AIFC
Members
This team has chosen to keep its participants anonymous.
Model
Please note that the model information below reflects the latest details provided by the team and does not show previous versions. Only the model summary questionnaires retain a submission history.
Model name
WRcast
Model type
Data-driven
Number of individuals supporting model development:
6-10
Maximum number of Central Processing Units (CPUs) supporting model development or forecast production:
8-48
Maximum number of Graphics Processing Units (GPUs) supporting model development or forecast production:
4-16
How would you best classify the IT system used for model development or forecast production:
Single node system
Model summary questionnaire for model WRcast
The list below shows all model summary questionnaires submitted for this model. Participants may submit an updated questionnaire whenever their methodology changes.
Questionnaires are displayed from the most recent to the earliest, with the latest version opened by default.
Which of the following descriptions best represent the overarching design of your forecasting model?
- Machine learning-based weather prediction.
- An empirical model that utilises historical weather patterns.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
Global ERA5 z500 and div200 + pre-processing
If any, what data does your model rely on for real-time forecasting purposes?
Global ERA5 z500 and div200
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
Global ERA5 z500 and div200
Please provide an overview of your final ML/AI model architecture (For example: key design features, specific algorithms or frameworks used, and any pre- or post-processing steps)
LSTM + custom model based on linear and convolutional ResNET layers
Have you published or presented any work related to this forecasting model? If yes, could you share references or links?
Part of the model is described in the arxiv.org/abs/2506.13758
Before submitting your forecasts to the AI Weather Quest, did you validate your model against observational or independent datasets? If so, how?
Part of the model has been validated against ERA5, as described in arxiv.org/abs/2506.13758
Did you face any challenges during model development, and how did you address them?
We are still facing many challenges, especially regarding the time-series forecast that represent the first part of the model. We are still addressing this issues.
Are there any limitations to your current model that you aim to address in future iterations?
The LSTM is currently not working
Are there any other AI/ML model components or innovations that you wish to highlight?
I am testing more advanced time-series forecasting models than LSTM
Who contributed to the development of this model? Please list all individuals who contributed to this model, along with their specific roles (e.g., data preparation, model architecture, model validation, etc) to acknowledge individual contributions.
This team has chosen to keep its participants anonymous.
Which of the following descriptions best represent the overarching design of your forecasting model?
- Machine learning-based weather prediction.
- An empirical model that utilises historical weather patterns.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
Global ERA5 z500 and div200 + pre-processing
If any, what data does your model rely on for real-time forecasting purposes?
Global ERA5 z500 and div200
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
Global ERA5 z500 and div200
Please provide an overview of your final ML/AI model architecture (For example: key design features, specific algorithms or frameworks used, and any pre- or post-processing steps)
LSTM + custom model based on linear and convolutional ResNET layers
Have you published or presented any work related to this forecasting model? If yes, could you share references or links?
Part of the model is described in the arxiv.org/abs/2506.13758
Before submitting your forecasts to the AI Weather Quest, did you validate your model against observational or independent datasets? If so, how?
Part of the model has been validated against ERA5, as described in arxiv.org/abs/2506.13758
Did you face any challenges during model development, and how did you address them?
We are still facing many challenges, especially regarding the time-series forecast that represent the first part of the model. We are still addressing this issues.
Are there any limitations to your current model that you aim to address in future iterations?
The LSTM is currently not working
Are there any other AI/ML model components or innovations that you wish to highlight?
I am testing more advanced time-series forecasting models than LSTM
Who contributed to the development of this model? Please list all individuals who contributed to this model, along with their specific roles (e.g., data preparation, model architecture, model validation, etc) to acknowledge individual contributions.
This team has chosen to keep its participants anonymous.
Which of the following descriptions best represent the overarching design of your forecasting model?
- Machine learning-based weather prediction.
- An empirical model that utilises historical weather patterns.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
Global ERA5 z500 and div200 + pre-processing
If any, what data does your model rely on for real-time forecasting purposes?
Global ERA5 z500 and div200
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
Global ERA5 z500 and div200
Please provide an overview of your final ML/AI model architecture (For example: key design features, specific algorithms or frameworks used, and any pre- or post-processing steps)
LSTM + custom model based on linear and convolutional ResNET layers
Have you published or presented any work related to this forecasting model? If yes, could you share references or links?
Part of the model is described in the arxiv.org/abs/2506.13758
Before submitting your forecasts to the AI Weather Quest, did you validate your model against observational or independent datasets? If so, how?
Part of the model has been validated against ERA5, as described in arxiv.org/abs/2506.13758
Did you face any challenges during model development, and how did you address them?
We are still facing many challenges, especially regarding the time-series forecast that represent the first part of the model. We are still addressing this issues.
Are there any limitations to your current model that you aim to address in future iterations?
The LSTM is currently not working
Are there any other AI/ML model components or innovations that you wish to highlight?
I am testing more advanced time-series forecasting models than LSTM
Who contributed to the development of this model? Please list all individuals who contributed to this model, along with their specific roles (e.g., data preparation, model architecture, model validation, etc) to acknowledge individual contributions.
This team has chosen to keep its participants anonymous.
Submitted forecast data in previous period(s)
Please note: Submitted forecast data is only publicly available once the evaluation of a full competitive period has been completed. See the competition's full detailed schedule with submitted data publication dates for each period here.
Access forecasts data