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tend to scale linearly with annual precipitation (i.e., wetter climates have larger rmse’s). This average is about 39% of the 30-yr monthly average precipitation for the contiguous United States. Table 1 also includes the ratio of the rmse to the standard deviation of all months in the test period for each forecast division, with the corresponding map shown in Fig. 5 . This is a comparison between method error and the variability of the test data. The unitless ratio ranges from about 0.4 to 0
tend to scale linearly with annual precipitation (i.e., wetter climates have larger rmse’s). This average is about 39% of the 30-yr monthly average precipitation for the contiguous United States. Table 1 also includes the ratio of the rmse to the standard deviation of all months in the test period for each forecast division, with the corresponding map shown in Fig. 5 . This is a comparison between method error and the variability of the test data. The unitless ratio ranges from about 0.4 to 0
available ( NOS 1999 ). The COFS nowcast serves the initial conditions for a daily 24-h forecast and the next day's assimilation cycle. The purpose of this paper is to present the results of comparisons between COFS temperature predictions with and without SST data assimilation. COFS SST predictions were evaluated for a 3-month period from 1 January to 31 March 1998, and subsurface predictions were assessed for February of 1998. A description of the version of the coastal ocean circulation model used
available ( NOS 1999 ). The COFS nowcast serves the initial conditions for a daily 24-h forecast and the next day's assimilation cycle. The purpose of this paper is to present the results of comparisons between COFS temperature predictions with and without SST data assimilation. COFS SST predictions were evaluated for a 3-month period from 1 January to 31 March 1998, and subsurface predictions were assessed for February of 1998. A description of the version of the coastal ocean circulation model used
to the 0.5-m depth of the observations ( Gilhousen 1987 ). The LST forecasts at buoy 45005 were also evaluated in terms of relative performance by comparing the predictions against an hourly LST climatology. Comparisons between lake forecasts, hindcasts, climatology, and observations were done using graphical techniques and statistical measures. These statistical measures included two absolute quantities, mean algebraic and absolute differences, one relative quantity, the index of agreement (IOA
to the 0.5-m depth of the observations ( Gilhousen 1987 ). The LST forecasts at buoy 45005 were also evaluated in terms of relative performance by comparing the predictions against an hourly LST climatology. Comparisons between lake forecasts, hindcasts, climatology, and observations were done using graphical techniques and statistical measures. These statistical measures included two absolute quantities, mean algebraic and absolute differences, one relative quantity, the index of agreement (IOA
baselines for comparison, questions of immediate interest are the degree to which model skill decreases as forecasts are extended to two days and beyond, and the comparative skills of mesocale models when mapped onto this standard 1° grid. These aspects will be the subject of immediate follow-on studies. REFERENCES Doswell, C. A., III, R. Davies-Jones, and D. L. Keller, 1990: On summary measures of skill in rare event forecasting based on contingency tables. Wea. Forecasting, 5, 576–585. 10
baselines for comparison, questions of immediate interest are the degree to which model skill decreases as forecasts are extended to two days and beyond, and the comparative skills of mesocale models when mapped onto this standard 1° grid. These aspects will be the subject of immediate follow-on studies. REFERENCES Doswell, C. A., III, R. Davies-Jones, and D. L. Keller, 1990: On summary measures of skill in rare event forecasting based on contingency tables. Wea. Forecasting, 5, 576–585. 10
some coherence to the fledgling science and technology of seasonal landfall forecasts. The purpose is twofold: 1) to compare the predictors used by the current suite of operational landfall forecasters, and 2) to continue our argument for a more probabilistic approach to the problem of seasonal hurricane prediction; an argument first articulated in Elsner and Bossak (2001) and refined in Elsner and Jagger (2004) . The present paper provides a comparison of hindcasts for the 2004 hurricane season
some coherence to the fledgling science and technology of seasonal landfall forecasts. The purpose is twofold: 1) to compare the predictors used by the current suite of operational landfall forecasters, and 2) to continue our argument for a more probabilistic approach to the problem of seasonal hurricane prediction; an argument first articulated in Elsner and Bossak (2001) and refined in Elsner and Jagger (2004) . The present paper provides a comparison of hindcasts for the 2004 hurricane season
