Weather4cast - Super-Resolution Rain Movie Prediction under Spatio-Temporal Shifts

Predict super-resolution rain movies in various regions of Europe
Transfer learning across space and time under strong shifts
Exploit data fusion to model ground-radar and multi-band satellite images

Introduction

The aim of the 2023 edition of the Weather4cast competition is to predict quantitatively future high resolution rainfall events from lower resolution satellite radiances. Ground-radar reflectivity measurements are used to calculate pan-European composite rainfall rates by the Operational Program for Exchange of Weather Radar Information (OPERA) radar network. While these are more precise, accurate, and of higher resolution than satellite data, they are expensive to obtain and not available in many parts of the world. We thus want to learn how to predict this high value rain rates from radiation measured by geostationary satellites operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).

Prediction task

Competition participants should predict the exact amount of rainfall for the next 8 hours in 32 time slots from an input sequence of 4 time slots of the preceeding hour. The input sequence consists of four 11-band spectral satellite images. These 11 channels show slightly noisy satellite radiances covering so-called visible (VIS), water vapor (WV), and infrared (IR) bands. Each satellite image covers a 15 minute period and its pixels correspond to a spatial area of about 12km x 12km. The prediction output is a sequence of 32 images representing rain rates from ground-radar reflectivities. Output images also have a temporal resolution of 15 minutes but have higher spatial resolution, with each pixel corresponding to a spatial area of about 2km x 2km. So in addition to predicting the weather in the future, converting satellite inputs to ground-radar outputs, this adds a super-resolution task due to the coarser spatial resolution of the satellite data

Weather4cast 2023 dataset

We provide data from 10 Eureopean regions selected based on their preciptation characteristics for 2019, 2020 and 2021. In total there then are 7 regions with full training data in both 2019 and 2020. Those regions then be used for training, while three additional regions provide a spatial transfer learning challenge in years 2019 and 2020. For all ten regions, the year 2021 provides a temporal transfer learning challenge.

Core Challege dataset

For the Core Challege we provide data from 7 Eureopean regions selected based on their preciptation characteristics for two years covering(boxi_0015, boxi_0034, boxi_0076, roxi_0004, roxi_0005, roxi_0006, and roxi_0007). This data covers the time February to December 2019 and January to December 2020.

The task is to predict exact amount of rain events 4 hours into the future from a 1 hour sequence of satellite images. Rain rates computed from OPERA ground-radar reflectivities provide a ground truth.

Transfer Learning Challege dataset

For the Transfer Learning Challege we provide satellite data for additional 3 regions (roxi_0008, roxi_0009 and roxi_0010) for years 2019 and 2020 and 10 all regions in 2021. New regions provide a spatial transfer learning challenge in years 2019 and 2020 and a temporal transfer learning challenge in 2021. For the seven regions with extensive training data in 2019 and 2020 this constitutes a pure temporal transfer learning challenge.

Get the data

You need to register for the competition and accept its Terms and Conditions to access the data:

Competition Data: Join and get the data

Data are provided in HDF-5 files, separately for each year and data type. In our canonical folder structure year/datatype/ the HRIT folder holds the satellite data and the OPERA folder provides the ground radar data. The file names reflect the different regions (boxi_####) and data splits (train, validation, and test). Ground truth for the test data split is of course withheld.

After downloading the data, your data files should thus be arranged in folders of the following structure:

2019/
    +-- HRIT/  ... sub-folder for satellite radiance datasets
        +-- boxi_0015.test.reflbt0.ns.h5
        +-- boxi_0015.train.reflbt0.ns.h5
        +-- boxi_0015.val.reflbt0.ns.h5
        +-- boxi_00XX…….
    +-- OPERA/  ... sub-folder for OPERA ground-radar rain rates
        +-- boxi_0015.train.rates.crop.h5
        +-- boxi_0015.val.rates.crop.h5
        +-- boxi_00XX…….
2020/
    +-- HRIT/  ... sub-folder for satellite radiance datasets
        +-- boxi_0015.test.reflbt0.ns.h5
        +-- boxi_0015.train.reflbt0.ns.h5
        +-- boxi_0015.val.reflbt0.ns.h5
        +-- boxi_00XX…….
    +-- OPERA/  ... sub-folder for OPERA ground-radar rain rates
        +-- boxi_0015.train.rates.crop.h5
        +-- boxi_0015.val.rates.crop.h5
        +-- boxi_00XX…….  
2021/
+-- HRIT/  ... sub-folder for satellite radiance datasets
        +-- boxi_0015.test.reflbt0.ns.h5
        +-- boxi_0015.train.reflbt0.ns.h5
        +-- boxi_0015.val.reflbt0.ns.h5
        +-- boxi_00XX…….

