10 research outputs found
Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model
Get PDFNumerical simulations of the storm tide that flooded the US Atlantic coastline during Hurricane Sandy (2012) are carried out using the National Weather Service (NWS) Sea Lakes and Overland Surges from Hurricanes (SLOSH) storm surge prediction model to quantify its ability to replicate the height, timing, evolution and extent of the water that was driven ashore by this large, destructive storm. Recent upgrades to the numerical model, including the incorporation of astronomical tides, are described and simulations with and without these upgrades are contrasted to assess their contributions to the increase in forecast accuracy. It is shown, through comprehensive verifications of SLOSH simulation results against peak water surface elevations measured at the National Oceanic and Atmospheric Administration (NOAA) tide gauge stations, by storm surge sensors deployed and hundreds of high water marks collected by the U.S. Geological Survey (USGS), that the SLOSH-simulated water levels at 71% (89%) of the data measurement locations have less than 20% (30%) relative error. The RMS error between observed and modeled peak water levels is 0.47 m. In addition, the model’s extreme computational efficiency enables it to run large, automated ensembles of predictions in real-time to account for the high variability that can occur in tropical cyclone forecasts, thus furnishing a range of values for the predicted storm surge and inundation threat
Environmental influences on tropical cyclone structure and intensity: A review of past and present literature
No full text61-74Tropical Cyclone (TC) track forecast skill has shown a steadily increasing trend in the north Atlantic basin over the last decade in contrast to little or no improvement in intensity forecast skill. This is attributable in part to a lack of in-situ observations to measure important inner-core processes and the inability of current operational numerical models to accurately resolve the inner-core dynamics. Consequently, much is unknown about TC intensity change, and the most skillful intensity forecasting techniques still rely upon climatology and persistence. The forecasting of rapid changes in intensity has been particularly difficult. The need for improved TC intensity forecasts has never been greater due to rapidly increasing population in coastal communities. This is the motivation for the present review, which seeks to discuss our current knowledge and highlight the most fruitful areas for future work. This is accomplished through a literature review of past and present research with emphasis on current gaps in knowledge
Comparison of TanDEM-X DEM with LiDAR Data for Accuracy Assessment in a Coastal Urban Area
Get PDFThe TanDEM-X (TDX) mission launched by the German Aerospace Center delivers unprecedented global coverage of a high-quality digital elevation model (DEM) with a pixel spacing of 12 m. To examine the relationships of terrain, vegetation, and building elevations with hydrologic, geologic, geomorphologic, or ecologic factors, quantification of TDX DEM errors at a local scale is necessary. We estimated the errors of TDX data for open ground, forested, and built areas in a coastal urban environment by comparing the TDX DEM with LiDAR data for the same areas, using a series of error measures including root mean square error (RMSE) and absolute deviation at the 90% quantile (LE90). RMSE and LE90 values were 0.49 m and 0.79 m, respectively, for open ground. These values, which are much lower than the 10 m LE90 specified for the TDX DEM, highlight the promise of TDX DEM data for mapping hydrologic and geomorphic features in coastal areas. The RMSE/LE90 values for mangrove forest, tropical hardwood hammock forest, pine forest, dense residential, sparse residential, and downtown areas were 1.15/1.75, 2.28/3.37, 3.16/5.00, 1.89/2.90, 2.62/4.29 and 35.70/51.67 m, respectively. Regression analysis indicated that variation in canopy height of densely forested mangrove and hardwood hammock was well represented by the TDX DEM. Thus, TDX DEM data can be used to estimate tree height in densely vegetated forest on nearly flat topography next to the shoreline. TDX DEM errors for pine forest and residential areas were larger because of multiple reflection and shadow effects. Furthermore, the TDX DEM failed to capture the many high-rise buildings in downtown, resulting in the lowest accuracy among the different land cover types. Therefore, caution should be exercised in using TDX DEM data to reconstruct building models in a highly developed metropolitan area with many tall buildings separated by narrow open spaces
Recent progress in storm surge forecasting
No full textInternational audienceThis report briefly summarizes recent progress in storm surge forecasts, one of topics discussed during the fourth International Workshop on Tropical Cyclone Landfall Process (IWTCLP 4) held during 5-8 December, 2017. In the workshop, improvement of storm surge forecasting system was mainly discussed with relevance to the problem of estimating the impacts of tropical cyclone landfall.To deal with storm surges, accurate TC condition (predictions and forecasts) as input, reasonable storm surge predictions (with forecasting systems), and effective advisories / warnings (i.e. useful information products) are necessary. Therefore, we need to improve storm surge related matters systematically: input, prediction system, and effective information.This report tries to highlight recent progress in the field of storm surges in relation to three key points: improvement in storm surge forecasting models / system, TC conditions as input for storm surge predictions, and informative products for end users
