Evaluation of PERSIANN system satellite-based estimates of tropical rainfall

By Soroosh Sorooshian, K. Hsu, Xiaogang Gao, Hoshin V. Gupta, B. Imam, D. Braithwaite. 2000

1,228 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/4d53d8e239d02cf85ad8581b9163058ac296f751

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Citations in the corpus

Meteorological Drought Analysis in the Lower Mekong Basin Using Satellite-Based Long-Term CHIRPS Product (2017, methodology): “Miao et al. [19] evaluated PERSIANN-CDR’s performance in capturing the behavior of daily extreme precipitation events in China from 1983 to 2016 and found that the performance of PERSIANN-CDR is good in most parts of China.”
Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System (2004, methodology): “In our earlier reported work, we developed the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) algorithm, using satellite longwave infrared (IR) images (Hsu et al. 1997, 1999; Sorooshian et al. 2000).”
Status and Outlook of Operational Satellite Precipitation Algorithms for Extreme-Precipitation Events (2003, methodology): “…3) determining the best approach for algorithm development: statistical/mathematical techniques (e.g., regression, neural networks–Zhang et al. 1994; Sorooshian et al. 2000) versus physically based approaches (e.g., cloud models); and 4) discerning the best way to display the estimates: as exact…”
An improved procedure for the validation of satellite-based precipitation estimates (2015, methodology): “…algorithms have been developed to exploit the complementary strengths of three different types of precipitation measuring sensors: Infrared (IR), Passive Microwave (PMW) Radiometers, and Precipitation Radar (PR) (e.g., Sorooshian et al., 2000; Joyce et al., 2004; Huffman et al., 2007).”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, neutral): “ocean wave heights, and real-time forecasting (Bowden et al., 2005; Dawson & Wilby, 2001; Deo & Naidu, 1998; Govindaraju & Rao, 2000; Gupta et al., 2000; Maier et al., 2010; Maier & Dandy, 1996; Sorooshian et al., 2000).”
Uncertainty analysis of bias from satellite rainfall estimates using copula method (2014, methodology): “…rainfall products used in this study are based on Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) (Sorooshian et al., 2000) and Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) adjusted product (3B42).”
Diurnal Variation in Precipitation over India during the Summer Monsoon Season: Observed and Model Predicted (2007, methodology): “…using Artificial Neural Network (PERSIANN), from the infrared observations from multiple geostationary satellites with improvements made by the instantaneous rain-rate estimates from the TRMM observations and also in situ gauge measurements (Hsu et al. 1997, 1999; Sorooshian et al. 2000).”
A near real-time satellite-based global drought climate data record (2012, methodology): “• The Precipitation Estimation from Remotely Sensed In- formation using Artificial Neural Networks (PERSIANN; Sorooshian et al 2000, Hsu et al 1997), which uses a neural network classification algorithm to derive precipitation estimates based on IR data after near real-time adjustment using…”
Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design (2018, methodology): “These include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product, version 7 (Huffman et al., 2007), the Global Precipitation Climatology Project (GPCP) dataset (Huffman et al., 2001), the Precipitation Estimation from Remotely Sensed Information ”