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Relevant bibliographies by topics / Satellite based rainfall

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Academic literature on the topic 'Satellite based rainfall'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Satellite based rainfall"

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

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

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La estimación de precipitación en tiempo real a partir de imágenes satelitales digitales (ISD) es una metodología ampliamente utilizada por meteorólogos e hidrólogos, su aplicación sobre una región superficial es indirecta, en la cual las resoluciones temporal y espacial de la información definen la precisión, los resultados obtenidos deben validarse empleando registros de redes pluviométricas. La finalidad del presente trabajo es presentar una metodología de acoplamiento temporal y espacial (ATE), para información con resolución de 15 minutos. La información empleada corresponde a valores del nivel digital (ND) en los pixeles de las ISD captadas por el satélite GOES-13 durante el desarrollo del ciclón tropical (CT) Paul. Fueron utilizados los registros de las estaciones meteorológicas automáticas (EMA) localizadas en la región hidrológica 10 Sinaloa. La lectura y el tratamiento digital de las ISD se realizaron empleando el código Fast Infrared Satellite Image Reader GOES 13 (FISIR-G13) desarrollado en lenguaje R. Se obtuvieron lecturas en vecindades de 9 pixeles geográficamente referenciados, generando series temporales del ND, a partir de los cuales se estimó la precipitación empleando el Hidroestimador (HE). La estandarización permitió contrastar ambas fuentes de información, como resultado se identificaron combinaciones de pixeles para el ATE. La evaluación estadística empleando el coeficiente de correlación de la intensidad estimada respecto de la intensidad observada muestra un mejor desempeño de la metodología desarrollada respecto del HE. La metodología establece el acoplamiento temporal de los valores estimados de precipitación empleando ISD respecto de los valores registrados en la EMA, con valores del coeficiente de correlación cercanos a 1.

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

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Abstract. This paper describes a new multi-sensor approach for continuously monitoring convective rain cells. It exploits lightning data from surface networks to propagate rain fields estimated from multi-frequency brightness temperature measurements taken by the AMSU/MHS microwave radiometers onboard NOAA/EUMETSAT low Earth orbiting operational satellites. Specifically, the method allows inferring the development (movement, morphology and intensity) of convective rain cells from the spatial and temporal distribution of lightning strokes following any observation by a satellite-borne microwave radiometer. Obviously, this is particularly attractive for real-time operational purposes, due to the sporadic nature of the low Earth orbiting satellite measurements and the continuous availability of ground-based lightning measurements – as is the case in most of the Mediterranean region. A preliminary assessment of the lightning-based rainfall propagation algorithm has been successfully made by using two pairs of consecutive AMSU observations, in conjunction with lightning measurements from the ZEUS network, for two convective events. Specifically, we show that the evolving rain fields, which are estimated by applying the algorithm to the satellite-based rainfall estimates for the first AMSU overpass, show an overall agreement with the satellite-based rainfall estimates for the second AMSU overpass.

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Lekula, Moiteela, Maciek W. Lubczynski, Elisha M. Shemang, and Wouter Verhoef. "Validation of satellite-based rainfall in Kalahari." Physics and Chemistry of the Earth, Parts A/B/C 105 (June 2018): 84–97. http://dx.doi.org/10.1016/j.pce.2018.02.010.

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Tajudin, Noraisyah, Norsuzila Ya’acob, Darmawaty Mohd Ali, and Nor Aizam Adnan. "Estimation of TRMM rainfall for landslide occurrences based on rainfall threshold analysis." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 3208. http://dx.doi.org/10.11591/ijece.v10i3.pp3208-3215.

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Landslide can be triggered by intense or prolonged rainfall. Precipitation data obtained from ground-based observation is very accurate and commonly used to do analysis and landslide prediction. However, this approach is costly with its own limitation due to lack of density of ground station, especially in mountain area. As an alternative, satellite derived rainfall techniques have become more favorable to overcome these limitations. Moreover, the satellite derived rainfall estimation needs to be validated on its accuracy and its capability to predict landslide which presumably triggered by rainfall. This paper presents the investigation of using the TRMM-3B42V7 data in comparison to the available rain-gauge data in Ulu Kelang, Selangor. The monthly average rainfall, cumulative rainfall and rainfall threshold analysis from 1998 to 2011 is compared using quantitative statistical criteria (Pearson correlation, bias, root mean square error, mean different and mean). The results from analysis showed that there is a significant and strong positive correlation between the TRMM 3B42V7 and rain gauge data. The threshold derivative from the satellite products is lower than the rain gauge measurement. The findings indicated that the proposed method can be applied using TRMM satellite estimates products to derive rainfall threshold for the possible landslide occurrence.

