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--- |
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license: apache-2.0 |
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datasets: |
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- openclimatefix/era5 |
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language: |
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- es |
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- en |
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metrics: |
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- mse |
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library_name: transformers |
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pipeline_tag: image-to-image |
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tags: |
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- climate |
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- transformers |
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- super-resolution |
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--- |
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# Europe Reanalysis Super Resolution |
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The aim of the project is to create a Machine learning (ML) model that can generate high-resolution regional reanalysis data (similar to the one produced by CERRA) by |
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downscaling global reanalysis data from ERA5. |
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This will be accomplished by using state-of-the-art Deep Learning (DL) techniques like U-Net, conditional GAN, and diffusion models (among others). Additionally, |
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an ingestion module will be implemented to assess the possible benefit of using CERRA pseudo-observations as extra predictors. Once the model is designed and trained, |
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a detailed validation framework takes the place. |
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It combines classical deterministic error metrics with in-depth validations, including time series, maps, spatio-temporal correlations, and computer vision metrics, |
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disaggregated by months, seasons, and geographical regions, to evaluate the effectiveness of the model in reducing errors and representing physical processes. |
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This level of granularity allows for a more comprehensive and accurate assessment, which is critical for ensuring that the model is effective in practice. |
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Moreover, tools for interpretability of DL models can be used to understand the inner workings and decision-making processes of these complex structures by analyzing |
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the activations of different neurons and the importance of different features in the input data. |
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This work is funded by [Code for Earth 2023](https://codeforearth.ecmwf.int/) initiative. The model **ConvSwin2SR** is released in Apache 2.0, making it usable without |
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restrictions anywhere. |
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# Table of Contents |
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- [Model Card for Europe Reanalysis Super Resolution](#model-card-for--model_id-) |
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- [Table of Contents](#table-of-contents) |
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- [Model Details](#model-details) |
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- [Model Description](#model-description) |
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- [Uses](#uses) |
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- [Direct Use](#direct-use) |
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- [Out-of-Scope Use](#out-of-scope-use) |
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- [Bias, Risks, and Limitations](#bias-risks-and-limitations) |
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- [Training Details](#training-details) |
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- [Training Data](#training-data) |
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- [Training Procedure](#training-procedure) |
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- [Preprocessing](#preprocessing) |
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- [Speeds, Sizes, Times](#speeds-sizes-times) |
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- [Evaluation](#evaluation) |
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- [Testing Data, Factors & Metrics](#testing-data-factors--metrics) |
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- [Testing Data](#testing-data) |
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- [Factors](#factors) |
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- [Metrics](#metrics) |
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- [Results](#results) |
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- [Technical Specifications](#technical-specifications-optional) |
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- [Model Architecture](#model-architecture) |
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- [Components](#components) |
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- [Configuration details](#configuration-details) |
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- [Loss function](#loss-function) |
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- [Computing Infrastructure](#computing-infrastructure) |
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- [Hardware](#hardware) |
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- [Software](#software) |
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- [Authors](#authors) |
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# Model Details |
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## Model Description |
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<!-- Provide a longer summary of what this model is/does. --> |
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We present the ConvSwin2SR tranformer, a vision model for down-scaling (from 0.25º to 0.05º) regional reanalysis grids in the mediterranean area. |
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- **Developed by:** A team of Predictia Intelligent Data Solutions S.L. & Instituto de Fisica de Cantabria (IFCA) |
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- **Model type:** Vision model |
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- **Language(s) (NLP):** en, es |
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- **License:** Apache-2.0 |
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- **Resources for more information:** More information needed |
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- [GitHub Repo](https://github.com/ECMWFCode4Earth/DeepR) |
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# Uses |
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## Direct Use |
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The primary use of the ConvSwin2SR transformer is to enhance the spatial resolution in the Mediterranean area of global reanalysis grids using a regional reanalysis grid |
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as groundtruth. This enhancement is crucial for more precise climate studies, which can aid in better decision-making for various stakeholders including policymakers, |
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researchers, and weather-dependent industries like agriculture, energy, and transportation. |
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## Out-of-Scope Use |
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The model is specifically designed for downscaling ERA5 reanalysis grids to the CERRA regional reanalysis grid and may not perform well or provide accurate results |
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for other types of geospatial data or geographical regions. |
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# Bias, Risks, and Limitations |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) |
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and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes |
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across protected classes; identity characteristics; and sensitive, social, and occupational groups. |
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# Training Details |
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## Training Data |
