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P. Goswami and S. Mallick

may primarily arise from the projection of model data on a given horizontal and vertical grid-to-point (station) observation. This part of the bias may be expected to be somewhat systematic in nature, arising, as it does, from an adopted grid and methodology for interpolation. Recently, Steed and Mass (2004) experimented with several different spatial techniques of applying bias correction to forecasts of temperature from a mesoscale model. Eckel and Mass (2005) applied bias correction on

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Iris Odak Plenković, Luca Delle Monache, Kristian Horvath, and Mario Hrastinski

procedure should be able to cope even with drastic changes in both the starting model and the AN forecast error. 3. NWP model data Three configurations of the operational limited-area mesoscale NWP model ALADIN ( ALADIN International Team 1997 ) of the Croatian Meteorological and Hydrological Service are used to generate 10-m wind speed forecasts: The operational limited-area mesoscale ALADIN model is launched twice a day (0000 UTC and 1200 UTC) at 8-km horizontal grid spacing (A8). The A8 model uses

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Ariel E. Cohen, Steven M. Cavallo, Michael C. Coniglio, Harold E. Brooks, and Israel L. Jirak

)–(i) The 21-h forecast of simulated composite reflectivity (dB Z ) for each WRF PBL member valid at 0900 UTC 13 Feb 2007 and (j) the observed mosaic composite reflectivity from the NCAR Mesoscale and Microscale Meteorology Laboratory Image Archive ( NCAR 2017 ) at 0900 UTC 13 Feb 2007. White ovals are overlaid in the panels to indicate regions of convection referenced within the main text, relevant for differences in convective mode between simulated convection and the observations. The color scale for

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David J. Stensrud, Geoffrey S. Manikin, Eric Rogers, and Kenneth E. Mitchell

guidance to forecasters on the evolution of the parameters used to evaluate the potential for heavy precipitation and severe thunderstorms. However, as our understanding of these types of events has improved, the important roles played by mesoscale features have been highlighted ( Maddox et al. 1979 , 1980 ; Olson 1985 ; Doswell 1987 ; Funk 1991 ; Doswell et al. 1993 ). During the warm season, Heideman and Fritsch (1988) show that over 80% of the more significant precipitation events 1 are

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Jonathan E. Thielen and William A. Gallus Jr.

1. Introduction Mesoscale convective systems (MCSs) play a crucial role climatologically in precipitation across the central United States. These systems account for roughly 30%–70% of the precipitation that occurs during the April–September period (warm season) in this region ( Ashley et al. 2003 ) and are therefore key phenomena of interest when seeking to improve the quantitative precipitation forecast (QPF) skill of models ( Fritsch et al. 1986 ). While this rainfall is essential to

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Yunji Zhang, Fuqing Zhang, David J. Stensrud, and Zhiyong Meng

deficiencies in the numerical models may all contribute to these uncertainties. Most works on practical predictability of mesoscale weather phenomena have been focused on relatively larger systems such as snowstorms (e.g., Zhang et al. 2002 ), tropical cyclones (e.g., Sippel and Zhang 2008 ; Zhang et al. 2014 ), and MCSs (e.g., Zhang et al. 2006 ; Melhauser and Zhang 2012 ; Wu et al. 2013 ; Wandishin et al. 2008 , 2010 ). Results showed that forecasts could be very sensitive to initial and model

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Steven E. Koch and Christopher O’Handley

waves can exert important controls upon convection and mesoscale precipitation patterns, but in general, the operational community mistakenly perceives gravity waves as being too inconsequential, or occurring too infrequently, or being too difficult to forecast and diagnose, to be worthy of consideration in a daily forecast environment. Issues that immediately arise in this weather forecasting context include the following: 1) What kinds of gravity waves are important to the weather? 2) How

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Miriam L. Rorig, Steven J. McKay, Sue A. Ferguson, and Paul Werth

weather predictions ( Mass et al. 2003 ). Because most fire weather forecasting tools use variables that are output by the mesoscale models, many value-added products are being generated in support of the fire community. For example, gridded next-day predictions of NFDRS indices are currently available for the continental United States ( ) and for the Pacific Northwest ( ) ( Hoadley et al. 2006 ). The current study uses

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Eric P. Grimit and Clifford F. Mass

Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (PSU–NCAR MM5; Grell et al. 1994 ) suggest diminishing returns as grid spacing drops below 12 km, when evaluated using standard measures of forecast skill ( Mass et al. 2002 ). Furthermore, numerical model forecasts can be very sensitive to slight changes in the larger-scale initial conditions ( Brooks et al. 1992 ). Recognition of such predictability issues has led to increased interest in developing

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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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