Cycling Variational Assimilation of Remotely Sensed Observations for Simulations of Hurricane Katrina S.-H. Chen 1 E. Lim 2, W.-C. Lee 3, C. Davis 2, M. Bell 3, Q. Xiao 2, H.-C. Lin 2, G. Holland 2 1 Land, Air, and Water Resources, University of California, Davis, CA, USA 2 Mesoscale and Microscale Meteorology Division/NCAR, Boulder, CO, USA 3 Earth Observing Laboratory/NCAR, Boulder, CO, USA IntroductionExperiment DesignObservationsResults and DiscussionSummary Tropical cyclones (TCs) at landfall are one of the most dangerous natural disasters. Accurate TC data analysis and forecasts are crucial for the protection of life and property. Despite recent progress in TC track forecasting, intensity forecasting remains unsatisfactory primarily due to complicated processes at multiple scales, including cloud-scale moist convection interacting with hurricane large-scale environmental flows. To gain understanding of TC multi-scale processes and thus improve forecasts of the rapid intensification or weakening of TCs, it is important to observe both the small-scale inner-core structure and the large-scale environmental flows that have a profound impact on TC evolution. Unfortunately, TC forecasting over the ocean remains a big challenge, especially during the period of most rapid intensification and weakening, because of the lack of in-situ observations. Consequently, specification of the model initial condition must rely on remotely-sensed techniques to retrieve the critical parameters over both the inner-core and the vast area covered by TCs. This study assimilated different scale observations from radar, satellite, and conventional instruments and assessed the impact of these data on Hurricane Katrina (2005) simulations during one of its rapid intensification periods (see the magenta lined box in Fig. 1) using the high-resolution Weather Research and Forecasting (WRF) model. Three sets of experiments, EXP1-3, with various initial times and data cycling periods were conducted. The data cycling periods, which were applied to the coarsest three domains, were 1800 UTC August 25 to 0000 UTC August 26 (6 h), 0000 UTC to 0600 UTC August 26 (6 h), and 1800 UTC August 25 to 0600 UTC August 26 (12 h) for EXP1-3, respectively. The different observations assimilated for each experiment are shown in Table 1. The first two sets (i.e. EXP1 and EXP2) evaluated the impact of assimilating different observations on Katrina simulations, while the last one (EXP3) examined the influence of different assimilation periods for radar data. In EXP3, satellite and conventional data were assimilated whenever data were available during the whole 12-h cycling period, but radar data were assimilated only every three hours over different time periods (i.e., 0h, 3h, 6h, 9h, and 12h). Thirty-six hour model integrations were then performed on all four domains after the data cycling. The simulated storms from EXP1 moved too slowly and meandered over southern Florida. No improvement was made for simulated tracks after the use of any observation (Fig. 3a). This is because the upper-level anticyclone did not intrude southward into the northern Gulf of Mexico (Fig. 4a vs. 4b). The results were greatly improved when the model started 6 h later (i.e., EXP2; Fig. 3b), in particular with the assimilation of observations. The error was ~50 km throughout the simulation period. The location of the upper-level anticyclone was comparable with that from AVN reanalysis (Fig. 4a vs. 4c). Simulated tracks from EXP3 were slightly deflected southward (Fig. 3c) because the upper-level anticyclone intruded slightly too far south (Fig. 4a vs. 4d). However, these tracks were much better than those from EXP1, which started data cycling with the same first guess at 1800 UTC 25 August Results from EXP1-3 further illustrate that the improvement to the simulated track was contributed by GTS data and/or satellite data from 0000 UTC to 0600 UTC August 26. Katrina was not only extremely intense but also exceptionally large. Unfortunately, the simulated Katrina was smaller than observed for all experiments conducted here (figure not shown). Results from EXP1 show that the assimilation of radar and conventional data had a positive impact on simulated storm intensities during the first 24 hours. The influence of satellite data was also positive, though less significant, but was able to extend over the whole 36-h simulation (Figs. 5a and 5d) since the coverage of satellite data was much larger than that of radar. Results from EXP2 show great improvement in both simulated storm intensities (Figs. 5b and 5e) and tracks after the use of observations. Finally, EXP1-3 (Fig. 5) indicated that the assimilation of radar data at an interval of every three hours for a 6-h time period is optimal. ▲ Fig. 3: Simulated tracks from (a) EXP1, (b) EXP2, and (3) EXP3. (a) (b) (c) ▲ Fig. 4: 300-mb geopotential height and wind vectors from (a) AVN reanalysis, (b) EXP1 ALL_3h, (c) EXP2 ALL_2h, and (c) EXP3 06h at 18Z 26 August (a) (b) (c)(d) (a)(b)(c)(d)(e) (f) ▲ Fig. 5: Simulated minimum sea level pressure (a-c) and maximum 10-m wind speed (d-f).  Simulated Katrina was smaller than the observed size.  The assimilation of observations improved simulated storm intensity for all three experiments and improved simulated storm track for EXP2 and EXP3.  Radar data influenced the first 24 hours of simulations, while the influence of satellite data lasted longer, but was less significant.  EXP3 confirmed that observations from 0000 UTC to 0600 UTC Aug 26 played a key role in improving simulated tracks.  Doppler radar data assimilation mainly contributed to the improvement of simulated hurricane intensity, in particular during the early time period of simulations  The assimilation of radar data at an interval of every three hours for a 6-h time period is close to an optimal setting. ▲ Fig. 1: (a) Best track positions, (b) observations (pts) and best track (line) maximum sustained surface wind speed, and (c) pressure observations (pts) and best track (line) minimum central pressure for Hurricane Katrina, August (Courtesy of National Hurricane Center) ▲ Fig. 2: (a) The positions of Katrina with respected to Miami radar, R, and the coverage of (b) QuikSCAT and (c) Special Sensor Microwave/Imager (SSM/I) satellite data from 18Z 25 August to 00Z 26 August, 2005 used in this study. The blue dot at the tip of Florida is Katrina’s position at 0000 UTC 26 August, (a) (b) (c) (a) (b) (c)