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Benjamin Jaimes and Lynn K. Shay

approximately 1 m s −1 (e.g., Molinari and Morrison 1988 ; Nowlin and Hubertz 1972 ), the effects of this energetic geostrophic variability need to be resolved to understand the modulated OML response to hurricane forcing. Deriving geostrophic flow from the shallow AXBTs (∼350 m) measurements is, however, not trivial, as the vertical scale of the mesoscale features is ∼800–1000 m. Nevertheless, the water mass homogeneity in the GOM beneath the thermocline (or below the 20°C isotherm depth, Fig. 2b

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Matthew J. Carrier, Hans Ngodock, Scott Smith, Gregg Jacobs, Philip Muscarella, Tamay Ozgokmen, Brian Haus, and Bruce Lipphardt

measurements consist of point measurements from acoustic Doppler current profilers (ADCP) or high-frequency (HF) radar surface current measurements that measure the speed and direction of the ocean currents at a fixed location in space. Lagrangian data are collected from data-gathering devices on board any passive tracer, such as drifters or surface floats. Eulerian observations can be assimilated directly into the ocean model as the form of the data matches that of the model variable. Lagrangian data, on

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

1. Introduction Computational fluid dynamics (CFD) codes are used at the microscale to predict atmospheric boundary layer flows over complex terrain for a variety of applications, ranging from the siting of wind turbines to predictions of flow in urban terrain for contaminant dispersion. CFD codes used for simulations of the atmospheric boundary layer often lack important features, such as incorporating atmospheric physics and regional weather effects. Additionally, it is often time consuming

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Peter G. Duynkerke and Phillip Hignett

1988) and detailed observations. We will use the observations made during the 1987 FIRE marine stratocumulus experiment. More specifically, we will usecloud-base height, inversion height, liquid water path,soundings of temperature and humidity, etc. Moreover,we will employ turbulence measurements made usinginstruments attached to the cable of a tethered balloon(Hignett 1991 ). Some of the turbulence data was already discussed by Hignett ( 1991 ); part of the data ispresented here for the first time

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David E. Kingsmill, Paul J. Neiman, F. Martin Ralph, and Allen B. White

the California coast ( Ralph et al. 1999 ). A major focus of the January–March 1998 effort was sampling the moisture-laden low-level jet that is often located in advance of the extratropical cyclone cold-frontal boundary, a channel of air that is embedded within a feature sometimes referred to as an atmospheric river ( Ralph et al. 2004 ). Offshore measurements were provided by a National Oceanic and Atmospheric Administration (NOAA) WP-3D research aircraft. The landfall of these storms was also

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Alexander Gohm, Günther Zängl, and Georg J. Mayr

1. Introduction The spatial resolution of the present-day numerical weather prediction models has outpaced routine meteorological networks. A thorough verification of the numerical results therefore requires higher-resolution observations that can in general only be collected in dedicated field campaigns. In contrast to in situ measurements, remote sensing instruments such as radar, lidar, sodar, and optical sensors are able to map atmospheric parameters continuously over a wide domain. These

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Yansen Wang, Wei-Kuo Tao, and Joanne Simpson

3%, respectively, compared with asimulation excluding the effects of ocean fluxes for asubtropical squall line. In this study, a two-dimensional cloud-resolvingmodel, the Goddard Cumulus Ensemble (GCE) Model,is linked with the TOGA COARE~ bulk flux algorithm(Fairall et al. 1996) to quantify the effects of oceansurface fluxes, including the latent heat flux, the sensible heat flux, and the momentum flux, on the development of a tropical convective system. Sensitivitytests are also performed to

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George H. Bryan, Nathan A. Dahl, David S. Nolan, and Richard Rotunno

as large-eddy simulation (LES). In principle, LES uses sufficiently small grid resolution to represent the largest and most energetic features in a turbulent flow. LES requires a subgrid turbulence model, which accounts for the effects of unresolved turbulence on resolved-scale fields. Subgrid models for LES are often designed based on theoretical conditions [e.g., chapter 6 of Wyngaard (2010) ]. However, the underlying assumption of LES is that resolved fluctuations contain most of the

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HOWARD C. SUMNER

-sive summary on the theory and practice of tlie measure-ment of the visual range. It is still the only book devoted wholly to this subject which, in some respects, has been neglected in this era of expanded transport. The concisely and carefully written thcoreticnl portions of the first edition have been largely retained in this new issue, with some small improvements in notation, and several important brief additions. Among the topics discussed in the new material are the following: Variation of the

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Frédéric Fabry and Juanzhen Sun

about data assimilation in a more conceptual way than usual. In particular, in this work, a greater emphasis will be put on the nature and characteristics of the data to be assimilated or of the model fields to be constrained. Data assimilation is explicitly designed to constrain model variables with noisy measurements. But for data assimilation to succeed, three additional conditions must be met well enough. First, the difference between the assumed atmospheric state x ′ and the true

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