only revising total rainfall and using historical rainfall distribution, calculate rainfall regression by individual point. This method will predict more heavy rainfall but more false alarm. Compare earlier I-TRaP using SSMIS, GSMaP with high spatial-temporal resolution is more useful for I-TRaP forecast, and more prediction of heavy rainfall.
Indu, J. "Uncertainty Analysis of Microwave Based Rainfall Estimates over a River Basin Using TRMM Orbital Data Products." Thesis, 2014. http://hdl.handle.net/2005/3005.
Повний текст джерела"Rainfall estimation in Southern Africa using meteosat data." Thesis, 2014. http://hdl.handle.net/10210/13086.
Повний текст джерелаSun, Tain-De, and 孫天德. "A study with artificial neural network on estimating rainfall by using satellite cloud image." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/37342336885135940578.
Повний текст джерела中原大學
土木工程研究所
94
There are debris flow disasters in recently years in Taiwan. Since present warning system is hard to reach satisfied results, debris flows frequently cause serious loss on not only human’s life but also their property. Heavy rain is one of the factors that caused debris flows. The precipitation usually concentrates during typhoon season from May to October every year. A question as to how to estimate typhoon rainfall rapidly and accurately has become very important for early warning of debris flows. The purpose of this study is to learn about estimating typhoon rainfall by using Artificial Neural Network (ANN) with cloud temperature. The Shihmen Reservoir and its watershed are taken as example area of study,and data of cloud temperature and rainfall of invading typhoon from 1996 to 2003 are collected in this study. A temperature-rainfall model is established to predict rainfall at 3 hours later in Shihmen Reservoir. Two results are found: Firstly, rain stations with similar geographic properties have similar temperature-rainfall models. It means that landform affects rainfall condition. The past references also demonstrate this. Secondly, for the same rain station, the cloud temperature relates highly with heavy rainfall. The results display that the model performed well especially in the big typhoon events. Although for the small typhoon events the model did not perform as good as for the big ones, the errors are still acceptable. The results of this paper could serve as a fine reference for predicting debris flow induced by typhoon invasion.