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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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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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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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Keith D. Sherburn and Matthew D. Parker

unique radar signatures associated with HSLC severe convection (e.g., McAvoy et al. 2000 ; Grumm and Glazewski 2004 ; Barker 2006 ; Clark 2011 ), rather than exploring environmental characteristics. Conventional techniques for forecasting significant severe weather have been noted to perform poorly in HSLC environments ( Guyer and Dean 2010 ; R. Thompson 2012, personal communication). Additionally, the compressed nature of HSLC convection ( Davies 1990 ; Markowski and Straka 2000 ; McAvoy et al

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Nathalie Voisin, John C. Schaake, and Dennis P. Lettenmaier

) describe a method of producing flood forecasts at two locations in the Ganges and Brahmaputra basins in Bangladesh that the Flood Forecasting and Warning System of Bangladesh integrates in their automated system. The streamflow forecasts are derived from European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) forecasts, Tropical Rainfall Monitoring Mission (TRMM) 3B42 simulations, the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004

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Sim D. Aberson

( Fig. 1a ). Stan made landfall earlier than forecast and dissipated over the mountainous terrain of Mexico 24 h after the initial time. No forecasts from 36 h onward, including the 36-h OFCL forecast of 90 kt, are verified in the currently used technique. In the second case, at 0600 UTC 11 November 2006, the tropical depression that became Tropical Storm Gamma was forecast to remain at 25-kt intensity for 24 h before dissipation ( Fig. 1b ). The best track shows that the system remained a tropical

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Cameron J. Nixon and John T. Allen

the 102 cases (≈35%), deviant in the first 5 min), thus in over a third of all cases, this offset does represent nearly the entire tornado path, and must be accounted for almost immediately upon tornado formation. This problem warrants further exploration into a technique that can forecast deviant tornadoes based on the environment. Fig . 6. The leftward deviant portions of all 102 tornadoes examined. These tracks are colored by NWS warning performance assessment, with green tracks indicating good

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