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Joel Michaelsen

model from the remaining cases, and tests it on the deleted case or cases. The procedureis nonparametric and can be applied to any automated model building technique. It can also provide importantdiagnostic information about influential cases in the dataset and the stability of the model. Two experimentswere conducted using cross-validation to estimate forecast skill in different predictive models of North Pacificsea surface temperatures (SSTs). The results indicate that bias, or artificial

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Robert R. Czys and Robert W. Scott

group according toTcc~ and PB. The physical basis of TccL and PB to implicitly represent a period of time for coalescence toproduce supercooled drizzle and raindrops is discussed. The technique performed well at forecasting the occurrenceand height of a_qernoon convective clouds. Aircraft measurements of supercooled raindrop concentrations showedthat a discriminator function, dependent only on Tccc and PB, gave a good indication oftbe presence or absenceof supercooled drizzle and raindrops in the

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Richard T. McNider, William M. Lapenta, Arastoo P. Biazar, Gary J. Jedlovec, Ronnie J. Suggs, and Jonathan Pleim

parameter spaces. Additionally, it addresses the day-to-day consistency of the retrieved heat capacities and issues with sensitivity and numerical solution techniques. Background on the heat capacity parameter Before discussing the satellite assimilation strategies, we review surface energy budget formulations and, specifically, the role of surface heat capacity in weather forecast and global climate models. Most early mesoscale models ( Mahrer and Pielke 1976 ; Physick 1976 ; McNider and Pielke 1981

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Stanley A. Changnon, Griffith M. Morgan Jr., Gary L. Achtemeier, Neil G. Towery, and Ronald C. Grosh

but will be more difficultand expensive than in less humid areas. Radar will be needed for short-term forecasting, aircraft operations,identification of potential hailstorms, and in the evaluation of seeding effectiveness. Weather forecastingby objective techniques will be valuable in both operations and evaluation, and adequate objective techniques have been largely developed. The overall shape of the proposed experiment is now clear. It willconsist of an impact monitoring effort, which will make

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Richard W. Katz, Allan H. Murphy, and Robert L. Winkler

decision-making problem is inherently dynamicin nature. As a result, a class of dynamic models known as Markov decision processes is considered. Acomputational technique called dynamic programming is used in conjunction with these models to determinethe optimal actions and to estimate the value of meteorological information. Some results concerning the value of frost forecasts to orchardists in ~he Yakima Valley of centralWashington are presented for the cases of red delicious apples, bartlett pears

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Frederick Sanders and Robert W. Burpee

tropical circulationsand of the information available to describe them, is found to be capable of providing a basis for a significantadvance of the state of the art of hurricane track forecasting in the range from 24-72 hr in regions of relatively dense rawinsonde data coverage. The distinctive features of the technique are application of the barotropic equation to tropospheric mean data computed from information at 10 constant-pressure levels, prognostic use of a stream function derived from direct

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Nikolay V. Balashov, Anne M. Thompson, and George S. Young

, probabilistic approaches to AQ forecasting have been recommended ( Dabberdt et al. 2004 ; Delle Monache et al. 2006a ; Delle Monache et al. 2006b ; Vautard et al. 2009 ). These and related studies (e.g., Garner and Thompson 2013 ; Djalalova et al. 2015 ) have generally applied meteorological model ensembles or postprocessing techniques to chemical transport models (CTMs) to quantify the uncertainty of AQ forecasts. A concise summary of these studies can be found in Zhang et al. (2012b) . Although some

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Michael J. Erickson, Joshua S. Kastman, Benjamin Albright, Sarah Perfater, James A. Nelson, Russ S. Schumacher, and Gregory R. Herman

probability-matched mean exhibited good spatial structure but produced values that were too high, while the conventional mean produced overly smoothed values that were too low ( Perfater and Albright 2017 ). Combining the probability-matched mean and conventional mean into a blended mean preserved the best aspects of both means. New to the 2017 FFaIR Experiment, the CSU-MLP is a machine-learning random-forest technique trained on 11 years of Global Ensemble Forecast System reforecasts to predict

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Jaemo Yang, Marina Astitha, Emmanouil N. Anagnostou, and Brian M. Hartman

1. Introduction Weather forecasting, applied to global and regional scales, has evolved with the use of multimodel or single-model ensembles ( Doblas-Reyes et al. 2005 ; Palmer et al. 2008 ; Weigel et al. 2009 ; Kirtman et al. 2014 ), data-assimilation techniques ( Barker et al. 2012 ; Wang et al. 2013 ; Ancell et al. 2015 ), and high-resolution grid spacing ( Roberts 2003 ; Speer et al. 2003 ; Steppeler et al. 2003 ; Gego et al. 2005 ; Schwartz et al. 2009 ) in an attempt to improve

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C. M. Penner

be rapidly computed by measurement of the areas of the quadrilaterals formed bythe cross-patterns of 1000-mb contours and 1000-500 mb thicknesses, and space mean (~) contours andff~--Z isolines. These charts are themselves of considerable use in short-range forecasting techniques. Asimple graphical combination of these charts results in an co-chart. The co-values, so derived, have beensuccessfully used in quantitative precipitation computations.1. Introduction Little practical use has

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