Neural networks in satellite rainfall estimation

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

Performance of High Resolution Satellite Rainfall Products over Data Scarce Parts of Eastern Ethiopia

Accurate estimation of rainfall in mountainous areas is necessary for various water resource-related applications. Though rain gauges accurately measure rainfall, they are rarely found in mountainous regions and satellite rainfall data can be used as an alternative source over these regions. This study evaluated the performance of three high-resolution satellite rainfall products, the Tropical ...

Multiple data fusion for rainfall estimation using a NARX-based recurrent neural network – the development of the REIINN model

Rainfall, one of the important elements of the hydrologic cycle, is also the most difficult to model. Thus, accurate rainfall estimation is necessary especially in localized catchment areas where variability of rainfall is extremely high. Moreover, early warning of severe rainfall through timely and accurate estimation and forecasting could help prevent disasters from flooding. This paper prese...

Self-organizing nonlinear output (SONO): A neural network suitable for cloud patch–based rainfall estimation at small scales

[1] Accurate measurement of rainfall distribution at various spatial and temporal scales is crucial for hydrological modeling and water resources management. In the literature of satellite rainfall estimation, many efforts have been made to calibrate a statistical relationship (including threshold, linear, or nonlinear) between cloud infrared (IR) brightness temperatures and surface rain rates ...

Investigating Error Metrics for Satellite Rainfall Data at Hydrologically Relevant Scales

This paper addresses the following open question: What set of error metrics for satellite rainfall data can advance the hydrologic application of new-generation, high-resolution rainfall products over land? The authors’ primary aim is to initiate a framework for building metrics that are mutually interpretable by hydrologists (users) and algorithm developers (data producers) and to provide more...