detailed comparison of their intensity to surface damage. Acknowledgments The SMART-R deployment and data collection and the power transmission damage summary were facilitated by the Salt River Project. The authors thank Charles Ester of the Salt River Project Water Operations Group for his continued support. The authors also thank Ami Arthur for providing the maps and hybrid scan height figures. This research was supported in part by an appointment to the National Oceanic and Atmospheric
detailed comparison of their intensity to surface damage. Acknowledgments The SMART-R deployment and data collection and the power transmission damage summary were facilitated by the Salt River Project. The authors thank Charles Ester of the Salt River Project Water Operations Group for his continued support. The authors also thank Ami Arthur for providing the maps and hybrid scan height figures. This research was supported in part by an appointment to the National Oceanic and Atmospheric
coarse approximation to facilitate the estimation of sea state parameters using the hurricane wind input. With the scaling model supplying the fetch and duration in the wind-wave growth functions, H s and T p are given in simple algebraic equations of U 10 and r along each radial transect. Furthermore, the radial dependency of wind speed can be approximated by a power function of r ; therefore, the formulas for H s and T p along a radial transect are given as algebraic equations of r
coarse approximation to facilitate the estimation of sea state parameters using the hurricane wind input. With the scaling model supplying the fetch and duration in the wind-wave growth functions, H s and T p are given in simple algebraic equations of U 10 and r along each radial transect. Furthermore, the radial dependency of wind speed can be approximated by a power function of r ; therefore, the formulas for H s and T p along a radial transect are given as algebraic equations of r
stratified climatology model is presented ( section 4 ), illustrating yet another statistical technique used in Australian seasonal (e.g., Stone et al. 1996 ) and weather (e.g., Stern 1980 ; Dahni and Stern 1995 ) forecasting. This has the opposite property of the predictand being continuous but the predictor discrete or categorical. Comparisons are made between the results of the three different model types, and some interesting similarities are found ( section 5 ), along with an application of the
stratified climatology model is presented ( section 4 ), illustrating yet another statistical technique used in Australian seasonal (e.g., Stone et al. 1996 ) and weather (e.g., Stern 1980 ; Dahni and Stern 1995 ) forecasting. This has the opposite property of the predictand being continuous but the predictor discrete or categorical. Comparisons are made between the results of the three different model types, and some interesting similarities are found ( section 5 ), along with an application of the
physics-based surge model. Owing to the notable performance of ensemble prediction, storm surge forecasts based on many ensemble members or multiple scenarios are becoming increasingly important in operational applications ( Bonnardot et al. 2016 ; Greenslade et al. 2017 ; Hasegawa et al. 2017 ). The NHC is developing maps of potential storm surge flooding ( https://www.nhc.noaa.gov/surge/inundation ) that are created from hundreds runs of the Sea, Lake, and Overland Surges from Hurricanes model
physics-based surge model. Owing to the notable performance of ensemble prediction, storm surge forecasts based on many ensemble members or multiple scenarios are becoming increasingly important in operational applications ( Bonnardot et al. 2016 ; Greenslade et al. 2017 ; Hasegawa et al. 2017 ). The NHC is developing maps of potential storm surge flooding ( https://www.nhc.noaa.gov/surge/inundation ) that are created from hundreds runs of the Sea, Lake, and Overland Surges from Hurricanes model
Holt et al. (2009) . The ensemble means of 2-m air temperature, sea level pressure, and 10-m wind at 0000 UTC 25 June 2005 are displayed in Fig. 2 . Fig . 2. Ensemble mean sea level pressure (hPa), 2-m air temperature (°C), and 10-m wind at 0000 UTC 25 Jun 2005. One full wind barb represents 10 m s −1 and a half barb 5 m s −1 . (Note that this figure has a correct map aspect ratio. All other map figures have exaggerated aspect ratios to allow for more convenient comparison of three plots.) After
Holt et al. (2009) . The ensemble means of 2-m air temperature, sea level pressure, and 10-m wind at 0000 UTC 25 June 2005 are displayed in Fig. 2 . Fig . 2. Ensemble mean sea level pressure (hPa), 2-m air temperature (°C), and 10-m wind at 0000 UTC 25 Jun 2005. One full wind barb represents 10 m s −1 and a half barb 5 m s −1 . (Note that this figure has a correct map aspect ratio. All other map figures have exaggerated aspect ratios to allow for more convenient comparison of three plots.) After
