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Julia H. Keller, Christian M. Grams, Michael Riemer, Heather M. Archambault, Lance Bosart, James D. Doyle, Jenni L. Evans, Thomas J. Galarneau Jr., Kyle Griffin, Patrick A. Harr, Naoko Kitabatake, Ron McTaggart-Cowan, Florian Pantillon, Julian F. Quinting, Carolyn A. Reynolds, Elizabeth A. Ritchie, Ryan D. Torn, and Fuqing Zhang


The extratropical transition (ET) of tropical cyclones often has an important impact on the nature and predictability of the midlatitude flow. This review synthesizes the current understanding of the dynamical and physical processes that govern this impact and highlights the relationship of downstream development during ET to high-impact weather, with a focus on downstream regions. It updates a previous review from 2003 and identifies new and emerging challenges and future research needs. First, the mechanisms through which the transitioning cyclone impacts the midlatitude flow in its immediate vicinity are discussed. This “direct impact” manifests in the formation of a jet streak and the amplification of a ridge directly downstream of the cyclone. This initial flow modification triggers or amplifies a midlatitude Rossby wave packet, which disperses the impact of ET into downstream regions (downstream impact) and may contribute to the formation of high-impact weather. Details are provided concerning the impact of ET on forecast uncertainty in downstream regions and on the impact of observations on forecast skill. The sources and characteristics of the following key features and processes that may determine the manifestation of the impact of ET on the midlatitude flow are discussed: the upper-tropospheric divergent outflow, mainly associated with latent heat release in the troposphere below, and the phasing between the transitioning cyclone and the midlatitude wave pattern. Improving the representation of diabatic processes during ET in models and a climatological assessment of the ET’s impact on downstream high-impact weather are examples for future research directions.

Open access
Markus Gross, Hui Wan, Philip J. Rasch, Peter M. Caldwell, David L. Williamson, Daniel Klocke, Christiane Jablonowski, Diana R. Thatcher, Nigel Wood, Mike Cullen, Bob Beare, Martin Willett, Florian Lemarié, Eric Blayo, Sylvie Malardel, Piet Termonia, Almut Gassmann, Peter H. Lauritzen, Hans Johansen, Colin M. Zarzycki, Koichi Sakaguchi, and Ruby Leung


Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions.

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


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
Clark Evans, Kimberly M. Wood, Sim D. Aberson, Heather M. Archambault, Shawn M. Milrad, Lance F. Bosart, Kristen L. Corbosiero, Christopher A. Davis, João R. Dias Pinto, James Doyle, Chris Fogarty, Thomas J. Galarneau Jr., Christian M. Grams, Kyle S. Griffin, John Gyakum, Robert E. Hart, Naoko Kitabatake, Hilke S. Lentink, Ron McTaggart-Cowan, William Perrie, Julian F. D. Quinting, Carolyn A. Reynolds, Michael Riemer, Elizabeth A. Ritchie, Yujuan Sun, and Fuqing Zhang


Extratropical transition (ET) is the process by which a tropical cyclone, upon encountering a baroclinic environment and reduced sea surface temperature at higher latitudes, transforms into an extratropical cyclone. This process is influenced by, and influences, phenomena from the tropics to the midlatitudes and from the meso- to the planetary scales to extents that vary between individual events. Motivated in part by recent high-impact and/or extensively observed events such as North Atlantic Hurricane Sandy in 2012 and western North Pacific Typhoon Sinlaku in 2008, this review details advances in understanding and predicting ET since the publication of an earlier review in 2003. Methods for diagnosing ET in reanalysis, observational, and model-forecast datasets are discussed. New climatologies for the eastern North Pacific and southwest Indian Oceans are presented alongside updates to western North Pacific and North Atlantic Ocean climatologies. Advances in understanding and, in some cases, modeling the direct impacts of ET-related wind, waves, and precipitation are noted. Improved understanding of structural evolution throughout the transformation stage of ET fostered in large part by novel aircraft observations collected in several recent ET events is highlighted. Predictive skill for operational and numerical model ET-related forecasts is discussed along with environmental factors influencing posttransition cyclone structure and evolution. Operational ET forecast and analysis practices and challenges are detailed. In particular, some challenges of effective hazard communication for the evolving threats posed by a tropical cyclone during and after transition are introduced. This review concludes with recommendations for future work to further improve understanding, forecasts, and hazard communication.

