Quantile Regression in Statistical Downscaling to Estimate Extreme Monthly Rainfall

Smoothed Quantile Regression for Statistical Downscaling of Extreme Events in Climate Modeling

Statistical downscaling is commonly used in climate modeling to obtain high-resolution spatial projections of future climate scenarios from the coarse-resolution outputs projected by global climate models. Unfortunately, most of the statistical downscaling approaches using standard regression methods tend to emphasize projecting the conditional mean of the data while paying scant attention to t...

Performance evaluation of artificial neural networks in statistical downscaling of monthly precipitation (Case study: Minab watershed)

Automated regression-based statistical downscaling tool

Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based appro...

High quantile regression for extreme events

For extreme events, estimation of high conditional quantiles for heavy tailed distributions is an important problem. Quantile regression is a useful method in this field with many applications. Quantile regression uses an L1-loss function, and an optimal solution by means of linear programming. In this paper, we propose a weighted quantile regression method. Monte Carlo simulations are performe...

Downscaling Modeling Using Support Vector Regression for Rainfall Prediction

Statistical downscaling is an effort to link global scale to local scale variable. It uses GCM model which usually used as a prime instrument in learning system of various climate. The purpose of this study is as a SD model by using SVR in order to predict the rainfall in dry season; a case study at Indramayu. Through the model of SD, SVR is created with linear kernel and RBF kernel. The result...