Abstract
Abstract. Warm conveyor belts (WCBs) affect the atmospheric dynamics in midlatitudes and are highly relevant for total and extreme precipitation in many parts of the extratropics. Thus, these airstreams and their effect on midlatitude weather should be well represented in numerical weather prediction (NWP) and climate models. This study applies newly developed convolutional neural network (CNN) models which allow the identification of footprints of WCB inflow, ascent, and outflow from a limited number of predictor fields at comparably low spatiotemporal resolution. The goal of the study is to demonstrate the versatile applicability of the CNN models to different datasets and that their application yields qualitatively and quantitatively similar results as their trajectory-based counterpart, which is most frequently used to objectively identify WCBs. The trajectory-based approach requires data at higher spatiotemporal resolution, which are often not available, and is computationally more expensive. First, an application to reanalyses reveals that the well-known relationship between WCB ascent and extratropical cyclones as well as between WCB outflow and blocking anticyclones is also found for WCB footprints identified with the CNN models. Second, the application to Japanese 55-year reanalyses shows how the CNN models may be used to identify erroneous predictor fields that deteriorate the models' reliability. Third, a verification of WCBs in operational European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts for three Northern Hemisphere winters reveals systematic biases over the North Atlantic with both the trajectory-based approach and the CNN models. The ensemble forecasts' skill tends to be lower when being evaluated with the trajectory approach due to the fine-scale structure of WCB footprints in comparison to the rather smooth CNN-based WCB footprints. A final example demonstrates the applicability of the CNN models to a convection-permitting simulation with the ICOsahedral Nonhydrostatic (ICON) NWP model. Our study illustrates that deep learning methods can be used efficiently to support process-oriented understanding of forecast error and model biases and opens numerous directions for future research.
Highlights
Extratropical cyclones are accompanied by coherent airstreams which ascend cross-isentropically from the lower to the upper troposphere within 2 d – so-called warm conveyor belts (WCBs; Browning et al, 1973; Harrold, 1973; Carlson, 1980)
WCBs of the trajectory-based climatology by Madonna et al (2014) are associated with extratropical cyclones. We investigate whether this relationship is found for WCBs identified with the convolutional neural network (CNN) models by matching objects of WCB ascent with cyclone objects
This suggests that the CNN models identify WCBs that are associated with extratropical cyclones and not just rapidly ascending airstreams which occur independently of extratropical cyclones, such as orographic ascent or convective systems
Summary
Extratropical cyclones are accompanied by coherent airstreams which ascend cross-isentropically from the lower to the upper troposphere within 2 d – so-called warm conveyor belts (WCBs; Browning et al, 1973; Harrold, 1973; Carlson, 1980). The latent heat release during the WCB ascent leads to a net cross-isentropic transport of lower-tropospheric low-PV air into the upper troposphere where it contributes along with its diabatically amplified divergent outflow to the formation of anticyclonic PV anomalies (e.g., Pomroy and Thorpe, 2000; Ahmadi-Givi et al, 2004; Grams et al, 2011; Bosart et al, 2017). These anticyclonic PV anomalies may trigger or modify downstream Rossby waves (Röthlisberger et al, 2018) or may contribute to the onset and maintenance of blocking anticyclones (e.g., Pfahl et al, 2015; Grams and Archambault, 2016; Steinfeld and Pfahl, 2019). Steinfeld and Pfahl (2019) found that almost 10 % of air masses in blocking anticyclones had ascended in WCBs during the 7 d before reaching the blocking region
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