1. Ensemble Kalman filter assimilation of Global-Hawk-based data from tropical cyclones Jason Sippel, Gerry Heymsfield, Lin Tian, and Scott Braun- NASAs GSFC Yonghui Weng and Fuqing Zhang – Penn State University

2. Background: HIWRAP basics HIWRAP is a conically scanning Doppler radar mounted upon NASAs Global Hawk UAV It was first used to observe Hurricane Karl in GRIP (2010) and is being used in HS3 Simulated-data results (Sippel et al. 2013) suggest HIWRAP Vr can be assimilated to improve hurricane analyses Background HIWRAP Schematic EnKF analysis from simulated data

3. Methods: Experiment setup Looking at Karl (2010) – only hurricane that HIWRAP data is available for WRF-EnKF from Zhang et al. (2009) with 30 members Similar setup as Sippel et al. (2013) OSSEs Methods Model domains 3-km nest Karl’s track

4. Methods: Problems and solutions Significant issues for GRIP data  Outer beam unavailable for Karl – inner beam observes more along vertical axis  Unfolding not possible for some legs  Heavy QC required for Vr Solutions  Compare assimilation of Vr with VWP data (avoids DA issues with w, and less heavy QC required)  Assimilate position & intensity (P/I) in addition to HIWRAP data to help fill in gaps Methods VWP Methodology

5. Methods: Processing Vr obs Methods Only keep data from 2-8 km where refl > 25 dBZ Bin 15 scans (10 km) into 2 km x 20° grid, keep median as SO Only keep 25% of SOs

6. Methods: Processing VWP obs Methods Bin VWP u and v components into 1 km x 1 km bins every 20 km along track and 1 km in altitude Select median value from bin as SO, no thinning

7. Methods: Assimilation details Methods Assimilate position first, then position + HIWRAP data + SLP P/I ROI: ~1200 km horizontal and 35-level vertical HIWRAP ROI: Zhang et al. SCL with 900/300/100- km horizontal and 26- level vertical Karl assimilation schematic Assumed errors: Minimimum SLP – 4 hPaVr – 3 m/s Position – 20 kmVWP – 1.5 m/s

8. Results: EnKF storm position All analyses better than NODA, error of mean similar Error generally less than assumed position error (20 km) Results Track evolution from EnKF analyses

9. Results: EnKF max intensity All experiments far superior to NODA Slight improvement upon PIONLY with HIWRAP data Min SLP generally lower in VR experiment Results Maximum intensity evolution from EnKF analyses

10. Results: Wind radii PIONLY produces a storm that is much too large, especially for smaller radii Analysis with HIWRAP data is in much better agreement with best-track Results Wind radii (km) evolution from various EnKF analyses

11. Results: Vertical structure PIONLY analysis (not shown) produces a shallower, broad vortex that looks unrealistic for a major hurricane Analyses with HIWRAP data are more realistic with tall, compact core VR analysis more intense by about 5 m/s by last cycle Results Azimuthal mean wind speeds at last cycle

12. Results: Deterministic forecasts All EnKF-initialized forecasts improve upon NODA Hard to tell difference in track forecasts among EnKF experiments Results Comparison of best track with NODA and EnKF-initialized track forecasts

13. Results: Deterministic forecasts All EnKF-initialized forecasts improve upon NODA VWP-initialized Vmax forecasts generally better than Vr- initialized forecasts Results Best track Vmax (m/s) compared with NODA and EnKF-initialized intensity forecasts

14. Results: Deterministic forecasts All EnKF-initialized forecasts improve upon NODA HIWRAP-initialized min SLP forecasts better than PI-ONLY but VWP-initialized are best Results Best track min SLP (hPa) compared with NODA and EnKF-initialized intensity forecasts

15. Summary + Future Work HIWRAP data appears to be useful for TC analysis and forecasting Despite difficulties with early HIWRAP data, EnKF analyses with HIWRAP Vr and VWP data produce accurate estimates of maximum intensity, location, and wind radii EnKF-initialized forecasts significantly improve upon NODA, but for this case VWP assimilation produces better forecasts (perhaps because horizontal winds are better constrained) Future work will examine the impacts of additional Global-Hawk- based data, including dropsondes, surface wind speeds from HIRAD and water vapor and temperature retrievals from S-HIS