Code release for the paper "TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting"

Franch, G., Maggio, V., Coviello, L. et al. TAASRAD19, a high-resolution weather radar reflectivity dataset for precipitation nowcasting. Sci Data 7, 234 (2020).

https://doi.org/10.1038/s41597-020-0574-8

The code includes the scripts for sequence extraction, a deep learning model for precipitation nowcasting and an online visualization build on TAASRAD19 dataset. The dataset can be downloaded from the following repositories:

• Radar scans years 2010 - 2016:
• Radar scans years 2017 - 2019:
• Radar sequences years 2010 - 2019:

A NETCDF version of the radar sequences (not needed for the code in this repository) is available here:

• Radar sequences years 2010 - 2019 (NETCDF):

All the code was developed and tested on Ubuntu 18.04 with python 3.6+.

Create a new virtualenv (for example with venv):

python3 -m venv .venv 
source .venv/bin/ctivate

Install all required packages in the virtualenv:

pip install \
   opencv-python PyYAML pandas \
   numba numpy scipy tqdm imageio \
   Pillow jupyterlab h5py umap-learn \
   joblib matplotlib

The nowcasting deep learning model was tested with mxnet 1.5.1 that can be installed with CPU support:

pip install mxnet==1.5.1.post0

or for CUDA 10.1 GPUs (see other versions at mxnet website) :

pip install mxnet-cu101==1.5.1.post0