Open access
Craig S. Schwartz and Ryan A. Sobash


“Neighborhood approaches” have been used in two primary ways to postprocess and verify high-resolution ensemble output. While the two methods appear deceptively similar, they define events over different spatial scales and yield fields with different interpretations: the first produces probabilities interpreted as likelihood of event occurrence at the grid scale, while the second produces probabilities of event occurrence over spatial scales larger than the grid scale. Unfortunately, some studies have confused the two methods, while others did not acknowledge multiple possibilities of neighborhood approach application and simply stated, “a neighborhood approach was applied” without supporting details. Thus, this paper reviews applications of neighborhood approaches to convection-allowing ensembles in hopes of clarifying the two methods and their different event definitions. Then, using real data, it is demonstrated how the two approaches can yield statistically significantly different objective conclusions about model performance, underscoring the critical need for thorough descriptions of how neighborhood approaches are implemented and events are defined. The authors conclude by providing some recommendations for application of neighborhood approaches to convection-allowing ensembles.

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P. L. Houtekamer and Fuqing Zhang


This paper reviews the development of the ensemble Kalman filter (EnKF) for atmospheric data assimilation. Particular attention is devoted to recent advances and current challenges. The distinguishing properties of three well-established variations of the EnKF algorithm are first discussed. Given the limited size of the ensemble and the unavoidable existence of errors whose origin is unknown (i.e., system error), various approaches to localizing the impact of observations and to accounting for these errors have been proposed. However, challenges remain; for example, with regard to localization of multiscale phenomena (both in time and space). For the EnKF in general, but higher-resolution applications in particular, it is desirable to use a short assimilation window. This motivates a focus on approaches for maintaining balance during the EnKF update. Also discussed are limited-area EnKF systems, in particular with regard to the assimilation of radar data and applications to tracking severe storms and tropical cyclones. It seems that relatively less attention has been paid to optimizing EnKF assimilation of satellite radiance observations, the growing volume of which has been instrumental in improving global weather predictions. There is also a tendency at various centers to investigate and implement hybrid systems that take advantage of both the ensemble and the variational data assimilation approaches; this poses additional challenges and it is not clear how it will evolve. It is concluded that, despite more than 10 years of operational experience, there are still many unresolved issues that could benefit from further research.


