Near-surface Temperature Prediction

Official source code for paper 《LS-NTP: Unifying Long- and Short-range Spatial Correlations for Near-surface Temperature Prediction》

Overall Architecture of LS-NTP

Environment Installation

conda env create -f LS_NTP.yaml

Data Preparation

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

Reproducibility

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.

Source Files Description

-- 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

Run

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.

Citation

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},
}

xuguangning1218/LS_NTP