Relevant bibliographies by topics / Satellite rainfall data

Academic literature on the topic 'Satellite rainfall data'

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Published: 14 March 2023

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Journal articles on the topic "Satellite rainfall data"

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Iryani, Sakura Yulia, Febrinasti Alia, Muhammad Abiyyi Tauhid, Ahmad Muhtarom, and Arie Putra Usman. "Utilization of GPM Satellite and PERSIANN Satellite Data for Estimated Monthly Rainfall in South Sumatera." UKaRsT 6, no. 2 (November 29, 2022): 174. http://dx.doi.org/10.30737/ukarst.v6i2.3482.

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Rainfall data are widely used to predict regional rainfall. Limited rainfall data is a problem that has an impact on decreasing accuracy, one of which is in the area of South Sumatra. This can be overcome by using satellites. However, to utilize satellitebased rainfall data, it is necessary to carry out an analysis to determine the accuracy of rainfall data. This research aims to evaluate rainfall data from the GPM satellite and PERSIANN satellite with validation and calibration analysis so that the value of rainfall data from the Satellite is close to the measurement result and can be used to estimate monthly rainfall. In this study, the data used were measured monthly rainfall in the field, GPM, and PERSIANN obtained from 9 South Sumatra districts for 2019 until 2021. The research method was validated using correlation coefficient, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Calibration is done using a combination method, a solver algorithm in Microsoft Excel, and manually. The estimated monthly rainfall analysis is carried out using the isohyet method with the IDW interpolation method. The research results were obtained based on the validation and calibration of monthly rainfall data showing that data from the GPM showing it is closer to the results of field rainfall measurements than the data obtained from PERSIANN satellite. Based on the results of research on satellite data that has been calibrated, it can be used to estimate monthly rainfall in the South Sumatra Region
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Kuntoro, A. A., R. K. Hapsari, M. B. Adityawan, M. Farid, Widyaningtias, and Radhika. "Estimation of Extreme Rainfall over Kalimantan Island based on GPM IMERG Daily Data." IOP Conference Series: Earth and Environmental Science 1065, no. 1 (July 1, 2022): 012036. http://dx.doi.org/10.1088/1755-1315/1065/1/012036.

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Abstract Rainfall is one of the critical data for water resources infrastructure planning. In many cases in developing countries such as Indonesia, rainfall stations are not evenly distributed. In many cases, regional development occurs much faster than the improvement of hydrological measurement instruments. The plan to move the capital city of Indonesia to Kalimantan is one example. Satellites rainfall products can be utilized, especially for areas with a limited number of rainfall stations. This study examines the potential use of Global Precipitation Measurement (GPM) satellite products to estimate the spatial distribution of rainfall in the Kalimantan region. Twenty years data of daily maximum rainfall from GPM satellite rainfall products in 2001-2020 were compared to twenty years data of daily maximum rainfall from 16 rainfall stations under the Meteorology, Climatology, and Geophysical Agency (BMKG), with data time spanning from the 1970s to 2020. The analysis results show a significant difference between extreme rainfall analysis computed by using station data and the satellite. The use of the correction function can increase the accuracy of the GPM rainfall product. It can be used as an alternative data source for a region with limited rainfall stations.
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Zeri, Sarah Jabbar, Mohammed Magdy Hamed, Xiaojun Wang, and Shamsuddin Shahid. "Utilizing Satellite Data to Establish Rainfall Intensity-Duration-Frequency Curves for Major Cities in Iraq." Water 15, no. 5 (February 22, 2023): 852. http://dx.doi.org/10.3390/w15050852.

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This study generates intensity-duration-frequency curves for three important cities in Iraq using Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation near real-time (GSMaP NRT), and gauge corrected (GSMaP GC) satellite precipitation datasets. Many probability distribution functions were used to fit the maximum yearly rainfall data. The Sherman equation was used to create intensity-duration-frequency (IDF) curves for rainfall intensities with 2-, 5-, 10-, 25-, 50-, and 100-year return periods, with the estimated coefficients of the best-fit distribution serving as the fitting parameters. The discrepancy between the IDF curves produced from the satellites and the observed data was used to bias correct the satellite IDF curves. The Generalized Extreme Value Distribution model best describes the hourly rainfall distribution of satellite data. GSMaP GC was the best option for creating IDF curves with higher correlations with observed data at Baghdad, Basra, and Mosul. The study indicates the necessity of gauge correction of satellite rainfall data to reduce under- and over-estimating observed rainfall. GSMaP GC can reasonably estimate rainfall in a predominantly arid climate region like Iraq. The generated IDF curves may be an important step toward achieving sustainable urban stormwater management in the country.
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Tama, Danny Riyadi, Lily Montarcih Limantara, Ery Suhartanto, and Yatnanta Padma Devia. "THE USAGE OF GPM DATA IN THE UNGAUGED WONOGIRI CATCHMENT." Journal of Southwest Jiaotong University 57, no. 6 (December 30, 2022): 1004–10. http://dx.doi.org/10.35741/issn.0258-2724.57.6.86.

