

# 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](https://codeforearth.ecmwf.int/) initiative.



#  Table of Contents

- [Model Card for Europe Reanalysis Super Resolution](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
  - [Model Description](#model-description)
- [Uses](#uses)
  - [Direct Use](#direct-use)
  - [Downstream Use [Optional]](#downstream-use-optional)
  - [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
  - [Recommendations](#recommendations)
- [Training Details](#training-details)
  - [Training Data](#training-data)
  - [Training Procedure](#training-procedure)
    - [Preprocessing](#preprocessing)
    - [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
    - [Testing Data](#testing-data)
    - [Factors](#factors)
    - [Metrics](#metrics)
  - [Results](#results)
- [Model Examination](#model-examination)
- [Technical Specifications [optional]](#technical-specifications-optional)
  - [Model Architecture and Objective](#model-architecture-and-objective)
  - [Compute Infrastructure](#compute-infrastructure)
    - [Hardware](#hardware)
    - [Software](#software)
- [Authors](#authors)

# Model Details

## Model Description

<!-- Provide a longer summary of what this model is/does. -->
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](https://github.com/ECMWFCode4Earth/DeepR)


# Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

## Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->




## Downstream Use [Optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
 



## Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->




# Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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) 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 across protected classes; identity characteristics; and sensitive, social, and occupational groups.


## Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->





# Training Details

## Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

More information on training data needed


## Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

### Preprocessing

More information needed

### Speeds, Sizes, Times

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

More information needed
 
# Evaluation

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

## Testing Data, Factors & Metrics

### Testing Data

<!-- This should link to a Data Card if possible. -->

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

<!-- 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. -->

- Mario Santa Cruz

- Antonio Pérez

- Javier Díez
{"language": ["es", "en"], "license": "apache-2.0", "library_name": "diffusers", "tags": ["climate", "diffusers", "super-resolution"], "datasets": ["openclimatefix/era5"], "metrics": ["mse"], "pipeline_tag": "image-to-image"}
diffusers tensorboard climate super-resolution image-to-image es en dataset:openclimatefix/era5 license:apache-2.0 has_space diffusers:UNet2DModel region:us
TAGS
#diffusers #tensorboard #climate #super-resolution #image-to-image #es #en #dataset-openclimatefix/era5 #license-apache-2.0 #has_space #diffusers-UNet2DModel #region-us 



# 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]](#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]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- 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
    - GitHub Repo


# 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.


## Recommendations







# Training Details

## Training Data



More information on training data needed


## Training Procedure



### Preprocessing

More information needed

### Speeds, Sizes, Times



More information needed
 
# Evaluation



## Testing Data, Factors & Metrics

### Testing Data



More information needed


### Factors



More information needed

### Metrics



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



- Mario Santa Cruz

- Antonio Pérez

- Javier Díez
# 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]](#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]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- 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
    - GitHub Repo # 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. ## Recommendations # Training Details ## Training Data



More information on training data needed ## Training Procedure ### Preprocessing

More information needed ### Speeds, Sizes, Times



More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data



More information needed ### Factors



More information needed ### Metrics



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



- Mario Santa Cruz

- Antonio Pérez

- Javier Díez
TAGS
#diffusers #tensorboard #climate #super-resolution #image-to-image #es #en #dataset-openclimatefix/era5 #license-apache-2.0 #has_space #diffusers-UNet2DModel #region-us 
 # 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]](#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]](#technical-specifications-optional)
  - Model Architecture and Objective
  - Compute Infrastructure
    - Hardware
    - Software
- 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
    - GitHub Repo # 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. ## Recommendations # Training Details ## Training Data



More information on training data needed ## Training Procedure ### Preprocessing

More information needed ### Speeds, Sizes, Times



More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data



More information needed ### Factors



More information needed ### Metrics



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



- Mario Santa Cruz

- Antonio Pérez

- Javier Díez