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Richard J. Keane, George C. Craig, Christian Keil, and Günther Zängl

Abstract

The emergence of numerical weather prediction and climate models with multiple or variable resolutions requires that their parameterizations adapt correctly, with consistent increases in variability as resolution increases. In this study, the stochastic convection scheme of Plant and Craig is tested in the Icosahedral Nonhydrostatic GCM (ICON), which is planned to be used with multiple resolutions. The model is run in an aquaplanet configuration with horizontal resolutions of 160, 80, and 40 km, and frequency histograms of 6-h accumulated precipitation amount are compared. Precipitation variability is found to increase substantially at high resolution, in contrast to results using two reference deterministic schemes in which the distribution is approximately independent of resolution. The consistent scaling of the stochastic scheme with changing resolution is demonstrated by averaging the precipitation fields from the 40- and 80-km runs to the 160-km grid, showing that the variability is then the same as that obtained from the 160-km model run. It is shown that upscale averaging of the input variables for the convective closure is important for producing consistent variability at high resolution.

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Mirjam Hirt, Stephan Rasp, Ulrich Blahak, and George C. Craig

Abstract

Kilometer-scale models allow for an explicit simulation of deep convective overturning but many subgrid processes that are crucial for convective initiation are still poorly represented. This leads to biases such as insufficient convection triggering and late peak of summertime convection. A physically based stochastic perturbation scheme (PSP) for subgrid processes has been proposed (Kober and Craig) that targets the coupling between subgrid turbulence and resolved convection. The first part of this study presents four modifications to this PSP scheme for subgrid turbulence: an autoregressive, continuously evolving random field; a limitation of the perturbations to the boundary layer that removes artificial convection at night; a mask that turns off perturbations in precipitating columns to retain coherent structures; and nondivergent wind perturbations that drastically increase the effectiveness of the vertical velocity perturbations. In a revised version, PSP2, the combined modifications retain the physically based coupling to the boundary layer scheme of the original scheme while removing undesirable side effects. This has the potential to improve predictions of convective initiation in kilometer-scale models while minimizing other biases. The second part of the study focuses on perturbations to account for convective initiation by subgrid orography. Here the mechanical lifting effect is modeled by introducing vertical and horizontal wind perturbations of an orographically induced gravity wave. The resulting perturbations lead to enhanced convective initiation over mountainous terrain. However, the total benefit of this scheme is unclear and we do not adopt the scheme in our revised configuration.

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Kirstin Kober, Annette M. Foerster, and George C. Craig

Abstract

Stochastic parameterizations allow the representation of the small-scale variability of parameterized physical processes. This study investigates whether additional variability introduced by a stochastic convection parameterization leads to improvements in the precipitation forecasts. Forecasts are calculated with two different ensembles: one considering large-scale and convective variability with the stochastic Plant–Craig convection parameterization and one considering only large-scale variability with the standard Tiedtke convection parameterization. The forecast quality of both ensembles is evaluated in comparison with radar observations for two case studies with weak and strong synoptic forcing of convection and measured with neighborhood and probabilistic verification methods. The skill of the ensemble based on the Plant–Craig convection parameterization relative to the ensemble with the Tiedtke parameterization strongly depends on the synoptic situation in which convection occurs. In the weak forcing case, where the convective precipitation is highly intermittent, the ensemble based on the stochastic parameterization is superior, but the scheme produces too much small-scale variability in the strong forcing case. In the future, the degree of stochastic variability could be tuned, and these results show that parameters should be chosen in a regime-dependent manner.

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Anna Agustí-Panareda, Suzanne L. Gray, George C. Craig, and Chris Thorncroft

Abstract

The transition that a tropical cyclone experiences as it moves into the extratropical environment (known as extratropical transition) can result in the decay or intensification of a baroclinic cyclone. The extratropical transition (ET) of Tropical Cyclone Lili (1996) in the North Atlantic resulted in a moderate extratropical development of a baroclinic cyclone. The impact of Lili in the extratropical development that occurred during its ET is investigated. Numerical experiments are performed using potential vorticity inversion and the Met Office Unified Model to forecast the extratropical development with and without the tropical cyclone in the initial conditions. In contrast with other case studies in the literature, Lili is shown to play a crucial role during its ET in the development of a baroclinic cyclone that occurred in the same region. A hypothesis of the possible scenarios of ET is presented that links the case-to-case variability of ET case studies in the literature with a classification of the life cycles of baroclinic cyclones.

