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Huw C. Davies

heat in a resting ambient atmosphere yields a net descent in the inner core of the initially warmed region with ascent above and below ( Fanelli and Bannon 2005 ). This contrast in the heating–ascent relationship is relevant in an NWP context. Longstanding challenges in NWP are to deliver reliable quantitative forecasting of precipitation, and to develop robust and effective techniques to assimilate/adjust model fields to account for misrepresented or unrepresented cloud diabatic heating. The

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Zhiyong Meng and Fuqing Zhang

EnKF in comparison to the variational data assimilation techniques. These advantages include the following: 1) the background error covariance is flow dependent, which reflects the error of the day; 2) the model and observation operator can be nonlinear; 3) it provides not only the best estimation of the state, but also the associated flow-dependent uncertainty; therefore, it can be seamlessly coupled with ensemble forecasting; 4) there is no need to code a tangent linear or adjoint model; 5) it is

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Loïk Berre and Gérald Desroziers

1. Introduction Usual data assimilation systems for numerical weather prediction (NWP), using Kalman filter or variational techniques, are based on a statistical combination of observations and a background, which is usually a short-term forecast. This statistical estimation requires the specification of spatial covariances of errors in these two kinds of information. As presented in Hollingsworth (1987) and Daley (1991 , p. 125), the role of background error covariances is to spatially

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Craig S. Schwartz and Ryan A. Sobash

-alone methods to produce probabilistic guidance (e.g., Jirak et al. 2012 ; Clark et al. 2013 ; Ben Bouallègue and Theis 2014 ; Schwartz et al. 2015a , b ; Sobash et al. 2016 ) and as techniques to verify deterministic and ensemble forecasts of a variety of meteorological fields, including precipitation (e.g., Roberts and Lean 2008 ), updraft helicity ( Sobash et al. 2011 , 2016 ; Clark et al. 2013 ; Yussouf et al. 2013a ), hail ( Gagne et al. 2015 ; Snook et al. 2016 ), reflectivity ( Stratman et

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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

assessment of cyclone intensity during ET. It is well known by operational forecasters (e.g., at La Réunion and the Joint Typhoon Warning Center; Fogarty 2010 ) that the empirical relationships between cloud patterns and cyclone intensity that underlie the Dvorak technique (DT; Dvorak 1984 ) and advanced Dvorak technique (ADT; Olander and Velden 2007 ) are less reliable during ET than at other times ( Velden et al. 2006 ). During ET, this decrease in reliability results in unrepresentative estimates

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Richard A. Anthes

to small-scale weather phenomena. Examples are measures of stability that are statistically related to thunderstorms, such as areas of convectiveinstability or threshold values of various indices (e.g.,lifted index, K-index). Reap and Foster (1979) discussresults of a statistical technique based on numericalmodel output parameters to forecast the probabilityof thunderstorms.sive number of points received the threshold amountof precipitation and a positive bias developed. 5) PROBABILITY ELLIPSES

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P. L. Houtekamer and Fuqing Zhang

Representing Model Uncertainty and Error in Numerical Weather and Climate Prediction Models , Shinfield Park, Reading, United Kingdom, ECMWF, 163 – 173 . Houtekamer , P. L. , and L. Lefaivre , 1997 : Using ensemble forecasts for model validation . Mon. Wea. Rev. , 125 , 2416 – 2426 , doi: 10.1175/1520-0493(1997)125<2416:UEFFMV>2.0.CO;2 . Houtekamer , P. L. , and H. L. Mitchell , 1998 : Data assimilation using an ensemble Kalman filter technique . Mon. Wea. Rev. , 126 , 796 – 811 , doi

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David M. Schultz and Philip N. Schumacher

convection may be present within mesoscale-model data. Proper use of model data for assessing slantwise convection, especially early in the simulation when model spinup may be important, mandates knowledge of the model characteristics. If model output is used wisely, then we believe it can be useful. Thus, operational implementation of MSI techniques in the forecast office tends to consist of the following methodology ( Grumm and Nicosia 1997 , 21–22; Wiesmueller and Zubrick 1998 , section 7). First

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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

research on the representation of model errors arising from diabatic processes using techniques such as stochastic physics. The research summarized in this review primarily focused on assessing the impact of ET on the short-to-medium-range forecast horizon. Preliminary results reveal a statistically significant correlation between monthly mean values of selected teleconnection indices and ET event counts, as well as significant departures from climatology on the subseasonal to seasonal time scale in

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Volkmar Wirth, Michael Riemer, Edmund K. M. Chang, and Olivia Martius

to surface weather that falls into the tail(s) of the respective local distribution (e.g., precipitation exceeding the 95th percentile). To the extent that weather events inherit predictability from larger-scale dynamical features such as RWPs ( Anthes et al. 1985 ), a better understanding of the RWPs may help to improve the weather forecast, and this is particularly relevant in case of extreme weather. An example is the episode in August 2002, when a quasi-stationary low pressure system over

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