Statistical Methods in the Atmospheric Sciences

By Philip H. Ramsey. 2005

1,011 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a5765169625611cbd0f322b26e7eff2bfb98c1ef

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Citations in the corpus

Impact of temperature and rainfall on rice production in Punjab, Pakistan (2020, background): “In other words, such function projects a future value along a line of best fit which is based on historical data (Wilks 2011; Winkler 1994; Winkler et al. 1996).”
Dual state-parameter estimation of hydrological models using ensemble Kalman filter (2005, background): “A large number of procedures exist to evaluate the ensemble forecasts [12,27,38,48].”
Cross-validation of bias-corrected climate simulations is misleading (2018, methodology): “in weather forecasting (Jolliffe and Stephenson, 2003; Mason, 2008; Wilks, 2006) and perfect predictor experiments of downscaling methods (Themeßl et al.”
Bias patterns and climate change signals in GCM-RCM model chains (2018, background): “The covariance of the data determines the angle of the ellipse (Wilks 2011).”
Forecast calibration and combination: A simple Bayesian approach for ENSO (2004, methodology): “2) EMPIRICAL MODEL CROSS VALIDATION To avoid artificial skill, the empirical model has been evaluated using a cross-validation ‘‘leave one out’’ method (Wilks 1995, his section 6.3.6).”
Combining probability forecasts (2010, methodology): “regression techniques to the output of a numerical weather prediction model and recent weather observations (Glahn and Lowry, 1972; Wilks, 2006).”
Prevalence, thresholds and the performance of presence–absence models (2014, background): “This equation shows that factoring out prevalence Pr(y=1) from the joint distribution Pr(p,y) leaves the conditional distributions Pr(p|y), which are used to calculate discrimination ability (Fig. 2g; Murphy & Winkler 1987; Pearce & Ferrier 2000; Wilks 2011).”
Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: A comparison (2014, background): “Further details can be found in the documentation of the Ensemble Verification System (Brown et al., 2010) as well as in reference works on forecast verification (Wilks, 2006; Jolliffe and Stephenson,850 2012).”
Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies (2014, background): “The skewness has strong sensitivity to outliers (see e.g.Wilks, 2006) but this is a desirable property when we are looking for the large but infrequent errors associated with snow-scattering situations.”
A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites (2009, background): “The latter is a generalization of the well known reliability diagram for probabilistic forecasts of two-category variables [Wilks, 1995].”
Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin (2017, methodology): “If the p value is less than or equal to 0.05 (5 %), the result is deemed statistically significant, i.e. there is a sig- nificant relationship between the gauge and satellite rainfall (Wilks, 2006).”