Accession Number:

AD1103872

Title:

Numerical Simulation of Rapid Weakening of Hurricane Joaquin with Assimilation of High Definition Sounding System Dropsondes during theTropical Cyclone Intensity Experiment: Comparison of Three and Four Dimensional Ensemble-Variational Data Assimilation

Descriptive Note:

Journal Article - Open Access

Corporate Author:

University of Utah Salt Lake City United States

Personal Author(s):

Report Date:

2019-05-07

Pagination or Media Count:

18.0

Abstract:

Observations from High-Definition Sounding System HDSS dropsondes, collected for Hurricane Joaquin during the Office of Naval Research Tropical Cyclone Intensity TCI field experiment in 2015, are assimilated into the NCEP Hurricane Weather Research and Forecasting HWRF Model. The Gridpoint Statistical Interpolation GSI-based hybrid three-dimensional and four-dimensional ensemblevariational 3DEnVar and 4DEnVar data assimilation configurations are compared. The assimilation of HDSS dropsonde observations can help HWRF initialization by generating consistent analysis between wind and pressure fields and can also compensate for the initial maximum surface wind errors in the absence of initial vortex intensity correction. Compared with GSI3DEnVar, the assimilation of HDSS dropsonde observations using GSI4DEnVar generates a more realistic initial vortex intensity and reproduces the rapid weakening RW of Hurricane Joaquin, suggesting that the assimilation of high-resolution inner-core observations e.g., HDSS dropsonde data based on an advanced data assimilation method e.g., 4DEnVar can potentially outperform the vortex initialization scheme currently used in HWRF. Additionally, the assimilation of HDSS dropsonde observations can improve the simulation of vortex structure changes and the accuracy of the vertical motion within the TC inner-core region, which is essential to the successful simulation of the RW of Hurricane Joaquin with HWRF. Additional experiments with GSI4DEnVar in different configurations also indicate that the performance of GSI4DEnVar can be further improved with a high-resolution background error covariance and a denser observational bin.

Subject Categories:

  • Meteorology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE