摘要
Extending an earlier study, the best track minimum sea level pressure （MSLP） data are assimilated for landfalling Hurricane Ike （2008） using an ensemble Kalman filter （EnKF）, in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS （Global Forecast System） analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity （Z） only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind （Vr） data （assimilated at 10-min intervals）, but assimilating MSLP at 60-rain intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly （noticeably） larger positive impact than Vr （Z） data. When V~ or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1 h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.
Extending an earlier study, the best track minimum sea level pressure （MSLP） data are assimilated for landfalling Hurricane Ike （2008） using an ensemble Kalman filter （EnKF）, in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS （Global Forecast System） analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity （Z） only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind （Vr） data （assimilated at 10-min intervals）, but assimilating MSLP at 60-rain intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly （noticeably） larger positive impact than Vr （Z） data. When V~ or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1 h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.
基金
Supported by the United States DOD ONR grants N00014-10-1-0133and N00014-10-1-0775
NSF grants OCI-0905040andAGS-0802888
National Basic Research and Development(973)Program of China(2013CB430103)
