Bias correction of daily satellite precipitation data using genetic algorithm

A comparative study of quantitative mapping methods for bias correction of ERA5 reanalysis precipitation data

This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 reanalysis precipitation data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 reanalysis precipitation data for the years 1989-2019 for 10 selected syno...

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORP...

Material Characterization of Structures Using Genetic Algorithm Vibetarional Data

On The Enhancement of Infrared Satellite Precipitation Estimates Using Genetic Algorithm Filter-Based Feature Selection

a methodology to enhance a satellite infrared – based high resolution rainfall retrieval algorithm is developed by intelligently selecting features based on a filter model. Our methodology for satellite-based rainfall estimation is similar to the PERSIANN-CCS approach. However, our algorithms are enriched by applying a filterbased feature selection using generic algorithm. The objective of usin...

Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction

When correcting for biases in general circulation model (GCM) output, for example when statistically downscaling for regional and local impacts studies, a common assumption is that the GCM biases can be characterized by comparing model simulations and observations for a historical period. We demonstrate some complications in this assumption, with GCM biases varying between mean and extreme valu...