Добірка наукової літератури з теми "Satellite rainfall estimation"
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Статті в журналах з теми "Satellite rainfall estimation"
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.
Повний текст джерелаShih, 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).
Повний текст джерелаTapiador, F. J., C. Kidd, K. L. Hsu, and F. Marzano. "Neural networks in satellite rainfall estimation." Meteorological Applications 11, no. 1 (March 2004): 83–91. http://dx.doi.org/10.1017/s1350482704001173.
Повний текст джерелаKuntoro, 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.
Повний текст джерелаNikolopoulos, E. I., E. Destro, V. Maggioni, F. Marra, and M. Borga. "Satellite Rainfall Estimates for Debris Flow Prediction: An Evaluation Based on Rainfall Accumulation–Duration Thresholds." Journal of Hydrometeorology 18, no. 8 (August 1, 2017): 2207–14. http://dx.doi.org/10.1175/jhm-d-17-0052.1.
Повний текст джерелаGoodarzi, Mohammad Reza, Roxana Pooladi, and Majid Niazkar. "Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran." Sustainability 14, no. 20 (October 12, 2022): 13051. http://dx.doi.org/10.3390/su142013051.
Повний текст джерелаManz, Bastian, Sebastián Páez-Bimos, Natalia Horna, Wouter Buytaert, Boris Ochoa-Tocachi, Waldo Lavado-Casimiro, and Bram Willems. "Comparative Ground Validation of IMERG and TMPA at Variable Spatiotemporal Scales in the Tropical Andes." Journal of Hydrometeorology 18, no. 9 (September 1, 2017): 2469–89. http://dx.doi.org/10.1175/jhm-d-16-0277.1.
Повний текст джерелаHong, Yang, Kuo-Lin Hsu, Soroosh Sorooshian, and Xiaogang Gao. "Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System." Journal of Applied Meteorology 43, no. 12 (December 1, 2004): 1834–53. http://dx.doi.org/10.1175/jam2173.1.
Повний текст джерелаDiem, Jeremy E., Joel Hartter, Sadie J. Ryan, and Michael W. Palace. "Validation of Satellite Rainfall Products for Western Uganda." Journal of Hydrometeorology 15, no. 5 (September 25, 2014): 2030–38. http://dx.doi.org/10.1175/jhm-d-13-0193.1.
Повний текст джерелаBellerby, T. J. "Satellite Rainfall Uncertainty Estimation Using an Artificial Neural Network." Journal of Hydrometeorology 8, no. 6 (December 1, 2007): 1397–412. http://dx.doi.org/10.1175/2007jhm846.1.
Повний текст джерелаДисертації з теми "Satellite rainfall estimation"
D'Souza, G. "Rainfall estimation over Africa using satellite data." Thesis, University of Bristol, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.384497.
Повний текст джерелаHsu, Kuo-Lin, Soroosh Sorooshian, Xiaogang Gao, and Hoshin Vijai Gupta. "Rainfall estimation from satellite infrared imagery using artificial neural networks." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1997. http://hdl.handle.net/10150/615703.
Повний текст джерелаHsu, Kuo-lin 1961. "Rainfall estimation from satellite infrared imagery using artificial neural networks." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/191209.
Повний текст джерелаMorland, June Christine. "Observations of surface microwave emission in the context of satellite rainfall estimation." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.245823.
Повний текст джерелаChadwick, Robin. "Multi-spectral satellite rainfall estimation over Africa using meteosat second generation data." Thesis, University of Reading, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542062.
Повний текст джерелаFaridhosseini, Alireza. "Evaluation of Summer Rainfall Estimation by Satellite Data using the ANN Model for the GCM Subgrid Distribution." Thesis, The University of Arizona, 1998. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_etd_hy0021_m_sip1_w.pdf&type=application/pdf.
Повний текст джерелаDiop, Mariane. "The influence of weather systems on satellite rainfall estimation with application to river flow." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.397102.