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This paper intends to establish the usage of Global Precipitation Measurement (GPM) data in the ungauged Wonogiri catchment. Along with technological developments in the field of remote sensing, such as satellite data, the rainfall measurements can be perfgormed by monitoring rainfall in the large areas, even in places that cannot be reached by the conventional equipment. This advantage can be used to obtain the amount of rain in an area du to the benefit of water resource management. The methodology consists of collecting the observed data (rainfall station) and rainfall satellite data, then calibrating the satellite data, and by the end is to validate the result of calibration. The use of satellite rain data needs to be corrected so the rain characteristics produced by the satellite approach the rainfall conditions produced by the observations. The corrections are made by multiplying each data interval against a certain coefficient so that the value generated by the satellite data approaches the characteristic value generated by the observed rain that is used as the Global Precipitation Measurement (GPM). The Global Precipitation Mesaurement (GPM) is required in the ungauged catchment that there is no observed rainfall data. Based on the results of trial and error, the results show that a correction coefficient value of 0 is produced for the interval 0 – 2 mm, 0.75 for the interval 2 – 20 mm, 0.8 for the interval 20 – 30 mm, 0.85 for the interval 30 – 50 mm and 0.95 for the value rain satellites larger than 50 mm.
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González, MH, and I. Velasco. "Rainfall area identification using satellite data." Climate Research 5 (1995): 259–67. http://dx.doi.org/10.3354/cr005259.

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Shih, Sun F. "GOES Satellite Data in Rainfall Estimation." Journal of Irrigation and Drainage Engineering 115, no. 5 (October 1989): 839–52. http://dx.doi.org/10.1061/(asce)0733-9437(1989)115:5(839).

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Wu, Qiaoyan, and Yilei Wang. "Comparison of Oceanic Multisatellite Precipitation Data from Tropical Rainfall Measurement Mission and Global Precipitation Measurement Mission Datasets with Rain Gauge Data from Ocean Buoys." Journal of Atmospheric and Oceanic Technology 36, no. 5 (May 2019): 903–20. http://dx.doi.org/10.1175/jtech-d-18-0152.1.

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AbstractThree satellite-derived precipitation datasets [the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) dataset, the NOAA Climate Prediction Center morphing technique (CMORPH) dataset, and the newly available Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) dataset] are compared with data obtained from 55 rain gauges mounted on floating buoys in the tropics for the period 1 April 2014–30 April 2017. All three satellite datasets underestimate low rainfall and overestimate high rainfall in the tropical Pacific Ocean, but the TMPA dataset does this the most. In the high-rainfall (higher than 4 mm day−1) Atlantic region, all three satellite datasets overestimate low rainfall and underestimate high rainfall, but the IMERG dataset does this the most. For the Indian Ocean, all three rainfall satellite datasets overestimate rainfall at some gauges and underestimate it at others. Of these three satellite products, IMERG is the most accurate in estimating mean precipitation over the tropical Pacific and Indian Oceans, but it is less accurate over the tropical Atlantic Ocean for regions of high rainfall. The differences between the three satellite datasets vary by region and there is a need to consider uncertainties in the data before using them for research.
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Zhou, Yuanyuan, Nianxiu Qin, Qiuhong Tang, Huabin Shi, and Liang Gao. "Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework." Remote Sensing 13, no. 6 (March 11, 2021): 1057. http://dx.doi.org/10.3390/rs13061057.

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The accuracy of the rain distribution could be enhanced by assimilating the remotely sensed and gauge-based precipitation data. In this study, a new nonparametric general regression (NGR) framework was proposed to assimilate satellite- and gauge-based rainfall data over southeast China (SEC). The assimilated rainfall data in Meiyu and Typhoon seasons, in different months, as well as during rainfall events with various rainfall intensities were evaluated to assess the performance of this proposed framework. In rainy season (Meiyu and Typhoon seasons), the proposed method obtained the estimates with smaller total absolute deviations than those of the other satellite products (i.e., 3B42RT and 3B42V7). In general, the NGR framework outperformed the original satellites generally on root-mean-square error (RMSE) and mean absolute error (MAE), especially on Nash-Sutcliffe coefficient of efficiency (NSE). At monthly scale, the performance of assimilated data by NGR was better than those of satellite-based products in most months, by exhibiting larger correlation coefficients (CC) in 6 months, smaller RMSE and MAE in at least 9 months and larger NSE in 9 months, respectively. Moreover, the estimates from NGR have been proven to perform better than the two satellite-based products with respect to the simulation of the gauge observations under different rainfall scenarios (i.e., light rain, moderate rain and heavy rain).
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Cheng, Ke S., and Sun F. Shih. "Rainfall Area Identification Using GOES Satellite Data." Journal of Irrigation and Drainage Engineering 118, no. 1 (January 1992): 179–90. http://dx.doi.org/10.1061/(asce)0733-9437(1992)118:1(179).

