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
The aim of the 2022 edition of the Weather4cast competition is to predict 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).
Competition participants should predict rainfall locations 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
For Stage 1 of the competition we provide data from three Eureopean regions selected based on their preciptation characteristics. The task is to predict rain events 8 hours into the future from a 1 hour sequence of satellite images. The models should output binary pixels, with 1 and 0 indicating rain or no rain respectively. Rain rates computed from OPERA ground-radar reflectivities provide a ground truth. Although we provide the rain rates, at this stage, only rain/no rain needs to be predicted for each pixel.
For Stage 1 we provide data from one year only, covering February to December 2019.
In Stage 2 additional data will be provided for 2020 and 2021. Years 2019 and 2020 can then be used for training, while test sets from 2021 assess model robustness to temporal shifts. Additional regions with different climatological characteristics test model robustsness under spatial shifts. There are thus additional files for the new regions and years and thus the folder structure for stage 2 has been expanded accordingly to include additional sub-folders with the data for 2020 and 2021. In total there then are 7 regions with full training data in both 2019 and 2020. 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. For the seven regions with extensive training data in 2019 and 2020 this constitutes a pure temporal transfer learning challenge. The three additional regions 2021 provide a combined spatial and temporal transfer learning challenge.
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…….
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.
- Stage-2 Competition: Join and get the data
+-- 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….
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
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
When using or referencing the Weather4cast Competition in general or the competition data please cite:
@article{seo2022domain,
title={Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift},
author={Seo, Minseok and Kim, Doyi and Shin, Seungheon and Kim, Eunbin and Ahn, Sewoong and Choi, Yeji},
journal={arXiv preprint arXiv:2212.02968},
year={2022}
}
@article{seo2022simple,
title={Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network},
author={Seo, Minseok and Kim, Doyi and Shin, Seungheon and Kim, Eunbin and Ahn, Sewoong and Choi, Yeji},
journal={arXiv preprint arXiv:2212.02952},
year={2022}
}