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
Some cool model...
- Developed by: More information needed
- Shared by [Optional]: More information needed
- Model type: Language model
- Language(s) (NLP): en, es
- License: apache-2.0
- Parent Model: More information needed
- Resources for more information: More information needed
Uses
Direct Use
Downstream Use [Optional]
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.
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Training Details
Training Data
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Training Procedure
Preprocessing
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Speeds, Sizes, Times
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Model Examination
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Authors
Mario Santa Cruz
Antonio Pérez
Javier Díez
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