Повний текст джерелаAhmad, Khalil Ali. "ESTIMATION OF OCEANIC RAINFALL USING PASSIVE AND ACTIVE MEASUREMENTS FROM SEAWINDS SPACEBORNE MICROWAVE SENSOR." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3015.
Повний текст джерелаPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
Ringard, Justine. "Estimation des précipitations sur le plateau des Guyanes par l'apport de la télédétection satellite." Thesis, Guyane, 2017. http://www.theses.fr/2017YANE0010/document.
Повний текст джерелаThe Guiana Shield is a region that is characterized by 90% of a primary rainforest and about 20% of the world’s freshwater reserves. This natural territory, with its vast hydrographic network, shows annual rainfall intensities up to 4000 mm/year; making this plateau one of the most watered regions in the world. In addition, tropical rainfall is characterized by significant spatial and temporal variability. In addition to climate-related aspects, the impact of rainfall in this region of the world is significant in terms of energy supply (hydroelectric dams). It is therefore important to develop tools to estimate quantitatively and qualitatively and at high spatial and temporal resolution the precipitation in this area. However, this vast geographical area is characterized by a network of poorly developed and heterogeneous rain gauges, which results in a lack of knowledge of the precise spatio-temporal distribution of precipitation and their dynamics.The work carried out in this thesis aims to improve the knowledge of precipitation on the Guiana Shield by using Satellite Precipitation Product (SPP) data that offer better spatial and temporal resolution in this area than the in situ measurements, at the cost of poor quality in terms of precision.This thesis is divided into 3 parts. The first part compares the performance of four products of satellite estimates on the study area and attempts to answer the question : what is the quality of these products in the Northern Amazon and French Guiana in spatial and time dimensions ? The second part proposes a new SPP bias correction technique that proceeds in three steps: i) using rain gauges measurements to decompose the studied area into hydro climatic areas ii) parameterizing a bias correction method called quantile mapping on each of these areas iii) apply the correction method to the satellite data for each hydro-climatic area. We then try to answer the following question : does the parameterization of the quantile mapping method on different hydro-climatic areas make it possible to correct the precipitation satellite data on the study area ? After showing the interest of taking into account the different rainfall regimes to implement the QM correction method on SPP data, the third part analyzes the impact of the temporal resolution of the precipitation data used on the quality of the correction and the spatial extent of potentially correctable SPP data (SPP data on which the correction method can be applied effectively). In summary, the objective of this section is to evaluate the ability of our method to correct on a large spatial scale the bias of the TRMM-TMPA 3B42V7 data in order to make the exploitation of this product relevant for different hydrological applications.This work made it possible to correct the daily satellite series with high spatial and temporal resolution on the Guiana Shield using a new approach that uses the definition of hydro-climatic areas. The positive results in terms of reduction of the bias and the RMSE obtained, thanks to this new approach, makes possible the generalization of this new method in sparselygauged areas
Cassé, Claire. "Impact du forçage pluviométrique sur les inondations du fleuve Niger à Niamey : Etude à partir de données satellitaires et in-situ." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30236/document.
