Statistical Downscaling of Climate Predictions
Downscaling using Analogs based on large scale fields.
Downscaling using Analogs based on coarse scale fields.
Downscaling using interpolation and bias adjustment.
Regrid or interpolate gridded data to a point location.
Downscaling using interpolation and linear regression.
Downscaling using interpolation and logistic regression.
Downscaling using interpolation and bias adjustment.
Regrid or interpolate gridded data to a point location.
Downscaling using interpolation and linear regression.
Downscaling using interpolation and logistic regression.
Statistical downscaling and bias correction of climate predictions. It includes implementations of commonly used methods such as Analogs, Linear Regression, Logistic Regression, and Bias Correction techniques, as well as interpolation functions for regridding and point-based applications. It facilitates the production of high-resolution and local-scale climate information from coarse-scale predictions, which is essential for impact analyses. The package can be applied in a wide range of sectors and studies, including agriculture, water management, energy, heatwaves, and other climate-sensitive applications. The package was developed within the framework of the European Union Horizon Europe projects Impetus4Change (101081555) and ASPECT (101081460), the Wellcome Trust supported HARMONIZE project (224694/Z/21/Z), and the Spanish national project BOREAS (PID2022-140673OA-I00). Implements the methods described in Duzenli et al. (2024) <doi:10.5194/egusphere-egu24-19420>.
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