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Soyoung Ha, Judith Berner, and Chris Snyder

1. Introduction An ensemble Kalman filter (EnKF) is suitable for the mesoscale analysis because it estimates multivariate flow-dependent background error covariance that can capture fast-varying meso- and small-scale features. However, even if “errors of the day” are well described in short-rage ensemble forecasts (to be well represented in the background error covariance), the mesoscale analysis is still challenging because of various factors, including the validity of the linear and Gaussian

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Katherine A. Lundquist, Fotini Katopodes Chow, and Julie K. Lundquist

1. Introduction Most mesoscale numerical weather prediction (NWP) models use terrain-following coordinates, which accommodate complex terrain by transforming the physical domain onto a Cartesian grid. Phillips (1957) first introduced this coordinate, using the variable sigma to represent the transformed vertical coordinate. This formulation simplifies the application of lower boundary conditions by aligning the lowest coordinate with the topography. Coordinate lines gradually

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Morten Køltzow, Barbara Casati, Eric Bazile, Thomas Haiden, and Teresa Valkonen

forecast accuracy by the use of optimized physics for the targeted area and finer horizontal and vertical resolution ( Jung et al. 2016 ). However, operational convection permitting resolution models have just recently started to appear for the Arctic domain. Müller et al. (2017) and Yang et al. (2018) describe added value from operational high-resolution HIRLAM–ALADIN Research on Mesoscale Operational NWP in Euromed (HARMONIE)–Applications of Research to Operations at Mesoscale (AROME) runs in the

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Donald C. Norquist

standard meteorological variables (temperature, wind, humidity, pressure) as produced by the theater numerical weather prediction (NWP) model. Cloud forecasts are required out to 36 h beyond the current (most recent observation) time, with a stated degree of accuracy. This paper describes the methods developed to provide the required cloud predictions and presents an assessment of their predictive skill. Because the cloud variables required are not explicitly predicted by any known mesoscale NWP

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Craig S. Schwartz, Glen S. Romine, Morris L. Weisman, Ryan A. Sobash, Kathryn R. Fossell, Kevin W. Manning, and Stanley B. Trier

storm-scale EnKF DA of radar observations for individual cases [e.g., Snyder and Zhang 2003 ; Zhang et al. 2004 ; Dowell at al. 2004 ; Putnam et al. (2014) , and references therein], only more recently have mesoscale EnKF DA systems been employed to initialize convection-allowing ensemble forecasts over meso- α - to synoptic-scale regions. For example, several case studies (e.g., Jones and Stensrud 2012 ; Melhauser and Zhang 2012 ; Jones et al. 2013 , 2015 ; Schumacher and Clark 2014 ) and

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Lisa S. Alexander, David M. L. Sills, and Peter A. Taylor

products when detecting/analyzing convective development along mesoscale boundaries. In other words, cell initiations are often first and better detected using data above the CAPPI 1-km level. Finally, it is important that boundary information be used to detect and nowcast the initiation of convective storms. A semiobjective, manual approach was used in this study for boundary identification, though it is recognized that this is too labor intensive for operational use by forecasters. High

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Mika Peace, Trent Mattner, Graham Mills, Jeffrey Kepert, and Lachlan McCaw

mesoscale environment. Several of these (e.g., Mills 2005 , 2008a ; Charney and Keyser 2010 ; Zimet et al. 2007 ) describe dynamical mixing of dry and high-momentum air from the mid–upper troposphere to above a fire site. In each of the events above, extreme fire behavior occurred in an environment where dry, high-momentum air was present in the midtroposphere. Each study proposed meteorological mechanisms by which the surface fire activity could be enhanced by mixing of the air mass from the mid

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Donna F. Tucker and Kristine S. Zentmire

well. Acknowledgments We are grateful to Dr. Edward Tollerud of the Forecast Systems Laboratory for providing the dataset with MCC times and locations. REFERENCES Anderson, C. J., and R. W. Arritt, 1998: Mesoscale convective complexes and persistent elongated convective systems over the United States during 1992 and 1993. Mon. Wea. Rev., 126, 578–599. 10.1175/1520-0493(1998)126<0578:MCCAPE>2.0.CO;2 Augustine, J. A., and K. W. Howard, 1988: Mesoscale convective complexes over the United States

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Shengjun Zhang, Tim Li, Xuyang Ge, Melinda Peng, and Ning Pan

code. Thus, even though the observed TC structure is given, there is an observation error associated with the observed data. We did include some level of reasonable observation noise. b. Application of the TCDI–3DVAR scheme to an operational forecast system The aforementioned dynamic initialization scheme has been implemented in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC; Hendricks et al. 2011 ). Figure 11 is a flowchart describing how the TCDI

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Dustan M. Wheatley and David J. Stensrud

high-resolution numerical weather prediction (NWP) products can serve as a proxy for the real atmosphere, but the initial conditions of NWP models are often devoid of important mesoscale features, a potential source of forecast error. Mesoscale surface data assimilation is one approach for improving model initialization/spinup and subsequently derived products. The present study emphasizes the potential role of including surface pressure observations in mesoscale ensemble data assimilation. To meld

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