Each HDF file provides a set of (multi-channel) images:

boxi_00XX.train.reflbt0.ns.h5 provides REFL-BT, which is a tensor of shape (20308, 11, 252, 252) representing 20,308 images with 11 channels of satellite radiances for region XX. These are the input training data. The order of the channels in the H5 file corresonds to the following order of the satellite channels: IR_016, IR_039, IR_087, IR_097, IR_108, IR_120,IR_134, VIS006, VIS008, WV_062, WV_073.
boxi_00XX.train.rates.crop.h5 provides rates.crop, which is a tensor of shape (20308, 11, 252, 252) representing OPERA ground-radar rain rates for the corresponding satellite radiances from the train dataset. Model output should be 1 or 0 for rain or no-rain predictions respectively.
boxi_00XX.val.reflbt0.ns.h5 provides REFL-BT, which is a tensor of shape (2160, 11, 252, 252) representing additional measured satellite radiances. This data can be used as input for independent model validation. There are 60 validation sequences and each validation sequence consists of images for 4 input time slots; while in addition we also provide images for the 32 output time slots please note that this is just to aid model development and that model predictions cannot use these. The validation data set thus holds 4x60 + 32x60 = 2,160 images in total.
boxi_00XX.val.rates.crop.h5 provides rates.crop, which is a tensor of shape (2160, 1, 252, 252) representing OPERA ground-radar rain rates for the corresponding satellite radiances from the validation dataset. Model output should be 1 or 0 for rain or no-rain predictions respectively.
boxi_00XX.test.reflbt0.ns.h5 provides REFL-BT, which is a tensor of a shape (240, 11, 252, 252) representing additional satellite radiances. This dataset gives the input data for your model predictions for submission to the leaderboard. There are 60 input sequences in total, as each test sequence consists of images for 4 time slots (4x60 = 240). Note that no satellite radiances are provided for the future, so this is a true prediction task.

Both input satellite radiances and output OPERA ground-radar rain rates are given for 252x252 pixel patches but please note that the spatial resolution of the satellite images is about six times lower than the resolution of the ground radar. This means that the 252x252 pixel ground radar patch corresponds to a 42x42 pixel center region in the coarser satellite resolution. The model target region thus is surrounded by a large area providing sufficient context as input for a prediction of future weather. In fact, fast storm clouds from one border of the input data would reach the center target region in about 7-8h.

Submission guide

For submissions you need to upload a ZIP format archive of HDF-5 files that follows the folder structure below. Optionally, each HDF-5 file can be compressed by gzip, allowing for simple parallelization of the compression step. You need to include model predictions for all the regions. For each region, an HDF file should provide submission, a tensor of type float32 and shape for the 8-hour Core and Transfer learning leaderobards (60, 1, 32, 252, 252) and (60, 1, 16, 252, 252) for the 4-hour Nowcasting leaderobard, representing your predictions for the 60 test samples of a region. You need to follow the file naming convention shown in the example below to indicate the target region. Predictions for different years need to be placed in separate folders as shown below. The folder structure must be preserved in the submitted ZIP file. Please note that for Stage 1 we only ask for predictions for the year 2019, and predictions are simply 1 or 0 to indicate rain or no rain events respectively. For the Core Challenge, we ask for predictions for a total of 7 regions in both 2019 and 2020. For the Transfer Learning Challenge predictions for 3 regions are required in years 2019 and 2020, and for all 10 regions in 2021. To simplify compilation of predictions, we will provide helper scripts in the Starter Kit.

+-- 2019 –
    +-- boxi_0015.pred.h5.gz   ...1 file per region for 60 test-sequence predictions of 32 time-slots each
    +-- boxi_00XX….
+-- 2020 –
    +-- boxi_0015.pred.h5.gz  
    +-- boxi_00XX….

