The following forecast portal displays all sub-seasonal forecasts submitted to the AI Weather Quest. As a reminder, participants are challenged to submit quintile probabilistic forecasts of near-surface air temperature, mean sea level pressure and accumulated rainfall at a days 19 to 25, and 26 to 32 lead time.
Participants have four days to submit their forecasts (days 1 to 4). On day 5, all submitted forecasts are extracted and pushed to the forecast portal. In other words, forecasts initialised on a Thursday will not be viewable on the forecast portal until the succeeding Monday.
On the home page of the AI Weather Quest forecast portal, you will be able to see each product uploaded to the forecast portal. A product, characterised by a large thumbnail on the right, is defined for each variable submitted by an individual model. For instance, say I have a model called bestS2S and I submit forecasts for each of the three variables, then three products would appear.
On the left you can filter products by the chosen parameter, team name and individual models.
When clicking on a forecast product, you can view all forecasts submitted to the AI Weather Quest for that individual model and variable. The title of the product is formatted as 'teamname: modelname forecast variable quintile probabilities', for example: 'bestS2S Mean sea level pressure quintile probabilities'.
On the left selection panel, you can filter the shown map by:
Additionally, to the left of each selection filter, you will see an overview button (four cyan blocks). If you click on the overview button, it will open all forecasts within that selection filter. For example, if you click on the overview button next to the quintile interval selection, then forecasts for each of the quintile intervals will be displayed.
Once choosing your chosen selection, the right map will display the forecast.
There are several tools that you may chose to leverage on the map itself:
Finally, below the forecast visualisation you will find a brief description of the forecast shown.
The AI Weather Quest would like to thank all those in the Forecasts and Services Department who supported the development of this forecast portal. Special thanks go to Cihan Sahin for his comprehensive guidance on the portal creation process and for developing new code to facilitate the integration of new teams and products.