Neural networks in satellite rainfall estimation

Neural networks in satellite rainfall estimation

Neural networks (NNs) have been successfully used in the environmental sciences over the last two decades. However, only a few review papers have been published, most of which cover image processing, classification, prediction and geophysical retrieval in general, while neglecting rainfall estimation issues. This paper reviews, without aiming to be exhaustive, NN approaches to satellite rainfal...

Utilizing satellite images and artificial neural networks in estimation of vegetation fraction in arid regions

Investigating rainfall estimation from radar measurements using neural networks

Rainfall observed on the ground is dependent on the four dimensional structure of precipitation aloft. Scanning radars can observe the four dimensional structure of precipitation. Neural network is a nonparametric method to represent the nonlinear relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and r...

Rainfall Estimation Using Satellite Data for Paraíba Do Sul

This work presents a self-consistent algorithm for near real-time rainfall estimation via infrared Geostationary Operational Environmental Satellite (GOES) (each 1⁄2 hour) data based on a validation simultaneous dataset from Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite, GOES and lightning data, for the basin of Paraíba do Sul river. The dataset corre...

Estimation of Rainfall and its Error Characteristics from Satellite Observations

Currently, rainfall retrieval over ocean from passive microwave satellite observations represents the best compromise between estimation accuracy and spatial data coverage. Infrared data is available with higher temporal frequency from geostationary satellites but the link of top-of-the-atmosphere infrared emission to near-surface rainfall is very indirect. Therefore, all algorithms have to exp...