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Yuxuan Yang, Lifeng Zhang, Bin Zhang, Wei You, Mingyang Zhang, and Binpeng Xie

and Chou 2006 ; Tian et al. 2008 ; Wang et al. 2010 ). The EVA method adopted in this paper, referred to as POD-4DEnVar, was proposed by Tian et al. (2008 , 2009 , 2011 ) and Tian and Feng (2015 ) and is based on proper orthogonal decomposition (POD) and ensemble forecasting. This method applies the POD technique to the 4DVar framework, and the observation perturbations are decomposed so as to extract the standard orthogonal basis of the forecast bias in a four-dimensional (4D) space, which

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Bruce A. Veenhuis

and Loughe 1998 ). The ensemble members sample the various sources of error that degrade NWP forecasts. To quantify the error in the underlying analysis, the ensemble members are initialized with perturbed initial conditions. Over the years, a range of perturbation techniques have been proposed including the breeding method ( Toth and Kalnay 1997 ), singular vectors ( Palmer et al. 1998 ), and the ensemble transform Kalman filter ( Wang and Bishop 2003 ). The numerical model itself is also a

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Paul J. Roebber

temperature and precipitation and that, in specific circumstances, this human intervention can add considerably to the value of those forecasts. While these and other studies have helped to clarify some outstanding questions regarding the forecast process, an issue that has not yet been addressed is the large-scale regime dependence of forecast technique; that is, given a profile of forecast information (e.g., 850-hPa temperature, dewpoint temperature, surface wind speed, etc., hereafter called cues

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Fan Han and Istvan Szunyogh

1. Introduction In a pair of papers, Keil and Craig (2007 , 2009 ; hereafter KC07 and KC09 , respectively) introduced a morphing-based, nonparametric optical flow technique ( Marzban et al. 2009 ) for the verification of precipitation forecasts. Their technique was most recently used by Geiß (2015) to examine the forecast cases of the Mesoscale Verification Intercomparison over Complex Terrain (MesoVICT) research project ( Dorninger et al. 2013 ). In an earlier paper ( Han and Szunyogh

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Russell L. Elsberry, Edward L. Weniger, and Denis H. Meanor

2142 MONTHLY WEATHER REVIEW VOLUME I16A Statistical Tropical Cyclone Intensity Forecast Technique Incorporating Environmental Wind and Vertical Wind Shear InformationRUSSELL L. ELSBERRY, EDWARD L. WENIOER AND DENIS H. IVIEANORDepartment of Meteorology, Naval Postgraduate School, Monterey, CaliforniaManuscript received 8 Februar~ 1988, in final form 1 April 19gg) An objective technique for

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Donald S. Foster and Ferdinand C. Bates

A technique for forecasting the size of hailstones accompanying thunderstorms is presented. Hailstone size is related to its terminal velocity which in turn is related to the updraft velocity of a thunderstorm as derived from parcel buoyancy. This updraft velocity is approximated from positive area measurements on a thermodynamic diagram. The technique is tested on proximity soundings taken near the site and prior to known hail occurrences.

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Andrew E. Mercer, Chad M. Shafer, Charles A. Doswell III, Lance M. Leslie, and Michael B. Richman

1995 ) and the probability of detection ( Wilks 1995 ) were used to evaluate the classification performance. Bayesian neural networks (BNNs; MacKay 1992 ) produced the largest Heidke skill score values, although the BNN suffered from significant false-alarm ratios ( Wilks 1995 ), which can be problematic for tornado forecasting. SVMs minimized this false-alarm ratio and only decreased the Heidke skill score slightly, so it was chosen as the best method. Other techniques were tested in T05

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Mei Xu, Gregory Thompson, Daniel R. Adriaansen, and Scott D. Landolt

. (2017) described an attempt to construct the time-lag-ensemble (TLE) average for the prediction of supercooled liquid water (SLW) using a set of individual HRRR-TLE forecast members. For one case study of a high-impact weather event, it was shown that the TLE technique increased, sometimes substantially, the number of correctly captured icing reports. Also as expected, there was a trade-off with the prediction of negative icing reports, due to the increase in the total predicted icing airspace

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Carolyn A. Reynolds

1. Introduction Given the demonstrated importance of accurate initial conditions for forecast skill (e.g., Rabier et al. 1996 ), as well as the importance of accurate reanalyses for atmospheric process studies, there has been much interest in techniques designed to diagnose analysis errors. Techniques for identifying the fastest growing part of the analysis error include adjoint and singular vector–based methods ( Rabier et al. 1996 ; Buizza et al. 1997 ). Because these

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Michael Scheuerer and Thomas M. Hamill

of observed precipitation ( Park et al. 2008 ; Hamill et al. 2008 ; Bougeault et al. 2010 ). To obtain reliable probabilistic guidance from ensemble precipitation forecasts, a number of statistical postprocessing techniques have been proposed, including nonparametric methods such as the analog method ( Hamill and Whitaker 2006 ; Hamill et al. 2015 ) or decision-tree methods ( Herman and Schumacher 2018 ; Whan and Schmeits 2018 ), and parametric approaches such as extended logistic regression

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