Recipes for reproducing Analysis-Ready & Cloud Optimized (ARCO) ERA5 datasets.
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Aug 6, 2026 - Python
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Recipes for reproducing Analysis-Ready & Cloud Optimized (ARCO) ERA5 datasets.
atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series
A large compression model for weather and climate data, which compresses a 400+ TB ERA5 dataset into a new 0.8 TB CRA5 dataset.
Functions and Python scripts to ingest ERA5 data into Google Earth Engine
TopoPyScale: a Python library to perform simplistic climate downscaling at the hillslope scale
Remote Sensing data - Earth observation data
A super lightweight Lagrangian model for calculating millions of trajectories using ERA5 data
Python package for downloading ECMWF reanalysis data and converting it into a time series format.
Benchmarking tools for applying AI/ML to data assimilation
Open source software for predicting solar high altitude balloon (SHAB) trajectories
This Python script automates the retrieval and visualization of tropospheric NO2 data from Sentinel-5P satellite's TROPOMI instrument, enabling efficient monitoring of atmospheric pollution patterns through automated data processing and visualization.
Code for building CanadaFireSat
Concurrent CDS API downloader with a textual TUI, script mode, and Python library interface.
Lake surface water evaporation modeling using remote-sensed water quality parameters (CHL, CDOM, TSM, temperature) and Bayesian-optimized LSTM/GRU hybrids validated against Penman-FAO.
RL-HAB is an open-source high altitude balloon (HAB) reinforcement learning simulation environment for training autonomous HAB agents.
[ DPhil project ] Extreme value theory and GANs to generate compound coastal hazards (wind speed + sea level pressure) from ERA5 reanalysis data over the Bay of Bengal. In development...
Main repo tracking/including early CANARI-ML work
This repository is the reproducible code of the paper Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK. This paper has been accepted in the NeurIPS 2023 Workshop: Tackling Climate Change with Machine Learning.
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