cerra_denoise_nn_pixel-scaled_no-shuffle

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
  • Model Description
• Uses
  • Direct Use
  • Downstream Use [Optional]
  • Out-of-Scope Use
• Bias, Risks, and Limitations
  • Recommendations
• Training Details
  • Training Data
  • Training Procedure
    • Preprocessing
    • Speeds, Sizes, Times
• Evaluation
  • Testing Data, Factors & Metrics
    • Testing Data
    • Factors
    • Metrics
  • Results
• Model Examination
• Technical Specifications [optional]
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
• Authors

Model Details

Model Description

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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
  • GitHub Repo

Uses

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Direct Use

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Downstream Use [Optional]

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Out-of-Scope Use

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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) 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

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Training Details

Training Data

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More information on training data needed

Training Procedure

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Preprocessing

More information needed

Speeds, Sizes, Times

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More information needed

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

Testing Data, Factors & Metrics

Testing Data

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More information needed

Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

More information needed

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

More information needed

Results

More information needed

Model Examination

More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed

Authors

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• Mario Santa Cruz

• Antonio Pérez

• Javier Díez