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J. V. Ratnam, Takeshi Doi, Willem A. Landman, and Swadhin K. Behera

1. Introduction Most of the subsistence farmers across South Africa depend on the onset of summer rains for planting maize, the staple food of the country ( Tadross et al. 2005 ; Moeletsi et al. 2011 ). Successful long-lead forecasting of the dates of onset would be beneficial to the farmers in planning their farming activities. There have been some studies, for example, Reason et al. (2005) , Tadross et al. (2005) , and Moeletsi et al. (2011) , to understand the processes associated with

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Stephen Cusack and Alberto Arribas

1. Introduction The future state of the atmosphere is influenced by chaotic internal dynamics (e.g., Lorenz 1969 ; Lau 1981 ; Hendon and Hartmann 1985 ; Branstator 1995 ) that can amplify the uncertainties in forecast system initialization and formulation to produce different possible seasonal climate states. Seasonal forecasting systems employ ensembles of simulations to sample these uncertainties, and the prediction of a meteorological quantity is most appropriately viewed as a

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Xiaosong Yang, Timothy DelSole, and Hua-Lu Pan

1. Introduction Leith (1978) proposed a novel method for empirically correcting a dynamical forecast model in which a term is added to the governing equations that subtracts the predicted tendency error at each time step. This type of empirical correction method differs from after-the-fact correction methods, such as subtracting the bias of a forecast, in that the former involves modifying the dynamical equations while the latter involves modifying the forecast after the unmodified model

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Suzana J. Camargo and Anthony G. Barnston

1. Introduction Tropical cyclones (TCs; see the appendix for a list of the acronyms used in this paper) are one of the most devastating types of natural disasters. Seasonal forecasts of TC activity could help the preparedness of coastal populations for an upcoming TC season and reduce economical and human losses. Currently, many institutions issue operational seasonal TC forecasts for various regions. In most cases, these are statistical forecasts, such as the Atlantic hurricane outlooks

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Roman Krzysztofowicz and W. Britt Evans

1. Introduction a. Forecasting stochastic process An element of sensible weather is typically forecasted and observed at predetermined times in the daily cycle. The associated sequence of predictands (variates whose realizations are forecasted) forms a discrete-time stochastic process , or a time series, which can be characterized by its marginal distribution functions and its temporal dependence structure (in particular, the autocorrelation structure). For a continuous element, such as

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Allen B. White, Daniel J. Gottas, Arthur F. Henkel, Paul J. Neiman, F. Martin Ralph, and Seth I. Gutman

1. Introduction The “snow level” is a term used by National Oceanic and Atmospheric Administration’s (NOAA) forecasters at the National Weather Service (NWS) to ascribe the altitude in the atmosphere where falling snow melts to rain. The snow level should be distinguished from another term used by forecasters, the “free atmosphere freezing level,” which is the altitude corresponding to the 0°C isotherm. However, in cloud physics and in other fields, the term “melting level” is often used in

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Kieran T. Bhatia, David S. Nolan, Andrea B. Schumacher, and Mark DeMaria

1. Introduction An accurate forecast of a major tropical cyclone (TC) landfall represents one of the most remarkable feats of the earth sciences. Forecasting agencies can now produce skillful 120-h forecasts of the intensity, timing, and location of a TC making landfall. These long-range TC forecasts appear particularly impressive within the context of other natural disasters, such as earthquakes, tornadoes, tsunamis, and volcanic eruptions, which can only be diagnosed hours or sometimes

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Barry H. Lynn, Guy Kelman, and Gary Ellrod

and Gilson 2009 ). Various diagnostic approaches have been developed to predict lightning in forecast models (e.g., McCaul et al. 2009 ; Dahl et al. 2011 ). Recently, Fierro et al. (2013) implemented a physics-based, explicit lightning scheme within the Weather Research and Forecasting (WRF) Model ( Skamarock et al. 2008 ) that treats space charges as state variables and explicitly solves for the three components of the ambient electric field. Additionally, Lynn et al. (2012) developed a

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Elisa Brussolo, Jost von Hardenberg, and Nicola Rebora

1. Introduction Precipitation intensity represents the crucial atmospheric variable for the operational assessment of hydrometeorological risks; unfortunately, it is also one of the most difficult to forecast reliably by current weather models. Uncertainty in quantitative precipitation forecasts (QPFs) arises as a result of measurement errors, incomplete observations, insufficiently resolved initial conditions, an incomplete representation of the physics of the problem, finite numerical

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Patrick T. Marsh, John S. Kain, Valliappa Lakshmanan, Adam J. Clark, Nathan M. Hitchens, and Jill Hardy

1. Introduction Rare meteorological events 1 that occur on small spatial and short temporal scales pose significant challenges to forecasters. This is related to the limited predictability of phenomena occurring on short time–space scales. However, these events compose a substantial portion of meteorological phenomena that negatively impact society, such as heavy rain, large hail, and tornadoes. Thus, accurate numerical guidance of these events would provide large societal benefits. Convection

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