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Harris, Amanda, Sayma Rahman, Faisal Hossain, Lance Yarborough, Amvrossios Bagtzoglou, and Greg Easson. "Satellite-based Flood Modeling Using TRMM-based Rainfall Products." Sensors 7, no. 12 (2007): 3416–27. http://dx.doi.org/10.3390/s7123416.

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Kästner, Martina, Francesca Torricella, and Silvio Davolio. "Intercomparison of satellite-based and model-based rainfall analyses." Meteorological Applications 13, no. 03 (2006): 213. http://dx.doi.org/10.1017/s1350482706002246.

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Nikolopoulos, E. I., E. Destro, V. Maggioni, F. Marra, and M. Borga. "Satellite Rainfall Estimates for Debris Flow Prediction: An Evaluation Based on Rainfall Accumulation–Duration Thresholds." Journal of Hydrometeorology 18, no. 8 (2017): 2207–14. http://dx.doi.org/10.1175/jhm-d-17-0052.1.

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Abstract Rainfall thresholds are often used in early warning systems to identify rainfall conditions that, when reached or exceeded, are likely to result in debris flows. Rain gauges are typically used for the definition of these thresholds. However, in mountainous areas in situ observations are often sparse or nonexistent. Satellite-based rainfall estimates offer a solution to overcome the coverage problem at the global scale but are associated with significant estimation uncertainty. Evaluating satellite-based rainfall thresholds is thus necessary to understand their potential and 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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Zhuge, Xiao-Yong, Fan Yu, and Cheng-Wei Zhang. "Rainfall Retrieval and Nowcasting Based on Multispectral Satellite Images. Part I: Retrieval Study on Daytime 10-Minute Rain Rate." Journal of Hydrometeorology 12, no. 6 (2011): 1255–70. http://dx.doi.org/10.1175/2011jhm1373.1.

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Abstract This study develops a method for both precipitation area and intensity retrievals based on multispectral geostationary satellite images. This method can be applied to continuous observation of large-scale precipitation so as to solve the problem from the measurements of rainfall radar and rain gauge. Satellite observation is instantaneous, whereas the rain gauge records accumulative data during a time interval. For this reason, collocated 10-min rain gauge measurements and infrared (IR) and visible (VIS) data from the FengYun-2C (FY-2C) geostationary satellite are employed to improve the accuracy of satellite rainfall retrieval. First of all, the rainfall probability identification matrix (RPIM) is used to distinguish rainfall clouds from nonrainfall clouds. This RPIM is more efficient in improving the retrieval accuracy of rainfall area than previous threshold combination screening methods. Second, the multispectral segmented curve-fitting rainfall algorithm (MSCFRA) is proposed and tested to estimate the 10-min rain rates. Rainfall samples taken from June to August 2008 are used to assess the performance of the rainfall algorithm. Assessment results show that the MSCFRA improves the accuracy of rainfall estimation for both stratiform cloud rainfall and convective cloud rainfall. These results are practically consistent with rain gauge measurements in both rainfall area division and rainfall intensity grade estimation. Furthermore, this study demonstrates that the temporal resolution of satellite detection is important and necessary in improving the precision of satellite rainfall retrieval.

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Dissertations / Theses on the topic "Satellite based rainfall"

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Teo, Chee-Kiat. "Application of satellite-based rainfall estimates to crop yield forecasting in Africa." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434333.

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Soytekin, Arzu. "Evaluating The Use Of Satellite." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612542/index.pdf.

