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Ensemble Kalman filter assimilation of Global-Hawk-based data from tropical cyclones

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. Background. Background: HIWRAP basics. HIWRAP Schematic.

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Ensemble Kalman filter assimilation of Global-Hawk-based data from tropical cyclones

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

  2. Background Background: HIWRAP basics HIWRAP Schematic • 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 EnKF analysis from simulated data

  3. Methods 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 3-km nest Model domains Karl’s track

  4. Methods Methods: Problems and solutions VWP Methodology • 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

  5. Methods Methods: Processing Vrobs • 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 Methods: Processing VWP obs • 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 Methods: Assimilation details Karl assimilation schematic • 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 Assumed errors: Minimimum SLP – 4 hPa Vr– 3 m/s Position – 20 km VWP – 1.5 m/s

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

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

  10. Results Results: Wind radii Wind radii (km) evolution from various EnKF analyses • 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

  11. Results Results: Vertical structure Azimuthal mean wind speeds at last cycle • 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

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

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

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

  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

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