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Teo, Chee-Kiat, and David I. F. Grimes. "Stochastic modelling of rainfall from satellite data." Journal of Hydrology 346, no. 1-2 (November 2007): 33–50. http://dx.doi.org/10.1016/j.jhydrol.2007.08.014.

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Dissertations / Theses on the topic "Satellite rainfall data"

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

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Jörpeland, Jon. "Data assimilation of GPS-RO atmospheric profile data for improved rainfall forecasts over West Africa." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-291564.

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Forecasting rainfall is of great importance for the farmers in West Africa. However, due too lack of reliable weather observations, rainfall forecats in West Africa are difficult and primarly based on satellite observations. This thesis will study a satellite dataset that could possible work as a substitute for weather balloon soundings and thus improving the rainfall forecasts. A satellite dataset with atmospheric temperature and humidity profiles, obtained from GPS-RO, was compared with radiosondes available from Abidjan, Bamako and Niamey, to study the potential of improving rainfall forecasts over West Africa. Two case studies with simulated weather forecasts with and without assimilated GPS-RO data was also compared. Data assimilation is used to produce an estimate of the atmospheric properties. Temperature profiles obtained from GPS-RO data showed insignificant bias compared to the radiosondes. Probable humidity sensor failure resulted in problem analysing the dew point temperature. From simulations, it was shown that GPS-RO assimilation may have a large impact on the forecasts and could potentially be a substitute for radiosondes in West Africa.
Regnprognoser är något som är viktigt för jordbrukare. I Västafrika saknas pålitliga väderobservationer och regnprognoser är istället baserade på satellit observationer. Denna uppsats riktar sig på att studera ett satellit dataset som har möjligheten att vara ett substitut för väderballongssonderingar och på så vis vara ett steg mot förbättrade regnprognoser. Ett dataset med atmosfäriska temperatur- och fuktighetsprofiler, erhållen från GPS-RO, jämfördes med radiosonderingar från Abidjan, Bamako och Niamey, för att studera dess potential för förbättrade regnprognoser över Västafrika. Två fallstudier med simulerade väderprognoser med och utan assimilerad GPS-RO data jämfördes också. Data assimilering används för att uppskatta de atmosfäriska egenskaperna. Temperaturprofilerna erhållna från GPS-RO data visade ingen signifikant skillnad jämfört med radiosonderingarna. Troligt sensorfel i fuktighetsgivarna från radiosonderingarna ledde till problem med analysen av daggpunktstemperaturen. Simuleringar visade att assimilation med GPS-RO kan ha stor påverkan på prognoserna och har potential att bli ett substitut för radiosonderingar i Västafrika.
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Enbäck, Henrik, and Charlotta Eriksson. "Hybrid Rainfall Estimates from Satellite, Lightning and Ground Station Data in West Africa." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-254757.

