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Hilke S. Lentink, Christian M. Grams, Michael Riemer, and Sarah C. Jones

Abstract

Extratropical transition (ET) can cause high-impact weather in midlatitude regions and therefore constitutes an ongoing threat at the end of a tropical cyclone’s (TC) life cycle. Most of the ET events occur over the ocean, but some TCs recurve and undergo ET along coastal regions; however, the latter category is less investigated. Typhoon Sinlaku (2008), for example, underwent ET along the southern coast of Japan. It was one of the typhoons that occurred during the T-PARC field campaign, providing unprecedented high-resolution observational data. Sinlaku is therefore an excellent case to investigate the impact of a coastal region, and in particular orography, on the evolution of ET. Here, observations from T-PARC are employed to verify high-resolution simulations of Sinlaku. In addition, a sensitivity simulation is performed with the orography of Japan removed. The presence of orography causes blocking of low-level, cool midlatitude air north of Japan. Without this inflow of cool air, ET is delayed. Only once Sinlaku moves away from the orographic barrier does the cool, dry environmental air penetrate equatorward, and ET continues. On a local scale, evaporatively cooled air from below Sinlaku’s asymmetric precipitation field could be advected toward the cyclone center when orography was favorable for it. Changes in the vortex structure, as known from mature TCs interacting with orography, were only minor due to the high translation speed during ET. This study corroborates that orography can impact ET by modulating both the synoptic-scale environmental conditions and the mesoscale cyclone structure during ET.

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Paolo Ghinassi, Georgios Fragkoulidis, and Volkmar Wirth

Abstract

Upper-tropospheric Rossby wave packets (RWPs) are important dynamical features, because they are often associated with weather systems and sometimes act as precursors to high-impact weather. The present work introduces a novel diagnostic to identify RWPs and to quantify their amplitude. It is based on the local finite-amplitude wave activity (LWA) of Huang and Nakamura, which is generalized to the primitive equations in isentropic coordinates. The new diagnostic is applied to a specific episode containing large-amplitude RWPs and compared with a more traditional diagnostic based on the envelope of the meridional wind. In this case, LWA provides a more coherent picture of the RWPs and their zonal propagation. This difference in performance is demonstrated more explicitly in the framework of an idealized barotropic model simulation, where LWA is able to follow an RWP into its fully nonlinear stage, including cutoff formation and wave breaking, while the envelope diagnostic yields reduced amplitudes in such situations.

Open access
Stephan Rasp and Sebastian Lerch

Abstract

Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear relationships between arbitrary predictor variables and forecast distribution parameters that are automatically learned in a data-driven way rather than requiring prespecified link functions. In a case study of 2-m temperature forecasts at surface stations in Germany, the neural network approach significantly outperforms benchmark postprocessing methods while being computationally more affordable. Key components to this improvement are the use of auxiliary predictor variables and station-specific information with the help of embeddings. Furthermore, the trained neural network can be used to gain insight into the importance of meteorological variables, thereby challenging the notion of neural networks as uninterpretable black boxes. Our approach can easily be extended to other statistical postprocessing and forecasting problems. We anticipate that recent advances in deep learning combined with the ever-increasing amounts of model and observation data will transform the postprocessing of numerical weather forecasts in the coming decade.

Open access
Andreas Schäfler, George Craig, Heini Wernli, Philippe Arbogast, James D. Doyle, Ron McTaggart-Cowan, John Methven, Gwendal Rivière, Felix Ament, Maxi Boettcher, Martina Bramberger, Quitterie Cazenave, Richard Cotton, Susanne Crewell, Julien Delanoë, Andreas Dörnbrack, André Ehrlich, Florian Ewald, Andreas Fix, Christian M. Grams, Suzanne L. Gray, Hans Grob, Silke Groß, Martin Hagen, Ben Harvey, Lutz Hirsch, Marek Jacob, Tobias Kölling, Heike Konow, Christian Lemmerz, Oliver Lux, Linus Magnusson, Bernhard Mayer, Mario Mech, Richard Moore, Jacques Pelon, Julian Quinting, Stephan Rahm, Markus Rapp, Marc Rautenhaus, Oliver Reitebuch, Carolyn A. Reynolds, Harald Sodemann, Thomas Spengler, Geraint Vaughan, Manfred Wendisch, Martin Wirth, Benjamin Witschas, Kevin Wolf, and Tobias Zinner

