EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models - Part 1: Development of deep learning model

Physical processes on the synoptic scale are important modulators of the large-scale extratropical circulation. In particular, rapidly ascending airstreams in extratropical cyclones, so-called warm conveyor belts (WCBs), modulate the upper-tropospheric Rossby wave pattern and are sources and magnifiers of forecast uncertainty. Thus, from a process-oriented perspective, numerical weather prediction (NWP) and climate models should adequately represent WCBs. The identification of WCBs usually involves Lagrangian air parcel trajectories that ascend from the lower to the upper troposphere within 2 d. This requires expensive computations and numerical data with high spatial and temporal resolution, which are often not available from standard output. This study introduces a novel framework that aims to predict the footprints of the WCB inflow, ascent, and outflow stages over the Northern Hemisphere from instantaneous gridded fields using convolutional neural networks (CNNs). With its comparably low computational costs and relying on standard model output alone, the new diagnostic enables the systematic investigation of WCBs in large data sets such as ensemble reforecast or climate model projections, which are mostly not suited for trajectory calculations. Building on the insights from a logistic regression approach of a previous study, the CNNs are trained using a combination of meteorological parameters as predictors and trajectory-based WCB footprints as predictands. Validation of the networks against the trajectory-based data set confirms that the CNN models reliably replicate the climatological frequency of WCBs as well as their footprints at instantaneous time steps. The CNN models significantly outperform previously developed logistic regression models. Including time-lagged information on the occurrence of WCB ascent as a predictor for the inflow and outflow stages further improves the models\u27 skill considerably. A companion study demonstrates versatile applications of the CNNs in different data sets including the verification of WCBs in ensemble forecasts. Overall, the diagnostic demonstrates how deep learning methods may be used to investigate the representation of weather systems and their related processes in NWP and climate models in order to shed light on forecast uncertainty and systematic biases from a process-oriented perspective

Geophysical Research

Contains research objectives and reports on two research projects.Joint Services Electronics Programs (U. S. Army, U.S. Navy, and U.S. Air Force) under Contract DA 36-039-AMC-03200(E)National Aeronautics and Space Administration (Grant NGR-22-009-131)National Aeronautics and Space Administration (Grant NGR-22-009-(114)

Geophysical Research

Contains reports on two research projects.Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DA 36-039-AMC-03200(E)National Aeronautics and Space Administration (Grant NGR-22-009-131)National Aeronautics and Space Administration (Grant NGR-22-009-(114)

Geophysical Research

Contains reports on two research projects.Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DA 28-043-AMC-02536(E)National Aeronautics and Space Administration (Grant NGR-22-009-131)National Aeronautics and Space Administration (Grant NGR-22-009-114)National Aeronautics and Space Administration (Contract NAS 12-436

EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models - Part 2: Model application to different datasets

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\u27 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\u27 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

Year-round sub-seasonal forecast skill for Atlantic-European weather regimes

Weather regime forecasts are a prominent use case of sub‐seasonal prediction in the midlatitudes. A systematic evaluation and understanding of year‐round sub‐seasonal regime forecast performance is still missing, however. Here we evaluate the representation of and forecast skill for seven year‐round Atlantic–European weather regimes in sub‐seasonal reforecasts from the European Centre for Medium‐Range Weather Forecasts. Forecast calibration improves regime frequency biases and forecast skill most strongly in summer, but scarcely in winter, due to considerable large‐scale flow biases in summer. The average regime skill horizon in winter is about 5 days longer than in summer and spring, and 3 days longer than in autumn. The Zonal Regime and Greenland Blocking tend to have the longest year‐round skill horizon, which is driven by their high persistence in winter. The year‐round skill is lowest for the European Blocking, which is common for all seasons but most pronounced in winter and spring. For the related, more northern Scandinavian Blocking, the skill is similarly low in winter and spring but higher in summer and autumn. We further show that the winter average regime skill horizon tends to be enhanced following a strong stratospheric polar vortex (SPV), but reduced following a weak SPV. Likewise, the year‐round average regime skill horizon tends to be enhanced following phases 4 and 7 of the Madden–Julian Oscillation (MJO) but reduced following phase 2, driven by winter but also autumn and spring. Our study thus reveals promising potential for year‐round sub‐seasonal regime predictions. Further model improvements can be achieved by reduction of the considerable large‐scale flow biases in summer, better understanding and modeling of blocking in the European region, and better exploitation of the potential predictability provided by weak SPV states and specific MJO phases in winter and the transition seasons.The overall sub‐seasonal forecast performance (biases and skill) for predicting seven year‐round Atlantic–European weather regimes is highest in winter and lowest in summer. The year‐round skill horizon is shortest for the European Blocking and longest for the Zonal Regime and Greenland Blocking (see figure). Furthermore, the winter skill horizon tends to be enhanced following a strong stratospheric polar vortex but reduced following a weak one. Madden–Julian Oscillation phases 4 and 7 tend to increase and phase 2 to decrease the year‐round skill horizon.Helmholtz‐Gemeinschaft http://dx.doi.org/10.13039/50110000165

Geophysics

Contains reports on four research projects.United States Air Force (Contract AF19(628)-500)Lincoln Laboratory (Purchase Order DDL BB-107

Observations of mesoscale and boundary-layer circulations affecting dust uplift and transport in the Saharan boundary layer

International audienceObservations of the Saharan boundary layer, made during the GERBILS field campaign, show that mesoscale land surface temperature variations (which were related to albedo variations) induced mesoscale circulations, and that mesoscale and boundary-layer circulations affected dust uplift and transport. These processes are unrepresented in many climate models, but may have significant impacts on the vertical transport and uplift of desert dust. Mesoscale effects in particular tend to be difficult to parameterise. With weak winds along the aircraft track, land surface temperature anomalies with scales of greater than 10 km are shown to significantly affect boundary-layer temperatures and winds. Such anomalies are expected to affect the vertical mixing of the dusty and weakly stratified Saharan Air Layer (SAL). Mesoscale variations in winds are also shown to affect dust loadings in the boundary-layer. In a region of local uplift, with strong along-track winds, boundary-layer rolls are shown to lead to warm moist dusty updraughts in the boundary layer. Large eddy model (LEM) simulations suggest that these rolls increased uplift by approximately 30%. The modelled effects of boundary-layer convection on uplift is shown to be larger when the boundary-layer wind is decreased, and most significant when the mean wind is below the threshold for dust uplift and the boundary-layer convection leads to uplift which would not otherwise occur