downscaling

The Intermediate Complexity Atmospheric Research model (ICAR)

Statistical climate downscaling in Python

Downscaling & bias correction of CMIP6 tasmin, tasmax, and pr for the R/CIL GDPCIR project

The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.

Deep Learning for empirical DownScaling. Python package with state-of-the-art and novel deep learning algorithms for empirical/statistical downscaling of gridded data

Scale down / "pause" Kubernetes workload (Deployments, StatefulSets, and/or HorizontalPodAutoscalers and CronJobs too !) during non-work hours.

A horizontal autoscaler for Kubernetes workloads

Diffusion for climate downscaling

TopoPyScale: a Python library to perform simplistic climate downscaling at the hillslope scale

Generalized Analog Regression Downscaling (GARD) code

Python Package for Empirical Statistical Downscaling. pyESD is under active development and all colaborators are welcomed. The purpose of the package is to downscale any climate variables e.g. precipitation and temperature using predictors from reanalysis datasets (eg. ERA5) to point scale. pyESD adopts many ML and AL as the transfer function.

A project on how to incorporate physics constraints into deep learning architectures for downscaling or other super--resolution tasks.

spateGAN-ERA5: deep learning based spatio-temporal downscaling of ERA5 precipitation

Probabilistic Downscaling of Climate Variables Using Denoising Diffusion Probabilistic Models

Given a global mean temperature pathway, generate random global climate fields consistent with it and with spatial and temporal correlation derived from an ESM

A collection of notebooks and tools for analyzing the LOCA dataset

Using global reanalyses for permafrost simulation

Statistical dowscaling of climate data at daily scale using quantile mapping (QPM) technique.

The official AtmoSwing repository

This repository contains three packages that assemble codes and scripts to downscale coarse-resolution reanalysis fields to finer resolutions, accounting for subgrid-scale variability and/or topographic effects.