license: apache-2.0
datasets:
- openclimatefix/era5
language:
- es
- en
metrics:
- mse
library_name: transformers
pipeline_tag: image-to-image
tags:
- climate
- transformers
- super-resolution
Europe Reanalysis Super Resolution
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 downscaling global reanalysis data from ERA5.
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, 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, a detailed validation framework takes the place.
It combines classical deterministic error metrics with in-depth validations, including time series, maps, spatio-temporal correlations, and computer vision metrics, disaggregated by months, seasons, and geographical regions, to evaluate the effectiveness of the model in reducing errors and representing physical processes. This level of granularity allows for a more comprehensive and accurate assessment, which is critical for ensuring that the model is effective in practice.
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 the activations of different neurons and the importance of different features in the input data.
This work is funded by Code for Earth 2023 initiative.
Table of Contents
- Model Card for Europe Reanalysis Super Resolution
- Table of Contents
- Model Details
- Uses
- Bias, Risks, and Limitations
- Training Details
- Evaluation
- Model Examination
- Technical Specifications [optional]
- Authors
Model Details
Model Description
A vision model for down-scaling (from 0.25º to 0.05º) regional reanalysis grids in the mediterranean area.
- Developed by: A team of Predictia Intelligent Data Solutions S.L. & Instituto de Fisica de Cantabria (IFCA)
- Model type: Vision model
- Language(s) (NLP): en, es
- License: Apache-2.0
- Resources for more information: More information needed
Uses
Out-of-Scope Use
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Training Details
Training Data
The dataset used is a composition of the ERA5 and CERRA reanalysis.
The spatial coverage of the input grids (ERA5) is defined below, and corresponds to a 2D array of dimensions (60, 42):
longitude: [-8.35, 6.6]
latitude: [46.45, 35.50]
On the other hand, the target high-resolution grid (CERRA) correspond to a 2D matrix of dimmension (240, 160):
longitude: [-6.85, 5.1]
latitude: [44.95, 37]
The data samples used for training corresponds to the period from 1981 and 2013 (both included) and from 2014 to 2017 for per-epoch validation.
Training Procedure
Preprocessing
More information needed
Speeds, Sizes, Times
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
The dataset used is a composition of the ERA5 and CERRA reanalysis.
The spatial coverage of the input grids (ERA5) and the target high-resolution grids (CERRA) is fixed for both training and testing.
The data samples used for testing corresponds to the period from 2018 to 2020 (both included).
Factors
More information needed
Metrics
More information needed
Results
More information needed
Model Examination
More information needed
Technical Specifications
Model Architecture and Objective
The model architecture is based on the original Swin2 arcthitecture for Super Resolution (SR) tasks. The library transformers is used to simplify the model design.
The main component of the model is a transformers.Swin2SRModel which increases x8 the spatial resolution of its inputs (Swin2SR only supports upscaling ratios power of 2).
As the real upscale ratio is ~5 and the output shape of the region considered is (160, 240), a Convolutional Neural Network (CNN) is included as a pre-process component which convert the inputs into a (40, 60) feature maps that can be fed to the Swin2SRModel.
This network is trained to learn the residuals of the bicubic interpolation.
The specific parameters of this networks are available in config.json.
Compute Infrastructure
The use of GPUs in deep learning projects significantly accelerates model training and inference, leading to substantial reductions in computation time and making it feasible to tackle complex tasks and large datasets with efficiency.
The generosity and collaboration of our partners are instrumental to the success of this projects, significantly contributing to our research and development endeavors.
AI4EOSC: AI4EOSC stands for "Artificial Intelligence for the European Open Science Cloud." The European Open Science Cloud (EOSC) is a European Union initiative that aims to create a federated environment of research data and services. AI4EOSC is a specific project or initiative within the EOSC framework that focuses on the integration and application of artificial intelligence (AI) technologies in the context of open science.
European Weather Cloud: The European Weather Cloud is the cloud-based collaboration platform for meteorological application development and operations in Europe. Services provided range from delivery of weather forecast data and products to the provision of computing and storage resources, support and expert advice.
Hardware
For our project, we have deployed two virtual machines (VMs), each featuring a dedicated Graphics Processing Unit (GPU). One VM is equipped with a 16GB GPU, while the other boasts a more substantial 20GB GPU. This resource configuration allows us to efficiently manage a wide range of computing tasks, from data processing to deep learning, and ultimately drives the successful execution of our project.
Software
The code used to train and evaluate this model is freely available through its GitHub Repository ECMWFCode4Earth/DeepR hosted in the ECWMF Code 4 Earth organization.
Authors
Mario Santa Cruz. Predictia Intelligent Data Solutions S.L.
Antonio Pérez. Predictia Intelligent Data Solutions S.L.
Javier Díez. Predictia Intelligent Data Solutions S.L.