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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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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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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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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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Jeng-Ming Chen, Russell L. Elsberry, Mark A. Boothe, and Lester E. Carr III

1. Introduction a. Characteristics of objective track guidance The objective track guidance that a tropical cyclone (TC) forecaster has available may be grouped into four categories ( Elsberry 1995 ): (i) empirical, for example, climatology, persistence of past motion, CLImatology and PERsistence (CLIPER), and analog techniques; (ii) statistical-synoptic, in which additional meteorological information is incorporated, usually via statistical regressions using

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Fengpeng Sun, Daniel B. Walton, and Alex Hall

1. Introduction In Walton et al. (2015 , hereinafter Part I) , we described a hybrid dynamical–statistical technique for downscaling the global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to a 2-km resolution over the greater Los Angeles region. As an example of its capabilities, we applied this technique to all available CMIP5 GCMs for the RCP8.5 anthropogenic greenhouse gas emissions scenario and projected the midcentury most likely (ensemble mean

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John W. Kidson and Craig S. Thompson

1. Introduction The horizontal resolution of current general circulation models (GCMs) is generally too coarse to represent regional climate variations on scales needed for assessment of their economic and social impact. The efforts to bridge this gap have been reviewed by Giorgi and Mearns (1991) and, subsequently, in the recent International Panel on Climate Change report ( IPCC 1995 ). The principal techniques involve a semiempirical (statistical) approach or the use of regional climate

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Robert M. White, Royce C. Derby, Duane S. Cooley, and Florence A. Seaver

contour heightsas functions of the preceding two-day sequence of contour heights at the same level. The results indicatethat the statistical procedure may provide useful forecasts for twenty-four hours over regions of the hemisphere with adequate data coverage. The utility of the system for extended-range forecasting is also discussed.1. IntroductionAll meteorological forecasting techniques imply theapplication of filters to sets of observed data. Insynoptic-subjective forecasting, the individual may

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