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J. M. Gutiérrez, D. San-Martín, S. Brands, R. Manzanas, and S. Herrera

values, so the methods detected to be nonrobust are those leading to wrong climate change signals with low values. For instance, critical differences of approximately 1°C are found when comparing analog and regression methodologies. Therefore, the proposed test for robustness based on warm historical periods provides an objective criterion for discarding non robust statistical downscaling techniques for climate change future projections. This is the case for the analog and pure weather typing methods

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Daniel B. Walton, Fengpeng Sun, Alex Hall, and Scott Capps

California domain considered, with the presence of a number of mountain ranges with wintertime snow coverage, including the San Gabriel and San Bernardino Mountain ranges. Pierce et al. (2013) found that when a pair of GCMs was dynamically downscaled, the average difference in the annual warming between the Southern California mountains and coast was twice that of two common statistical downscaling techniques, bias correction with spatial disaggregation (BCSD) and bias correction with constructed

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Meghan J. Mitchell, Brian Ancell, Jared A. Lee, and Nicholas H. Smith

rotor swept area, partially due to systematic errors related to deficiencies in model physics parameterizations. These errors can be partially addressed with statistical postprocessing techniques that use statistical models over training data periods to relate model forecasts to observations. One common and established technique is model output statistics (MOS). MOS uses a multiple linear regression to correct systematic errors in a forecast model by using deterministic NWP forecasts of certain

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Meghan J. Mitchell, Brian Ancell, Jared A. Lee, and Nicholas H. Smith

rotor swept area, partially due to systematic errors related to deficiencies in model physics parameterizations. These errors can be partially addressed with statistical postprocessing techniques that use statistical models over training data periods to relate model forecasts to observations. One common and established technique is model output statistics (MOS). MOS uses a multiple linear regression to correct systematic errors in a forecast model by using deterministic NWP forecasts of certain

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Cristián Chadwick, Jorge Gironás, Sebastián Vicuña, Francisco Meza, and James McPhee

suitable alternative to cope with GCM uncertainty when dealing with climate change, this paper develops an ensemble technique for the mapping of GCM changes to local stations, in which both the local climate variability and the GCMs’ statistics are preserved (i.e., the technique is unbiased). The approach extracts future changes from annual precipitation and temperature time series derived from multiple GCM runs. A statistical framework combining these changes allows for using the needed trend

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I. Martínez-Zarzoso

; Cai et al. 2016 ; Coniglio and Pesce 2015 ). For the method used to estimate the statistical relationship between migration and climate change, the authors that focus on bilateral migration use the gravity model of trade, estimated with the most recent techniques proposed in the trade literature. Most of them include a number of fixed effects (dummy variables) to control for unobservable factors related to the destination country’s migration policies, time-invariant origin country factors, and

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Andrew E. Mercer, Michael B. Richman, Howard B. Bluestein, and John M. Brown

) was to use the “perfect prog” approach, in which it is assumed that the forecast model is “perfect” and the dependent data sample used to derive the statistical technique is based solely on observations. Consistent with the perfect prog approach, we chose to use the actual rawinsondes, rather than, for example, the North American Regional Reanalysis dataset, to define the subsynoptic environment, since we wanted to capture aspects of the stratification and hodograph upstream of the Continental

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Marc Bocquet, Carlos A. Pires, and Lin Wu

. Hypothesis testing is a well-developed topic in statistics, and many techniques meant to test the Gaussianity of random variables exist. Among the many tests of normality available in the statistical literature, the skewness and kurtosis, which are directly defined by the cumulants of a distribution, have been used very early. Lawson and Hansen (2004) have used them to assess how differently stochastic and deterministic ensemble-based filters handle non-Gaussianity. There are many other tests such as

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

type correctly, objective statistical and learning methods are employed on the WRF output. Statistical techniques are commonly utilized in meteorological studies (i.e., Reap and Foster 1979 ; Michaels and Gerzoff 1984 ; Billet et al. 1997 ; Marzban et al. 1999 ; Schmeits et al. 2005 ). Learning methods, such as support vector machines (SVMs; Haykin 1999 ), are not so widely used in meteorology but have been applied to previous severe weather studies. For example, Trafalis et al. (2005

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BANNER I. MILLER, ELBERT C. HILL, and PETER P. CHASE

540MONTHLY WEATHER REVIEWVal. 96, No. 8A REVISED TECHNIQUE FOR FORECASTING HURRICANE MOVEMENT BY STATISTICAL METHODSBANNER 1. MILLER*, ELBERT C. HILL**, and PETER P. CHASE**National Hurricane Research Laboratory and **National Hurricane Center, ESSA, Miami, Fla.ABSTRACTThe NHC-64 statistical equations for predicting the movement of hurricanes have been in operational use for 4 yr.These equations have continued to perform well. Following the 1966 hurricane season, however, it

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