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Most of the working population in Ghana are farmers. It is of importance for them to know where and when precipitation will occur to prevent crop losses due to droughts and floodings. In order to have a sustainable agriculture, improved rainfall forecasts are needed. One way to do that is to enhance the initial conditions for the rainfall models. In the mid-latitudes, in-situ rainfall observations and radar data are used to monitor weather and measure rainfall. However, due to the lack of station data and the present absence of a radar network in West Africa, other rainfall estimates are needed as substitutes. The rainfall amount in convective systems, dominating in West Africa, is coupled to their vertical structure. Therefore, satellite measurements of cloud top temperatures and microwave scatter, as well as the number of lightning, can be used to estimate the amount of rainfall. In this report, derived rainfall estimates from satellites and the use of lightning data are analysed to see how well they estimate the actual rainfall amount. The satellite datasets used in this report are NOAA RFE2.0, NOAA ARC2, and the EUMETSAT MPE. The datasets were compared to in-situ measurements from GTS- and NGO collaborating observation stations in order to verify which satellite dataset that best estimates the rainfall or, alternatively, if a combination between two or all the datasets is a better approach. Lightning data from Vaisala GLD360 have been compared to GTS-station data and RFE2.0 to see if a relation between the number of lightning and rainfall amount could be found. It was also tested whether a combination between the satellite- and lightning data could be a better estimate than the two approaches separately. Rainfall estimates from RFE2.0 alone showed the best correlation to GTS- and the NGO collaborating station data. However, a difference in how well RFE2.0 estimated rainfall at GTS-stations compared to reference stations was seen. Comparing RFE2.0 to GTS-stations showed a better correlation, probably due to the use of these observations in the build up of RFE2.0. Even though RFE2.0 showed the best correlation compared to other datasets, satellite estimates showed in general poor skill in catching the actual rainfall amount, strongly underestimating heavy rainfall and somewhat overestimating lighter rainfall. This is probably due to the rather basic assumptions that the cloud top temperature is directly coupled to rain rate and also the poor temporal resolution of the polar orbiting satellites (carrying microwave sensors). Better instruments and algorithms need to be developed to be able to use satellite datasets as an alternative to rainfall measurements in West Africa. Furthermore, due to the lack of station data, only tentative results between GLD360 and GTS-stations could be made, showing a regime dependence. When further analysed to RFE2.0, a stronger temporal dependence, i.e. seasonal variation, rather than a spatial one was seen, especially during the build up of the monsoon. However, due to poor rainfall estimates from RFE2.0, no accurate rainfall-lightning relation could be made but trends regarding the relation were seen. The use of GLD360 showed to be an effective way to erase false precipitation from satellite estimates as well as locating the trajectory of convective cells. To be able to further analyse rainfall/lightning relation, more measurements of the true rainfall is needed from e.g. a radar.
Majoriteten av Ghanas befolkning arbetar inom jordbrukssektorn. Det är viktigt för jordbrukarna att veta när och var nederbörd kommer att falla för att deras skörd inte ska bli förstörd av till exempel torka eller översvämningar. Det behövs därför bättre nederbördsprognoser för ett hållbart jordbruk. Ett sätt att få mer noggranna prognoser är att förbättra initialvärden till nederbördsmodellerna. Vid de mellersta breddgraderna på norra halvklotet används nederbördsmätningar från in-situ stationer samt data från radarsystem som initialvärden, men på grund av få mätstationer och inget radarsystem i västra Afrika behövs alternativa nederbördsestimater. Nederbörden i västra Afrika domineras av konvektiva system, vars regnmängd är kopplad till dess vertikala struktur. Satellitmätningar av molntoppstemperaturen och mikrovågornas spridning och absorption, liksom antalet blixtar är också relaterat till molnets struktur och kan därför användas för att estimera nederbördsmängden. I den här rapporten analyserades nederbördsestimater från satellitdata samt användning av blixtdata för att undersöka hur bra metoderna är på att estimera den verkliga nederbördsmängden. Satellitdataseten som analyserades var NOAA RFE2.0, NOAA ARC2 och EUMETSAT MPE. Dataseten jämfördes med in-situ mätningar från GTS-stationer samt observationerfrån NGO-samarbetande jordbrukare för att verifiera vilket satellitdataset som ger det bästa nederbördsestimatet, alternativt att en kombination mellan två eller alla dataset ger det bästa estimatet. Vidare har blixtdata från Vaisala GLD360 jämförts med GTS-stationer och RFE2.0 för att se om antalet blixtar är relaterat till nederbördsmängden. Slutligen har det också undersökts om en kombination mellan satellit- och blixtdata är ett bättre än de två metoderna separat. Nederbördsestimater från RFE2.0 visade på bäst korrelation med både GTS- och NGO-stationer. En tydlig skillnad noterades dock i RFE2.0:s förmåga att estimera nederbörd vid jämförelse mellan de två stationsdataseten. En bättre korrelation mellan RFE2.0 och GTS-stationerna påvisades, troligen för att RFE2.0 använder dessa observationer i uppbyggnaden av datasetet. Även om RFE2.0 visade på bäst korrelation i jämförelse med ARC2 och MPE var samtliga satellitdataset dåliga på att estimera den verkliga nederbördsmängden. De underestimerar starkt stora mängder nederbörd samtidigt som de överestimerar små mängder. Anledningen är troligen det relativt enkla antagandet att molntoppstemperaturen är direkt kopplad till molnets regnmängd samt den dåliga tidsupplösningen på de polära satelliterna som är utrustade med mikrovågssensorer. För att satellitdataseten ska kunna användas som ett alternativt nederbördsestimat i Västafrika behövs bättre mätinstrument och algoritmer. Vid analysen mellan GLD360 och GTS-stationer kunde, på grund av för få stationsdata, endast övergripande resultat erhållas. Ett områdesberoende gick dock att urskilja som vid en ytterligare analys mellan GLD360 och RFE2.0 visade på ett större säsongsberoende, särskilt under uppbyggnaden av monsunperioden i april och maj. Eftersom RFE2.0 visade sig ha dåliga nederbördsestimat kunde ingen noggrann koppling hittas, utan resultatet visade på trender samt möjligheter att kunna använda blixtdata som ett alternativt nederbördsestimat. Till exempel visade det sig att GLD360 kunde användas som ett verktyg för att sålla bort falsk nederbörd från satellitestimat samt identifiera trajektorien för ett konvektivt system. För en djupare analys i att relatera blixtar och nederbörd i Västafrika krävs bättre tekniker för att estimera nederbörd eller fler in-situ observationer.
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Faridhosseini, Alireza. "Evaluation of Summer Rainfall Estimation by Satellite Data using the ANN Model for the GCM Subgrid Distribution." Thesis, The University of Arizona, 1998. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_etd_hy0021_m_sip1_w.pdf&type=application/pdf.