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For the process of social and economic development, hydropower energy has an important role such as being renewable, clean, and having less impact on the environment. In decision of the hydropower potential of a study area, the preliminary condition is the availability of the gages in the area. However, in Turkey, the gages in working order are limited and getting decreased in recent years. Therefore, the satellite based precipitation estimates has been gaining importance to predict runoff for ungauged basins. In this study, &Ccedil<br>oruh basin, which is located in the north-eastern part of Turkey, is selected to perform hydrologic modeling. The input precipitation data for the model are provided from the observations at meteorological stations and the Tropical Rainfall Measuring Mission (TRMM) satellite products (3B42 and 3B43). TRMM satellite is used to monitor and study the rainfall distribution. The precipitation radar on the TRMM is the first radar to make precipitation estimation from the space. Using both precipitation data, HEC-HMS, being well known hydrological model, is applied to the &Ccedil<br>oruh Basin for 2005 and 2003 water years. To distinguish the differences in the runoff simulations and water budget, comparisons are done with respect to flow monitoring stations. Statistical criteria show that model simulation results obtained from TRMM 3B42 products are promising in estimating the water potential in ungauged basins.

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de, 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.

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Esta tesis explora la precisión de dos métodos de estimación de precipitación, Auto-Estimator y CRR (Convective Rainfall Rate), generados a partir de imágenes infrarrojas y visibles del Meteosat. Ambos métodos junto con una serie de correcciones de la intensidad de lluvia estimada se aplican y se verifican en dos casos de inundaciones acaecidas en zonas mediterráneas. El primer caso ocurrió en Albania del 21 al 23 de septiembre de 2002 y el segundo, conocido como caso Montserrat, ocurrió en Cataluña la noche del 9 al 10 se junio de 2000. Por otro lado se investiga la posibilidad de realizar calibraciones de ambos métodos directamente con datos de estaciones pluviométricas cuando lo común es calibrar con datos de radares meteorológicos. También se propone cambios en algunas de las correcciones ya que parecen mejorar los resultados y se propone una nueva corrección muy eficiente que utiliza las descargas eléctricas para determinar la zonas más convectivas y de mayor precipitación de los sistemas nubosos.<br>This 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.

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Roy, Tirthankar, Hoshin V. Gupta, Aleix Serrat-Capdevila, and Juan B. Valdes. "Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model." COPERNICUS GESELLSCHAFT MBH, 2017. http://hdl.handle.net/10150/623239.

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Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making <q>diagnostic structural improvements</q> to hydrologic models whenever possible.

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SHRESTHA, Mandira Singh. "Bias-Adjustment of Satellite-Based Rainfall Estimates over the Central Himalayas of Nepal for Flood Prediction." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/142254.

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Yilmaz, Koray Kamil. "Towards Improved Modeling for Hydrologic Predictions in Poorly Gauged Basins." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/195252.

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In most regions of the world, and particularly in developing countries, the possibility and reliability of hydrologic predictions is severely limited, because conventional measurement networks (e.g. rain and stream gauges) are either nonexistent or sparsely located. This study, therefore, investigates various systems methods and newly available data acquisition techniques to evaluate their potential for improving hydrologic predictions in poorly gaged and ungaged watersheds.Part One of this study explores the utility of satellite-remote-sensing-based rainfall estimates for watershed-scale hydrologic modeling at watersheds in the Southeastern U.S. The results indicate that satellite-based rainfall estimates may contain significant bias which varies with watershed size and location. This bias, of course, then propagates into the hydrologic model simulations. However, model performance in large basins can be significantly improved if short-term streamflow observations are available for model calibration.Part Two of this study deals with the fact that hydrologic predictions in poorly gauged/ungauged watersheds rely strongly on a priori estimates of the model parameters derived from observable watershed characteristics. Two different investigations of the reliability of a priori parameter estimates for the distributed HL-DHMS model were conducted. First, a multi-criteria penalty function framework was formulated to assess the degree of agreement between the information content (about model parameters) contained in the precipitation-streamflow observational data set and that given by the a priori parameter estimates. The calibration includes a novel approach to handling spatially distributed parameters and streamflow measurement errors. The results indicated the existence of a significant trade-off between the ability to maintain reasonable model performance while maintaining the parameters close to their a priori values. The analysis indicates those parameters responsible for this discrepancy so that corrective measures can be devised. Second, a diagnostic approach to model performance assessment was developed based on a hierarchical conceptualization of the major functions of any watershed system. "Signature measures" are proposed that effectively extract the information about various watershed functions contained in the streamflow observations. Manual and automated approaches to the diagnostic model evaluation were explored and were found to be valuable in constraining the range of parameter sets while maintaining conceptual consistency of the model.

