A Probabilistic TC Genesis Forecast Tool Utilizing an Ensemble of Global Models
Research Sponsored by the NOAA Joint Hurricane Testbed (JHT)
The mission of the Joint Hurricane Testbed is to transfer more rapidly and smoothly new technology,
research results, and observational advances of the United States Weather Research Program (USWRP)
, its sponsoring agencies, the academic community and other groups into improved tropical cyclone
analysis and prediction at operational centers. The main activities of the JHT are to
Identify new techniques, models, observing systems, etc. with potential for improving forecast
guidance, via an announcement of opportunity and a proposal, review, and funding process.
Establish and maintain an infrastructure to facilitate the modification and transfer of research
applications into the operational computing, communication, and display environment.
Complete tests in a quasi-operational environment of tools, techniques, etc. provided by funded
researchers, with metrics for scientific performance, ease-of-use, and time constraints.
Prepare documentation, training, and performance evaluations of successfully transferred
products to facilitate use and support in operations.
The specific goals of the FSU research are to:
Better utilize operational models to provide TC genesis forecasts.
Give explicit probabilistic forecasts of TC genesis out to 120 h to serve as guidance for NHC’s
operational 48 h GTWO and NHC’s experimental 120 h TC genesis forecasts, using:
A previously published algorithm to objectively identify TCs in the model fields,
An evaluation of model performance from prior seasons, and
An evaluation of confidence based upon multiple model (dis)agreement of genesis.
Conduct case studies of several recent TCs to identify any potential model biases.
Refine the probabilities of genesis by developing multiple logistic regression equations for each
model that include physically relevant predictors beyond those used in H13.
Screen the predictors using forward selection.
Verify TC genesis forecasts in real-time during the season so forecasters are aware of how the
models are performing to date during a given season.
Show the track and intensity of model TCs to aid pre-TC track and intensity forecasts.