A recently completed Collaborative Science, Technology, and Applied Research (CSTAR) project with North Carolina State University and over a half dozen WFOs in the Southeast examined ways to add science and improve inland wind and wind gust forecasts associated with tropical cyclones. One outcome of this project is the development of a GFE methodology in which forecasters create grids of wind reductions (from the NHC TCM guidance) and wind gust factors (applied to the wind to determine the wind gust). This methodology was initially tested last year with limited but favorable results. This approach should result in improved forecasts with better inclusion of science as forecasters now have the opportunity to vary these grids both spatially and temporally, they can now more efficiently collaborate across WFO boundaries resulting in improved consistency, and the grids are persistent from shift to shift. This year, forecasters now have an opportunity to evaluate an experimental bias correction scheme for the NHC TCM product that was developed at NC State. This bias correction attempts to account for systematic biases in the TCM product that become problematic when forecasters downscale the NHC TCM product into 2.5 km2 gridded wind forecasts.
These two CSTAR outcomes (the wind reduction methodology and the NC State bias correction) have been incorporated into the TCMWindTool for operational evaluation this year by six NWS Weather Forecast Offices (WFOs) in the Southeast. The development of Tropical Depression One in the Atlantic on Monday evening provided these WFOs with the first opportunity to begin using these CSTAR research to operations deliverables.
The first image below is the wind forecast using these new CSTAR supported techniques to produce a meteorologically sound, consistent forecast by WFOs AKQ, MHX, ILM, and RAH. The second image shows the Wind Reduction Factor grid used by the WFOs to account for the decrease of wind speeds because of friction over land, fetch, air mass stability, and other influences. The ability to edit this reduction factor over time and space and to see the values used by other WFOs is a great asset to the forecaster.