Starter kit

This repository provides a starter kit accompanying the Weather4cast 2023 competition that includes example code to get you up to speed quickly. Please note that its use is entirely optional. The sample code includes a dataloader, some helper scripts, and a Unet-3D baseline model, some parameters of which can be set in a configuration file.

To obtain the baseline model, you will need the wget command installed - then you can run

./mk_baseline.sh

to fetch and patch a basic 3D U-Net baseline model.

You will need to download the competition data separately. The sample code assumes that the downloaded data are organized in the following folder structure:

+-- data
    +-- 2019 –
        +-- HRIT --
            +-- boxi_0015.test.reflbt0.ns.h5
            +-- boxi_0015.train.reflbt0.ns.h5
            +-- boxi_0015.val.reflbt0.ns.h5
            +-- boxi_0034.test.reflbt0.ns.h5
            +-- boxi_0034.train.reflbt0.ns.h5
            +-- boxi_0034.val.reflbt0.ns.h5
            +-- boxi_0076.test.reflbt0.ns.h5
            +-- boxi_0076.train.reflbt0.ns.h5
            +-- boxi_0076.val.reflbt0.ns.h5
        +-- OPERA -- 
            +-- boxi_0015.train.rates.crop.h5
            +-- boxi_0015.val.rates.crop.h5
            +-- boxi_0034.train.rates.crop.h5
            +-- boxi_0034.val.rates.crop.h5
            +-- boxi_0076.train.rates.crop.h5
            +-- boxi_0076.val.rates.crop.h5
    +-- 2020 –
        +-- HRIT/  ... sub-folder for satellite radiance datasets
            +-- boxi_0015.test.reflbt0.ns.h5
            +-- boxi_0015.train.reflbt0.ns.h5
            +-- boxi_0015.val.reflbt0.ns.h5
            +-- boxi_00XX…….
        +-- OPERA/  ... sub-folder for OPERA ground-radar rain rates
            +-- boxi_0015.train.rates.crop.h5
            +-- boxi_0015.val.rates.crop.h5
            +-- boxi_00XX…….

The path to the parent folder data needs to be provided as the data_root parameter in the config_baseline.yaml file.

Environment

We provide Conda environments for the sample code which can be recreated locally. An environment with libraries current at release can be recreated from the file w4cNew.ymlusing the following command:

conda env create -f w4cNew.yml

If you want to use older libraries for compatibility reasons, we also provide an earlier environment in w4c.yml. Finally, if you want to create an environment in the future, we also provide a script mk_env.sh to get you started. Note that all this can easily run for an hour or more, depending on your machine and setup.

To activate the environment please run

conda activate w4cNew

or

conda activate w4c

respectively.

Training

We provide a script train.py with all the necessary code to train and explore a modified version of a 3D variant of the U-Net. The script supports training from scratch or fine tuning from a provided checkpoint. The same script can also be used to evaluate model predictions on the validation data split using the flag --mode val, or to generate submissions from the test data using the flag --mode predict. In all cases please ensure you have set the correct data path in config_baseline.yaml and activated the w4c environment.

Example invocations:

Training the model on a single GPU:

python train.py --gpus 0 --config_path config_baseline.yaml --name name_of_your_model

If you have more than one GPU you can select which GPU to use, with numbering starting from zero.

Fine tuning the model on 4 GPUs starting with a given checkpoint:

python train.py --gpus 0 1 2 3 --mode train --config_path config_baseline.yaml --checkpoint "lightning_logs/PATH-TO-YOUR-MODEL-LOGS/checkpoints/YOUR-CHECKPOINT-FILENAME.ckpt" --name baseline_tune

Validation

Training will create logs and checkpoint files that are saved in the lightning_logs directory. To validate your model from a checkpoint you can for example run the following command (here for two CPUs):

python train.py --gpus 0 1 --mode val  --config_path config_baseline.yaml  --checkpoint "lightning_logs/PATH-TO-YOUR-MODEL-LOGS/checkpoints/YOUR-CHECKPOINT-FILENAME.ckpt" --name baseline_validate

8-hours vs 4-hours predicions

You can use the Starting Kit to train your network for both 4-hours and 8-hours prediction tasks:

8-hours Core and Transfer Learning Leaderboards: to train your model for 8-hours leaderboards you need to set up len_seq_predict to 32 in the config_baseline.yaml file.
4-hours Nowcasting Leaderboard: to train your model for the 4-hours leaderboard you need to set up len_seq_predict to 16 in the config_baseline.yaml file.