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Friesen, Bruce. "Assimilation of satellite based rainfall estimates with the Canadian Precipitation Analysis." 2014. http://hdl.handle.net/1993/30078.

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The Canadian Precipitation Analysis (CaPA) produces a gridded product by assimilating data from stations and the Global Environmental Multiscale (GEM) model. This project assesses the performance of the satellite based rainfall estimates for Canada, and the results of their assimilation with CaPA. The satellite based estimates considered are those from the Climate Prediction Center Morphing method (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Relative to the Second Generation of Daily Adjusted Precipitation for Canada (APC2), all satellite products are shown to generally underestimate rainfall, however convective events result in an overestimation. Skill scores show that the satellite products possess the most skill for eastern Canada and decreasingly so westward. When assimilated with CaPA, the satellite products express decreased skill for light rainfall and potential improvements for larger events. While central Canada experiences the greatest improvements, all regions benefit the most from June through August.

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Pyng, Lee Jaw, and 李兆萍. "A study on the Anomalous Mei-Yu rainfall pattern based on Infrared Satellite data." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/18722171249114809250.

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Ou, Mi-Lim Smith Eric A. "Short-range QPF over Korean Peninsula using nonhydrostatic mesoscale model & "Future Time" data assimilation based on rainfall nowcasting from GMS satellite measurements." 2003. http://etd.lib.fsu.edu/theses/available/etd-11102003-010208.

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Thesis (Ph. D.)--Florida State University, 2003.<br>Advisor: Dr. Eric A. Smith, Florida State University, College of Arts and Sciences, Dept. of Meteorology. Title and description from dissertation home page (viewed Mar. 02, 2003). Includes bibliographical references.

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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.

