PERSIANNPrecipitation Estimation from Remotely Sensed Information using Artificial Neural Networks
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Although several IR-based rainfall datasets already exist [e.g., CHIRP, Hydro-Estimator, PERSIANN, PERSIANN-CCS, PERSIANN-Climate Data Record (PERSIANN-CDR), and Tropical Applications of Meteorology using Satellite Data and Ground-Based Observations (TAMSAT)], none of these meet all of our requirements: i) quasi-global coverage over land and ocean, ii) temporal coverage from the 1980s to the near present, iii) spatial resolution [less than or equal to] 0.1[degrees], iv) temporal resolution [less than or equal to] 3 h, and v) no gauge corrections.
Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall.
Braithwaite, "Evaluation of PERSIANN system satellite-based estimates of tropical rainfall," Bulletin of the American Meteorological Society, vol.
The PERSIANN precipitation datasets are created from IR brightness temperature observations using an artificial neural network method [34,35].
Results indicate that CMORPH products capture most of the rainy events better than TMPA and PERSIANN products in the study domain.
Both TMPA and CMORPH have equivalent MRV in the small rainy season, about 18-20%, whereas PERSIANN products performed on MRV above 30%.
Marra, 2015: An El Nino Southern Oscillation (ENSO) based Precipitation Climatology for the United States Affiliated Pacific Islands (USAPI) Using the PERSIANN Climate Data Record.
TRMM PERSIANN GSMaP Average KF-E-IFS 0.944 0.949 0.989 0.961 KF-KF-IFS 0.938 0.938 0.924 0.933 BMJ-BMJ-IFS 0.966 0.967 0.996 0.976 GD-GD-IFS 0.888 0.907 0.853 0.883 NGE-NGE-IFS 0.947 0.935 0.968 0.950 KF-E-ERA 0.975 0.916 0.949 0.947 KF-KF-ERA 0.857 0.885 0.817 0.853 BMJ-BMJ-ERA 0.943 0.949 0.996 0.963 GD-GD-ERA 0.850 0.898 0.834 0.861 NGE-NGE-ERA 0.955 0.975 0.986 0.972
2004), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN; Hsu et al.
PERSIANN primarily uses infrared brightness temperature data from geostationary satellites to estimate rainfall rate, updating its parameters using PMW observations from low-orbital satellites.
[8] investigated the streamflow simulation abilities of 3B42V7, 3B42RTV7, CMORPH, and PERSIANN using the Variable Infiltration Capacity (VIC) hydrologic model in the upper Yellow and Yangtze River Basins on the Tibetan Plateau.
The original PERSIANN, first established by Hsu et al.