Metadata-Version: 2.3
Name: opticalflowleo
Version: 0.8.8
Summary: Optical Flow derived winds from overlapping LEO granules
Project-URL: Homepage, https://gitlab.ssec.wisc.edu/rink/windflow_leo
Author-email: "T.Rink" <rink@ssec.wisc.edu>
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# windflow_leo
A package for deriving atmospheric winds from overlapping granules of satellites in a LEO (low earth orbit)
train formation, using dense feature tracking of CrIS retrievals of specific humidity (JPSS) or AVHRR imagery (MetOp).

This package runs inference on a pre-trained model based on WindFlow: Dense feature tracking of atmospheric winds with deep optical flow:

Vandal, T., Duffy, K., McCarty, W., Sewnath, A., & Nemani, R. (2022). Dense feature tracking of atmospheric winds with deep optical flow, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

Note: this project uses git submodules. Use this command to clone:  

`git clone --recursive https://gitlab.ssec.wisc.edu/rink/windflow_leo.git`

To install and run:  
`conda env create -f opticalflowleo.yml`  
`conda activate opticalflowleo
`pip install opticalflowleo`