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Kevin Bachmann, Christian Keil, George C. Craig, Martin Weissmann, and Christian A. Welzbacher

Abstract

We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

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David L. A. Flack, Suzanne L. Gray, Robert S. Plant, Humphrey W. Lean, and George C. Craig

Abstract

Convection-permitting ensembles have led to improved forecasts of many atmospheric phenomena. However, to fully utilize these forecasts the dependence of predictability on synoptic conditions needs to be understood. In this study, convective regimes are diagnosed based on a convective time scale that identifies the degree to which convection is in equilibrium with the large-scale forcing. Six convective cases are examined in a convection-permitting ensemble constructed using the Met Office Unified Model. The ensemble members were generated using small-amplitude buoyancy perturbations added into the boundary layer, which can be considered to represent turbulent fluctuations close to the grid scale. Perturbation growth is shown to occur on different scales with an order of magnitude difference between the regimes [O(1) km for cases closer to nonequilibrium convection and O(10) km for cases closer to equilibrium convection]. This difference reflects the fact that cell locations are essentially random in the equilibrium events after the first 12 h of the forecast, indicating a more rapid upscale perturbation growth compared to the nonequilibrium events. Furthermore, large temporal variability is exhibited in all perturbation growth diagnostics for the nonequilibrium regime. Two boundary condition–driven cases are also considered and show similar characteristics to the nonequilibrium cases, implying that caution is needed to interpret the time scale when initiation is not within the domain. Further understanding of perturbation growth within the different regimes could lead to a better understanding of where ensemble design improvements can be made beyond increasing the model resolution and could improve interpretation of forecasts.

Open access
Marlene Baumgart, Paolo Ghinassi, Volkmar Wirth, Tobias Selz, George C. Craig, and Michael Riemer

Abstract

Two diagnostics based on potential vorticity and the envelope of Rossby waves are used to investigate upscale error growth from a dynamical perspective. The diagnostics are applied to several cases of global, real-case ensemble simulations, in which the only difference between the ensemble members lies in the random seed of the stochastic convection scheme. Based on a tendency equation for the enstrophy error, the relative importance of individual processes to enstrophy-error growth near the tropopause is quantified. After the enstrophy error is saturated on the synoptic scale, the envelope diagnostic is used to investigate error growth up to the planetary scale. The diagnostics reveal distinct stages of the error growth: in the first 12 h, error growth is dominated by differences in the convection scheme. Differences in the upper-tropospheric divergent wind then project these diabatic errors into the tropopause region (day 0.5–2). The subsequent error growth (day 2–14.5) is governed by differences in the nonlinear near-tropopause dynamics. A fourth stage of the error growth is found up to 18 days when the envelope diagnostic indicates error growth from the synoptic up to the planetary scale. Previous ideas of the multiscale nature of upscale error growth are confirmed in general. However, a novel interpretation of the governing processes is provided. The insight obtained into the dynamics of upscale error growth may help to design representations of uncertainty in operational forecast models and to identify atmospheric conditions that are intrinsically prone to large error amplification.

