precipitation is one of important parameters of climatology and atmospheric science that have more importance in human life. recently, extensive flood and drought entered many damage to most parts of the world. precipitation forecasting and alerts management role is responsible for these problems. today, artificial neural networks are one of developed method that applied for estimate and predic...

A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are in...

This study integrates a Box-Cox power transformation procedure into a common trend two-phase regression model based test (the PMFred algorithm) for detecting changepoints, to make the test applicable to non-Gaussian data series, such as non-zero daily precipitation amounts or wind speeds. The detection power aspects of the transformed method (transPMFred) are assessed by a simulation study, whi...

Downscaling is a critical step to bridge the gap between large-scale climate information and local-scale impact assessment. This study presents novel deep learning approach: Super Resolution Deep Residual Network (SRDRN) for downscaling daily precipitation temperature. approach was constructed based on an advanced convolutional neural network with residual blocks batch normalizations. The data ...

Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM...

This study systematically explores existing and new optimization techniques for analog ensemble (AnEn) post-processing of hourly to daily precipitation forecasts over the complex terrain southwest British Columbia, Canada. An AnEn bias-corrects a target model forecast by searching past dates with similar (i.e., analogs), using verifying observations as members. The weather variables predictors)...