Obtaining salient information from many forecasts

Modern weather forecasting is moving towards ensemble prediction by default alongside the increased use of Machine Learning (ML) models and ensembles over a range of resolutions. From the user perspective, this will mean having to deal with a very large number of forecasts from different sources, often with different biases. There could potentially be many hundreds of ensemble members made available, with increased time frequency. At the European Centre for Medium Range Weather Forecasts (ECMWF) there are already 152 members available if the medium-range and sub-seasonal ensembles are used together out to 15 days, and with the addition of a ML ensemble the number increases to over 200.

For downstream applications that have limited resources, the problem becomes one of picking out the most salient and representative information from all those forecasts to extract the most important forecast messages. There is likely to be an increasing need for forms of “storylines” or “representative forecasts” that provide context, enable simpler communication and can be used effectively in downstream models. Clearly, there is still a vital place for probabilistic outputs, but on their own they limit the full extent of what could be usefully obtained.

Here we present highlights from work that investigates combining ensembles, member extraction and verification, making use of the different ensembles available at ECMWF with a focus on the prediction of synoptic weather patterns and regimes over a range of scales. The findings make use of a new diagnostic called CURV that categorises the degree of cyclonic or anticyclonic curvature from Mean Sea Level Pressure or Geopotential Height fields.