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The datasets that are mainly used in the project can be found in the following Copernicus Climate Data Store catalogue entries: |
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- [ERA5 hourly data on single levels from 1940 to present](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview) |
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- [CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-cerra-single-levels?tab=overview) |
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1. Input low-resolution grids (ERA5): |
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The input grids are structured as a 3D array with dimensions of (time, 60, 44), where 60 and 44 are the number of grid points along the longitude and latitude axes, |
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respectively. Geographically, these grids cover a longitude range from -8.35 to 6.6 and a latitude range from 46.45 to 35.50. |
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This implies that the data covers a region extending from a westernmost point at longitude -8.35 to an easternmost point at longitude 6.6, and from a |
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northernmost point at latitude 46.45 to a southernmost point at latitude 35.50. |
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2. Target high-resolution grids (CERRA): |
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They are represented as a 3D array with larger dimensions of (time, 240, 160), indicating a finer grid resolution compared to the input grids. Here, 240 and 160 are |
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the number of grid points along the longitude and latitude axes, respectively. The geographical coverage for these high-resolution grids is defined by a longitude |
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range from -6.85 to 5.1 and a latitude range from 44.95 to 37. This region extends from a westernmost point at longitude -6.85 to an easternmost point at longitude 5.1, |
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and from a northernmost point at latitude 44.95 to a southernmost point at latitude 37. |
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![spatial-coverages](spatial-coverages.png) |
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The dataset's temporal division is structured to optimize model training and subsequent per-epoch validation. |
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The training duration spans 29 years, commencing in January 1985 and culminating in December 2013. |
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Sequentially, the validation phase begins, covering the period from January 2014 to December 2017. This 4-year interval is solely dedicated to evaluating the model's |
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aptitude on data it hasn't been exposed to during training. This separation ensures the model's robustness and its capability to make dependable predictions for the |
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validation period. |
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## Training Procedure |
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### Preprocessing |
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The preprocessing of climate datasets ERA5 and CERRA, extracted from the Climate Data Store (CDS), is a critical step before their utilization in training models. |
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This section defines the preprocessing steps undertaken to homogenize these datasets into a common format. The steps include unit standardization, coordinate system |
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rectification, and grid interpolation. The methodology employed in each step is discussed comprehensively in the following paragraphs: |
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- Unit Standardization: A preliminary step in the preprocessing pipeline involved the standardization of units across both datasets. |
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This was imperative to ensure a uniform unit system, facilitating a seamless integration of the datasets in later stages. |
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- Coordinate System Rectification: The coordinate system of the datasets was rectified to ensure a coherent representation of geographical information. |
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Specifically, the coordinates and dimensions were renamed to a standardized format with longitude (lon) and latitude (lat) as designated names. |
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The longitude values were adjusted to range from -180 to 180 instead of the initial 0 to 360 range, while latitude values were ordered in ascending order, |
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thereby aligning with conventional geographical coordinate systems. |
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- Grid Interpolation: The ERA5 dataset is structured on a regular grid with a spatial resolution of 0.25º, whereas the CERRA dataset inhabits a curvilinear grid with |
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a Lambert Conformal projection of higher spatial resolution (0.05º). To overcome this disparity in the grid system, a grid interpolation procedure is performed. |
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This step is crucial to align the datasets onto a common format, a regular grid (with different spatial resolutions), thereby ensuring consistency in spatial |
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representation. The interpolation transformed the CERRA dataset to match the regular grid structure of the ERA5 dataset, keeping its initial spatial resolution |
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of 0.05º (5.5 km). |
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### Speeds, Sizes, Times |
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- Training time: The training duration for the ConvSwin2SR model is notably extensive, clocking in at 3,648 days to complete a total of 100 epochs with |
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a batch size of 2 for a total number of batches equal to ~43000. |
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- Model size: The ConvSwin2SR model is a robust machine learning model boasting a total of 12,383,377 parameters. |
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This size reflects a substantial capacity for learning and generalizing complex relationships within the data, enabling the model to |
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effectively upscale lower-resolution reanalysis grids to higher-resolution versions. |
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- Inference speed: The ConvSwin2SR model demonstrates a commendable inference speed, particularly when handling a substantial batch of samples. |
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Specifically, when tasked with downscaling 248 samples, which is synonymous with processing data for an entire month at 3-hour intervals, |
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the model completes the operation in a mere 21 seconds. This level of efficiency is observed in a local computing environment outfitted with 16GB o |
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f RAM and 4GB of GPU memory. |
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# Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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In terms of spatial dimensions, both the input grids from ERA5 and the target high-resolution grids from CERRA remain consistent throughout the training and testing phases. |
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This spatial consistency ensures that the model is evaluated under the same geographic conditions as it was trained, allowing for a direct comparison of its performance |
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across different temporal segments. |
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The testing data samples correspond to the three-year period from 2018 to 2020, inclusive. This segment is crucial for assessing the model's real-world applicability and |
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its performance on the most recent data points, ensuring its relevance and reliability in current and future scenarios. |
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## Results |
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In our evaluation, the proposed model displayed a significant enhancement over the established baseline, which employs bicubic interpolation for the same task. |
