Using forecasts to protect Federal facilities in the path of Hurricane Isabel

Hurricane Isabel made landfall as a Category 2 Hurricane on 18 September 2003, on the North Carolina Outer Banks between Cape Lookout and Cape Hatteras, then coursed northwestward through Pamlico Sound and west of Chesapeake Bay where it downgraded to a tropical storm. Wind damage on the west and southwest shores of Pamlico Sound and the western shore of Chesapeake Bay was moderate, but major damage resulted from the storm tide. The NOAA, National Ocean Service, National Centers for Coastal Ocean Sciences, Center for Coastal Fisheries and Habitat Research at Beaufort, North Carolina and the Center for Coastal Environmental Health and Biomedical Research Branch at Oxford, Maryland have hurricane preparedness plans in place. These plans call for tropical storms and hurricanes to be tracked carefully through NOAA National Weather Service (NWS) watches, warnings, and advisories. When a hurricane watch changes to a hurricane warning for the areas of Beaufort or Oxford, documented hurricane preparation plans are activated. Isabel exacted some wind damage at both Beaufort and Oxford. Storm tide caused damage at Oxford, where area-wide flooding isolated the laboratory for many hours. Storm tide also caused damage at Beaufort. Because of their geographic locations on or near the open ocean (Beaufort) or on or near large estuaries (Beaufort and Oxford), storm tide poses a major threat to these NOAA facilities and the safety of federal employees. Damage from storm surge and windblown water depends on the track and intensity of a storm. One tool used to predict storm surge is the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model of the NWS, which provides valuable surge forecasts that aid in hurricane preparation.

User’s Guide to ADMB2R: A Set of AD Model Builder Output Routines Compatible with the R Statistics Language

ADMB2R is a collection of AD Model Builder routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 ADMB2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the ADMB2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer ADMB2R to others in the hope that they will find it useful. (PDF contains 30 pages)

User’s Guide to C2R: A Set of C Language Output Routines Compatible with the R Statistics Language

C2R is a collection of C routines for saving complex data structures into a file that can be read in the R statistics environment with a single command.1 C2R provides both the means to transfer data structures significantly more complex than simple tables, and an archive mechanism to store data for future reference. We developed this software because we write and run computationally intensive numerical models in Fortran, C++, and AD Model Builder. We then analyse results with R. We desired to automate data transfer to speed diagnostics during working-group meetings. We thus developed the C2R interface to write an R data object (of type list) to a plain-text file. The master list can contain any number of matrices, values, dataframes, vectors or lists, all of which can be read into R with a single call to the dget function. This allows easy transfer of structured data from compiled models to R. Having the capacity to transfer model data, metadata, and results has sharply reduced the time spent on diagnostics, and at the same time, our diagnostic capabilities have improved tremendously. The simplicity of this interface and the capabilities of R have enabled us to automate graph and table creation for formal reports. Finally, the persistent storage in files makes it easier to treat model results in analyses or meta-analyses devised months—or even years—later. We offer C2R to others in the hope that they will find it useful. (PDF contains 27 pages)