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John W. Nielsen-Gammon and David A. Gold

Abstract

Advances in computer power, new forecasting challenges, and new diagnostic techniques have brought about changes in the way atmospheric development and vertical motion are diagnosed in an operational setting. Many of these changes, such as improved model skill, model resolution, and ensemble forecasting, have arguably been detrimental to the ability of forecasters to understand and respond to the evolving atmosphere. The use of nondivergent wind in place of geostrophic wind would be a step in the right direction, but the advantages of potential vorticity suggest that its widespread adoption as a diagnostic tool on the west side of the Atlantic is overdue. Ertel potential vorticity (PV), when scaled to be compatible with pseudopotential vorticity, is generally similar to pseudopotential vorticity, so forecasters accustomed to quasigeostrophic reasoning through the height tendency equation can transfer some of their intuition into the Ertel-PV framework. Indeed, many of the differences between pseudopotential vorticity and Ertel potential vorticity are consequences of the choice of definition of quasigeostrophic PV and are not fundamental to the quasigeostrophic system. Thus, at its core, PV thinking is consistent with commonly used quasigeostrophic diagnostic techniques.

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Eric G. Hoffman

Abstract

In the last decade, Fred Sanders was often critical of current surface analysis techniques. This led to his promoting the use of surface potential temperatures to distinguish between fronts, baroclinic troughs, and non-frontal baroclinic zones, and to the development of a climatology of surface baroclinic zones. In this paper, criticisms of current surface analysis techniques and the usefulness of surface potential temperature analyses are discussed. Case examples are used to compare potential temperature analyses and current National Centers for Environmental Prediction analyses.

The 1-yr climatology of Sanders and Hoffman is reconstructed using a composite technique. Annual and seasonal mean potential temperature analyses over the continental United States, southern Canada, northern Mexico, and adjacent coastal waters are presented. In addition, gridpoint frequencies of moderate and strong potential temperature gradients are calculated. The results of the mean potential temperature analyses show that moderate and strong surface baroclinic zones are favored along the coastlines and the slopes of the North American cordillera. Additional subsynoptic details, not found in Sanders and Hoffman, are identified. The availability of the composite results allows for the calculation of potential temperature gradient anomalies. It is shown that these anomalies can be used to identify significant frontal baroclinic zones that are associated with weak potential temperature gradients. Together the results and reviews in this paper show that surface potential temperature analyses are a valuable forecasting and analysis tool allowing analysts to distinguish and identify fronts, baroclinic troughs, and nonfrontal baroclinic zones.

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Howard B. Bluestein

Abstract

The nature of the different types of surface boundaries that appear in the southern plains of the United States during the convectively active season is reviewed. The following boundaries are discussed: fronts, the dryline, troughs, and outflow boundaries, The boundaries are related to their environment and to local topography. The role these boundaries might play in the initiation of convective storms is emphasized. The various types of boundary-related vertical circulations and their dynamics are discussed. In particular, quasigeostrophic and semigeostrophic dynamics, and the dynamics of solenoidal circulations, density currents, boundary layers, and gravity waves are considered.

Miscellaneous topics pertinent to convective storms and their relationship to surface boundaries such as along-the-boundary variability, boundary collisions, and the role of vertical shear are also discussed. Although some cases of storm initiation along surface boundaries have been well documented using research datasets collected during comprehensive field experiments, much of what we know is based only on empirical forecasting and nowcasting experience. It is suggested that many problems relating to convective-storm formation need to be explored in detail using real datasets with new observing systems and techniques, in conjunction with numerical simulation studies, and through climatological studies.