Повний текст джерелаSince the development of satellite based remote sensing in the 1970s, many missions have been dedicated to monitoring the terrestrial atmosphere and surfaces. Some of these satellites are dedicated to the Tropics with specific orbits. Megha-Tropiques (MT) is devoted to the water and energy cycle in the tropical atmosphere and provides an enhanced sampling for rainfall estimation in the tropical region. This PhD work was initiated within MT hydro-meteorological activities, with the objective of assessing the hydrological potential of satellite rainfall products in the Tropics. The world most important rivers lay in tropical areas where the in situ observation networks are deficient. Alternative information is therefore needed for water resource management and alert systems. The present work focuses on the Niger River a basin which has undergone drastic climatic variations leading to disasters such as droughts and floods. Since 1950, the Niger has been through 3 main climatic periods: a wet period (1950-1960), a long and intense drought period (1970-1980) and since 1990 a partial recovery of the rainfall. These climatic variations and the anthropic pressure, have modified the hydrological behaviour of the basin. Since 2000, the middle Niger River has been hit by an increase of floods hazards during the so-called Red flood period. In Niamey city, the highest river levels and the longest flooded period were recorded in 2003, 2010, 2012 and 2013, leading to heavy casualties and property damage. This study combines hydrological modelling and a variety of rainfall estimation products (satellite and in-situ) to meet several objectives: (i) the simulation of the Niamey Red flood and the detection of floods (during the recent period 2000-2013) (ii) the study of the propagation of satellite rainfall errors in hydrological modelling (iii) the evaluation of the role of rainfall variability, and surface conditions, in the changes of the Red flood in Niamey since the 50s. The global model ISBA-TRIP, is run with a resolution of 0.5° and 3h, and several rainfall products were used as forcing. Products derived from gauges (KRIG, CPC), pure satellite products (TAPEER, 3B42RT, CMORPH, PERSIANN) and mixed satellite products adjusted by rain gauges (3B42v7, RFE2, PERSIANN-CDR). This work confirms the hydrological potential of satellite rainfall products and proposes an original approach to overcome their biases. It highlights the need for documenting the errors associated with the rainfall products and the error structure. Finally, the hydrological modelling results since the 1950s have given a new understanding of the relative role of rainfall and surface conditions in the drastic increase of flood risk in Niamey
Книги з теми "Satellite rainfall estimation"
Shrestha, Mandira. Satellite rainfall estimation in the Hindu Kush-Himalayan Region. Kathmandu: International Centre for Integrated Mountain Development, 2008.
Знайти повний текст джерелаGriffith, Cecilia Girz. The estimation from satellite imagery of summertime rainfall over varied space and time scales. Boulder, Colo: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1987.
Знайти повний текст джерелаA, Vila Daniel, ed. Satellite rainfall estimation over South America: Evaluation of two major events. Washington, DC: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, 2003.
Знайти повний текст джерелаUnited 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.
Знайти повний текст джерелаSiyyid, Alward Nawazish. The use of Meteosat satellite data for spatial rainfall estimations and hydrological simulations. Birmingham: Aston University. Department of Civil Engineering, 1993.
Знайти повний текст джерелаJohnson, Michelle L. Estimating precipitation over the Amazon Basin from satellite and in-situ measurements. Middleton, Del: Legates Consulting, 2003.
Знайти повний текст джерелаSatellite rainfall estimation in the Hindu Kush-Himalayan region. Kathmandu: International Centre for Integrated Mountain Development, 2008.
Знайти повний текст джерелаRain 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.
Знайти повний текст джерелаЧастини книг з теми "Satellite rainfall estimation"
Hsu, K. L., H. V. Gupta, X. Gao, and S. Sorooshian. "Rainfall Estimation from Satellite Imagery." In Water Science and Technology Library, 209–34. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-015-9341-0_12.
Повний текст джерелаHsu, Kuo-Lin, Ali Behrangi, Bisher Imam, and Soroosh Sorooshian. "Extreme Precipitation Estimation Using Satellite-Based PERSIANN-CCS Algorithm." In Satellite Rainfall Applications for Surface Hydrology, 49–67. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_4.
Повний текст джерелаTapiador, Francisco J., Chris Kidd, Vincenzo Levizzani, and Frank S. Marzano. "Neural Network tools for Satellite Rainfall Estimation." In Measuring Precipitation From Space, 149–61. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_12.
Повний текст джерелаHong, Yang, Robert F. Adler, George J. Huffman, and Harold Pierce. "Applications of TRMM-Based Multi-Satellite Precipitation Estimation for Global Runoff Prediction: Prototyping a Global Flood Modeling System." In Satellite Rainfall Applications for Surface Hydrology, 245–65. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2915-7_15.
Повний текст джерелаJobard, Isabelle, and Michel Desbois. "Combination of Satellite Microwave and Infrared Measurements for Rainfall Estimation." In Global Precipitations and Climate Change, 265–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79268-7_16.
