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Source code of the paper Deep learning for precipitation nowcasting: A benchmark and a new model

If you use the code or find it helpful, please cite the following paper:

@inproceedings{xingjian2017deep,
    title={Deep learning for precipitation nowcasting: a benchmark and a new model},
    author={Shi, Xingjian and Gao, Zhihan and Lausen, Leonard and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-kin and Woo, Wang-chun},
    booktitle={Advances in Neural Information Processing Systems},
    year={2017}
}

Installation

Requires Python 3.5 or newer!

Both Windows and Linux are supported.

Install the package

python3 setup.py develop
# Use --user if you have no privilege
python3 setup.py develop --user

You will also need the python plugin of opencv:

pip3 install opencv-contrib-python

In addition, you will need to install FFMpeg + X264 (See FAQ).

For windows users it may be difficult to install some required packages like numba, ffmpeg or opencv-python. We strongly recommend you to use Anaconda and install them by commands like conda install numba. To install opencv-python on windows, you can download the wheel file from https://www.lfd.uci.edu/~gohlke/pythonlibs/.

If you want to run the deep models in the paper, e.g., TrajGRU, you will need to install MXNet. We've tested our code under MXNet v0.12.0. Also, in order to run the ROVER algorithm, install the python wrapper of VarFlow by following the guide in VarFlow.

IMPORTANT! You are able to run the HKO-7 benchmark environment without MXNet or VarFlow. You can proceed to use the HKOIterator and HKOBenchmarkEnv after you have installed the python package + Opencv-Python + FFMpeg with X264 encoding enabled and have downloaded the data. (See sections below for more reference).