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Michael J. Erickson, Joseph J. Charney, and Brian A. Colle

1. Introduction a. Background There is no universal definition for what represents a fire weather index (FWI). This is because an FWI can be developed using many different techniques depending on its intended purpose. For fire applications on very fine temporal and spatial scales (~1–10 s and ~1–10 m) an index that includes representations of combustion and head transport processes can be employed ( Sullivan 2009 ). For larger spatial and temporal scales, the relevant physical processes

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Mathieu R. Gerbush, David A. R. Kristovich, and Neil F. Laird

improved winter weather forecasting in the Great Lakes region. The Great Lakes Ice Cover–Atmospheric Flux (GLICAF) experiment was conducted with a primary goal of using aircraft to collect unprecedented boundary layer observations over a pack ice–covered Lake Erie. This paper describes the data collection during GLICAF and analysis techniques in section 2 , presents the observed relationships between ice cover and boundary layer properties and heat fluxes in section 3 , and discusses the findings in

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Fei Chen, Kevin W. Manning, Margaret A. LeMone, Stanley B. Trier, Joseph G. Alfieri, Rita Roberts, Mukul Tewari, Dev Niyogi, Thomas W. Horst, Steven P. Oncley, Jeffrey B. Basara, and Peter D. Blanken

1. Introduction This paper evaluates a land-state initialization technique for high-resolution coupled Weather Research and Forecast (WRF)–land surface model (LSM) numerical weather forecasts. Subjects discussed in this article include the concept of a high-resolution land data assimilation system (HRLDAS) based on the “Noah” land surface model, its configuration for nested grids, the time required for its spinup to reach quasi-equilibrium state, its sensitivity to various atmospheric forcing

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Bouchra Nasri, Yves Tramblay, Salaheddine El Adlouni, Elke Hertig, and Taha B. M. J. Ouarda

interest for impact studies, there is a need to develop downscaling techniques tailored for extreme precipitation ( Fowler et al. 2007 ; Maraun et al. 2010a ). To overcome the limitations of climate models in reproducing extremes ( Sillmann et al. 2013 ), several studies have used covariates in nonstationary extreme precipitation frequency analysis (e.g., Vrac and Naveau 2007 ; Aissaoui-Fqayeh et al. 2009 ; Beguería et al. 2010 ; Friederichs 2010 ; Kallache et al. 2011 ; Tramblay et al. 2011

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Michael Weniger and Petra Friederichs

: total cloud cover (TClC) and eight channels of spectral radiance. TClC is derived for the observational data as the fraction of cloudy pixels in a grid box using the NWC SAF MSG v2010 algorithm, which has been developed by the Satellite Application Facility for supporting nowcasting and very short-range forecasting (SAFNWC). The algorithm is based on a multispectral thresholding technique ( Derrien and Le Gléau 2005 , 2010 ). The COSMO model uses a parameterization based on relative humidity in its

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Antony Millner

1. Introduction It has long been recognized that seasonal forecasts hold tremendous potential value for managing climate risks ( Mjelde et al. 1998 ; Messina et al. 1999 ; Palmer 2002 ). Despite this widely accepted assertion, relatively little of that potential value is extracted by actual forecast users ( Rayner et al. 2005 ; Vogel and O’Brien 2006 ), often despite an increase in forecast skill over the past decade ( Saha et al. 2006 ). There is growing awareness in the forecasting

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Jessica Gartzke, Robert Knuteson, Grace Przybyl, Steven Ackerman, and Henry Revercomb

1. Introduction A “climatology” of radiosonde-derived indices provides a valuable historical context for the forecasting of severe weather. Relating this climatology to near-real-time observations from meteorological sensors on satellites could provide a valuable tool in assessing the risk of severe weather ( Doswell 2004 ; Breznitz 1984 ; Barnes et al. 2007 ; Golden and Adams 2000 ; Rothfusz et al. 2014 ; Cintineo et al. 2014 ). For example, convective available potential energy (CAPE) is

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S. Abhilash, A. K. Sahai, N. Borah, S. Joseph, R. Chattopadhyay, S. Sharmila, M. Rajeevan, B. E. Mapes, and A. Kumar

the original atmospheric or oceanic states, as described in Abhilash et al. (2013 , 2014a) . Imperfect model uncertainties are more challenging to represent. In a posterior approach, ensemble predictions from different models are pooled to produce a final forecast probability distribution. In any multimodel approach, the independent skills of the participating models are combined in a judicious manner to reinforce the total skill of the multimodel ensemble (MME) mean (or other statistics). One

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Zengchao Hao, Fanghua Hao, Youlong Xia, Vijay P. Singh, Yang Hong, Xinyi Shen, and Wei Ouyang

critical importance to provide drought information ahead of time for early warning to aid decision makers for drought management. In recent decades, various dynamical (or physical) and statistical models have been developed for drought prediction. Dynamic models of the climate and ocean system provide seasonal climate forecasts of variables such as precipitation that can be used for meteorological drought prediction ( Quan et al. 2012 ; Yoon et al. 2012 ; Mo and Lyon 2015 ), which in turn can be used

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D. S. Wilks

1. Introduction Climate “normals” are statistical estimates of present and/or near-future climate means for such quantities as monthly or seasonal temperature or precipitation. These estimates play a large role in societal perceptions of climate ( Hulme et al. 2009 ), are used in design and planning settings of economic significance (e.g., Dixon and Shulman 1984 ; Kunkel and Court 1990 ), and provide simple, empirical forecasts at lead times of nearly 1 year and longer ( Huang et al. 1996

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