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Craig H. Bishop, Teddy R. Holt, Jason Nachamkin, Sue Chen, Justin G. McLay, James D. Doyle, and William T. Thompson

resolution-dependent initial condition perturbations but easily could if the adjoint of a nested model was available. The breeding ensemble generation technique ( Toth and Kalnay 1993 , 1997 ) when applied to the mesoscale ( Stensrud et al. 1999 ) creates mesoscale analysis perturbations from mesoscale forecast perturbations and hence, in principle, provides initial condition perturbations at all scales resolved by the limited-area model (LAM). However, as pointed out by Wang and Bishop (2003) , the

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Meghan J. Mitchell, Brian Ancell, Jared A. Lee, and Nicholas H. Smith

rotor swept area, partially due to systematic errors related to deficiencies in model physics parameterizations. These errors can be partially addressed with statistical postprocessing techniques that use statistical models over training data periods to relate model forecasts to observations. One common and established technique is model output statistics (MOS). MOS uses a multiple linear regression to correct systematic errors in a forecast model by using deterministic NWP forecasts of certain

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Meghan J. Mitchell, Brian Ancell, Jared A. Lee, and Nicholas H. Smith

rotor swept area, partially due to systematic errors related to deficiencies in model physics parameterizations. These errors can be partially addressed with statistical postprocessing techniques that use statistical models over training data periods to relate model forecasts to observations. One common and established technique is model output statistics (MOS). MOS uses a multiple linear regression to correct systematic errors in a forecast model by using deterministic NWP forecasts of certain

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Domingo Muñoz-Esparza, Robert D. Sharman, and Wiebke Deierling

involvement. Another component that could be improved in the current GTG algorithm is the selection of the diagnostics that constitute the final ensemble combination. The current method employs a forward-selection optimization technique that maximizes the skill of the ensemble prediction for a given statistical metric of relevance. As the forecasting skill metric, the area under the receiving operating characteristic curve (AUC) is typically used, which represents the degree or measure of separability of

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David Ahijevych, James O. Pinto, John K. Williams, and Matthias Steiner

meteorologists yet has shown promise in several other complex weather prediction applications, as described below. Statistical models have long been a part of weather forecasting. For example, model output statistics (MOS) based on multiple linear regressions are routinely used to compensate for systematic model biases and to generate reliable probabilistic forecasts of precipitation, cloud cover, and other variables ( Glahn and Lowry 1972 ). Analog statistical techniques identify similar past weather

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Shiqiu Peng, Yineng Li, and Lian Xie

instability, too many local minimum points, and nonpositive definite features of the background error covariance. 5. Conclusions and discussion In this study, we employ the adjoint technique to adjust the parameters of wind stress drag coefficient in the three-dimensional POM for improving storm surge forecasts. The identical twin experiments are performed by assigning different error sources. The twin experimental results indicate that it is an efficient and practical way to reduce errors in storm

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Austin Coleman and Brian Ancell

value and cost exponentially more in terms of computational resources ( Kain et al. 2008 ). Thus, horizontal grid spacings of 2–4 km are common in operational ensemble systems to provide valuable probabilistic forecast guidance for severe convection. While model physics improvements, high resolution, and advancements in data assimilation techniques benefit the predictability of the atmosphere generally, other postprocessing techniques that harness ensemble information specific to various high

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Hsiao-Chung Tsai and Russell L. Elsberry

storm, Tsai and Elsberry (2014) gave a higher weight for those analogs that better matched the 3–5-day tracks, because they hypothesized that the track was a primary determinant of the intensity changes in that time interval. Tsai and Elsberry (2016) demonstrated that this simple analog technique, which can be calculated in a few minutes on a desktop computer, was more accurate than the regional numerical model intensity guidance in the 3–5-day forecast intervals. Tsai and Elsberry (2015) then

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Hsiao-Chung Tsai and Russell L. Elsberry

1. Introduction Tsai and Elsberry (2015b) developed a weighted analog technique called the Weighted Analog Intensity Atlantic (WAIA) for 5-day intensity and intensity spread predictions of Atlantic tropical cyclones (TCs) that is similar to the Tsai and Elsberry (2014) technique for western North Pacific TCs called the Weighted Analog Intensity Pacific (WAIP). These simple techniques are based on rankings of the 10 best historical track analogs to match the official track forecast and

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Philip A. Lutzak

studies of undular bores elsewhere, summarizes a set of necessary conditions associated with the development of bores in this region and an operational technique for forecasting them. Particular attention is focused on the seasonal influences that produce these conditions, as they are unique to this region of North America and are also nearly optimal for producing a wave cloud signature visible in satellite images. 2. Theory a. Density current and bore properties In order for a frontal boundary to

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