Although Regional Climate Models (RCMs) are powerful tools for describing small scale climate conditions, their direct use in impact studies is still challenging since they are commonly biased. According to scientific literature, the most adopted method to provide ‘corrected’ climate scenarios, consists in the application of different post-processing techniques in cascade to Regional Climate Models. Furthermore, another critical issue of impact studies is the need of precipitation at sub-daily scale, since hydrogeological instability is often caused by rainfall of high intensity but short duration. However precipitation time series are usually available only on daily scale, so there is the need to disaggregate these series. In literature different stochastic rainfall disaggregation models have been developed, that, starting from precipitation data at daily scale, provide series of precipitation at sub-daily scale.
In order to handle these problems, REMHI (Regional Models and Geo-Hydrological Impacts) Division of CMCC has implemented different bias correction techniques, based on linear scaling and quantile mapping approaches, both for temperature and precipitation data and a temporal downscaling tool for precipitation data. All these tools have been integrated in Clime, a software for climate data analysis developed by the REMHI Division. This work represents a complete guide to these Bias Correction and Temporal Downscaling tools
- Keywords: Bias Correction; Climate Data Analysis; Linear Scaling; Quantile Mapping; Temporal Downscaling; Disaggregation; Climate Data Processing in GIS Enviroment; Software Clime