Повний текст джерелаOuallouche, Fethi, Karim Labadi, Yacine Mohia, Mourad Lazri, and Soltane Ameur. "Artificial Intelligence for Satellite Image Processing: Application to Rainfall Estimation." In Lecture Notes in Electrical Engineering, 165–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6581-4_14.
Повний текст джерелаAnagnostou, Emmanouil N., and Themis G. Chronis. "The Worth of Long-Range Lightning Observations on Overland Satellite Rainfall Estimation." In Measuring Precipitation From Space, 135–48. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_11.
Повний текст джерелаRientjes, Tom H. M., Alemseged T. Haile, Ambro S. M. Gieske, Ben H. P. Maathuis, and Emad Habib. "Satellite Based Cloud Detection and Rainfall Estimation in the Upper Blue Nile Basin." In Nile River Basin, 93–107. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0689-7_4.
Повний текст джерелаLasmono, 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.
Повний текст джерелаColli, M., M. Stagnaro, A. Caridi, L. G. Lanza, A. Randazzo, M. Pastorino, D. D. Caviglia, and A. Delucchi. "A Field Experiment of Rainfall Intensity Estimation Based on the Analysis of Satellite-to-Earth Microwave Link Attenuation." In Lecture Notes in Electrical Engineering, 137–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11973-7_17.
Повний текст джерелаТези доповідей конференцій з теми "Satellite rainfall estimation"
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.
Повний текст джерелаMartins 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.
Повний текст джерелаVasavi, S., P. V. Sai Krishna, P. D. L. Nikhita Sri, M. Navena, and C. HariKiran. "Rainfall Estimation From Satellite Images Using Cloud Classifications." In 2022 IEEE North Karnataka Subsection Flagship International Conference (NKCon). IEEE, 2022. http://dx.doi.org/10.1109/nkcon56289.2022.10126540.
Повний текст джерелаGharanjik, 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.
Повний текст джерелаPutra, 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.
Повний текст джерелаSulistio, Emmilia Monica Andrianni, Anisa Budi Lestari, Rizaldi Ramadhan, Raden Mochammad Dwi Mulya, Chray Fanly Jovini Tambengi, Eko Wardoyo, and Imma Redha Nugraheni`. "Verification of the effect of quality control implementation to increase accuracy of rainfall estimation in Lombok areas." In Sixth International Symposium on LAPAN-IPB Satellite, edited by Tien Dat Pham, Kasturi D. Kanniah, Kohei Arai, Gay Jane P. Perez, Yudi Setiawan, Lilik B. Prasetyo, and Yuji Murayama. SPIE, 2019. http://dx.doi.org/10.1117/12.2541852.
Повний текст джерелаMugnai, Clio, Francesco Sermi, Fabrizio Cuccoli, and Luca Facheris. "Rainfall estimation with a commercial tool for satellite internet in KA band: Model evolution and results." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7325908.
Повний текст джерелаPasaribu, Octo Mario, Aris Poniman, Andrian Andaya Lestari, Yosef Prihanto, Asep Adang Supriyadi, and Yahya Darmawan. "Exploration of CHIRPS Satellite Data as Rainfall Estimation Data in Medan City and Deli Serdang Regency." In 2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS). IEEE, 2022. http://dx.doi.org/10.1109/agers56232.2022.10093448.
Повний текст джерелаMugnai, Clio, Fabrizio Cuccoli, and Francesco Sermi. "Rainfall estimation with a commercial tool for satellite internet in Ka band: concept and preliminary data analysis." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2014. http://dx.doi.org/10.1117/12.2067263.
Повний текст джерелаSaggese, 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.
Повний текст джерелаЗвіти організацій з теми "Satellite rainfall estimation"
Shrestha, M., P. K. Mool, and S. R. Bajracharya. Satellite Rainfall Estimation in the Hindu Kush-Himalayan Region. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2008. http://dx.doi.org/10.53055/icimod.492.
Повний текст джерелаShrestha, M., P. K. Mool, and S. R. Bajracharya. Satellite Rainfall Estimation in the Hindu Kush-Himalayan Region. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2008. http://dx.doi.org/10.53055/icimod.492.
Повний текст джерела