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Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting, and climate diagnostic applications. This information also aids in the refinement of physical assumptions within algorithms by identifying geographical regions and seasons where existing algorithm physics may be incorrect or incomplete. Examination of relative errors between independent estimates derived from satellite microwave data is particularly important over regions with limited surface-based equipments for measuring rain rate such as the global oceans and tropical continents. In this context, analysis of microwave based satellite datasets from the Tropical Rainfall Measuring Mission (TRMM) enables to not only provide information regarding the inherent uncertainty within the current TRMM products, but also serves as an opportunity to prototype error characterization methodologies for the TRMM follow-on program, the Global Precipitation Measurement (GPM) . Most of the TRMM uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year). Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. This is more so over land regions, where the highly varying land surface emissivity offers a myriad of complications, hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present thesis fosters the development of uncertainty analysis using instantaneous satellite orbital data products (latest version 7 of 1B11, 2A25, 2A23, 2B31, 2A12) derived from the passive and active microwave sensors onboard TRMM satellite, namely TRMM Microwave Imager (TMI) and precipitation radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Two approaches are taken up to examine uncertainty. While the first approach analyses independent contribution of error from these orbital data products, the second approach examines their combined effect. Based on the first approach, analysis conducted over the land regions of Mahanadi basin, India investigates three sources of uncertainty in detail. These include 1) errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. The second approach is hinged on evaluating the performance of rainfall estimates from each of these orbital data products by accumulating them within a spatial domain and using error decomposition methodologies. Microwave radiometers have taken unprecedented satellite images of earth’s weather, proving to be a valuable tool for quantitative estimation of precipitation from space. However, as mentioned earlier, with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. One such source of uncertainty is contributed by improper detection of rainfall signature within radiometer footprints. To date, the most-advanced passive microwave retrieval algorithms make use of databases constructed by cloud or numerical weather model simulations that associate calculated microwave brightness temperature to physically plausible sample rain events. Delineation of rainfall signature from microwave footprints, also known as rain/norain classification (RNC) is an essential step without which the succeeding retrieval technique (using the database) gets corrupted easily. Although tremendous advances have been made to catapult RNC algorithms from simple empirical relations formulated for computational expedience to elaborate computer intensive schemes which effectively discriminate rainfall, a number of challenges remain to be addressed. Most of the algorithms that are globally developed for land, ocean and coastal regions may not perform well for regional catchments of small areal extent. Motivated by this fact, the present work develops a regional rainfall detection algorithm based on scattering index methodology for the land regions of study area. Performance evaluation of this algorithm, developed using low frequency channels (of 19 GHz, 22 GHz), are statistically tested for individual case study events during 2011 and 2012 Indian summer monsoonal months. Contingency table statistics and performance diagram show superior performance of the algorithm for land regions of the study region with accurate rain detection observed in 95% of the case studies. However, an important limitation of this approach is comparatively poor detection of low intensity stratiform rainfall. The second source of uncertainty which is addressed by the present thesis, involves prediction of overland rainfall using TMI low frequency channels. Land, being a radiometrically warm and highly variable background, offers a myriad of complications for overland rain retrieval using microwave radiometer (like TMI). Hence, land rainfall algorithms of TRMM TMI have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in TMI ocean algorithm). In the present study, sensitivity analysis is conducted using spearman rank correlation coefficient as the indicator, to estimate the best combination of TMI low frequency channels that are highly sensitive to near surface rainfall rate (NSR) from PR. Results indicate that, the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors, but also aids in surface noise reduction over a predominantly vegetative land surface background. Further, the variations of rainfall signature in these channel combinations were seldom assessed properly due to their inherent uncertainties and highly non linear relationship with rainfall. Copula theory is a powerful tool to characterize dependency between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this work proposes a regional model using Archimedean copulas, to study dependency of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from TMI and PR. Studies conducted for different rainfall regimes over the study area show suitability of Clayton and Gumbel copula for modeling convective and stratiform rainfall types for majority of the intraseasonal months. Further, large ensembles of TMI Tb (from the highly sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, 95th) of both convective and stratiform rainfall types. Comparatively greater ambiguity was observed in modeling extreme values of convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal superior performance of the proposed copula based technique. Another persistent source of uncertainty inherent in low earth orbiting satellites like TRMM arise due to sampling errors of non negligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. This study investigates sampling uncertainty of seasonal rainfall estimates from PR, based on 11 years of PR 2A25 data product over the Indian subcontinent. A statistical bootstrap technique is employed to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall was found to exhibit seasonal variations. To give a practical demonstration of the implications of bootstrap technique, PR relative sampling errors over the sub tropical river basin of Mahanadi, India were examined. Results revealed that bootstrap technique incurred relative sampling errors of <30% (for 20 grid), <35% (for 10 grid), <40% (for 0.50 grid) and <50% (for 0.250 grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. In order to study the effect of sampling type on relative sampling uncertainty, the study compares the resulting error estimates with those obtained from latin hypercube sampling. Based on this study, it may be concluded that bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in in-situ validation data. One of the important goals of TRMM Ground Validation Program has been to estimate the random and systematic uncertainty associated with TRMM rainfall estimates. Disentangling uncertainty in seasonal rainfall offered by independent observations of TMI and PR enables to identify errors and inconsistencies in the measurements by these instruments. Motivated by this thought, the present work examines the spatial error structure of daily precipitation derived from the version 7 TRMM instantaneous orbital data products through comparison with the APHRODITE data over a subtropical region namely Mahanadi river basin of the Indian subcontinent for the seasonal rainfall of 6 years from June 2002 to September 2007. The instantaneous products examined include TMI and PR data products of 2A12, 2A25 and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products was quantified based on the performance metrics derived from the contingency table. For the seasonal daily precipitation over 10x10 grids, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition technique, performed to disentangle systematic and random errors, testified that the multiplicative error model representing rainfall from 2A12 algorithm, successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results indicate that even though the radiometer derived 2A12 is known to suffer from many sources of uncertainties, spatial and temporal analysis over the case study region testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications in decision making, prior to incorporating the resulting orbital products for basin scale hydrologic modeling. The current missions of GPM envision a constellation of microwave sensors that can provide instantaneous products with a relatively negligible sampling error at daily or higher time scales. This study due to its simplicity and physical approach offers the ideal basis for future improvements in uncertainty modeling in precipitation.

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Books on the topic "Satellite based rainfall"

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author, Rajbhandari Rupak, Bajracharya Sagar R. author, and International Centre for Integrated Mountain Development, eds. Validation of NOAA CPC_RFE satellite-based rainfall estimates in the Central Himalayas. International Centre for Integrated Mountain Development, 2013.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Fortune, Michael A. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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Optimal combining of ground-based sensors for the purpose of validating satellite-based rainfall estimates: Final report. Dept. of Civil and Environmental Engineering and Iowa Institute of Hydraulic Research, 1991.

