Official source code for paper 《LS-NTP: Unifying Long- and Short-range Spatial Correlations for Near-surface Temperature Prediction》
conda env create -f LS_NTP.yaml
- Download the required temperature dataset from ERA5 offical site through here and the required temperature dataset from NCEP offical site through here.
Or you can download the preprocessing data from my google drive through here.- Or you can download the preprocessing data from my baidu drive through here
We provide one of the five runs best-validated models for both ERA5 and NCEP datasets in here. You can reproduce the result reported in the paper using this best-validated model.
-- data # dataset folder
-- era5 # the ERA5 dataset
-- ncep # the NCEP dataset
-- dataprovider # data reader and normalizer
-- era5.py # dataloader in train, validate, test for ERA5
-- ncep.py # dataloader in train, validate, test for NCEP
-- normalizer.py # data normalizer, including std, maxmin
-- figure # figure provider
-- network.png # architecture of LS-NTP model
-- model # proposed model
-- lsconv.py # the proposed LS-Conv
-- lsconvlstm.py # the ConvLSTM with LS-Conv
-- model.py # model loader, saver, procedure of train, validate, and test
-- network.py # the LS-NTP
-- save # model save path
-- era5 # best model on ERA5 (one of five runs)
-- ncep # best model on NCEP (one of five runs)
LS_NTP.yaml # conda environment for the project
lsntp_era5.config # model configure for ERA5
lsntp_ncep.config # model configure for NCEP
Run_era5.ipynb # jupyter visualized code for the whole procedure on ERA5
Run_ncep.ipynb # jupyter visualized code for the whole procedure on NCEP
When the conda environment and datasets are ready, you can train or reproduce our result by running file Run_era5.ipynb
or Run_ncep.ipynb
.
If you find this code or idea useful, please cite our work:
@article{XU2022242,
title = {LS-NTP: Unifying long- and short-range spatial correlations for Near-surface Temperature Prediction},
journal = {Neural Networks},
volume = {155},
pages = {242-257},
year = {2022},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2022.07.022},
url = {https://www.sciencedirect.com/science/article/pii/S0893608022002787},
author = {Guangning Xu and Xutao Li and Shanshan Feng and Yunming Ye and Zhihua Tu and Kenghong Lin and Zhichao Huang},
}