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arXiv DOI

Localized Weather

This repository contains the code and data for the paper "Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data".

The paper presents a novel multi-modal method that downscales gridded weather forecasts, such as ERA5 and HRRR, to provide accurate off-grid predictions. The model leverages both global data and local weather station observations from MADIS to make predictions that reflect both large-scale atmospheric dynamics and local weather patterns.

The model is evaluated on a surface wind prediction task and shows significant improvement over baseline methods, including ERA5 and HRRR interpolation and a multi-layer perceptron.

Use the following citation when the data or the model are used:

Yang, Q.; Giezendanner, J.; Civitarese, D. S.; Jakubik, J.; Schmitt E.; Chandra, A.; Vila, J.; Hohl, D.; Hill, C.; Watson, C.; Wang, S.; Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data. arXiv, October 2024. https://doi.org/10.48550/arXiv.2410.12938

Model and data

Data

The data for training, testing and validation can be found on Zenodo.

The following data is available:

  • Shapefile of the Northeastern United States (NE-US, extracted from NWS)
  • Shapefile containing the location and number of observations (2019-2023) of the MADIS stations in NE-US
  • Processed hourly averaged MADIS data for the NE-US (2019-2023)
  • ERA5 re-analysis data for the NE-US (2019-2023), gridded and interpolated
  • HRRR analysis and forecast data for the NE-US (2019-2023), gridded and interpolated

For MADIS, ERA5 and HRRR, the following variables are available:

  • u and v component of wind vector at 10 meters above ground
  • temperature at 2 meters above ground
  • dewpoint at 2 meters above ground

The data can also be generated from scratch. For this, code is available under Source/DataDownload/{ERA5/MADIS/HRRR} for the raw data download, and in the respective data loaders for the data processing (Source/Dataloader/{ERA5/HRRR/MetaStation/Madis/MixDataMLP}.py).

Code

The code is organised as follows (in Source/):

  • LocalLauncher.py, and SLURM_job_launcher.py are two different launchers all eventually pointing at Main.py (the arguments from the slurm job launcher are parsed by Arg_Parser.py)
  • Main.py contains the main code loop
  • EvaluateModel contains the code for the evaluation of the model, as well as the back propagation
  • the folder Dataloader/ contains the data loaders for MADIS, HRRR and ERA5, and the combination of them, and the folder Network/ the code for the network construction, for both the internal (MADIS) and external (ERA5 to MADIS) connections
  • Modules/GNN/MPNN.py contains the code for the heterogeneous message passing neural network and calls GNN_Layer_Internal/External.py, the message passing sequences between the networks
  • Modules/Transformer/ViT.py contains the code for the transformer and calls Transformer/StationsEmbedding.py.

Code inputs

The code expects the following data structure, you need to specify the root data path in the launcher:

RootDataPath/
├── ERA5
│   ├── Interpolated
│   │   ├── era5interpolated_e2m_1_Nearest_2019_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│   │   ├── era5interpolated_e2m_1_Nearest_2020_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│   │   ├── era5interpolated_e2m_1_Nearest_2021_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│   │   ├── era5interpolated_e2m_1_Nearest_2022_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│   │   └── era5interpolated_e2m_1_Nearest_2023_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│   └── Processed
│       ├── era5_2019_-83_-65_37_49.nc
│       ├── era5_2019_e2m_1_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       ├── era5_2019_e2m_8_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       ├── era5_2020_-83_-65_37_49.nc
│       ├── era5_2020_e2m_8_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       ├── era5_2021_-83_-65_37_49.nc
│       ├── era5_2021_e2m_8_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       ├── era5_2022_-83_-65_37_49.nc
│       ├── era5_2022_e2m_8_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       ├── era5_2023_-83_-65_37_49.nc
│       └── era5_2023_e2m_8_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
├── HRRR
│   └── Northeastern
│       ├── Interpolated
│       │   ├── HRRRinterpolated_h2m_1_Nearest_2019_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       │   ├── HRRRinterpolated_h2m_1_Nearest_2020_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       │   ├── HRRRinterpolated_h2m_1_Nearest_2021_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       │   ├── HRRRinterpolated_h2m_1_Nearest_2022_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       │   └── HRRRinterpolated_h2m_1_Nearest_2023_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
│       └── Processed
│           └── Yearly
│               └── Madis
│                   └── -80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9
│                       ├── 2019.nc
│                       ├── 2020.nc
│                       ├── 2021.nc
│                       ├── 2022.nc
│                       └── 2023.nc
├── Shapefiles
│   └── Regions
│       ├── northeastern_buffered.cpg
│       ├── northeastern_buffered.dbf
│       ├── northeastern_buffered.geojson
│       ├── northeastern_buffered.prj
│       ├── northeastern_buffered.qmd
│       ├── northeastern_buffered.shp
│       └── northeastern_buffered.shx
└── madis
    ├── processed
    │   └── Meta-2019-2023
    │       ├── madis_2019_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
    │       ├── madis_2020_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
    │       ├── madis_2021_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
    │       ├── madis_2022_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
    │       └── madis_2023_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.nc
    └── stations
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered.cpg
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered.dbf
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered.prj
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered.shp
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered.shx
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.cpg
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.dbf
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.prj
        ├── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.shp
        └── stations_2019_2023_-80.53_-66.94_38.92_47.47northeastern_buffered_filtered_0.9.shx