  • Introduction...4490

  • Popular flavors of the EnKF algorithm...4491

    1. General description...4491

    2. Stochastic and deterministic filters...4492

      1. The stochastic filter...4492

      2. The deterministic filter...4492

    3. Sequential or local filters...4493

      1. Sequential ensemble Kalman filters...4493

      2. The local ensemble transform Kalman filter...4494

    4. Extended state vector...4494

    5. Issues for the development of algorithms...4495

  • Use of small ensembles...4495

    1. Monte Carlo methods...4495

    2. Validation of reliability...4497

    3. Use of group filters with no inbreeding...4498

    4. Sampling error due to limited ensemble size: The rank problem...4498

    5. Covariance localization...4499

      1. Localization in the sequential filter...4499

      2. Localization in the LETKF...4499

      3. Issues with localization...4500

    6. Summary...4501

  • Methods to increase ensemble spread...4501

    1. Covariance inflation...4501

      1. Additive inflation...4501

      2. Multiplicative inflation...4502

      3. Relaxation to prior ensemble information...4502

      4. Issues with inflation...4503

    2. Diffusion and truncation...4503

    3. Error in physical parameterizations...4504

      1. Physical tendency perturbations...4504

      2. Multimodel, multiphysics, and multiparameter approaches...4505

      3. Future directions...4505

    4. Realism of error sources...4506

  • Balance and length of the assimilation window...4506

    1. The need for balancing methods...4506

    2. Time-filtering methods...4506

    3. Toward shorter assimilation windows...4507

    4. Reduction of sources of imbalance...4507

  • Regional data assimilation...4508

    1. Boundary conditions and consistency across multiple domains...4509

    2. Initialization of the starting ensemble...4510

    3. Preprocessing steps for radar observations...4510

    4. Use of radar observations for convective-scale analyses...4511

    5. Use of radar observations for tropical cyclone analyses...4511

    6. Other issues with respect to LAM data assimilation...4511

  • The assimilation of satellite observations...4512

    1. Covariance localization...4512

    2. Data density...4513

    3. Bias-correction procedures...4513

    4. Impact of covariance cycling...4514

    5. Assumptions regarding observational error...4514

    6. Recommendations regarding satellite observations...4515

  • Computational aspects...4515

    1. Parameters with an impact on quality...4515

    2. Overview of current parallel algorithms...4516

    3. Evolution of computer architecture...4516

    4. Practical issues...4517

    5. Approaching the gray zone...4518

    6. Summary...4518

  • Hybrids with variational and EnKF components...4519

    1. Hybrid background error covariances...4519

    2. E4DVar with the α control variable...4519

    3. Not using linearized models with 4DEnVar...4520

    4. The hybrid gain algorithm...4521

    5. Open issues and recommendations...4521

  • Summary and discussion...4521

    1. Stochastic or deterministic filters...4522

    2. The nature of system error...4522

    3. Going beyond the synoptic scales...4522

    4. Satellite observations...4523

    5. Hybrid systems...4523

    6. Future of the EnKF...4523


Types of Filter Divergence...4524

  1. Classical filter divergence...4524

  2. Catastrophic filter divergence...4524

    APPENDIX B...4524

    Systems Available for Download...4524


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Bogdan Antonescu, David M. Schultz, Fiona Lomas, and Thilo Kühne


A synthesis of tornado observations across Europe between 1800 and 2014 is used to produce a pan-European climatology. Based on regional tornado-occurrence datasets and articles published in peer-reviewed journals, the evolution and the major contributions to tornado databases for 30 European countries were analyzed. Between 1800 and 2014, 9563 tornadoes were reported in Europe with an increase from 8 tornadoes per year between 1800 and 1850 to 242 tornadoes per year between 2000 and 2014. The majority of the reports came from northern, western, and southern Europe, and to a lesser extent from eastern Europe where tornado databases were developed after the 1990s. Tornadoes occur throughout the year with a maximum in June–August for most of Europe and in August–November for southern Europe. Tornadoes occur more frequently between 1300 and 1500 UTC over most of Europe and between 0900 and 1100 UTC over southern Europe. Where intensity was known, 74.7% of tornadoes were classified as F0 and F1, 24.5% as F2 and F3, and 0.8% as F4 and F5. Comparing this intensity distribution over Europe with the intensity distribution for tornadoes in the United States shows that tornadoes over western and eastern Europe are more likely to be supercellular tornadoes and those over northern and southern Europe are likely to also include nonsupercellular tornadoes.

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Huw C. Davies


A two-component study is undertaken of the classical quasigeostrophic (QG) omega equation. First, a reappraisal is undertaken of extant formulations of the equation’s so-called forcing function. It pinpoints shortcomings of various formulations and prompts consideration of alternative forms. Particular consideration is given to the contribution of flow deformation to the forcing function, and to the role of the advection of the geostrophic flow by the thermal wind (the R vector). The latter is closely related to the Q vector, the horizontal component of the ageostrophic vorticity, and the forcing function itself. The reexamination promotes further examination of the physical interpretation and diagnostic use of the omega equation particularly for assessing richly structured subsynoptic flow features.

Second, consideration is given to the dynamics associated with the equation and its more general utility. It is shown that the R vector is intrinsic to a quasigeostrophic cascade to finer-scaled flow, and that a fundamental feature of the QG omega equation—the in-phase relationship between cloud-diabatic heating and the attendant vertical velocity—has important potential ramifications for the assimilation of data in numerical weather prediction (NWP) models. Finally, it is shown that, in the context of considering NWP model output, mild generalizations of the quasigeostrophic R vector retain interpretative value for flow settings beyond geostrophy and warrant consideration when addressing some contemporary NWP challenges.

