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Jonathan Poterjoy

Snyder 2000 ; Lorenc 2003 ; Buehner 2005 ; Liu et al. 2008 ; Zhang et al. 2009 ). The above-mentioned strategies approximate error distributions for observations and model forecasts using Gaussian probabilities, causing them to be suboptimal when the model dynamics are nonlinear, or when the assimilated observations relate nonlinearly to the model state. Despite their limitations, techniques that rely on Gaussian assumptions have performed well for operational weather prediction and research (e

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Yalei You, Nai-Yu Wang, Ralph Ferraro, and Patrick Meyers

. 2015 ), for example, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), Global Satellite Mapping of Precipitation (GSMaP; Kubota et al. 2007 ), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN; Ashouri et al. 2015 ). In fact, the precipitation

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S. Wang, G. H. Huang, B. W. Baetz, and W. Huang

. Therefore, a factorial design is useful for taking into account various combinations of α -cut levels applied to fuzzy variables, revealing the potential correlations among fuzzy variables and enhancing the flexibility in practical problems. The factorial design is a powerful statistical technique to measure model outputs by systematically varying model parameters (factors) with each having a discrete set of levels ( Montgomery 2000 ; Wang and Huang 2015 ). In a factorial design, an experimental run

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Laura Slivinski and Chris Snyder

–334 . Bickel , P. , B. Li , and T. Bengtsson , 2008 : Sharp failure rates for the bootstrap particle filter in high dimensions. Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh , B. Clarke and S. Ghosal, Eds., Institute of Mathematical Statistics, 318–329 . Bocquet , M. , C. A. Pires , and L. Wu , 2010 : Beyond Gaussian statistical modeling in geophysical data assimilation . Mon. Wea. Rev. , 138 , 2997 – 3023 , doi: 10.1175/2010MWR3164

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Daniel Hodyss, William F. Campbell, and Jeffrey S. Whitaker

statistic, , can readily be calculated by using the same techniques as in section 3a , and is found to vanish (i.e. ). Equation (3.14) shows that there are two components of the expected squared difference between the sample mean estimate and the true posterior mean: one owing to non-Gaussianity and the other due to sampling error. Equation (3.14) can be used to calculate the MSE in our posterior mean estimate as and is the VP about the true posterior mean. The MSE ( ) of has three terms

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R. Andrew Weekley, R. Kent Goodrich, and Larry B. Cornman

time series ( Ma and Perkins 2003 ). Other techniques for image segmentation include the level set method discussed in Airouche et al. (2009) . Mathematical morphology is discussed in Shih (2009) . Various clustering techniques are compared in Kettaf et al. (1996) and could be used to find plumes in lidar images as well. Some morphology techniques are used in the present paper to classify certain features associated with hard targets. Plumes can be found as a range (statistical) anomaly using

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Guang Wen, Alain Protat, Peter T. May, William Moran, and Michael Dixon

has been described in detail in Part I . A Gaussian mixture was adopted to model the PDFs of polarimetric variables and temperature for a given hydrometeor type with minimal bias. The Gaussian mixture should have better performance than a parameterization of the PDFs with a single shape, such as a multivariate Gaussian function and other shapes used in statistical classifiers. These PDFs are then used in a Bayesian classifier. As described in Part I , the cluster-based method has some advantages

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Laurie Trenary and Timothy DelSole

principal goal of this work is to understand the source of AMO predictability in long control simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive. Rather than use a single index to represent the AMOC variability, a novel optimization technique is used to identify structures of oceanic meridional overturning streamfunction most related to AMO ( DelSole and Tippett 2009 ; Jia and DelSole 2011 ). This method maximizes the squared correlation between the AMO and the

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Daniel Hodyss, Elizabeth Satterfield, Justin McLay, Thomas M. Hamill, and Michael Scheuerer

techniques. While ensemble weather prediction systems have improved greatly, the predictions are still frequently affected by systematic errors, including biased ensemble mean forecasts and often an insufficiency of ensemble spread. Consequently, much attention has been paid in recent years to statistical postprocessing techniques, whereby the current guidance is adjusted based on relationships noted between past forecasts and observations/analyses. In many circumstances, the goal is to produce a

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Emerson LaJoie and Timothy DelSole

attribution studies, and it assumes that internal variability is independent in time, has a known distribution, and is additive relative to the variability of the forced component (e.g., Allen and Tett 1999 ; Jones et al. 2013 ; Imbers et al. 2014 ). To the extent that this statistical model is correct, changes in internal variability due to anthropogenic forcing can be estimated using ensemble techniques and then compared with estimates of internal variability simulated from preindustrial control runs

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