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Fuqing Zhang and Jason A. Sippel

which society will always have to cope, more so given that coastal populations vulnerable to hurricanes are still on the rise ( Pielke 1997 ). Inherent uncertainties in hurricane forecasts illustrate the need for developing advanced ensemble prediction systems to provide event-dependent probabilistic forecasts and risk assessment. In practice, despite an increasing role and demonstrated benefits of using ensembles in aiding deterministic hurricane forecasting ( Krishnamurti et al. 1999 ; Zhang et

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Ping Lu, Ning Lin, Kerry Emanuel, Daniel Chavas, and James Smith

, and risk assessment of TC rainfall is therefore an important task. Water plays an essential role in TCs; evaporation of seawater into the air is the most important source of energy driving TCs ( Emanuel 1986 , 1991 ). TCs typically form from cloud clusters over large bodies of warm water. The inflowing air toward the low-pressure center experiences a large increase in entropy owing to surface enthalpy fluxes. Air then rises nearly moist adiabatically in the eyewall and eventually loses its excess

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Kerry Emanuel and Fuqing Zhang

track error as well as errors in the forecast of the storm’s oceanic and atmospheric thermodynamic environment. This suggests that these other sources of error are important in the actual forecast errors. To further explore the growth of intensity error in a perfect model framework, we now examine the divergence of pairs of CHIPS simulations in which somewhat more realistic perturbations are added to the environmental shear and storm track. 3. Error growth in a tropical cyclone risk assessment model

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Michael T. Montgomery and Roger K. Smith

contribution of supergradient flow to azimuthal winds, its computation relies on dynamical diagnostics. PI s on the other hand, is a straightforward thermodynamic bound on surface winds, a quantity that is more relevant to hurricane risk assessment.” We are puzzled by this remark since the formula for PI s [Eq. (16)] depends on a knowledge of k s * , k 10 , and T out , none of which are known a priori, but must be determined by running a numerical model (see their section 3b). Indeed, as can be seen

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Romeo Alexander, Zhizhen Zhao, Eniko Székely, and Dimitrios Giannakis

which the training data is the nearest neighbor (in data space) to the data observed at initialization time : that is, With the analog identified, the weights are then set to Dirac delta functions centered at the analog data point , and the forecasting function in (1) becomes A major drawback to this method is the risk of the resulting forecast being highly nonsmooth in its argument, as the “best” analog jumps around. Such nonsmooth behavior should be avoided in climate applications and

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Robert M. Chervin

univariatestatistical tests are presented which permit a straightforward assessment of the extent to which observedand GCM simulated climates agree or differ with respect to various first- and second-moment measures (i.e.,ensemble averages and standard deviations) of the climate. As an example of this approach, the verticallyaveraged transient heat flux,V'T' is considered as a basic climate element and ensemble averages andstandard deviations of this climate element are objectively compared for the same number of

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Alexey Yu. Karpechko, Douglas Maraun, and Veronika Eyring

: Quantitative performance metrics for stratospheric-resolving chemistry-climate models . Atmos. Chem. Phys. , 8 , 5699 – 5713 . Weigel , A. P. , R. Knutti , M. A. Liniger , and C. Appenzeller , 2010 : Risks of model weighting in multimodel climate projections . J. Climate , 23 , 4175 – 4191 . Whetton , P. , I. Macadam , J. Bathols , and J. O’Grady , 2007 : Assessment of the use of current climate patterns to evaluate regional enhanced greenhouse response patterns of climate

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David C. Fritts, Ling Wang, Marvin A. Geller, Dale A. Lawrence, Joe Werne, and Ben B. Balsley

, 1999 ; Balsley et al. 1998 , 2003 , 2013 ; Muschinski and Wode 1998 ; Nastrom and Eaton 2001 ; Chuda et al. 2007 ; Fritts et al. 2004 , 2013 ). Instabilities and turbulence at small spatial scales are of considerable interest throughout the atmosphere because of their local effects, their roles in weather and climate, the risks they pose for aircraft, and their implications for atmospheric and astronomical observations (e.g., Reitar 1969 ; Coulman 1969 ; McIntyre 1990 ; Garratt 1994

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Mark J. Stevens and Gerald R. North

is present in the observed data and that it hasthe same phase as the model-generated signal, whichimplies a scaling factor greater than zero. This allowsus to use a one-tailed test of the null hypothesis. In theGFDL case, the value for a of 1.30 is 1.94Crtota~ fromzero. This implies a risk in rejecting the null hypothesisof 2.6%, or a confidence level of 97.4%. In the MPIcase, the value for a of 0.93 is 1.75Crtotal from zero. Thisimplies a risk of rejecting the null hypothesis of 4.0%,or a

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Daniel R. Chavas, Ning Lin, and Kerry Emanuel

circulation ( Merrill 1984 ; Liu and Chan 1999 ; Knaff and Zehr 2007 ; Dean et al. 2009 ; Chavas and Emanuel 2010 ; Knaff et al. 2014 ), while in risk analysis size typically refers to the radius of maximum wind due to its relevance to damage potential ( Irish et al. 2008 ; Lin et al. 2012 ). Perspectives aside, determining which of these metrics is more or less “correct” from a physical standpoint depends principally on their covariability. Ultimately, then, the proper interpretation of any

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