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Ken Dixon, Clifford F. Mass, Gregory J. Hakim, and Robert H. Holzworth

1. Introduction Advances in computing power have made it possible for operational regional forecast systems to reach convection-permitting horizontal grid spacing (Δ x ≤ 4 km), alleviating the need for a cumulus parameterization scheme (CPS) and creating the opportunity to assimilate convective-scale observations to improve forecasts ( Weisman et al. 1997 ; Kain et al. 2008 ). Today, radar reflectivity and lightning-flash-rate observations are assimilated through latent heating into the

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Carole Peubey and William Bell

1. Introduction This paper aims to quantify the sensitivity of numerical weather prediction (NWP) analysis and forecast accuracy to frequency shifts in microwave channel passbands. Any shift in the center frequency of a passband will change the optical depth characteristics of the atmosphere sampled by this passband. This effect will be largest for passbands that are close to sharp absorption lines, such as the O 2 absorption lines used for temperature sounding at 50–60 GHz or the H 2 O lines

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Sung-Min Kim and Hyun Mee Kim

1. Introduction Data assimilation (DA) aims at estimating the atmospheric state as accurately as possible using observations and the background state (i.e., a short-term forecast) at the analysis time. The estimated atmospheric state is called the analysis state. There are many DA methods: optimal interpolation (OI), three- or four-dimensional variational data assimilation (3DVAR or 4DVAR, respectively; Courtier et al. 1994 ), and ensemble Kalman filter (EnKF; Evensen 1994 ). 4DVAR and EnKF

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Shiqiu Peng, Yineng Li, and Lian Xie

; Donelan et al. 2004 ), the linear parameterization [Eq. (2) ] is still widely used in storm surge forecasting. Therefore, we still employ the formula of by Large and Pond (1981) in this study with a correction under the large wind speed (i.e., is set to be unchanging with the wind velocity when the wind speed is larger than 30 m s −1 ). We will demonstrate that even with this simple linear parameterization, obtaining an optimal value for can lead to improvements in storm surge forecasting

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Timothy C. Y. Chui, David Siuta, Gregory West, Henryk Modzelewski, Roland Schigas, and Roland Stull

1. Introduction: Background and motivation The field of numerical weather prediction (NWP) has made progress across all aspects of the forecasting process within the past century ( Bauer et al. 2015 ). Though much of the progress has been a result of research conducted at national and international organizations like the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF), developments have also been made throughout academia

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E. V. Stanev, F. Ziemer, J. Schulz-Stellenfleth, J. Seemann, J. Staneva, and K.-W. Gurgel

1. Introduction The geographical area of the present research is the German Bight in the southern North Sea ( Fig. 1 ), which is characterized by very shallow water, complex bathymetry, and mesotidal conditions. The Coastal Observing System for Northern and Arctic Seas (COSYNA), which was recently deployed in this area, integrates near-real-time measurements with numerical models in a preoperational way and provides continuously coastal ocean state estimates and forecasts. The measurement suite

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Alexandra Simpson, Merrick Haller, David Walker, Patrick Lynett, and David Honegger

1. Introduction A real-time, wave-by-wave forecasting system has a number of potential applications. For example, the performance of wave energy converters (WEC) can be improved when the device’s behavior is tuned via adaptive control on a wave-by-wave basis ( Brekken 2011 ; Li et al. 2012 ; Korde 2014 ; O’Sullivan and Lightbody 2017 ). Also, maritime operation tasks such as cargo and personnel transfer, helicopter landing, high speed navigation, and small craft recovery would benefit from

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Alain Caya, Mark Buehner, and Tom Carrieres

northern populations to adapt. The direct use of accurate and timely sea ice information, such as the operational products of the Canadian Ice Service (CIS), can result in significant economic and safety benefits for all such activities. An important secondary impact of more accurate sea ice information is improved numerical weather prediction (NWP) for northern regions, especially when coupled ice–ocean–atmosphere forecast models are used. An example of this is the experimental Gulf of St. Lawrence

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Zaizhong Ma, Lars Peter Riishøjgaard, Michiko Masutani, John S. Woollen, and George D. Emmitt

and subsequent forecast failures ( Marseille et al. 2008 ). Direct observations of wind profiles have been recognized as the most urgently needed observation type for climate studies as well as numerical weather prediction. Space-based Doppler wind lidar (DWL) has been identified as the key technology necessary to meet the global wind profiling requirement ( Emmitt 1987 ). Two measurement techniques—namely, coherent and direct detection—are available for the measuring of Mie and Rayleigh

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Hyun Mee Kim and Dae-Hui Kim

1. Introduction The initial conditions obtained by assimilating the model background and observations are used to predict weather in numerical weather prediction (NWP). Because individual observations assimilated to produce the initial conditions for the prediction contribute differently to the performance of forecasts, the impact of individual observations on the forecasts needs to be evaluated quantitatively to improve the performance of the NWP. The impact of real observations on forecasts

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