TITLE

Ensemble Data Assimilation Applied to RAINEX Observations of Hurricane Katrina (2005)

AUTHOR(S)
Torn, Ryan D.; Hakim, Gregory J.
PUB. DATE
September 2009
SOURCE
Monthly Weather Review;Sep2009, Vol. 137 Issue 9, p2817
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
An ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting model is applied to generate ensemble analyses and forecasts of Hurricane Katrina (2005) and the surrounding area every 6 h over the lifetime of the storm on a nested domain. Analyses are derived from assimilating conventional in situ observations, reconnaissance dropsondes, including data taken during the Hurricane Rainband and Intensity Exchange Experiment (RAINEX), and tropical cyclone position estimates. Observation assimilation at individual times consistently reduces errors in tropical cyclone position, but not necessarily in intensity; however, withholding observations leads to significantly larger errors in both quantities. Analysis increments for observations near the tropical cyclone are dominated by changes in vortex position, and these increments increase the asymmetric structure of the storm. Data denial experiments indicate that dropsondes deployed in the synoptic environment provide minimal benefit to the outer domain; however, dropsondes deployed within the tropical cyclone lead to significant reductions in position and intensity errors on the inner domain. Specifically, errors in the inner domain ensemble-mean 6-h forecasts of minimum pressure are 70% larger when dropsonde data is not assimilated. Precipitation fields are qualitatively similar to Tropical Rainfall Measuring Mission (TRMM) satellite estimates, although model values are double the values of the satellite estimate. Moreover, the spinup period and initial imbalance in EnKF-initialized WRF forecasts is less than starting the model from a GFS analysis. Ensemble-mean 48-h forecasts initialized with EnKF analyses have track and intensity errors that are 50% smaller than GFS and NHC official forecasts.
ACCESSION #
44216855

 

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