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United States. National Environmental Satellite, Data, and Information Service., ed. Automated satellite-based estimates of precipitation: An assessment of accuracy. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 1998.

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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. Space Sciences and Engineering Center at the University of Wisconsin-Madison, 1991.

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Book chapters on the topic "Satellite based rainfall"

1

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. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_4.

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Chiu, Long S., Si Gao, and Dong-Bin Shin. "Climate-Scale Oceanic Rainfall Based on Passive Microwave Radiometry." In Satellite-based Applications on Climate Change. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5872-8_15.

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Kuligowski, Robert J. "The Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) for High-Resolution, Low-Latency Satellite-Based Rainfall Estimates." In Satellite Rainfall Applications for Surface Hydrology. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_3.

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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. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_15.

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Tarnavsky, Elena, and Rogerio Bonifacio. "Drought Risk Management Using Satellite-Based Rainfall Estimates." In Advances in Global Change Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35798-6_28.

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Cattani, Elsa, Andrés Merino, and Vincenzo Levizzani. "Rainfall Trends in East Africa from an Ensemble of IR-Based Satellite Products." In Advances in Global Change Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35798-6_17.

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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. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0689-7_4.

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Muntohar, Agus Setyo, Olga Mavrouli, Victor G. Jetten, Cees J. van Westen, and Rokhmat Hidayat. "Development of Landslide Early Warning System Based on the Satellite-Derived Rainfall Threshold in Indonesia." In Understanding and Reducing Landslide Disaster Risk. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60311-3_26.

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Colli, M., M. Stagnaro, A. Caridi, et al. "A Field Experiment of Rainfall Intensity Estimation Based on the Analysis of Satellite-to-Earth Microwave Link Attenuation." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11973-7_17.

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Moazami, S., and S. Golian. "Ensemble-Based Multivariate Sensitivity Analysis of Satellite Rainfall Estimates Using Copula Model." In Sensitivity Analysis in Earth Observation Modelling. Elsevier, 2017. http://dx.doi.org/10.1016/b978-0-12-803011-0.00014-8.

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Conference papers on the topic "Satellite based rainfall"

1

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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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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Xianghu Li and Qi Zhang. "Validation of satellite based rainfall data in Poyang Lake catchment." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5965145.

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Munir, Sarfraz, and Rubar Dizayee. "Satellite and Ground Based Rainfall Measurement Relationship in Kurdistan Region, Iraq." In 2020 6th International Engineering Conference “Sustainable Technology and Development" (IEC). IEEE, 2020. http://dx.doi.org/10.1109/iec49899.2020.9122813.

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Chaurasia, Kuldeep, Unnam Tarun, Sarala Guddanti Venkata Sai, and Komal Soni. "AI based prediction of daily rainfall from satellite observation for disaster management." In SPIE Future Sensing Technologies, edited by Christopher R. Valenta, Joseph A. Shaw, and Masafumi Kimata. SPIE, 2020. http://dx.doi.org/10.1117/12.2580628.

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Black, Emily, Elena Tarnavsky, Helen Greatrex, et al. "Exploiting Satellite-Based Rainfall for Weather Index Insurance: The Challenges of Spatial and Temporal Aggregation." In 1st International Electronic Conference on Remote Sensing. MDPI, 2015. http://dx.doi.org/10.3390/ecrs-1-f002.

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Xianghu Li, Qi Zhang, and Yunliang Li. "Validation the applicability of satellite based rainfall data for runoff simulation and water balance analysis." In 2011 International Symposium on Water Resource and Environmental Protection (ISWREP). IEEE, 2011. http://dx.doi.org/10.1109/iswrep.2011.5893051.

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Rizaludin Mahmud, Mohd, and Mazlan Hashim. "Determination of forest water yield in Malaysian tropical watershed using calibrated satellite-based rainfall data." In 2010 International Conference on Science and Social Research (CSSR). IEEE, 2010. http://dx.doi.org/10.1109/cssr.2010.5773895.

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Ahuna, Mary N., Thomas J. Afullo, and Akintunde A. Alonge. "Rainfall rate prediction based on artificial neural networks for rain fade mitigation over earth-satellite link." In 2017 IEEE AFRICON. IEEE, 2017. http://dx.doi.org/10.1109/afrcon.2017.8095546.

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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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