TensorBoard

You can of course also use TensorBoard to track and visualize model evaluation metrics during the training process. The standard TensorBoard command line is:

tensorboard --logdir ./lightning_logs

This should confirm that TensorBoard has started. For the default port, you point your browser to http://localhost:6006.

Generating a submission

Submission files can be generated from a trained model based on the model paramters saved in the checkpoint file. To generate predictions from your model checkpoint you can run the train.py script as below:

train.py --gpus 0 --mode predict --config_path config_baseline.yaml --checkpoint "lightning_logs/PATH-TO-YOUR-MODEL-LOGS/checkpoints/YOUR-CHECKPOINT-FILENAME.ckpt"

The code currently does not support generating a prediction for more than one region/year at a time.

The results are saved in a single HDF-5 file named boxi_00XX.pred.h5 in the ./submssion/YEAR/ folder, where boxi_00XX is the name of the region defined in the predict section your config file. A sample configuration is shown below:

predict:
  region_to_predict: boxi_0015
  year_to_predict: 2019

To generate predictions for multiple regions this needs to be run with a separate configuration file for each region.

After generating prediction files for all the regions, please pack them into a single ZIP file (keeping the year/ folder structure) and submit them to the respective Weather4cast leaderboards when they are available.

Please remember to set up the len_seq_predict paramter to either 16 or 32, depenging on the task for which you are generating the submssion.

Automated generation of submissions (helper scripts)

Considering the much increased number of individual predictions to collect for a leaderboard submissions, we will provide helper scripts mk_pred_core.sh and ./mk_pred_transfer.sh that can be used to generate and compile all individual predictions from a single model. The scripts display help text and diagnostics. Note that the use of these scripts is entirely optional because you may prefer to apply different models for different regions. You can provide both an output directory and a GPU ID to generate multiple predictions in parallel. The script will typically run for 20-40 minutes on a recent GPU system.

Example invocation for interactive use:

./mk_pred_core.sh config_baseline_w4c23-pred.yaml 'lightning_logs/yourModelName/checkpoints/yourCheckPointName.ckpt' yourSubmissionName 0 2>&1 | tee yourSubmission.core.log

Plotting the results

To analyze the quality of your predictions, you can plot the output images generated in validation or prediction mode. They will be saved in the plots directory as PDF files. Each PDF file is generated separately for a single batch analysis.

To plot the results, you need to set up the plot_results parameter in the experiment section of the config_baseline.yaml file. Plots are generated for analyzed batches, and of them starts with the input satellite sequence. By changing the value of in_channel_to_plot, you can define which of the 11 input satellite channels you want to be plotted. In validation mode, the plots will include both target and prediction sequences, while in prediction mode, the plots will include only predictions.

As plotting is slow, by default, the plot_results parameter is set to `False``.

The image below presetns exemplary plot results generated in validation mode. First row representes input sequecne, second row represents target (ground truth) seqnece and the third row represens prediction.

Code and Scientific Abstract

At the end of the competition paricpants are required to provide:

A short scientific paper (Scientific Abstract) with a sufficiently detailed description of your approach (4-6 pages plus references)
The code and models with their learned weights that you used for your predictions, with explanations of how to reproduce you submissions.

We will notify participants of how to provide their scientific abstract. For the code, you will need to submit it to a public repository like GitHub, providing a link to download the model's learned weights. Ideally, your repository should at least contain:

a) A list of dependencies. In the case of using Python, we suggest using conda/pip to generate them: conda env export > environment.yml. Make sure that your code can be executed from a fresh environment using the provided list of requirements: conda env create -f environment.yml.
b) Code, models, and a folder with all model weights.
c) An out-of-the-box script to use your best model to generate predictions. The script will read the inputs for the model from a given path and region, using its test folder (like the one used for the leaderboard), and save the outputs on a given path. The path to the folder containing the weights to be loaded by the models can also be an argument of the script.

