Statistical climate downscaling in Python

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

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

The Intermediate Complexity Atmospheric Research model (ICAR)

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

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.

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

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

Diffusion for climate downscaling

Generalized Analog Regression Downscaling (GARD) code

Probabilistic Downscaling of Climate Variables Using Denoising Diffusion Probabilistic Models

Python tool for downsizing Microsoft PowerPoint presentations (pptx) files.

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

Generate stocastic Gaussian realization constrained to a coarse scale image.

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

Code repository associated with "Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification" (Getter, Bessac, Rudi, Feng).

A horizontal autoscaler for Kubernetes workloads

Awesome-AI4Earth: a curated list of machine learning in Earth System, especially for weather and climate.

Cost saving K8s controller to scale down and up of resources during non-business hours