As an output of the EENSAT Research Component, a research article is published at Atmospheric Research Journal by the PhD student at Mekelle University, Ethiopia. The research article is on the validation and bias correction of selected satellite rainfall estimation products.
Daily rainfall is the most important and demanded input of water resources studies, challenged by typically low density and/or poor quality of in-situ observations. However, the satellite earth observation, through freely available web-based products, can provide complementary rainfall data. Such data is however, typically affected by substantial error, particularly at daily temporal resolution. Therefore, effective methods and protocols of rainfall downscaling, validation, and bias-correction are needed. This research aims to validate two downscaled satellite-derived daily rainfall products against in-situ observations and merge the downscaled products with in-situ observations to improve their accuracy. Validation of the downscaled products was carried out using descriptive statistics, categorical statistics and bias decomposition methods, introducing novel protocol with new bias indicators for each of the evaluation methods. The validation showed large biases of the satellite-derived rainfall products. To correct biases of the downscaled rainfall products, each downscaled satellite rainfall products was merged with the in-situ observed rainfall applying Geographically Weighted Regression algorithm and using rainfall dependence on altitude as explanatory variable. The merging approach substantially improved the accuracy of the satellite rainfall products. This study confirmed that merging approach could substantially reduce daily bias of satellite rainfall products, even in topographically complex areas, such as the UTB. Further improvement of the method application, can be achieved by densifying raingauge network and eventually by adding accuracy-effective explanatory variable(s).
Full article can be obtained here.