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John Bjørnar Bremnes

several spline configurations. To further increase robustness forecasts from these could potentially be combined. Averaging forecasts generated by splines with one and two interior knots gave approximately 0.1% improvement relative to the former. Thus, only minor improvements on the overall performance could be expected. The bootstrap is another averaging technique which is applied in many situations to reduce estimation uncertainty and thereby also increasing the forecast skill. A test with 50

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Constantin Junk, Luca Delle Monache, and Stefano Alessandrini

quantiles from the cumulative distribution function at levels for ( Gneiting et al. 2005 ). c. Analog ensemble An analog ensemble can be estimated using a set of M past verifying measurements that correspond to the M past forecasts (analogs), which are most similar to a current forecast. While Hamill and Whitaker (2006) used the analog ensemble as a technique to calibrate an existing ensemble forecast, Delle Monache et al. (2013b) proposed the analog ensemble as a mean to generate an

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Qingnong Xiao, Xiaoyan Zhang, Christopher Davis, John Tuttle, Greg Holland, and Patrick J. Fitzpatrick

advanced data assimilation technique together with high-resolution aircraft observations within the inner core can enhance the initial vortex definition and improve subsequent forecast skill for intensity. Simulations based on high-resolution analysis will provide more detailed dynamics and thermodynamics of the vortex structure, eyewall, eye, and inner and spiral rainbands near the eyewall (e.g., Liu et al. 1997 ; Zhu et al. 2004 ; Yau et al. 2004 ; Wong and Chan 2006 ; Krishnamurti et al. 2005

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Caren Marzban and Scott Sandgathe

1. Introduction It has become evident that the performance of numerical weather prediction (NWP) models must be assessed within a framework that acknowledges the existence of “objects” in the spatial field of observations and forecasts. Standard verification techniques ignore the spatial structure of forecast and observation fields and treat errors inappropriately. For example, a misplaced forecast’s contribution to the mean squared error is independent of the magnitude of the displacement. Or

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Daniel Hodyss

1. Introduction Numerical prediction through state estimation in the geophysical sciences strives for high resolution and longer cycling intervals and/or assimilation windows. High resolution, which presently refers to resolving the mesoscale, provides the ability to resolve those details of the circulation that are important to making a useful forecast, such as convection along frontal boundaries and the internal dynamics within a tropical cyclone. The cycling interval refers to the time

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Javier Amezcua, Eugenia Kalnay, and Paul D. Williams

model representing inertial oscillations of the simple harmonic type ( Williams 2009 ). In the present paper, the filter will be implemented and tested in the Simplified Parameterizations, Primitive Equation Dynamics (SPEEDY) model ( Molteni 2003 ), a relatively simple nonlinear atmospheric general circulation model (AGCM). Our objectives are to examine whether the use of the RAW filter changes either the climatology or the skill of weather forecasts, or both. For the first objective, we will

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Hui-Ling Chang, Huiling Yuan, and Pay-Liam Lin

system was developed by the Central Weather Bureau (CWB) in partnership with the National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Systems Division (NOAA/ESRL/GSD), for the purpose of improving the capability of short-range QPFs for severe weather systems. At present, major forecast centers pay more and more attention to advanced data assimilation schemes. The three-dimensional variational data assimilation (3DVAR) technique has been widely used in operational

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Paul J. Roebber

and Vallée 2002 ), N -day running mean bias removal (e.g., Cheng and Steenburgh 2007 ), and weighted MOS (e.g., Baars and Mass 2005 ). Other bias removal methods include incorporating observations made after the time of forecast issuance but prior to verification (e.g., Nipen et al. 2011 ; Huang et al. 2012 ), and the use of analogs ( Delle Monache et al. 2011 ). Evolutionary programs [EP; see Fogel (1999) for a historical overview of the technique] have been shown in recent studies to

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Luca Delle Monache, F. Anthony Eckel, Daran L. Rife, Badrinath Nagarajan, and Keith Searight

as AnEn requires only a single model forecast, as opposed to the multiple model runs of an NWP ensemble. Another advantage is that forecast uncertainty is based solely upon past observations, thereby eliminating the need to simulate all sources of NWP forecast uncertainty via sophisticated and computationally intensive techniques, and perhaps also avoiding the need for postprocessing calibration. The AnEn attempts to capture flow-dependent error growth by assigning the observed errors from

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Yanfeng Zhao, Donghai Wang, and Jianjun Xu

. 2015 ). Consequently, starting from the perspective of the better predictability of the large-scale circulation, an effort for extending the forecast lead time for precipitation, especially for heavy rains and PSR events, is worthwhile and reasonable to enhance disaster prevention and mitigation capabilities. For the large-scale circulation, spectral nudging (SN) is a scale-selective interior constraint technique ( von Storch et al. 2000 ) in a regional model. When the large-scale systems develop

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