Abstract

The North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) explored the impact of diabatic processes on disturbances of the jet stream and their influence on downstream high-impact weather through the deployment of four research aircraft, each with a sophisticated set of remote sensing and in situ instruments, and coordinated with a suite of ground-based measurements. A total of 49 research flights were performed, including, for the first time, coordinated flights of the four aircraft: the German High Altitude and Long Range Research Aircraft (HALO), the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Dassault Falcon 20, the French Service des Avions Français Instrumentés pour la Recherche en Environnement (SAFIRE) Falcon 20, and the British Facility for Airborne Atmospheric Measurements (FAAM) BAe 146. The observation period from 17 September to 22 October 2016 with frequently occurring extratropical and tropical cyclones was ideal for investigating midlatitude weather over the North Atlantic. NAWDEX featured three sequences of upstream triggers of waveguide disturbances, as well as their dynamic interaction with the jet stream, subsequent development, and eventual downstream weather impact on Europe. Examples are presented to highlight the wealth of phenomena that were sampled, the comprehensive coverage, and the multifaceted nature of the measurements. This unique dataset forms the basis for future case studies and detailed evaluations of weather and climate predictions to improve our understanding of diabatic influences on Rossby waves and the downstream impacts of weather systems affecting Europe.

Open access
Volkmar Wirth, Michael Riemer, Edmund K. M. Chang, and Olivia Martius

Abstract

Rossby wave packets (RWPs) are Rossby waves for which the amplitude has a local maximum and decays to smaller values at larger distances. This review focuses on upper-tropospheric transient RWPs along the midlatitude jet stream. Their central characteristic is the propagation in the zonal direction as well as the transfer of wave energy from one individual trough or ridge to its downstream neighbor, a process called “downstream development.” These RWPs sometimes act as long-range precursors to extreme weather and presumably have an influence on the predictability of midlatitude weather systems. The paper reviews research progress in this area with an emphasis on developments during the last 15 years. The current state of knowledge is summarized including a discussion of the RWP life cycle as well as Rossby waveguides. Recent progress in the dynamical understanding of RWPs has been based, in part, on the development of diagnostic methods. These methods include algorithms to identify and track RWPs in an automated manner, which can be used to extract the climatological properties of RWPs. RWP dynamics have traditionally been investigated using the eddy kinetic energy framework; alternative approaches based on potential vorticity and wave activity fluxes are discussed and put into perspective with the more traditional approach. The different diagnostics are compared to each other and the strengths and weaknesses of individual methods are highlighted. A recurrent theme is the role of diabatic processes, which can be a source for forecast errors. Finally, the paper points to important open research questions and suggests avenues for future research.

Open access
Joël Arnault, Thomas Rummler, Florian Baur, Sebastian Lerch, Sven Wagner, Benjamin Fersch, Zhenyu Zhang, Noah Kerandi, Christian Keil, and Harald Kunstmann

Abstract

Precipitation is affected by soil moisture spatial variability. However, this variability is not well represented in atmospheric models that do not consider soil moisture transport as a three-dimensional process. This study investigates the sensitivity of precipitation to the uncertainty in the representation of terrestrial water flow. The tools used for this investigation are the Weather Research and Forecasting (WRF) Model and its hydrologically enhanced version, WRF-Hydro, applied over central Europe during April–October 2008. The model grid is convection permitting, with a horizontal spacing of 2.8 km. The WRF-Hydro subgrid employs a 280-m resolution to resolve lateral terrestrial water flow. A WRF/WRF-Hydro ensemble is constructed by modifying the parameter controlling the partitioning between surface runoff and infiltration and by varying the planetary boundary layer (PBL) scheme. This ensemble represents terrestrial water flow uncertainty originating from the consideration of resolved lateral flow, terrestrial water flow uncertainty in the vertical direction, and turbulence parameterization uncertainty. The uncertainty of terrestrial water flow noticeably increases the normalized ensemble spread of daily precipitation where topography is moderate, surface flux spatial variability is high, and the weather regime is dominated by local processes. The adjusted continuous ranked probability score shows that the PBL uncertainty improves the skill of an ensemble subset in reproducing daily precipitation from the E-OBS observational product by 16%–20%. In comparison to WRF, WRF-Hydro improves this skill by 0.4%–0.7%. The reproduction of observed daily discharge with Nash–Sutcliffe model efficiency coefficients generally above 0.3 demonstrates the potential of WRF-Hydro in hydrological science.

