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Joint Hurricane Testbed Logo

JHT 2011-2013 Goals


The assimilation of non-NOAA and non-AF GPS dropwindsonde data into NOAA numerical models: Sim Aberson, (NOAA/OAR/AOML)

  • Test the assimilation of dropwindsonde data from NASA and NSF aircraft that regularly participate in field programs.

Improvements in Statistical Tropical Cyclone Forecast Models: Renate Brummer (CSU/CIRA), John Knaff (NOAA/NESDIS) and Mark DeMaria (NOAA/NESDIS)

  • Improve the operational Statistical Hurricane Intensity Prediction Scheme (SHIPS) and the Logistic Growth Equation Model (LGEM) by:
    1. Separating the persistence component of LGEM from the other inputs that are available throughout the forecast period
    2. Developing versions of the SHIPS and LGEM models specifically for the Gulf of Mexico region; and
    3. Improving the databases used to develop SHIPS and LGEM through use of the NCEP's new coupled reanalysis system.

Development of a Real-Time Automated Tropical Cyclone Surface Wind Analysis: Renate Brummer (CSU/CIRA), Mark DeMaria, (NOAA/NESDIS) and John Knaff, (NOAA/NESDIS)

  • Create a real-time and fully automated surface wind analysis system by combining the existing satellite- based six-hourly multi-platform tropical cyclone surface wind analysis (MTCSWA) and aircraft reconnaissance data
  • Initially the center location will be determined using a combination of operational best track and aircraft-based center positions

Development of a Probabilistic Tropical Cyclone Genesis Prediction Scheme: Jason Dunion, (Univ. of Miami/CIMAS, AOML), John Kaplan, (NOAA/OAR/AOML), Andrea Schumacher, (CSU/CIRA) and Joshua Cossuth, (FSU/COAPS)

  • Develop a storm-centric TC Genesis Index -an objective tool for identifying the probability of TC genesis (0-48 hr and 0- 120 hr) in the North Atlantic basin.
  • Incorporate two new predictors: total precipitable water (TPW) and Dvorak T-numbers.
  • These and other top TC genesis predictors will be used to develop an objective genesis index (GI) that can be employed to provide estimates of the probability of TC genesis over a period of 0-48 and 0-120 h utilizing linear discriminant analysis.

Improving the operational TC models at NOAA/NCEP and Navy/FNMOC: Isaac Ginis, (Univ. of Rhode Island) and Morris Bender, (NOAA/OAR/GFDL)

  • Upgrade the air-sea momentum and heat flux parameterizations and the upper ocean mixing in the operational GFDL, GFDN, and HWRF models.
  • Upgrade the ocean model domain configuration, initialization, and data assimilation in the operational GFDL, GFDN, and HWRF models.
  • Upgrade the GFDL/GFDN atmospheric model physics and initialization scheme. Implement a unified GFDL/GFDN version control framework.
  • Test and implement the 37 GHz ring pattern RI index on the SHIPS RI index.

Enhancement of SHIPS-RI index using satellite 37 GHz microwave ring pattern: Haiyan Jiang, (Florida Intl Univ.)

  • Refine the 37-GHz ring pattern RI index by using the first-year's testing results and more data from microwave sensors besides TMI.
  • Incorporate and implement the 37-GHz ring pattern RI index into the current version of the SHIPS RI index for operational use in the ATL and EPA basins.

Improvement to the SHIPS Rapid Intensification Index: John Kaplan, (NOAA/OAR/AOML), Charles Sampson, (DOD/Naval Research Lab.), Chris Rozoff, (NOAA/NCDC), Jim Kossin, (NOAA/NCDC) and Chris Velden, (Univ. of Wisconsin/CIMSS)

  • Develop additional versions of the RII for lead times out to 48-h, as well as an ensemble RII and an RII-based consensus aid that can be used to make intensity forecasts out to 48-h in each basin.
  • Automate the evaluation of new structural predictors (i.e. microwave imagery) for inclusion in new versions of RII will be completed.

Updating the secondary eyewall formation probabilistic model, completing new climatologies of intensity and structure changes associated with eyewall replacement cycles, and construction of new forecast guidance tools based on the new climatologies: Jim Kossin (NOAA/NCDC)

  • Improve the present model skill a) Modification or addition of features b) Include a second probabilistic model based on logistic regression, c) Optimized model feature selection

Introducing diagnostic variables towards extending the SHIPS algorithm for hurricane intensity forecasts: T.N. Krishnamurti, (Florida State Univ.)

  • Modify the SHIPS algorithm by using different as predictors in replacement of (or in addition to) original SHIPS predictors, which include the vertical differential of heating in the complete potential vorticity equation, the conversion of vertical shear vorticity into curvature vorticity, the transformation of divergent kinetic energy into rotational kinetic energy, etc.

Improved automation and performance of VORTRAC intensity guidance: Wen-Chau Lee, (NCAR/EOL), Paul Harasti, (DOD/Naval Research Lab.) and Michael Bell, (Naval Postgraduate School)

  • Utilize existing operational data streams to make VORTRAC nearly automatic, reducing the amount of required user interaction while maintaining the capability to intervene and make adjustments.
  • Use AWIPS to provide reference pressure observations.
  • Add high-resolution WSR-88D Level III radar data to augment the Level II data.
  • Create possible enhancements to the radar algorithms, data display, and output capabilities, with the goal of maximizing the accuracy and effectiveness of the intensity and RMW diagnoses.

Improved SFMR surface wind measurements in intense rain conditions: Eric Uhlhorn (NOAA/OAR/AOML)

  • Deliver improved SFMR surface winds in such conditions incrementally in two steps:
  • Utilize the expanded SFMR and GPS dropwindsonde database to compute a statistically-based correction to real-time SFMR surface wind speeds.
  • Use new corrected values to provide a basis for evaluating a new coupled wind and rain geophysical model function.

Validation of HWRF forecasts with satellite observations and potential use in vortex initialization: Tomislava Vukicevic, (NOAA/OAR/AOML) and Tom Greenwald, (Univ. of Wisconsin/CIMSS)

  • Develop new capabilities for the operational HWRF (Hurricane Weather Research and Forecast) system such as:
    1. Satellite data simulator: Develop new software within the post-processing component of HWRF operational forecast system for simulating a wide range of satellite observations using an operational version of CRTM (Community Radiative Transfer Model).
    2. Forecast verification in satellite data: Develop a new set of forecast verification diagnostics in satellite data space based on comparison of simulated and actual satellite observations in terms of brightness temperature (TB).
    3. Initialization diagnostics: Using ensemble of forecasts, correlation statistics between thermodynamical and hydrologic variables and the simulated satellite observations will be evaluated.
  • Create and transition the new operational tools to provide intensity and structure forecast guidance.


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Page last modified: Friday, 20-Jan-2012 14:21:33 UTC