# Precipitation-Nowcasting
**Repository Path**: mirrors_Hzzone/Precipitation-Nowcasting
## Basic Information
- **Project Name**: Precipitation-Nowcasting
- **Description**: pytorch implemention of trajGRU.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 2
- **Created**: 2022-01-11
- **Last Updated**: 2025-09-22
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
### Introduction
This repo has implemented a pytorch-based encoder-forecaster model with RNNs including (TrajGRU, ConvLSTM) to do precipitation nowcasting. For more information about TrajGRU, please refer to [HKO-7](https://github.com/sxjscience/HKO-7).
If you are interested in my implementation of ConvLSTM and TrajGRU, please see [ConvLSTM](https://github.com/Hzzone/Precipitation-Nowcasting/blob/master/nowcasting/models/convLSTM.py) and [TrajGRU](https://github.com/Hzzone/Precipitation-Nowcasting/blob/master/nowcasting/models/trajGRU.py). It is assumed that the input shape should be 
. All of my implementation have been proved to be effective in HKO-7 Dataset. Hopefully it helps your research.
### Train
Firstly you should apply for HKO-7 Dataset from [HKO-7](https://github.com/sxjscience/HKO-7), and modify somelines in config.py to find the dataset path.
Secondly and last, run `python3 experiments/trajGRU_balanced_mse_mae/main.py`, and then run `python3 experiments/trajGRU_frame_weighted_mse/main.py` since I have finetuned the model on the basis of model trained in last step.
### Environment
Python 3.6+, PyTorch 1.0 and Ubuntu or macOS.
### Demo
### Performance
The performance on HKO-7 dataset is below.
| CSI |
HSS |
Balanced MSE |
Balanced MAE |
 |
 |
 |
 |
 |
|
|
|
|
|
|
|
| 0.5496 |
0.4772 |
0.3774 |
0.2863 |
0.1794 |
0.6713 |
0.6150 |
0.5226 |
0.4253 |
0.2919 |
5860.97 |
15062.46 |
### Download
[Dropbox Pretrained Model](https://www.dropbox.com/sh/i5goltdq83dmkvc/AABBe5wTuEQF5j3VSMszVQSaa?dl=0)
### Citation
```
@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}
}
@inproceedings{xingjian2015convolutional,
title={Convolutional LSTM network: A machine learning approach for precipitation nowcasting},
author={Xingjian, SHI and Chen, Zhourong and Wang, Hao and Yeung, Dit-Yan and Wong, Wai-Kin and Woo, Wang-chun},
booktitle={Advances in neural information processing systems},
pages={802--810},
year={2015}
}
```