Assimilation of GOES Infrared Brightness Temperatures with an Ensemble Kalman Filter: Track and Intensity Impacts for Hurricane
نویسندگان
چکیده
Data assimilation using ensemble Kalman filters (EnKF) has led to significant improvements in atmospheric state estimation. The advantages of EnKF over common operational assimilation methods such as three-dimensional variational (3D-VAR) methods and its impressive performance in the assimilation of radar data at convective scales have led to its increasing popularity. While most previous studies have involved the assimilation of conventional observations only, this study presents an innovative approach in the EnKF assimilation scheme that involves the assimilation of GOES -12 channel 3 (6.5 micron) and channel 4 (i.e. 10.7 micron) brightness temperature data. In this study, the potential of the assimilation of GOES-12 infrared brightness temperature data was explored in the context of track and intensity forecasts for hurricane Rita from the 2005 Atlantic hurricane season. The experiments were run at two different resolutions. In the lower resolution experiments (60 km horizontal grid spacing) results show that the assimilation of GOES brightness temperatures improved the representation of TC structure and produced better track and intensity forecasts when compared to the control experiment (CTL), which involved conventional observations only. RMS errors and calibration values of different fields produced by the assimilation of GOES-12 brightness temperatures generally compared well to the CTL results and in certain cases performed better than the CTL. An example of this is the microphysical fields, where the marked improvements shown by the assimilation of GOES radiance data is due to the close relationship between radiance and microphysics. It is also shown that the assimilation of radiance data eliminated a spurious cyclone that
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