Open access
Yuefei Zeng, Tijana Janjić, Alberto de Lozar, Stephan Rasp, Ulrich Blahak, Axel Seifert, and George C. Craig

Abstract

Different approaches for representing model error due to unresolved scales and processes are compared in convective-scale data assimilation, including the physically based stochastic perturbation (PSP) scheme for turbulence, an advanced warm bubble approach that automatically detects and triggers absent convective cells, and additive noise based on model truncation error. The analysis of kinetic energy spectrum guides the understanding of differences in precipitation forecasts. It is found that the PSP scheme results in more ensemble spread in assimilation cycles, but its effects on the root-mean-square error (RMSE) are neutral. This leads to positive impacts on precipitation forecasts that last up to three hours. The warm bubble technique does not create more spread, but is effective in reducing the RMSE, and improving precipitation forecasts for up to 3 h. The additive noise approach contributes greatly to ensemble spread, but it results in a larger RMSE during assimilation cycles. Nevertheless, it considerably improves the skill of precipitation forecasts up to 6 h. Combining the additive noise with either the PSP scheme or the warm bubble technique reduces the RMSE within cycles and improves the skill of the precipitation forecasts, with the latter being more beneficial.

Open access
Christopher J. Cox, Robert S. Stone, David C. Douglas, Diane M. Stanitski, George J. Divoky, Geoff S. Dutton, Colm Sweeney, J. Craig George, and David U. Longenecker

Abstract

Linkages between atmospheric, ecological, and biogeochemical variables in the changing Arctic are analyzed using long-term measurements near Utqiaġvik (formerly Barrow), Alaska. Two key variables are the date when snow disappears in spring, as determined primarily by atmospheric dynamics, precipitation, air temperature, winter snow accumulation, and cloud cover, and the date of onset of snowpack in autumn that is additionally influenced by ocean temperature and sea ice extent. In 2015 and 2016 the snow melted early at Utqiaġvik owing mainly to anomalous warmth during May of both years attributed to atmospheric circulation patterns, with 2016 having the record earliest snowmelt. These years are discussed in the context of a 115-yr snowmelt record at Utqiaġvik with a trend toward earlier melting since the mid-1970s (–2.86 days decade–1, 1975–2016). At nearby Cooper Island, where a colony of seabirds, black guillemots, have been monitored since 1975, timing of egg laying is correlated with Utqiaġvik snowmelt with 2015 and 2016 being the earliest years in the 42-yr record. Ice out at a nearby freshwater lagoon is also correlated with Utqiaġvik snowmelt. The date when snow begins to accumulate in autumn at Utqiaġvik shows a trend toward later dates (+4.6 days decade–1, 1975–2016), with 2016 being the latest on record. The relationships between the lengthening snow-free season and regional phenology, soil temperatures, fluxes of gases from the tundra, and to regional sea ice conditions are discussed. Better understanding of these interactions is needed to predict the annual snow cycles in the region at seasonal to decadal scales and to anticipate coupled environmental responses.

Open access
George C. Craig, Andreas H. Fink, Corinna Hoose, Tijana Janjić, Peter Knippertz, Audine Laurian, Sebastian Lerch, Bernhard Mayer, Annette Miltenberger, Robert Redl, Michael Riemer, Kirsten I. Tempest, and Volkmar Wirth

Abstract

Prediction of weather is a main goal of atmospheric science. Its importance to society is growing continuously due to factors such as vulnerability to natural disasters, the move to renewable energy sources, and the risks of climate change. But prediction is also a major scientific challenge due to the inherently limited predictability of a chaotic atmosphere, and has led to a revolution in forecasting methods as we have moved to probabilistic prediction. These changes provide the motivation for Waves to Weather (W2W), a major national research program in Germany with three main university partners in Munich, Mainz, and Karlsruhe. We are currently in the second 4-year phase of our planned duration of 12 years and employ 36 doctoral and post-doctoral scientists. In the context of this large program, we address what we have identified to be the most important and challenging scientific questions in predictability of weather, namely upscale error growth, errors associated with cloud processes, and probabilistic prediction of high impact weather. This paper presents some key results of the first phase of W2W and discusses how they have influenced our understanding of predictability. The key role of interdisciplinary research linking atmospheric scientists with experts in visualization, statistics, numerical analysis, and inverse methods will be highlighted. To ensure a lasting impact on research in our field in Germany and internationally, we have instituted innovative programs for training and support of early career scientists, and to support education, equal opportunities, and outreach, which are also described here.

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