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Richard A. Anthes

components on the forecastrather than the details of each component. The main conclusion of the paper is that further increases in the overall skill of operational regionalforecasts are likely to occur through improvements in all the components of limited-area models. Improvements in various components developed and tested in research models are currently being incorporated inseveral operational models, and some modest but significant improvements in regional forecast skill arelikely over the next

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Clark Evans, Kimberly M. Wood, Sim D. Aberson, Heather M. Archambault, Shawn M. Milrad, Lance F. Bosart, Kristen L. Corbosiero, Christopher A. Davis, João R. Dias Pinto, James Doyle, Chris Fogarty, Thomas J. Galarneau Jr., Christian M. Grams, Kyle S. Griffin, John Gyakum, Robert E. Hart, Naoko Kitabatake, Hilke S. Lentink, Ron McTaggart-Cowan, William Perrie, Julian F. D. Quinting, Carolyn A. Reynolds, Michael Riemer, Elizabeth A. Ritchie, Yujuan Sun, and Fuqing Zhang

produce hazards (such as Hurricane Sandy) and/or generate hazards downstream [e.g., Hurricane Katia in 2011 as described by Grams and Blumer (2015) ; Typhoon Nabi in 2005 as described by Harr et al. (2008) ]. Fig . 1. A two-stage ET classification based on Klein et al. (2000) . The onset and completion times correspond to the definitions of Evans and Hart (2003) . The “tropical” and “extratropical” labels indicate approximately how the system would be regarded by an operational forecast center

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David M. Schultz

theoretical, observational, modeling, and diagnostic research approaches. The last goal is to look toward the future of improved understanding and forecasting of prefrontal troughs in section 4c through developing closer relationships between the research- and operational-meteorology communities. a. Synthesis The 10 mechanisms for prefrontal troughs and wind shifts that were presented in section 3 can be simplified even further by classifying the nature of the process that produces the prefrontal

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Zhiyong Meng and Fuqing Zhang

-model Observing System Simulation Experiments (OSSEs) to more real-data, real-time, quasi-operational applications ( Tong and Xue 2005 ; Barker 2005 ; Zhang et al. 2006 ; Chen and Snyder 2007 ; Meng and Zhang 2007 ; Fujita et al. 2007 , 2008 ; Hacker et al. 2007 ; Meng and Zhang 2008a , b ; Torn and Hakim 2008a , 2009a ; Zhang et al. 2009a ; Aksoy et al. 2009 , 2010 ). The first pseudo-operational regional-scale EnKF system, based on the Weather Research and Forecasting model (WRF), was

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Craig S. Schwartz and Ryan A. Sobash

.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2 Clark , A. J. , W. A. Gallus Jr. , and M. L. Weisman , 2010 : Neighborhood-based verification of precipitation forecasts from convection-allowing NCAR WRF Model simulations and the operational NAM . Wea. Forecasting , 25 , 1495 – 1509 , doi: 10.1175/2010WAF2222404.1 . 10.1175/2010WAF2222404.1 Clark , A. J. , and Coauthors , 2011 : Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble . Mon. Wea

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P. L. Houtekamer and Fuqing Zhang

forecasts [the Ensemble Prediction System (EPS) item in Table 3 ]. This is a perfect implementation of the Monte Carlo method to the extent that the forecast model is indeed independent of the ensemble of input perturbations. Since the model can be integrated with no knowledge or analysis of its inner logic, it can be considered a black box. Going toward an operational implementation of such an ensemble prediction system, the main difficulty is how to obtain a realistic multivariate covariance matrix

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David M. Schultz and Philip N. Schumacher

cumulonimbus, large precipitation rates, or lightning precludes the existence of slantwise convection. In fact, some evidence suggests otherwise. 6. Nature of the banding Perhaps the most operationally significant use of MSI is to assess the nature of the precipitation banding (orientation, spacing, number of bands, movement, etc.) that may occur. The goal in operational forecasting is to anticipate the occurrence and nature of the large gradients in precipitation that can result if

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Tammy M. Weckwerth and David B. Parsons

focus more on local measurements. The need for a more extensive research radiosonde network for SESAME was probably due to the improvements in the capabilities of forecast models, assimilation systems, and operational measurements (e.g., satellite sensing, profiling networks, and flight-level data). Many papers in this special issue utilize the multiscale observations obtained during IHOP_2002 (e.g., Wakimoto et al. 2006 ; Murphey et al. 2006 ; Cai et al. 2006 ; Markowski et al. 2006 ; Demoz et

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Julia H. Keller, Christian M. Grams, Michael Riemer, Heather M. Archambault, Lance Bosart, James D. Doyle, Jenni L. Evans, Thomas J. Galarneau Jr., Kyle Griffin, Patrick A. Harr, Naoko Kitabatake, Ron McTaggart-Cowan, Florian Pantillon, Julian F. Quinting, Carolyn A. Reynolds, Elizabeth A. Ritchie, Ryan D. Torn, and Fuqing Zhang

transport, which may impact both the occurrence frequency and predictability of subseasonal regimes on basin to hemispheric length scales. The Subseasonal to Seasonal Project database ( ; Vitart et al. 2017 ), which provides access to subseasonal to seasonal forecasts from 11 operational centers, could be a valuable resource for such investigations. On still longer time scales, the influence of a warming climate on the downstream impact of ET, in particular, is another aspect that

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Loïk Berre and Gérald Desroziers

increasing attention recently, in particular in the context of ensemble variational data assimilation for global NWP. b. Error simulation techniques in variational data assimilation The first versions of operational variational schemes often used background error covariances, which were estimated with the so-called NMC method ( Parrish and Derber 1992 ; Rabier et al. 1998 ; Berre 2000 ; Ingleby 2001 ). This method is based on covariances of differences typically between 24- and 48-h forecasts valid at

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