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Chadwick, Robin. "Multi-spectral satellite rainfall estimation over Africa using meteosat second generation data." Thesis, University of Reading, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542062.

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Bonifacio, Rogerio. "Vegetation amnd rainfall studies in Sahelian and Saharan Africa using satellite data." Thesis, University of Reading, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.259812.

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Pscheidt, Ieda [Verfasser]. "Generating high resolution precipitation conditional on rainfall observations and satellite data / Ieda Pscheidt." Bonn : Universitäts- und Landesbibliothek Bonn, 2017. http://d-nb.info/1149154195/34.

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Siyyid, 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/.

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Satellite information, in combination with conventional point source measurements, can be a valuable source of information. This thesis is devoted to the spatial estimation of areal rainfall over a region using both the measurements from a dense and sparse network of rain-gauges and images from the meteorological satellites. A primary concern is to study the effects of such satellite assisted rainfall estimates on the performance of rainfall-runoff models. Low-cost image processing systems and peripherals are used to process and manipulate the data. Both secondary as well as primary satellite images were used for analysis. The secondary data was obtained from the in-house satellite receiver and the primary data was obtained from an outside source. Ground truth data was obtained from the local Water Authority. A number of algorithms are presented that combine the satellite and conventional data sources to produce areal rainfall estimates and the results are compared with some of the more traditional methodologies. The results indicate that the satellite cloud information is valuable in the assessment of the spatial distribution of areal rainfall, for both half-hourly as well as daily estimates of rainfall. It is also demonstrated how the performance of the simple multiple regression rainfall-runoff model is improved when satellite cloud information is used as a separate input in addition to rainfall estimates from conventional means. The use of low-cost equipment, from image processing systems to satellite imagery, makes it possible for developing countries to introduce such systems in areas where the benefits are greatest.
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Assiri, Mazen Ebraheem. "Investigation of Arabian rainfall climate and its teleconnections using satellite, gauge and NWP model data." Thesis, University of Reading, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558779.

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Water is essential for life. In the Arabian Peninsula (AP), rainfall is irregular, infrequent and low. Climate studies show that the AP receives between less than SOmm per year and more than 2S0mm per year depending upon location. Therefore rainfall monitoring and modelling are very important in optimising the use of this scarce resource. Monitoring rainfall using satellite observations is an alternative method which can solve the problem of the inadequate rainfall monitoring by ground-based methods (raingauge and radar). In this project, rainfall variability over the study area was shown using raingauges observations. The Tropical Applications of Meteorology using SATellite (TAMSAT) approach has been tried to estimate rainfall in the Arabian Peninsula. It depends on the use of cold cloud duration based only on thermal infra- red imagery. Then, the rainfall estimates were utilized to evaluate the rainfall ERA- Interim reanalysis dataset over the study area, and the tele-connection between the rainfall variability over the southwest mountainous region of the AP and the southwesterly monsoon was derived using the ERA-Interim data. The results show that the AP has extreme temporal/spatial variation of rainfall. Most of the' study area receives rainfall between October and May while rainfall occurs in the southwest region (SWAP) throughout the year by getting two rainy seasons (winter and summer). It was found that the TAMSAT approach performs well over the SWAP during summer which led to conducting an evaluation of rainfall ERA-Interim date only over this region. The evaluation has shown that the rainfall reanalysis data captures the summer intra/inter-annual rainfall pattern while its rainfall values are overestimated specifically for September. The analysis of the connection between rainfall variability over the SWAP and the southwesterly monsoon gave indication of the monsoon influences. The main finding is the relationship between weak (break)/strong (active) monsoon and the increase/decrease of rainfall over the study area in terms of providing perceptible water which enhances the creation of rainy clouds.
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Bottomley, Laura Jones. "The application of IBM PC's and distrometers in a satellite propagation experiment." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/90919.

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This thesis describes the use of a distrometer and two IBM-PC's to collect data in a large propagation experiment. The uses and methods of collecting drop size distribution are discussed as are the uses of IBM-PC's for both data collection and control. Methods of requiring the PC's to operate in real time are also included.
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Books on the topic "Satellite rainfall data"

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

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Smith, P. L. Further studies to extend and test the area-time-integral technique applied to satellite data, period, 1 July - 31 December, 1993: Semiannual status report on grant no. NAG 5-386. [Washington, DC: National Aeronautics and Space Administration, 1993.

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United States. National Aeronautics and Space Administration., ed. Collection and analysis of radar rainfall and satellite data for the Darwin TRMM experiment: For the period of 1 December 1990 to 31 May 1991 : a final report ... Madison, Wis: Space Science and Engineering Center at the University of Wisconsin-Madison, 1991.

