Improvement of seasonal forecast correction by using weather regimes

In order to develop seasonal forecast applications, raw forecast data generally need to be corrected to remove their systematic errors and drifts in time. In the climate community, methods based on quantile mapping techniques are quite common for their easy implementation. In the framework of the SECLI-FIRM project, we have tested a refinement of quantile mapping by conditioning the correction to weather regimes, in order to take large-scale circulation into account. For that purpose, we have used ADAMONT, a tool originally developed by Météo-France to correct climate projection scenarios. It was applied on four C3S seasonal forecast models over Europe, using ERA5 as a reference. Three parameters were treated at daily time-step: 2-metre temperature, precipitation and 10-metre wind-speed.

One of the main objectives of this study was to better understand the role weather regimes can play, if/when/where/for which parameter we gain in quality and predictability. For instance, a series of experiments were conducted on an idealized case of “perfect forecasts” of weather regimes, to point out the maximum benefits we could expect from the method.

Another focus of research was to test some strategies to optimize the positive impact of the introduction of weather regimes, by selecting members in one model ensemble or by using a multi-model approach. The selection was based on a sub-sampling of the best members in terms of weather regime frequency forecast, in order to determine the needed precision of weather regime forecast, for it to be useful in the correction.

We will present the main results of this work and some operational perspectives.