- Fairbanks, Alaska
- https://uaf-snap.org/
- https://stackexchange.com/users/5860578/charlie-parr
Starred repositories
Implementation of the Prithvi WxC Foundation Model and Downstream Tasks
Search and download Copernicus Sentinel satellite images
Implementation of the Aurora model for atmospheric forecasting
Pipelines and utilites for working with CMIP6 data
Climatology of Southeast Alaska atmospheric rivers that resulted in extreme precipitation.
AI driven development in your terminal. Designed for large, real-world tasks.
Python toolbox to process large vector files faster.
Jupyterlite extension to allow code to be pre-run in the repl app
Carbon Dioxide Standard Operating Procedure (SOP)
scivision: a framework for scientific image analysis
A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data
A framework for composing Neural Processes in Python
A Python package for tackling diverse environmental prediction tasks with NPs.
A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model.
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Preparing a Species of Greatest Conservation Need (SGCN) modeled presence/absence dataset for inclusion in SNAP's Northern Climate Reports
Python library for working with any SpatioTemporal Asset Catalog (STAC)
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
A real world full-stack application using LlamaIndex
LlamaIndex is a data framework for your LLM applications
🦜🔗 Build context-aware reasoning applications
Spatial Representations for Artificial Intelligence - a Python library toolkit for geospatial machine learning focused on creating embeddings for downstream tasks
🐢 Open-Source Evaluation & Testing for ML models & LLMs
llama3 implementation one matrix multiplication at a time
The Senseiver: attention-based global field reconstruction from sparse observations
Python package for earth-observing satellite data processing
Flexible and user-friendly toolkit for the bias correction of climate models and associated evaluation.