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Manning, Robert Michael. Space communication link propagation data for selected cities within the multiple beam and steerable antenna coverage areas of the Advanced Communications Technology Satellite. [Washington, DC]: National Aeronautics and Space Administration, 1988.

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

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B, Hinton Barry, United States. National Aeronautics and Space Administration., and University of Wisconsin--Madison. Space Science and Engineering Center., eds. Use of microwave satellite data to study variations in rainfall over the Indian Ocean: Final report, 1 August 1986 through 28 February 1990. Madison, Wis: Space Science and Engineering Center, University of Wisconsin-Madison, 1990.

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

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Toulmin, Camilla. Land, Investment, and Migration. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198852766.001.0001.

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How do people survive and thrive in the uncertain and risk-prone Sahel? This long-term study portrays the people of Dlonguébougou in Central Mali, to show how they have adapted to change over the last 35 years, shaping new strategies and finding new sources of cash. Drawing on my 2 years in the village in 1980–1982, published in Cattle, Women, and Wells: Managing Household Survival in the Sahel (OUP 1992), I have revisited the people to explore the village economy and society today. A tripling in population, unpredictable rainfall, and the arrival of the Chinese have forced people into new ways of both making ends meet and building up wealth—some are doing much better than others. Using a combination of infographics, satellite images, interviews, and survey data, my research presents the different strategies and fortunes of individuals and their families, the search for new cash incomes, the shift of people from village to town, and the erosion of collective solidarity at household and village levels. Overall, people’s fortunes have been mixed. Many people acknowledge they have become financially better off, but they are no longer so rooted in the life and landscape of millet farming, which had structured household relationships and village society. Land has become much scarcer, and the villagers can no longer exert much power over the wider society and environment. In eight chapters, Land, Investment, and Migration is written in an engaging style, with plenty of illustrations and material from interviews.
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Book chapters on the topic "Satellite rainfall data"

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Hossain, Faisal, Ling Tang, Emmanouil N. Anagnostou, and Efthymios I. Nikolopoulos. "A Practical Guide to a Space-Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data." In Satellite Rainfall Applications for Surface Hydrology, 145–67. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_9.

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Kuligowski, Robert J. "Satellite Rainfall Information for Flood Preparedness and Response." In Use of Satellite and In-Situ Data to Improve Sustainability, 31–39. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9618-0_4.

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Hoang, Thanh-Van, Tien-Yin Chou, Yao-Min Fang, Chun-Tse Wang, Ming Chang Tsai, Quoc Dinh Nguyen, Quoc Huy Nguyen, Quang Thanh Bui, and Quoc Tuan Nguyen. "Application of Global Satellite Positioning and Automatic Monitoring in Slopeland Disaster Prevention." In Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022, 147–58. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18471-0_12.

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AbstractThe Global Positioning System (GPS) is satellite-based, with receiving equipment worldwide utilizing geographic positioning satellites in Earth orbit. The system is unaffected by the radio positioning system, so it provides highly accurate three-dimensional positioning, velocity, and time data to users. In this paper, Alishan Township, Chiayi County, central Taiwan, is selected to test an automatic real-time monitoring system comprising of one machine with multiple GPS antennas. To this end, the Alishan Public Works Section installed advanced measuring instruments and a landslide-monitoring system composed of a high-efficiency transmission system. A pre- and post-rainfall data survey was conducted on this slope section. Together with the rainfall records, real-time ground-slip monitoring data was collected, and subsequently analyzed to understand the disaster situation and ground slip characteristics of the Alishan Highway following an earthquake (1998). In the future, more effective management values will be set to reduce the loss of slope disasters to conserve land and public safety. For the first time in Taiwan, this paper presents displacement data indicating that after the typhoon rains, a maximum surface movement velocity of 2.5 cm every six days is attained, while the total displacement per month is as high as 10 cm. These data can be used as for the remediation of this section of the Alishan Highway.
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Tesfaye, Argaw, and Arragaw Alemayehu. "Climate Change and Variability on Food Security of Rural Household: Central Highlands, Ethiopia." In African Handbook of Climate Change Adaptation, 379–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_188.

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AbstractThis chapter analyzes the impact of climate change and variability on food security of rural households in the central highlands of Ethiopia taking Basona Werana district as a case study site. Data were obtained from 123 households selected using simple random sampling from three agro ecological zones. Key informant interviews and focus group discussion (FDG) were used to supplement the data obtained from household survey. The monthly rainfall and temperature data are for 56 points of 10 × 10 km grids reconstructed from weather stations and meteorological satellite observations, which cover the period between 1983 and 2016. Standardized rainfall anomaly (SRA), linear regression (LR), and coefficient of variation (CV) are used to examine inter-annual and intra-annual variability of rainfall. Annual and seasonal rainfalls show decreasing trends over the period of observation. The decreasing trends in annual and March–May (Belg) rainfall totals exhibit statically significant decreasing trends at p = 0.05 level. Kiremt (June–September) shows statically significant decreasing trends at p = 0.1 level. Mean annual maximum and minimum temperatures show statically significant increasing trends at p = 0.05 level. More than 80% of households perceived that the climate is changing and their livelihoods (crop and livestock production) are impacted. The district belongs to one of the most vulnerable areas to climate change and variability in the country where large proportions of households (62%) are under different food insecurity classes. Results suggest that local level investigations are useful in developing context-specific climate change adaptation.
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Satyawardhana, Haries, Erma Yulihastin, Gammamerdianti, Candra Nur Ihsan, and Eka P. Wulandari. "Evaluation of CCAM Seasonal Prediction by GSMaP Satellite Rainfall Data in Indonesia." In Springer Proceedings in Physics, 183–92. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0308-3_14.

