# "`-''-/").___..--''"`-._
# (`6_ 6 ) `-. ( ).`-.__.`) WE ARE ...
# (_Y_.)' ._ ) `._ `. ``-..-' PENN STATE!
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# Author: Weiming Hu <weiming@psu.edu>
#
# Geoinformatics and Earth Observation Laboratory (http://geolab.psu.edu)
# Department of Geography and Institute for Computational and Data Sciences
# The Pennsylvania State University
Training a deep network for weather analogs! This method was developed to identify weather analogs with a Machine Learning similarity metric.
The Parallel Analog Ensemble is an implementation that uses the original statistical metric proposed by Dr. Delle Monache. However, the Deep Analogs seek to use a deep network for weather analog identification.
Hu, W., Cervone, G., Young, G. et al. Machine Learning Weather Analogs for Near-Surface Variables. Boundary-Layer Meteorol (2023). https://doi.org/10.1007/s10546-022-00779-6
pip install git+https://github.com/Weiming-Hu/DeepAnalogs.git
deep_analogs_train -h
Currently, this package is still under active development. So please use it at your own discretion. But I'm also happy to answer any questions or provide help.