Search Results

You are looking at 41 - 50 of 14,766 items for :

  • Regression analysis x
  • All content x
Clear All
Paula J. Brown, Raymond S. Bradley, and Frank T. Keimig

–Southern Oscillation (ENSO) and the Arctic Oscillation (AO) ( Griffiths and Bradley 2007 ; Leathers et al. 2008 ). While a comprehensive analysis of the effect of such mechanisms is beyond the scope of this study, an exploratory analysis was performed. Leathers et al. evaluated the association between teleconnection patterns and temperature/precipitation values using significant variables obtained from a stepwise, multiple linear regression (MLR). Six teleconnection patterns were investigated: the Arctic

Full access
Simon A. Crooks and Lesley J. Gray

-biennial oscillation (QBO) in lower-stratospheric equatorial winds, but not when the full dataset was used in their analysis. Since then several other observational studies have been performed. In a regression analysis using 18 years (1980–97) of Stratospheric and Microwave Sounding Units(SSU/MSU) temperature data corrected and compiled by the National Centers for Environmental Prediction (NCEP)–Climate Prediction Center (CPC), Hood (2004) , referred to hereafter as H2004 ) found a vertical three-cell pattern of

Full access
Santiago Beguería and Sergio M. Vicente-Serrano

number of parameters. The spatial database was then sampled at the locations corresponding to the climatic stations. The regression analysis was performed upon this database using SPSS, version 12, software, and the final maps showing the distribution of the three parameters were obtained using ArcView, version 3.2, GIS. We used a jackknife cross-validation method to validate the maps of GP parameters. A recursive procedure removed one station at a time and calculated the regression model with the

Full access
Daniel Gombos and Ross N. Hoffman

incoming observations in advance of relatively slow data assimilations ( Gombos 2009 ), and 3) identify, for purposes including supplementary forecast guidance and adaptive observing, the antecedent atmospheric features to which the ExDS is most sensitive ( Gombos et al. 2012 ). Ensemble regression is a multivariate linear inverse technique that uses ensemble model output to make inferences about the linear relationships between vector-valued forecast and/or analysis fields, often gridded “maps” of

Full access
Michael K. Tippett and Timothy DelSole

is defined to be The matrix is square with positive diagonal entries and is thus invertible. Therefore, the simple LR and CA forecasts are In the language of principal component analysis (PCA), the columns of the matrices and are the empirical orthogonal function (EOFs) and principal components (PCs), respectively, of the anomaly data . The factors of serve to normalize the PCs to have unit variance since the columns of are unit vectors with zero mean. Principal component regression

Full access
George B. Frisvold and Anand Murugesan

measures ( Bishop et al. 2010 ; Chouinard et al. 2008 ; Sheeder and Lynne 2011 ). They test this theory through discrete choice regression analysis, where the adoption regression depends on a reduced form specification of utility V = V ( x , ω ). The papers find that many variables equivalent to ω above are individually, statistically significant and that the predictive power of the models improve when these behavioral variables are included. Equation (1) further acknowledges that farm

Full access
P. Friederichs and A. Hense

dynamical and statistical approaches. This study presents a novel approach for statistical downscaling or recalibration that derives extremal quantile forecasts of precipitation. In the case of normally distributed response variables (e.g., temperature), only the expectation value and a measure of variance are needed to provide the complete probabilistic behavior of a response variable. The expectation value is estimated by standard linear regression using least squares methods. The variance is derived

Full access
Bob Glahn

probabilistic guidance forecasts; linear regression applied to forecasting events for that purpose was called regression estimation of event probabilities (REEP) by Bob Miller (1968) . REEP was easy to use, and predictor selection, either forward, backward, or stepwise—whatever the developer preferred—from a large number of potential predictors, typically over 100, was straightforward. Such postprocessing was especially useful because NWP did not at first provide forecasts of the elements desired, such as

Full access
Michael E. Mann, Scott Rutherford, Eugene Wahl, and Caspar Ammann

conclusions of MRWA05 using an implementation of the “regularized expectation maximization (RegEM)” procedure that does not suffer from the technical issue SK07 note. SK07 confuse the RegEM climate field reconstruction (CFR) method, which does not in general suffer from the issue they raise, with one particular implementation of the method as employed by MRWA05 (and previously by Rutherford et al. 2005 ). In that particular implementation, “ridge regression” was used to accomplish the

Full access
Matthew Potoski, R. Urbatsch, and Cindy Yu

weather-based effects on attitudes toward global warming and presidential approval. This implies that accurately measuring public opinion requires adjusting standard survey results with conditioning strategies, such as weights, propensity score matching, or regression-based controls relating to weather. 2. Temperature, public opinion, and surveys Though many components of weather (e.g., precipitation or windiness) can affect behavior, we focus on deviations from normal temperature. People have

Full access