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Olaniyan, Olumide A., Vincent O. Ajayi, Kamoru A. Lawal, and Ugbah Paul Akeh. "Impact of Moisture Flux and Vertical Wind Shear on Forecasting Extreme Rainfall Events in Nigeria." In African Handbook of Climate Change Adaptation, 1127–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_98.

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AbstractThis chapter investigates extreme rainfall events that caused flood during summer months of June–September 2010–2014. The aim is to determine the impact of horizontal moisture flux divergence (HMFD) and vertical wind shear on forecasting extreme rainfall events over Nigeria. Wind divergence and convective available potential energy (CAPE) were also examined to ascertain their threshold values during the events. The data used include rainfall observation from 40 synoptic stations across Nigeria, reanalyzed datasets from ECMWF at 0.125° × 0.125° resolution and the Tropical Rainfall Measuring Mission (TRMM) dataset at resolution of 0.25° × 0.25°. The ECMWF datasets for the selected days were employed to derive the moisture flux divergence, wind shear, and wind convergence. The derived meteorological parameters and the CAPE were spatially analyzed and superimposed on the precipitation obtained from the satellite data. The mean moisture flux and CAPE for some northern Nigerian stations were also plotted for 3 days prior to and 3 days after the storm. The result showed that HMFD and CAPE increased few days before the storm and peak on the day of the storms, and then declined afterwards. HMFD values above 1.0 × 10−6 g kg−1 s−1 is capable of producing substantial amount of rainfall mostly above 50 mm while wind shear has a much weaker impact on higher rainfall amount than moisture availability. CAPE above 1000 Jkg−1 and 1500 Jk−1 are favorable for convection over the southern and northern Nigeria, respectively. The study recommends quantitative analysis of moisture flux as a valuable short-term severe storm predictor and should be considered in the prediction of extreme rainfall.
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Olaniyan, Olumide A., Vincent O. Ajayi, Kamoru A. Lawal, and Ugbah Paul Akeh. "Impact of Moisture Flux and Vertical Wind Shear on Forecasting Extreme Rainfall Events in Nigeria." In African Handbook of Climate Change Adaptation, 1–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42091-8_98-1.

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AbstractThis chapter investigates extreme rainfall events that caused flood during summer months of June–September 2010–2014. The aim is to determine the impact of horizontal moisture flux divergence (HMFD) and vertical wind shear on forecasting extreme rainfall events over Nigeria. Wind divergence and convective available potential energy (CAPE) were also examined to ascertain their threshold values during the events. The data used include rainfall observation from 40 synoptic stations across Nigeria, reanalyzed datasets from ECMWF at 0.125° × 0.125° resolution and the Tropical Rainfall Measuring Mission (TRMM) dataset at resolution of 0.25° × 0.25°. The ECMWF datasets for the selected days were employed to derive the moisture flux divergence, wind shear, and wind convergence. The derived meteorological parameters and the CAPE were spatially analyzed and superimposed on the precipitation obtained from the satellite data. The mean moisture flux and CAPE for some northern Nigerian stations were also plotted for 3 days prior to and 3 days after the storm. The result showed that HMFD and CAPE increased few days before the storm and peak on the day of the storms, and then declined afterwards. HMFD values above 1.0 × 10−6 g kg−1 s−1 is capable of producing substantial amount of rainfall mostly above 50 mm while wind shear has a much weaker impact on higher rainfall amount than moisture availability. CAPE above 1000 Jkg−1 and 1500 Jk−1 are favorable for convection over the southern and northern Nigeria, respectively. The study recommends quantitative analysis of moisture flux as a valuable short-term severe storm predictor and should be considered in the prediction of extreme rainfall.
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Meneghello, Géri Eduardo, Letícia Burkert Méllo, Ritâ De Cassia Fraga Damé, Francisco Amaral Villela, Maria Clotilde Carré Chagas Neta, Suelen Cristiane Riemer da Silveira, Claúdia Fernanda Almeida Teixeira-Granda, and Roberta Machado Karsburg. "Comparison Between Estimated Rainfall Estimated by the Tropical Rainfall Measuring Mission (TRMM) Satellite and Data Observed in the Lagoa Mirim/RS Basin, Brazil." In INCREaSE 2019, 97–110. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30938-1_8.

