BrAInfall
Members
First name (team leader)
Mehrdad
Last name
Mohannazadeh
Organisation name
BrAInfall
Organisation type
Academic (Student)
Organisation location
Germany
First name
Ehsan
Last name
Modiri
Organisation name
UFZ
Organisation type
Research Organisation (Academic, Independent, etc.)
Organisation location
Germany
Models
Model name
PreAImhyd
Number of individuals supporting model development:
1-5
Maximum number of Central Processing Units (CPUs) supporting model development or forecast production:
48-1,000
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 PreAImhyd
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)
1- Data used: The three inputs (pressure, precipitation, and temperature), that are readily available for downloading as provided by ECMWF.
2- The initial condition is made by running the LSTM with the three inputs for the previous 28 days from the time of prediction.
If any, what data does your model rely on for real-time forecasting purposes?
pressure, precipitation, and temperature
What types of datasets were used for model training? (For example: observational datasets, reanalysis data, NWP outputs or satellite data)
only observational datasets
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)
for each grid-cell, three lstms crunch the time-series of the inputs (pressure, precipitation, and temperature) and the output of all the lstms are passed to a feed-forward network, with hope to capture teleconnection effects. The output layer has dimensions (3 x #grid-cells) .
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?
Yes, we would like to add a new model and improve our current model to capture temperature and pressure more accurately.
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.
Mehrdad Mohannazadeh: model development and structuring the forecast chain
Ehsan Modiri: pre and post processing of inputs/forcing and structuring the forecast chain
Model name
LSTMMAP
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:
High-Performance Computing (HPC) Cluster
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