PERSIANN

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PERSIANN, "Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks",[1][2] is a satellite-based precipitation retrieval algorithm that provides near real-time rainfall information. The algorithm uses infrared (IR) satellite data from global geosynchronous satellites as the primary source of precipitation information. Precipitation from IR images is based on statistical relationship between cloud top temperature and precipitation rates. The IR-based precipitation estimates are then calibrated using satellite microwave data available from low Earth orbit satellites (e.g., Tropical Rainfall Measuring Mission Microwave Imager, Special Sensor Microwave Imager, Advanced Microwave Scanning Radiometer‐Earth observing system). The calibration technique relies on an adaptive training algorithm that updates the retrieval parameters when microwave observations become available (approximately at 3 hours intervals).

The PERSIANN satellite precipitation data sets have been validated with ground-based observations and other satellite data products.[3][4][5][6][7][8] The PERSIANN data has been used in a wide variety of studies including hydrologic modeling,[9] drought monitoring,[10] soil moisture analysis,[11] and flood forecasting.[12] The PERSIANN data are freely available to the public.

References[edit]

  1. ^ Hsu, Kou-lin; X. Gao, S. Sorooshian, and H. Gupta (1997). "Precipitation estimation from remotely sensed information using artificial neural networks". J. Appl. Meteorol. 36 (9): 1176–1190. doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2. 
  2. ^ Sorooshian, Soroosh; K. Hsu, X. Gao, H. Gupta, B. Imam, and D. Braithwaite (2000). "Evolution of the PERSIANN system satellite‐based estimates of tropical rainfall". Bull. Am. Meteorol. Soc. 81 (9): 2035–2046. doi:10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2. 
  3. ^ AghaKouchak, A.; Behrangi, A.; Sorooshian, S.; Hsu, K.; Amitai, E. (26 January 2011). "Evaluation of satellite-retrieved extreme precipitation rates across the central United States". Journal of Geophysical Research 116 (D2). doi:10.1029/2010JD014741. 
  4. ^ Tian, Yudong; Peters-Lidard, Christa D.; Eylander, John B.; Joyce, Robert J.; Huffman, George J.; Adler, Robert F.; Hsu, Kuo-lin; Turk, F. Joseph; Garcia, Matthew; Zeng, Jing (16 December 2009). "Component analysis of errors in satellite-based precipitation estimates". Journal of Geophysical Research 114 (D24). doi:10.1029/2009JD011949. 
  5. ^ Mehran, Ali; AghaKouchak, Amir (1 March 2013). "Capabilities of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations". Hydrological Processes: n/a. doi:10.1002/hyp.9779. 
  6. ^ Yilmaz, Koray K.; Hogue, Terri S.; Hsu, Kuo-lin; Sorooshian, Soroosh; Gupta, Hoshin V.; Wagener, Thorsten (1 August 2005). "Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting". Journal of Hydrometeorology 6 (4): 497–517. doi:10.1175/JHM431.1. 
  7. ^ Sapiano, M. R. P.; Arkin, P. A. (1 February 2009). "An Intercomparison and Validation of High-Resolution Satellite Precipitation Estimates with 3-Hourly Gauge Data". Journal of Hydrometeorology 10 (1): 149–166. doi:10.1175/2008JHM1052.1. 
  8. ^ AghaKouchak, Amir; Mehran, Ali; Norouzi, Hamidreza; Behrangi, Ali (1 May 2012). "Systematic and random error components in satellite precipitation data sets". Geophysical Research Letters 39 (9): n/a–n/a. doi:10.1029/2012GL051592. 
  9. ^ Behrangi, Ali; Khakbaz, Behnaz; Jaw, Tsou Chun; AghaKouchak, Amir; Hsu, Kuolin; Sorooshian, Soroosh (1 February 2011). "Hydrologic evaluation of satellite precipitation products over a mid-size basin". Journal of Hydrology 397 (3–4): 225–237. doi:10.1016/j.jhydrol.2010.11.043. 
  10. ^ AghaKouchak, Amir; Nakhjiri, Navid (1 December 2012). "A near real-time satellite-based global drought climate data record". Environmental Research Letters 7 (4): 044037. doi:10.1088/1748-9326/7/4/044037. 
  11. ^ Juglea, S., Kerr, Y. H., Mialon, A., Lopez-Baeza, E., Braithwaite, D., & Hsu, K. (2010). . Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station. 
  12. ^ Chiang, Yen-Ming; Hsu, Kuo-Lin; Chang, Fi-John; Hong, Yang; Sorooshian, Soroosh (1 July 2007). "Merging multiple precipitation sources for flash flood forecasting". Journal of Hydrology 340 (3–4): 183–196. doi:10.1016/j.jhydrol.2007.04.007. 

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