scienceAI
Members
This team has chosen to keep its participants anonymous.
Models
Model name
findforecast
Number of individuals supporting model development:
1-5
Maximum number of Central Processing Units (CPUs) supporting model development or forecast production:
< 8
Maximum number of Graphics Processing Units (GPUs) supporting model development or forecast production:
< 4
How would you best classify the IT system used for model development or forecast production:
Cloud computing system
Model summary questionnaire for model findforecast
Please note that the list below shows all questionnaires submitted for this model.
They are displayed from the most recent to the earliest, covering each 13-week competition period in which the team competed with this model.
Which of the following descriptions best represent the overarching design of your forecasting model?
- Machine learning-based weather prediction.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
Model is trained from scratch.
If any, what data does your model rely on for real-time forecasting purposes?
Some variables from the HRES data for the initialization day (Thursday) and upto 4 previous days data.
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
observational datasets ERA5, HRES
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)
Combination of a gencast-inspired diffusion model and an XGBoost classifier. Used XGBoost and Keras frameworks.
Have you published or presented any work related to this forecasting model? If yes, could you share references or links?
No
Before submitting your forecasts to the AI Weather Quest, did you validate your model against observational or independent datasets? If so, how?
We checked RPSS scores for dates in 2023 and 2024
Did you face any challenges during model development, and how did you address them?
No answer.
Are there any limitations to your current model that you aim to address in future iterations?
No answer.
Are there any other AI/ML model components or innovations that you wish to highlight?
No answer.
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.
Model name
ngcm
Number of individuals supporting model development:
1-5
Maximum number of Central Processing Units (CPUs) supporting model development or forecast production:
< 8
Maximum number of Graphics Processing Units (GPUs) supporting model development or forecast production:
< 4
How would you best classify the IT system used for model development or forecast production:
Cloud computing system
Model summary questionnaire for model ngcm
Please note that the list below shows all questionnaires submitted for this model.
They are displayed from the most recent to the earliest, covering each 13-week competition period in which the team competed with this model.
Which of the following descriptions best represent the overarching design of your forecasting model?
- Machine learning-based weather prediction.
- Ensemble-based model, aggregating multiple predictions to assess uncertainty and variability.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
Model trained from scratch on top of refrorecasts from an existing general circulation model to aggregate predictions.
If any, what data does your model rely on for real-time forecasting purposes?
Some variables from the HRES data for the initialization day (Thursday) and forecasts from a general circulation model upto the desired 32 days lead time.
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
Reforecasts from a general circulation model with upto 100 realizations for 40 day lead times.
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)
Mostly baseline. Otherwise a correction light GBM model on some forecasts from a general circulation model.
Have you published or presented any work related to this forecasting model? If yes, could you share references or links?
no
Before submitting your forecasts to the AI Weather Quest, did you validate your model against observational or independent datasets? If so, how?
evaluated on RPSS for initialization dates in the year 2020.
Did you face any challenges during model development, and how did you address them?
No answer.
Are there any limitations to your current model that you aim to address in future iterations?
No answer.
Are there any other AI/ML model components or innovations that you wish to highlight?
No answer.
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.
Model name
zephyr
Number of individuals supporting model development:
1-5
Maximum number of Central Processing Units (CPUs) supporting model development or forecast production:
< 8
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:
Cloud computing system
Model summary questionnaire for model zephyr
Please note that the list below shows all questionnaires submitted for this model.
They are displayed from the most recent to the earliest, covering each 13-week competition period in which the team competed with this model.
Which of the following descriptions best represent the overarching design of your forecasting model?
- Machine learning-based weather prediction.
- Statistical model focused on generating quintile probabilities.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
None
If any, what data does your model rely on for real-time forecasting purposes?
Some variables from the HRES data for the initialization day
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
observational datasets ERA5, HRES
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)
Mostly baseline. Or in some cases similar to "findforecast" model which uses a combination of a gencast-inspired diffusion model and an XGBoost classifier. Used XGBoost and Keras frameworks.
Have you published or presented any work related to this forecasting model? If yes, could you share references or links?
no
Before submitting your forecasts to the AI Weather Quest, did you validate your model against observational or independent datasets? If so, how?
We checked RPSS scores for dates in 2023 and 2024
Did you face any challenges during model development, and how did you address them?
No answer.
Are there any limitations to your current model that you aim to address in future iterations?
No answer.
Are there any other AI/ML model components or innovations that you wish to highlight?
No answer.
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