Scale down / "pause" Kubernetes workload (Deployments, StatefulSets, and/or HorizontalPodAutoscalers and CronJobs too !) during non-work hours.
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Updated
Sep 3, 2024 - Python
Scale down / "pause" Kubernetes workload (Deployments, StatefulSets, and/or HorizontalPodAutoscalers and CronJobs too !) during non-work hours.
A vertical autoscaler for Kubernetes workloads
TopoPyScale: a Python library to perform simplistic climate downscaling at the hillslope scale
Scale down Kubernetes deployments after work hours
A high resolution tool for snow cover reconstruction studies
Downscaling spatial resolution of geo-spatial data (TROPOMI SIF) using auxiliary data (MODIS) by application of U-NET and Local Binary Patterns
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.
Deep Learning for empirical DownScaling. Python package with state-of-the-art and novel deep learning algorithms for empirical/statistical downscaling of gridded data
Multi-Fidelity Gaussian Processes (MFGP) applied to downscaling sparse climatic data.
Python package to reconstruct and extend observational climate series through empricial downscaling of large-scale models
Statistical climate downscaling in Python
Diffusion for climate downscaling
Scripts that I've used during grad school for data collection, analysis, visualization, cleaning, wrangling, etc., for classes, project reports, and manuscripts.
The official AtmoSwing repository
Downscaling & bias correction of CMIP6 tasmin, tasmax, and pr for the R/CIL GDPCIR project
Cost saving K8s controller to scale down and up of resources during non-business hours
The Intermediate Complexity Atmospheric Research model (ICAR)
Generalized Analog Regression Downscaling (GARD) code
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).
Awesome-AI4Earth: a curated list of machine learning in Earth System, especially for weather and climate.
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