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Stanley G. Benjamin, John M. Brown, Gilbert Brunet, Peter Lynch, Kazuo Saito, and Thomas W. Schlatter

progress in forecasting within four historical eras and for eight components of forecasting. This 100-yr period is divided here into these four eras: “Era 1” (1919–39: maps only; observations and extrapolation/advection techniques), “Era 2” (1939–56: increasing science understanding; application especially to aviation; birth of computers), “Era 3” (1956–85: the advent of NWP and dawn of remote sensing), and “Era 4” (1985–2018: weather forecasting, and especially NWP, mature and penetrate virtually all

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Harold E. Brooks, Charles A. Doswell III, Xiaoling Zhang, A. M. Alexander Chernokulsky, Eigo Tochimoto, Barry Hanstrum, Ernani de Lima Nascimento, David M. L. Sills, Bogdan Antonescu, and Brad Barrett

aftermath of the event. They spent much of the next decade trying to understand the environmental conditions and atmospheric patterns in which tornadoes form in order to improve forecasts for the Air Force. This process represents a connection that has been repeated over the decades of forecast problems leading to research topics, which in turn led to improved forecast techniques. Fig . 18-3. Composite charts showing important factors for tornadoes from (a) 20 Mar and (b) 25 Mar 1948, the date that

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Sue Ellen Haupt, Branko Kosović, Scott W. McIntosh, Fei Chen, Kathleen Miller, Marshall Shepherd, Marcus Williams, and Sheldon Drobot

. Observational tools to reduce risk with regard to the background solar wind, CME directionality, CME and prominence magnetic polarization, and so on are all critically wedded to information technology, data assimilation, and the array of numerical modeling techniques that have been extensively developed over past decades in the solar–terrestrial physics community. A truly critical need for future space weather understanding (and increased forecast skill) is the full characterization of the sun’s global

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Sue Ellen Haupt, Steven Hanna, Mark Askelson, Marshall Shepherd, Mariana A. Fragomeni, Neil Debbage, and Bradford Johnson

forecasts ( Mahoney et al. 2012 ; Wilczak et al. 2015 ; Haupt et al. 2014 ; Cheng et al. 2017 ). For solar energy, instruments such as sky cameras ( Chow et al. 2011 ; Marquez and Coimbra 2013 ; Huang et al. 2013 , Quesada-Ruiz et al. 2014 ; Nguyen and Kleissl 2014 ; Chu et al. 2015 ; Peng et al. 2015 ) as well as pyranometer observations can be combined with other meteorological information to provide relatively accurate forecasts. Methods used for these techniques include Markov process

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M. Haeffelin, S. Crewell, A. J. Illingworth, G. Pappalardo, H. Russchenberg, M. Chiriaco, K. Ebell, R. J. Hogan, and F. Madonna

microware radiometer, and a rain gauge, all operating 24 hours each day. A crucial aspect is to have a common calibration standard for the instruments, so techniques were developed for automatically calibrating cloud lidars ( O’Connor et al. 2004 ) and cloud radar ( Hogan et al. 2003a ) using the properties of the meteorological targets themselves. The evaluation of the representation of clouds in seven European operational forecast models as reported by Illingworth et al. (2007) and Bouniol et al

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Christa D. Peters-Lidard, Faisal Hossain, L. Ruby Leung, Nate McDowell, Matthew Rodell, Francisco J. Tapiador, F. Joe Turk, and Andrew Wood

operational pathways toward harnessing predictability have also differed, leading to multiple views on forecasting development strategies. For example, despite substantial progress in groundwater modeling (as noted above in section 2d ) and understanding interactions between surface water and groundwater ( Brunner et al. 2017 ), groundwater-level short- to medium-range forecasting relies to a greater extent on statistical and machine-learning techniques (e.g., Daliakopoulos et al. 2005 ) than on process

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Maike Ahlgrimm, Richard M. Forbes, Jean-Jacques Morcrette, and Roel A. J. Neggers

1. Introduction The European Centre for Medium-Range Weather Forecasts (ECMWF) is one of the leading centers in operational global numerical weather prediction (NWP) and provides forecasts for days to monthly and seasonal time scales across a range of resolutions. Parameterization of subgrid physical processes are a key part of the model [the Integrated Forecast System (IFS)] and, for global application, must be appropriate for all meteorological regimes and regions across a wide range of

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