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Reliable forecasts of quasi‐stationary, recurrent, and persistent large‐scale atmospheric circulation patterns—so‐called weather regimes—are crucial for various socio‐economic sectors, including energy, health, and agriculture. Despite steady progress, probabilistic weather regime predictions still exhibit biases in the exact timing and amplitude of weather regimes. This study thus aims at advancing probabilistic weather regime predictions in the North Atlantic–European region through ensemble post‐processing. Here, we focus on the representation of seven year‐round weather regimes in sub‐seasonal to seasonal reforecasts of the European Centre for Medium‐Range Weather Forecasts (ECMWF). The manifestation of each of the seven regimes can be expressed by a continuous weather regime index, representing the projection of the instantaneous 500‐hPa geopotential height anomalies (A) onto the respective mean regime pattern. We apply a two‐step ensemble post‐processing involving first univariate ensemble model output statistics and second ensemble copula coupling, which restores the multivariate dependence structure. Compared with current forecast calibration practices, which rely on correcting the field by the lead‐time‐dependent mean bias, our approach extends the forecast skill horizon for daily/instantaneous regime forecasts moderately by 1 day (from 13.5 to 14.5 days). Additionally, to our knowledge our study is the first to evaluate the multivariate aspects of forecast quality systematically for weather regime forecasts. Our method outperforms current practices in the multivariate aspect, as measured by the energy and variogram score. Still, our study shows that, even with advanced post‐processing, weather regime prediction becomes difficult beyond 14 days, which likely points towards intrinsic limits of predictability for daily/instantaneous regime forecasts. The proposed method can easily be applied to operational weather regime forecasts, offering a neat alternative for cost‐ and time‐efficient post‐processing of real‐time weather regime forecasts.
Reliable forecasts of quasi‐stationary, recurrent, and persistent large‐scale atmospheric circulation patterns—so‐called weather regimes—are crucial for various socio‐economic sectors, including energy, health, and agriculture. Despite steady progress, probabilistic weather regime predictions still exhibit biases in the exact timing and amplitude of weather regimes. This study thus aims at advancing probabilistic weather regime predictions in the North Atlantic–European region through ensemble post‐processing. Here, we focus on the representation of seven year‐round weather regimes in sub‐seasonal to seasonal reforecasts of the European Centre for Medium‐Range Weather Forecasts (ECMWF). The manifestation of each of the seven regimes can be expressed by a continuous weather regime index, representing the projection of the instantaneous 500‐hPa geopotential height anomalies (A) onto the respective mean regime pattern. We apply a two‐step ensemble post‐processing involving first univariate ensemble model output statistics and second ensemble copula coupling, which restores the multivariate dependence structure. Compared with current forecast calibration practices, which rely on correcting the field by the lead‐time‐dependent mean bias, our approach extends the forecast skill horizon for daily/instantaneous regime forecasts moderately by 1 day (from 13.5 to 14.5 days). Additionally, to our knowledge our study is the first to evaluate the multivariate aspects of forecast quality systematically for weather regime forecasts. Our method outperforms current practices in the multivariate aspect, as measured by the energy and variogram score. Still, our study shows that, even with advanced post‐processing, weather regime prediction becomes difficult beyond 14 days, which likely points towards intrinsic limits of predictability for daily/instantaneous regime forecasts. The proposed method can easily be applied to operational weather regime forecasts, offering a neat alternative for cost‐ and time‐efficient post‐processing of real‐time weather regime forecasts.
Abstract. Warm conveyor belts (WCBs) are coherent ascending airstreams in extratropical cyclones. They are a major source of moisture for the extratropical upper troposphere and lower stratosphere (UTLS), where moisture acts as a potent greenhouse gas and WCB-associated cirrus clouds contribute to cloud radiative forcing. However, the processes controlling WCB moisture transport and cloud properties are poorly characterised. Furthermore, recent studies have revealed (embedded) convection as a ubiquitous feature of WCBs, highlighting the importance of understanding their updraught and microphysical structure. We present a Lagrangian investigation of WCB moisture transport for a case from the WISE (Wave-driven ISentropic Exchange) campaign based on a convection-permitting simulation. Lagrangian non-dimensional metrics of the moisture budget suggest that the ascent timescale (τ600) strongly controls the end-of-ascent total moisture content, which is largest for slowly ascending trajectories (τ600≥20 h, 30 % of all WCB trajectories). This is due to relatively warm end-of-ascent temperatures and the strong temperature control on transported water vapour. Deviations from equilibrium water vapour condensate partitioning are largest for slow trajectories due to faster glaciation and lower ice crystal numbers. A local moisture transport minimum at intermediate τ600 results from a shift towards a riming-dominated precipitation formation pathway and decreasing outflow temperatures with decreasing τ600. The fastest trajectories (τ600≤5 h, 5 % of all WCB trajectories) transport the largest condensate mass to the UTLS due to less efficient condensate loss and produce the longest-lived outflow cirrus clouds. Models that parameterise convection may under-represent these processes, potentially impacting weather forecasts and climate predictions.
Abstract. Many fundamental concepts of synoptic-scale extratropical dynamics are based on the quasi-geostrophic equations of a dry atmosphere. This “dry dynamics” provides the essential understanding of, for example, the formation of extratropical cyclones and the propagation of Rossby waves and makes potential vorticity (PV) a materially conserved quantity. Classically, for extratropical weather systems, the importance of so-called “diabatic effects”, e.g. surface fluxes, phase changes of water in clouds, and radiation, has been regarded as secondary compared to the dry dynamical processes. As outlined in this review article, research during recent decades has modified this view of the role of diabatic processes. A combination of complementary research approaches revealed that the nonlinear dynamics of extratropical cyclones and upper-tropospheric Rossby waves is affected – in some cases strongly – by diabatic processes. Despite the violation of material PV conservation in the presence of diabatic processes, the concept of PV has been of utmost importance to identify and quantify the role of diabatic processes and to integrate their effects into the classical understanding based on dry dynamics. This review first summarises the theoretical concepts of diabatic PV modification, moist PV, and slantwise moist convection and provides a concise overview of early research on diabatic effects until the late 1970s. Two poorly predicted high-impact cyclones affecting eastern North America then triggered an impressive diversity of efforts to investigate the role of diabatic processes in rapid cyclone intensification in the last 2 decades of the 20th century. These research activities, including the development of sophisticated diagnostics, growing applications of the Lagrangian perspective, real-case and idealised numerical experiments, and dedicated field experiments, are reviewed in detail. This historical perspective provides insight about how societal relevance, international collaboration, technical development, and creative science contributed to establishing this important theme of dynamical meteorology. The second part of the review then more selectively outlines important achievements in the last 2 decades in our understanding of how diabatic effects, in particular those related to cloud microphysics, affect the structure, dynamics, and predictability of different types of extratropical cyclones and their mesoscale substructures, upper-tropospheric blocks, Rossby waves, and interactions. A novel aspect is the relevance of research on diabatic processes for climate change research. The review closes by highlighting important implications of investigating diabatic processes in extratropical weather systems for the broader field of weather and climate dynamics and its fundamentals and representation in numerical models.
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