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Robert Wood


This paper reviews the current knowledge of the climatological, structural, and organizational aspects of stratocumulus clouds and the physical processes controlling them. More of Earth’s surface is covered by stratocumulus clouds than by any other cloud type making them extremely important for Earth’s energy balance, primarily through their reflection of solar radiation. They are generally thin clouds, typically occupying the upper few hundred meters of the planetary boundary layer (PBL), and they preferably occur in shallow PBLs that are readily coupled by turbulent mixing to the surface moisture supply. Thus, stratocumuli favor conditions of strong lower-tropospheric stability, large-scale subsidence, and a ready supply of surface moisture; therefore, they are common over the cooler regions of subtropical and midlatitude oceans where their coverage can exceed 50% in the annual mean. Convective instability in stratocumulus clouds is driven primarily by the emission of thermal infrared radiation from near the cloud tops and the resulting turbulence circulations are enhanced by latent heating in updrafts and cooling in downdrafts. Turbulent eddies and evaporative cooling drives entrainment at the top of the stratocumulus-topped boundary layer (STBL), which is stronger than it would be in the absence of cloud, and this tends to result in a deepening of the STBL over time. Many stratocumulus clouds produce some drizzle through the collision–coalescence process, but thicker clouds drizzle more readily, which can lead to changes in the dynamics of the STBL that favor increased mesoscale variability, stratification of the STBL, and in some cases cloud breakup. Feedbacks between radiative cooling, precipitation formation, turbulence, and entrainment help to regulate stratocumulus. Although stratocumulus is arguably the most well-understood cloud type, it continues to challenge understanding. Indeed, recent field studies demonstrate that marine stratocumulus precipitate more strongly, and entrain less, than was previously thought, and display an organizational complexity much larger than previously imagined. Stratocumulus clouds break up as the STBL deepens and it becomes more difficult to maintain buoyant production of turbulence through the entire depth of the STBL.

Stratocumulus cloud properties are sensitive to the concentration of aerosol particles and therefore anthropogenic pollution. For a given cloud thickness, polluted clouds tend to produce more numerous and smaller cloud droplets, greater cloud albedo, and drizzle suppression. In addition, cloud droplet size also affects the time scale for evaporation–entrainment interactions and sedimentation rate, which together with precipitation changes can affect turbulence and entrainment. Aerosols are themselves strongly modified by physical processes in stratocumuli, and these two-way interactions may be a key driver of aerosol concentrations over the remote oceans. Aerosol–stratocumulus interactions are therefore one of the most challenging frontiers in cloud–climate research. Low-cloud feedbacks are also a leading cause of uncertainty in future climate prediction because even small changes in cloud coverage and thickness have a major impact on the radiation budget. Stratocumuli remain challenging to represent in climate models since their controlling processes occur on such small scales. A better understanding of stratocumulus dynamics, particularly entrainment processes and mesoscale variability, will be required to constrain these feedbacks.


  1. Introduction...2

  2. Climatology of stratocumulus...4

    1. Annual mean...4

    2. Temporal variability...6

    3. Spatial scales of organization1...0

  3. The stratocumulus-topped boundary layer...11

    1. Vertical structure of the STBL...11

    2. Liquid water...14

    3. Entrainment interfacial layer...15

  4. Physical processes controlling stratocumulus...16

    1. Radiative driving of stratocumulus...16

    2. Turbulence...21

    3. Surface fluxes...24

    4. Entrainment...25

    5. Precipitation...26

  5. Microphysics...27

    1. Cloud droplet concentration and controlling factors...27

    2. Microphysics of precipitation formation...29

  6. Interactions between physical processes...32

    1. Maintenance and regulating feedbacks...32

    2. Microphysical–macrophysical interactions...34

    3. Interactions between the STBL and large-scale meteorology...35

    4. Formation...36

    5. Dissipation and transition to other cloud types...36

  7. Summary...40

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Zhiyong Meng and Fuqing Zhang


Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance and is best known by varying forms of ensemble Kalman filters (EnKFs). The EnKF has recently emerged as one of the primary alternatives to the variational data assimilation methods widely used in both global and limited-area numerical weather prediction models. In addition to comparing the EnKF with variational methods, this article reviews recent advances and challenges in the development and applications of the EnKF, including its hybrid with variational methods, in limited-area models that resolve weather systems from convective to meso- and regional scales.

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