Citation

When using or referencing the Weather4cast Competition in general or the competition data please cite:

@InProceedings{pmlr-v220-gruca22a,
  title = 	 {Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts},
  author =       {Gruca, Aleksandra and Serva, Federico and Lliso, Lloren\c{c} and R\'ipodas, Pilar and Calbet, Xavier and Herruzo, Pedro and Pihrt, Ji\v{r}\'{\i} and Raevskyi, Rudolf and \v{S}im\'{a}nek, Petr and Choma, Matej and Li, Yang and Dong, Haiyu and Belousov, Yury and Polezhaev, Sergey and Pulfer, Brian and Seo, Minseok and Kim, Doyi and Shin, Seungheon and Kim, Eunbin and Ahn, Sewoong and Choi, Yeji and Park, Jinyoung and Son, Minseok and Cho, Seungju and Lee, Inyoung and Kim, Changick and Kim, Taehyeon and Kang, Shinhwan and Shin, Hyeonjeong and Yoon, Deukryeol and Eom, Seongha and Shin, Kijung and Yun, Se-Young and {Le Saux}, Bertrand and Kopp, Michael K and Hochreiter, Sepp and Kreil, David P},
  booktitle = 	 {Proceedings of the NeurIPS 2022 Competitions Track},
  pages = 	 {292--313},
  year = 	 {2022},
  editor = 	 {Ciccone, Marco and Stolovitzky, Gustavo and Albrecht, Jacob},
  volume = 	 {220},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {28 Nov--09 Dec},
  publisher =    {PMLR},
  url = 	 {https://proceedings.mlr.press/v220/gruca22a.html},
}


@INPROCEEDINGS{9672063,  
author={Herruzo, Pedro and Gruca, Aleksandra and Lliso, Llorenç and Calbet, Xavier and Rípodas, Pilar and Hochreiter, Sepp and Kopp, Michael and Kreil, David P.},  
booktitle={2021 IEEE International Conference on Big Data (Big Data)},   
title={High-resolution multi-channel weather forecasting – First insights on transfer learning from the Weather4cast Competitions 2021},   
year={2021},  
volume={},  
number={},  
pages={5750-5757},  
doi={10.1109/BigData52589.2021.9672063}
}

@inbook{10.1145/3459637.3482044,
author = {Gruca, Aleksandra and Herruzo, Pedro and R\'{\i}podas, Pilar and Kucik, Andrzej and Briese, Christian and Kopp, Michael K. and Hochreiter, Sepp and Ghamisi, Pedram and Kreil, David P.},
title = {CDCEO'21 - First Workshop on Complex Data Challenges in Earth Observation},
year = {2021},
isbn = {9781450384469},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3459637.3482044},
booktitle = {Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management},
pages = {4878–4879},
numpages = {2}
}

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Weather4cast - Super-Resolution Rain Movie Prediction under Spatio-Temporal Shifts

Contents

Introduction

Prediction task

Weather4cast 2023 dataset

Core Challege dataset

Transfer Learning Challege dataset

Get the data

Submission guide

Starter kit

Environment

Training

Validation

8-hours vs 4-hours predicions

TensorBoard

Generating a submission

Automated generation of submissions (helper scripts)

Plotting the results

Code and Scientific Abstract

Citation

Credits

About

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Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
data		data
images		images
models		models
plots		plots
submission		submission
utils		utils
.gitignore		.gitignore
COPYING		COPYING
LICENSE		LICENSE
README.md		README.md
mk_baseline.sh		mk_baseline.sh
mk_env.sh		mk_env.sh
mk_pred_core.sh		mk_pred_core.sh
mk_pred_transfer.sh		mk_pred_transfer.sh
train.py		train.py
w4c.yml		w4c.yml
w4cNew.yml		w4cNew.yml

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freddy-feng/weather4cast-2023

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Weather4cast - Super-Resolution Rain Movie Prediction under Spatio-Temporal Shifts

Contents

Introduction

Prediction task

Weather4cast 2023 dataset

Core Challege dataset

Transfer Learning Challege dataset

Get the data

Submission guide

Starter kit

Environment

Training

Validation

8-hours vs 4-hours predicions

TensorBoard

Generating a submission

Automated generation of submissions (helper scripts)

Plotting the results

Code and Scientific Abstract

Citation

Credits

About

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