「Statistical Methods in the Atmospheric Sciences(2nd edition), International Geophysics Series 91」, Daniel S. Wilks著, Academic Press, 2005年11月, 648頁, $94.95, ISBN978-0-12-751966-1(本だな)

By 松山洋. 2010

658 citations recorded by the source corpus.

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Citations in the corpus

Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation (2008, methodology): “4a, would be assigned near-zero skill [see the interpretation of the related " attributes diagram " in Hsu and Murphy (1986) and in Wilks (2006), p. 292].”
Water for food: The global virtual water trade network (2011, methodology): “For example, the Gamma distribution has been often used to explain precipitation data, with important applications in hydrologic models, such as flooding or drought estimates [Wilks, 2006].”
Analysis of Convectively Coupled Kelvin Waves in the Indian Ocean MJO (2008, methodology): “…tests were applied to determine the significance of the differences between composites generated for the convectively active and suppressed phases of the MJO by comparing the two relevant bootstrap distributions and assuming the null hypothesis that the two means are the same (e.g., Wilks 2006).”
Surface ozone-temperature relationships in the eastern US: A monthly climatology for evaluating chemistry-climate models (2011, methodology): “The Spearman's rank correlation (Wilks, 2006), a nonparametric statistic (i.e. does not assume Gaussian characteristics ), for all correlation analyses yielded little numerical difference from the Pearson correlation coefficients.”
Parameterizing turbulent exchange over sea ice in winter (2010, background): “The median is the ‘‘most common robust and resistant measure of central tendency’’ (Wilks 2006, p. 26).”
A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending (2014, methodology): “A detailed description of those verification scores can be found in Hamill and Colucci (1997), Mason (1982), Jolliffe and Stephenson (2003), Stanski et al. (1989), and Wilks (2006).”
Toward an Improved Multimodel ENSO Prediction (2015, background): “The six models include 1 Probabilistic reliability (Murphy 1973;Wilks 2006) refers to the condition that for a sufficiently large set of all forecasts of a given probability for an event (such as a 40% likelihood for an El Niño), the corresponding relative frequency of later observed occurrence of…”
Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions (2011, methodology): “Finally, we evaluate the Spearman correlation co-efﬁcient that can be thought of as the Pearson correlation coefﬁcient between the ranked variables, and is referred to here as the ‘‘rank correlation’’ (Wilks 2006).”
Uncertainty analysis of bias from satellite rainfall estimates using copula method (2014, methodology): “Also, in order to assess the rain detecting skill of SREs, two categorical statistical indices (Wilks, 2006) including the probability of detection (POD) and false alarm ratio (FAR) are used (Eqs.”
Visualizing Multiple Measures of Forecast Quality (2009, background): “As has been amply discussed in the literature, forecast verification is a complex and multidimensional problem (e.g., Murphy 1993; Brooks and Doswell 1996; Katz and Murphy 1997; Wilks 2006; Morss et al. 2008 and many others).”
Bayesian Changepoint Analysis for Extreme Events (Typhoons, Heavy Rainfall, and Heat Waves): An RJMCMC Approach (2010, methodology): “A Poisson process is a proper probability model for describing independent, rare event counts (e.g., Wilks 2006).”
Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction (2010, methodology): “…approach is based on regression , using model output statistics (MOS) schemes based on multiple linear regression for linear variables, such as temperature or pressure, and logistic regression for binary variables, such as precipitation occurrence or freezing (Glahn and Lowry 1972; Wilks 2006a).”
Comparison of six methods for the interpolation of daily, European climate data (2008, background): “However, because of its insensitivity to biases and errors in variance, the correlation coefficient should be considered as a measure of potential skill [Murphy and Epstein, 1989; Wilks, 2006].”
Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes (2014, methodology): “…will therefore also discuss the quality of our model with respect to the forecast verification attributes of reliability, resolution, and sharpness (Wilks 2006; Jolliffe and Stephenson 2003). a. Model selection results and goodness of fit We carry out themodel selection separately for winter and…”
Combining probability forecasts (2010, methodology): “The MOS probability forecasts are statistical forecasts that apply logistic regression techniques to the output of a numerical weather prediction model and recent weather observations (Glahn and Lowry 1972; Wilks 2006).”