Arcana
Members
First name (team leader)
Yuchen
Last name
Zhang
Organisation name
Envision Energy
Organisation type
Large Tech Company
Organisation location
China
First name
Lanxiang
Last name
Xing
Organisation name
Tsinghua University Envision Sustainability Research Institute
Organisation type
Academic (Student)
Organisation location
China
First name
Huakun
Last name
Luo
Organisation name
Tsinghua University Envision Sustainability Research Institute
Organisation type
Academic (Student)
Organisation location
China
First name
Yuchen
Last name
Ma
Organisation name
Tsinghua University
Organisation type
Academic (Student)
Organisation location
China
First name
Mingsheng
Last name
Long
Organisation name
Tsinghua University
Organisation type
Research Organisation (Academic, Independent, etc.)
Organisation location
China
Models
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
ArcanaSpherical
Model type
Number of individuals supporting model development:
1-5
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:
16-64
How would you best classify the IT system used for model development or forecast production:
High-Performance Computing (HPC) Cluster
Model summary questionnaire for model ArcanaSpherical
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.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
We used ERA5 reanalysis data and HRES 0h for input.
If any, what data does your model rely on for real-time forecasting purposes?
We use HRES 0h data for initialization.
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
ERA5 reanalysis data.
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)
We use daily mean ERA5 dataset and a spherical-based transformer model to predict.
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?
No.
Did you face any challenges during model development, and how did you address them?
No.
Are there any limitations to your current model that you aim to address in future iterations?
We are trying to extend the forecast lead time.
Are there any other AI/ML model components or innovations that you wish to highlight?
No
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.
Yuchen Zhang provided ideas, Lanxiang Xing conducted main experiment, Huakun Luo and Yuchen Ma processed data and validated the model.
Model name
ArcanaSphericalv2
Model type
Number of individuals supporting model development:
1-5
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:
16-64
How would you best classify the IT system used for model development or forecast production:
High-Performance Computing (HPC) Cluster
Model summary questionnaire for model ArcanaSphericalv2
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.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
We used ERA5 reanalysis data for input.
If any, what data does your model rely on for real-time forecasting purposes?
No.
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
ERA5 reanalysis data.
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)
We use daily mean ERA5 dataset and a spherical-based transformer model to predict.
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?
No.
Did you face any challenges during model development, and how did you address them?
No.
Are there any limitations to your current model that you aim to address in future iterations?
We are trying to extend the forecast lead time.
Are there any other AI/ML model components or innovations that you wish to highlight?
No.
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.
Yuchen Zhang provided ideas, Lanxiang Xing conducted main experiment, Huakun Luo and Yuchen Ma processed data and validated the model.
Model name
ArcanaSphericalBeta
Model type
Number of individuals supporting model development:
1-5
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:
16-64
How would you best classify the IT system used for model development or forecast production:
High-Performance Computing (HPC) Cluster
Model summary questionnaire for model ArcanaSphericalBeta
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.
What techniques did you use to initialise your model? (For example: data sources and processing of initial conditions)
We used ERA5 reanalysis data and HRES 0h for input.
If any, what data does your model rely on for real-time forecasting purposes?
We use HRES 0h data for initialization.
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
ERA5 reanalysis data.
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)
We use daily mean ERA5 dataset and a spherical-based transformer model to predict.
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?
No.
Did you face any challenges during model development, and how did you address them?
No.
Are there any limitations to your current model that you aim to address in future iterations?
We are trying to extend the forecast lead time.
Are there any other AI/ML model components or innovations that you wish to highlight?
No.
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.
Yuchen Zhang provided ideas, Lanxiang Xing conducted main experiment, Huakun Luo and Yuchen Ma processed data and validated the model.
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