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

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Jumianti, Nining, Marzuki Marzuki, Wendi Harjupa, Risyanto, and Muhammad Fadhlan Putranto. "Prediction of Extreme Rainfall of September 9, 2020 in Padang City Based on Clouds Brightness Temperature Difference from Himawari-8 Satellite Data." In Springer Proceedings in Physics, 771–87. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0308-3_61.

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Conference papers on the topic "Satellite rainfall data"

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

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Abiola, Semire Folasade, Rosmiwati Mohd-Mokhtar, Widad Ismail, Norizah Mohamad, and J. S. Mandeep. "Satellite and ground data rainfall characterization in Malaysia." In 2011 International Conference on Space Science and Communication (IconSpace). IEEE, 2011. http://dx.doi.org/10.1109/iconspace.2011.6015856.

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Perera, Helani, Miyuru Gunathilake, and Upaka Rathnayake. "Satellite Rainfall Products for analysis of Rainfall trends for Mahaweli River Basin." In The SLIIT International Conference on Engineering and Technology 2022. Faculty of Engineering, SLIIT, 2022. http://dx.doi.org/10.54389/zzug8067.

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The presence of accurate and spatiotemporal data is of utmost importance in hydrological studies for river basins. However, limited ground-measured rainfall data restrict the accuracy of these analyses. Data scarcities can often be seen not only in many developing countries but also in the developed world. Therefore, much attention is given to alternative techniques to accomplish the data requirement. Precipitation data extraction from satellite precipitation products is one of the frequently used techniques in the absence of ground-measured rainfall data. The Mahaweli River Basin (MRB) is the largest river basin in Sri Lanka and it covers 1/6th of the total land area of the country. Mahaweli River is the heart of the country and the water of it is being used for many activities, including hydropower development, water supply, irrigation, etc. Therefore, analyzing rainfall trends of MRB is interesting and worthwhile for many stakeholders of the river basin. Therefore, this research investigates the suitability of Satellite Rainfall Products (SRP’s) as an alternative for Rain Gauge measured data in the MRB by performing trend analysis between the two datasets. Six precipitation products, namely PERSIANN, PERSIANNCCS, PERSIANN-CDR, GPM IMERG V06, TRMM-3B42 V7, TRMM-3B42RT V7 were extracted for 10-35 years for 14 locations of the MRB spatially distributed in the three climatic zones of the catchment. Non-parametric tests, including the Mann-Kendall test and Sen’s slope estimator tests, were used to detect the possible rainfall trends in precipitation products. Significant increasing trends were observed for both ground-measured and SRP’s in the annual scale while mixed results were observed in monthly and seasonal scales. The trends from ground-measured rainfall and SRP’s were compared and the suitability of SRP’s as an alternative technique was stated. KEYWORDS: ground-measured rainfall data, Mahaweli River Basin, rainfall trends, satellite precipitation products, PERSIANN, IMERG, TRMM
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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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Gogoua, Gogoua Habib, Franziska Tügel, and Reinhard Hinkelmann. "Scenarios definition for extreme rainfall events based on a combination of field and satellite rainfall data." In Proceedings of the 39th IAHR World Congress From Snow to Sea. Spain: International Association for Hydro-Environment Engineering and Research (IAHR), 2022. http://dx.doi.org/10.3850/iahr-39wc2521711920221681.

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Budhakooncharoen, Saisunee. "Rainfall Estimate for Flood Management Using Meteorological Data from Satellite Imagery." In Ninth Biennial Conference on Engineering, Construction, and Operations in Challenging Environments. Reston, VA: American Society of Civil Engineers, 2004. http://dx.doi.org/10.1061/40722(153)31.

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Yasmeen, Zarina, Muhammad Jehanzeb Masud Cheema, Saddam Hussain, Zainab Haroon, Sadaf Amin, and Muhammad Sohail Waqas. "Downscaling of Satellite Rainfall Data Using Remotely Sensed NDVI and Topographic Datasets." In The 1st International Precision Agriculture Pakistan Conference 2022 (PAPC 2022)—Change the Culture of Agriculture. Basel Switzerland: MDPI, 2023. http://dx.doi.org/10.3390/environsciproc2022023040.

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

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Nunez, A., V. Pastoriza, F. Machado, P. Marino, F. P. Fontan, M. Carpacho, and U. C. Fiebig. "On the spatial structure of rainfall rate: Merging radar and rain gauge data." In 2008 IEEE International Workshop on Satellite and Space Communications (IWSSC). IEEE, 2008. http://dx.doi.org/10.1109/iwssc.2008.4656739.

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Shukla, Anoop Kumar, C. S. P. Ojha, and R. D. Garg. "Comparative study of trmm satellite predicted rainfall data with rain gauge data over himalayan basin." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8651413.

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