Abstract
PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) is a satellite-based rainfall estimation algorithm. It uses local cloud textures from longwave infrared images of the geostationary environmental satellites to estimate surface rainfall rates based on an artificial neural network algorithm. Model parameters are frequently updated from rainfall estimates provided by low-orbital passive microwave rainfall estimates. The PERSIANN algorithm has been evolving since 2000, and has generated near real-time rainfall estimates continuously for global water and energy studies. This paper presents the development of the PERSIANN algorithm in the past 10 years. In addition, the validation and merging PERSIANN rainfall with ground-based rainfall measurements for hydrologic applications are also discussed.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