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Marlene Baumgart, Michael Riemer, Volkmar Wirth, Franziska Teubler, and Simon T. K. Lang

Abstract

Synoptic-scale error growth near the tropopause is investigated from a process-based perspective. Following previous work, a potential vorticity (PV) error tendency equation is derived and partitioned into individual contributions to yield insight into the processes governing error growth near the tropopause. Importantly, we focus here on the further amplification of preexisting errors and not on the origin of errors. The individual contributions to error growth are quantified in a case study of a 6-day forecast. In this case, localized mesoscale error maxima have formed by forecast day 2. These maxima organize into a wavelike pattern and reach the Rossby wave scale around forecast day 6. Error growth occurs most prominently within the Atlantic and Pacific Rossby wave patterns. In our PV framework, the error growth is dominated by the contribution of upper-level, near-tropopause PV anomalies (near-tropopause dynamics). Significant contributions from upper-tropospheric divergent flow (prominently associated with latent heat release below) and lower-tropospheric anomalies [tropospheric-deep (i.e., baroclinic) interaction] are associated with a misrepresentation of the surface cyclone development in the forecast. These contributions are, in general, of smaller importance to error growth than near-tropopause dynamics. This result indicates that the mesoscale errors generated near the tropopause do not primarily project on differences in the subsequent baroclinic growth, but instead directly project on the tropopause evolution and amplify because of differences in the nonlinear Rossby wave dynamics.

Open access
Peter Vogel, Peter Knippertz, Andreas H. Fink, Andreas Schlueter, and Tilmann Gneiting

Abstract

Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, the performance of nine operational global ensemble prediction systems (EPSs) is analyzed relative to climatology-based forecasts for 1–5-day accumulated precipitation based on the monsoon seasons during 2007–14 for three regions within northern tropical Africa. To assess the full potential of raw ensemble forecasts across spatial scales, state-of-the-art statistical postprocessing methods were applied in the form of Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), and results were verified against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated and unreliable, and often underperform relative to climatology, independently of region, accumulation time, monsoon season, and ensemble. The differences between raw ensemble and climatological forecasts are large and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable, and strongly improve on the raw ensembles but, somewhat disappointingly, typically do not outperform climatology. Most EPSs exhibit slight improvements over the period 2007–14, but overall they have little added value compared to climatology. The suspicion is that parameterization of convection is a potential cause for the sobering lack of ensemble forecast skill in a region dominated by mesoscale convective systems.

Open access
Stephan Rasp, Tobias Selz, and George C. Craig

Abstract

The statistical theory of convective variability developed by Craig and Cohen in 2006 has provided a promising foundation for the design of stochastic parameterizations. The simplifying assumptions of this theory, however, were made with tropical equilibrium convection in mind. This study investigates the predictions of the statistical theory in real-weather case studies of nonequilibrium summertime convection over land. For this purpose, a convection-permitting ensemble is used in which all members share the same large-scale weather conditions but the convection is displaced using stochastic boundary layer perturbations. The results show that the standard deviation of the domain-integrated mass flux is proportional to the square root of its mean over a wide range of scales. This confirms the general applicability and scale adaptivity of the Craig and Cohen theory for complex weather. However, clouds tend to cluster on scales of around 100 km, particularly in the morning and evening. This strongly impacts the theoretical predictions of the variability, which does not include clustering. Furthermore, the mass flux per cloud closely follows an exponential distribution if all clouds are considered together and if overlapping cloud objects are separated. The nonseparated cloud mass flux distribution resembles a power law. These findings support the use of the theory for stochastic parameterizations but also highlight areas for improvement.

Open access
Thomas Engel, Andreas H. Fink, Peter Knippertz, Gregor Pante, and Jan Bliefernicht

Abstract

Two extreme, high-impact events of heavy rainfall and severe floods in West African urban areas (Ouagadougou on 1 September 2009 and Dakar on 26 August 2012) are investigated with respect to their atmospheric causes and statistical return periods. In terms of the synoptic–convective dynamics, the Ouagadougou case is truly extraordinary. A succession of two slow-moving African easterly waves (AEWs) caused record-breaking values of tropospheric moisture. The second AEW, one of the strongest in recent decades, provided the synoptic forcing for the nighttime genesis of mesoscale convective systems (MCSs). Ouagadougou was hit by two MCSs within 6 h, as the strong convergence and rotation in the AEW-related vortex allowed a swift moisture refueling. An AEW was also instrumental in the overnight development of MCSs in the Dakar case, but neither the AEW vortex nor the tropospheric moisture content was as exceptional as in the Ouagadougou case. Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation data show some promise in estimating centennial return values (RVs) using the “peak over threshold” approach with a generalized Pareto distribution fit, although indications for errors in estimating extreme rainfall over the arid Sahel are found. In contrast, the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) dataset seems less suitable for this purpose despite the longer record. Notably, the Ouagadougou event demonstrates that highly unusual dynamical developments can create extremes well outside of RV estimates from century-long rainfall observations. Future research will investigate whether such developments may become more frequent in a warmer climate.

Open access