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Hurricane Regional and Global Model Evaluation and Improvement Project

Principal Investigator and Project Manager:

Collaborating Scientists:
  • Dr. Jian-Wen Bao
    NOAA/ESRL
    Physical Sciences Division
    Boulder, CO 80305

Primary NOAA Collaborative  Laboratories

  • NOAA/AOML
    4301 Rickenbacker Causeway
    Miami, FL 33149
  • NOAA/ESRL
    325 Broadway
    Boulder, CO 80305

Objectives:

Improve NOAA's Hurricane Weather Research and Forecasting (HWRF) and Flow­ following finite-volume Icosahedral (FIM) model performance through a systematic evaluation process, whereby model biases are documented, understood, and ultimately eliminated by implementing accurate observation-based physical parameterizations.

Project Evaluation and Improvement Methodology:

  • Compare output from discrete aspects of the HWRF regional and FIM global hurricane modeling systems (e.g. air-sea interaction) with similar observationally based corollaries over appropriate spatial and time scales.
  • Emphasize analyses that highlight wave number 0/1 (i.e. mean/asymmetric) structure.
  • Based on results from these inter-comparisons, identify and begin work in specific areas where model improvement may be possible (e.g. model physics enhancement, parameterization modification, etc.).
  • Identify areas within the hurricane system that cannot currently be effectively evaluated due to inadequate data sampling.
  • Target these "observational gaps" by strategically utilizing NOAA's Hurricane Field Program and other data acquisition opportunities (e.g. partner field programs, satellite measurements, new sensor/platform technologies) in order to improve our ability to effectively evaluate and ultimately improve model fields.
  • Conduct targeted OSE/OSSE experiments through the OSSE Testbed to help determine the
  • optimal mix of new and existing observations that are most likely to result in improved future forecasts of hurricane intensity change.
  • As model improvements are made, conduct process-oriented simulations that investigate key elements of the hurricane forecast system in order to improve basic physical understanding and better quantify model sensitivity and overall variability.
  • Conduct periodic updates to appropriate NOAA leadership as progress is made.
  • Maintain existing funding and explore avenues for additional resources as opportunities arise.

 Proposed Project Stage I:  Evaluate HWRF and FIM Air-Sea Interaction Fields:

The hurricane Model Evaluation and Improvement Project (hereafter referred to as "the Project") is envisioned as a multi-stage effort that includes several iterative phases. The first stage that is being proposed under this agreement is an in-depth model/observational inter-comparison of the hurricane air-sea environment. It is proposed that this particular region of the storm be analyzed first since it is believed that existing air-sea observations can adequately describe the mean and asymmetric state associated with this region of the hurricane (i.e. wave number 0, 1). As such, properly scaled analyses derived from model output should enable a realistic and reasonably accurate assessment of model performance (relative to observations) as it relates to this critically important region of the storm.

Hurricane  Air-Sea Interaction  Evaluation and Improvement  Methodology:

  • Establish a clear representation of air-sea conditions based on decades of measurements collected within the inner and outer core hurricane environment over a wide array of storm conditions. Compare observed fields with comparable/appropriately-scaled model air-sea analyses over a wide array of storm conditions (e.g. hurricane, tropical storm, intensifying, weakening, fast/slow moving, etc.).
  • Conduct model/observational comparisons utilizing various flow-relative frameworks (e.g. earth­relative, storm motion-relative, shear-relative).
  • Consistent with the Project's objectives, analyses and inter-comparisons would concentrate on model/observational differences associated with mean and asymmetric structure (i.e. waves 0/1 ).
  • Assess near surface atmospheric and oceanic model performance.
  • As specific areas of improvement are targeted, work closely with key ESRL, HRD, AOML and EMC personnel to improve atmospheric model surface layer, upper ocean physics and associated parameterization routines.
  • Once improvements to the modeling system have been made, conduct targeted idealized modeling studies in order to improve physical understanding, document model variability and to ensure that findings are correct for the right (physical) reason(s).
  • Highlight any "observational gaps" that may exist within the air-sea interactive environment and provide an assessment of how to best target such gaps going forward (e.g. new field experiments, new observing platforms (UAS, new/improved sensors, etc.).
  • As appropriate, work closely with ESRL, AOML and other collaborative scientists to implement targeted OSE/OSSE experiments designed to determine the optimal mix of new and existing observations most likely to improve future forecasts of hurricane intensity change.
  • Once/if significant changes have been made to the model, conduct simulations that highlight the improvements that were achieved (e.g. improved physical representation of the air-sea environment (mean and asymmetric fields), appropriate variability attained, and (hopefully) a measure of improved forecast accuracy).

Future Goals:
  • Assemble comprehensive observational databases (e.g. buoy, GPS dropsonde, Doppler radar, microphysics, etc.).
  • Establish an appropriate framework for comparing numerical model output with observations (e.g. sampling, statistics, etc.).
  • Systematically evaluate numerous physical and dynamical aspects of the HWRF and FIM coupled model systems against historical observational databases at HRD and other NOAA and non-NOAA institutions.
  • Seek to eliminate model biases by developing observation-based parameterizations of physical processes.
  • By effectively using new and existing observations, target and eliminate data gaps that impede model evaluation.


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