Ensemble Kalman filter assimilation of Global-Hawk-based data from tropical cyclones
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Transcript of Ensemble Kalman filter assimilation of Global-Hawk-based data from tropical cyclones
Ensemble Kalman filter assimilation of Global-Hawk-based data from tropicalcyclones
Jason Sippel, Gerry Heymsfield, Lin Tian, and Scott Braun- NASAs GSFC
Yonghui Weng and Fuqing Zhang – Penn State University
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
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
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
Methods: Processing Vr obsMethods
• 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
Methods: Processing VWP obsMethods
• 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
Methods: Assimilation detailsMethods
• 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 hPa Vr – 3 m/sPosition – 20 km
VWP – 1.5 m/s
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
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
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
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
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
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
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
Summary + Future WorkHIWRAP 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