HYT
Members
This team has chosen to keep its participants anonymous.
Model
Model name
WenDingNet
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:
Single node system
Model summary questionnaire for model WenDingNet
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)
z-score normalized ERA5 data.
If any, what data does your model rely on for real-time forecasting purposes?
data from atmospheric, oceanic and terrestrial data.
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
ERA5 reanalysis data only.
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)
encoder-informer-decoder architecture.
Have you published or presented any work related to this forecasting model? If yes, could you share references or links?
not yet.
Before submitting your forecasts to the AI Weather Quest, did you validate your model against observational or independent datasets? If so, how?
never, but will be conduct in the near future.
Did you face any challenges during model development, and how did you address them?
the cost of training a model for gloabl forecast is a little expensive, so the resolution is limited to a caoser degree.
Are there any limitations to your current model that you aim to address in future iterations?
currently, the model do not introduce 'anomaly' information in the desgin, it will be beneficial to incorperate them during training for better anomaly forecast.
Are there any other AI/ML model components or innovations that you wish to highlight?
that's all~
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