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Specifically, our model achieved a noteworthy 34.93% reduction in Mean Absolute Error (MAE), a metric indicative of the average magnitude of errors between |
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predicted and actual values. Furthermore, there was a near 30% improvement in the Root Mean Square Error (RMSE), which measures the square root of the average |
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of squared differences between predictions and actual values. |
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These metrics not only underscore the model's capability to predict with greater precision but also emphasize its reduced propensity for errors. |
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In comparison to the bicubic interpolation baseline, our model's superior predictive accuracy is evident, positioning it as a more reliable tool for this task. |
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- Mean absolute error (MAE): |
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![mae](metric_global_map_diff_var-mae.png) |
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- Root mean squared error (RMSE): |
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![rmse](metric_global_map_diff_var-rmse.png) |
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# Technical Specifications |
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## Model Architecture |
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Our model's design is deeply rooted in the Swin2 architecture, specifically tailored for Super Resolution (SR) tasks. |
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We've harnessed the [transformers library](https://github.com/huggingface/transformers) to streamline and simplify the model's design. |
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![Model Architecture](architecture.png) |
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### Components |
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- **Transformers Component**: Central to our model is the [transformers.Swin2SRModel](https://huggingface.co/docs/transformers/model_doc/swin2sr#transformers.Swin2SRModel). This component amplifies the spatial resolution of its inputs by a factor of 8. Notably, Swin2SR exclusively supports upscaling ratios that are powers of 2. |
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- **Convolutional Neural Network (CNN) Component**: Given that our actual upscale ratio is approximately 5 and the designated output shape is (160, 240), |
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we've integrated a CNN. This serves as a preprocessing unit, transforming inputs into (20, 30) feature maps suitable for the Swin2SRModel. |
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The underlying objective of this network is to master the residuals stemming from bicubic interpolation. |
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### Configuration Details |
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For those inclined towards the intricacies of the model, the specific parameters governing its behavior are meticulously detailed in the |
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[config.json](https://huggingface.co/predictia/convswin2sr_mediterranean/blob/main/config.json). |
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### Loss function |
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The Swin2 transformer optimizes its parameters using a composite loss function that aggregates multiple L1 loss terms to enhance its predictive |
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accuracy across different resolutions and representations: |
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1. **Primary Predictions Loss**: |
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- This term computes the L1 loss between the primary model predictions and the reference values. It ensures that the transformer's outputs |
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closely match the ground truth across the primary spatial resolution. |
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2. **Downsampled Predictions Loss**: |
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- Recognizing the importance of accuracy across varying resolutions, this term calculates the L1 loss between the downsampled versions of the |
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predictions and the reference values. By incorporating this term, the model is incentivized to preserve critical information even when the data is represented |
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at a coarser scale. |
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3. **Blurred Predictions Loss**: |
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- To ensure the model's robustness against small perturbations and noise, this term evaluates the L1 loss between blurred versions of the |
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predictions and the references. This encourages the model to produce predictions that maintain accuracy even under slight modifications in the data representation. |
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## Computing Infrastructure |
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Leveraging GPUs in deep learning initiatives greatly amplifies the pace of model training and inference. This computational edge not only diminishes the total |
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computational duration but also equips us to proficiently navigate complex tasks and extensive datasets. |
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Our profound gratitude extends to our collaborative partners, whose invaluable contribution and support have been cornerstones in the fruition of this project. |
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Their substantial inputs have immensely propelled our research and developmental strides. |
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- **AI4EOSC**: Representing "Artificial Intelligence for the European Open Science Cloud," AI4EOSC functions under the aegis of the European Open Science Cloud (EOSC). |
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Initiated by the European Union, EOSC endeavors to orchestrate a cohesive platform for research data and services. AI4EOSC, a distinct arm within EOSC, concentrates |
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on embedding and leveraging artificial intelligence (AI) techniques within the open science domain. |
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- **European Weather Cloud**: Serving as a cloud-centric hub, this platform catalyzes collective efforts in meteorological application design and operations |
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throughout Europe. Its offerings are manifold, ranging from disseminating weather forecast data to proffering computational prowess, expert counsel, and |
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consistent support. |
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### Hardware Specifications |
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Our endeavor harnesses the capabilities of two virtual machines (VMs), each embedded with a dedicated GPU. One VM is equipped with a 16GB GPU, while its counterpart |
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is equipped with an even potent 20GB GPU. This strategic hardware alignment proficiently caters to diverse computational needs, spanning data orchestration to model |
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fine-tuning and evaluation, ensuring the seamless flow and success of our project. |
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### Software Resources |
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For enthusiasts and researchers inclined towards a deeper probe, our model's training and evaluation code is transparently accessible. |
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Navigate to our GitHub Repository [ECMWFCode4Earth/DeepR](https://github.com/ECMWFCode4Earth/DeepR) under the ECWMF Code 4 Earth consortium. |
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### Authors |
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<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> |
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- Mario Santa Cruz. Predictia Intelligent Data Solutions S.L. |
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- Antonio Pérez. Predictia Intelligent Data Solutions S.L. |
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- Javier Díez. Instituto de Física de Cantabria (IFCA) |
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