Abstract

The scientific community has long acknowledged the importance of high-temporal-resolution radar observations to advance science research and improve high-impact weather prediction. Development of innovative rapid-scan radar technologies over the past two decades has enabled radar volume scans of 10–60 s compared to 3–5 min with traditional parabolic dish research radars and the WSR-88D radar network. This review examines the impact of rapid-scan radar technology, defined as radars collecting volume scans in 1 min or less, on atmospheric science research spanning different subdisciplines and evaluates the strengths and weaknesses of the use of rapid-scan radars. In particular, a significant body of literature has accumulated for tornado and severe thunderstorm research and forecasting applications, in addition to a growing number of studies of convection. Convection research has benefited substantially from more synchronous vertical views, but could benefit more substantially by leveraging multi-Doppler wind retrievals and complementary in situ and remote sensors. In addition, several years of forecast evaluation studies are synthesized from radar testbed experiments, and the benefits of assimilating rapid-scan radar observations are analyzed. Although the current body of literature reflects the considerable utility of rapid-scan radars to science research, a weakness is that limited advancements in understanding of the physical mechanisms behind observed features have been enabled. There is considerable opportunity to bridge the gap in physical understanding with the current technology using coordinated efforts to include rapid-scan radars in field campaigns and expanding the breadth of meteorological phenomena studied.

Abstract

Monthly Weather Review, one of the oldest continuously published meteorological journals in the world, publishes its 150th volume this year. In January 1873, the U.S. War Department’s Army Signal Service began producing this monthly report summarizing weather across the United States. Its first issue consisted of a one-page narrative of weather conditions and storms and one chart depicting tracks of low pressure centers during that month. In 1891, Monthly Weather Review continued as a government publication with the transfer of the nation’s weather service from the military to the newly established U.S. Weather Bureau. Over time and sometimes erratically, it grew into a scientific journal. In 1974, Monthly Weather Review was transferred to the American Meteorological Society, who continues to publish it to this day (although a 2003 proposal might have ended it). This Historical Review discusses some of the journal’s history and impact, as well as its legacy. This review also compiles for the first time a complete list of Monthly Weather Review editors. The research published within Monthly Weather Review has included highly cited, ground-breaking articles on weather and climate phenomena (e.g., extratropical and tropical cyclones, El Niño–Southern Oscillation, Madden–Julian oscillation), general circulation modeling, and numerical weather prediction. The data published in the early issues have been used—and continue to be used to this day—for a variety of applied research and historical analysis purposes. The composition and content of Monthly Weather Review have changed over the past century and a half, continuing to evolve with the modern publishing landscape, with color figures at no additional cost, open-access articles, open data, and, in the near future, embedded figure animations.

Abstract

About 140 years ago, Lord Kelvin derived the equations describing waves that travel along the axis of concentrated vortices such as tornadoes. Although Kelvin’s vortex waves, also known as centrifugal waves, feature prominently in the engineering and fluid dynamics literature, they have not attracted as much attention in the field of atmospheric science. To remedy this circumstance, Kelvin’s elegant derivation is retraced, and slightly generalized, to obtain solutions for a hierarchy of vortex flows that model basic features of tornado-like vortices. This treatment seeks to draw attention to the important work that Lord Kelvin did in this field, and reveal the remarkably rich structure and dynamics of these waves. Kelvin’s solutions help explain the vortex breakdown phenomenon routinely observed in modeled tornadoes, and it is shown that his work is compatible with the widely used criticality condition put forth by Benjamin in 1962. Moreover, it is demonstrated that Kelvin’s treatment, with the slight generalization, includes unstable wave solutions that have been invoked to explain some aspects of the formation of multiple-vortex tornadoes. The analysis of the unstable solutions also forms the basis for determining whether, for example, an axisymmetric or a spiral vortex breakdown occurs. Kelvin’s work thus helps explain some of the visible features of tornado-like vortices.

Abstract

Data assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as additive Gaussian noise, specified by their covariance matrices $\mathsf{Q}$ and $\mathsf{R}$ , respectively. These error covariances, and specifically their respective amplitudes, determine the weights given to the background (i.e., the model forecasts) and to the observations in the solution of data assimilation algorithms (i.e., the analysis). Consequently, $\mathsf{Q}$ and $\mathsf{R}$ matrices significantly impact the accuracy of the analysis. This review aims to present and to discuss, with a unified framework, different methods to jointly estimate the $\mathsf{Q}$ and $\mathsf{R}$ matrices using ensemble-based data assimilation techniques. Most of the methods developed to date use the innovations, defined as differences between the observations and the projection of the forecasts onto the observation space. These methods are based on two main statistical criteria: 1) the method of moments, in which the theoretical and empirical moments of the innovations are assumed to be equal, and 2) methods that use the likelihood of the observations, themselves contained in the innovations. The reviewed methods assume that innovations are Gaussian random variables, although extension to other distributions is possible for likelihood-based methods. The methods also show some differences in terms of levels of complexity and applicability to high-dimensional systems. The conclusion of the review discusses the key challenges to further develop estimation methods for $\mathsf{Q}$ and $\mathsf{R}$ . These challenges include taking into account time-varying error covariances, using limited observational coverage, estimating additional deterministic error terms, or accounting for correlated noise.

Abstract

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.

Abstract

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.

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.

Abstract

“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.

Abstract

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.

Contents

• 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

APPENDIX A...4524

Types of Filter Divergence...4524

1. Classical filter divergence...4524

2. Catastrophic filter divergence...4524

   APPENDIX B...4524

   Systems Available for Download...4524

   References...4525

Abstract

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.