Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 1: Method and case studies

Abstract

Abstract. Strong winds associated with extratropical cyclones are one of the most dangerous natural hazards in Europe. These high winds are mostly associated with five mesoscale dynamical features: the warm (conveyor belt) jet (WJ); the cold (conveyor belt) jet (CJ); cold frontal convection (CFC); strong cold-sector winds (CS); and, at least in some storms, the sting jet (SJ). The timing within the cyclone's life cycle, the location relative to the cyclone core and some further characteristics differ between these features and, hence, likely also the associated forecast errors. Here, we present a novel objective identification approach for these high-wind features using a probabilistic random forest (RF) based on each feature’s most important characteristics in near-surface wind, rainfall, pressure and temperature evolution. As the CJ and SJ are difficult to distinguish in near-surface observations alone, these two features are considered together here. A strength of the identification method is that it works flexibly and is independent of local characteristics and horizontal gradients; thus, it can be applied to irregularly spaced surface observations and to gridded analyses and forecasts of different resolution in a consistent way. As a reference for the RF, we subjectively identify the four storm features (WJ, CS, CFC, and CJ and SJ) in 12 winter storm cases between 2015 and 2020 in both hourly surface observations and high-resolution reanalyses of the German Consortium for Small-scale Modeling (COSMO) model over Europe, using an interactive data analysis and visualisation tool. The RF is then trained on station observations only. The RF learns physically consistent relations and reveals the mean sea level pressure (tendency), potential temperature, precipitation amount and wind direction to be most important for the distinction between the features. From the RF, we get probabilities of each feature occurring at the single stations, which can be interpolated into areal information using Kriging. The results show a reliable identification for all features, especially for the WJ and CFC. We find difficulties in the distinction of the CJ and CS in extreme cases, as the features have rather similar meteorological characteristics. Mostly consistent results in observations and reanalysis data suggest that the novel approach can be applied to other data sets without the need for adaptation. Our new software RAMEFI (RAndom-forest-based MEsoscale wind Feature Identification) is made publicly available for straightforward use by the atmospheric community and enables a wide range of applications, such as working towards a climatology of these features for multi-decadal time periods (see Part 2 of this paper; Eisenstein et al., 2022d), analysing forecast errors in high-resolution COSMO ensemble forecasts and developing feature-dependent post-processing procedures.