1. EnKF Assimilation of Simulated HIWRAP Radial Velocity Data Jason Sippel and Scott Braun - NASAs GSFC Yonghui Weng and Fuqing Zhang - PSU

2. Global Hawk flown for 2012- 2014 hurricane seasons – 26-h flight time; 19-km altitude – ‘Over-storm’ payload: HIWRAP Doppler radar (Ku & Ka) HAMSR sounder (Tb for qv & T above precip) HIRAD radiometer (sfc. winds) – ‘Environmental’ payload: Dropsondes (u, v, qv, T) S-HIS sounder (clear-air radiances for qv & T) Cloud Physics Lidar (aerosols) (maybe) TWILITE Doppler Lidar Motivation: NASA’s HS3 Campaign Background Global Hawk at NASA’s Dryden hangar

3. Motivation: Past WRF-EnKF success Same ensemble Kalman filter (EnKF) setup as used for: – WSR-88D Vr assimilation in Hurricane Humberto (Zhang et al. 2009, MWR) – P3 Vr assimilation in Hurricane Katrina (Weng and Zhang 2011, MWR) – Penn. State University HFIP real-time WRF/EnKF Proven successful, even with difficult storms Background Humberto: Obs vs. EnKF analysis

4. Objectives 1.Generate a 48-h ensemble without data assimilation 2.Select ‘truth’ realizations for simulated data experiments 3.Assimilate simulated HIWRAP observations with an ensemble Kalman filter (EnKF) 4.Assess quality of analyses and forecasts

5. WRF-EnKF system EnKF from Zhang et al. (2009) WRF-ARW V3.1.1, 27/9/3 km 30-member ensemble, IC/BCs from WRF-VAR + GFS Ensemble integrated 12 h to generate mesoscale covariance WSM-6 mp for assimilation; GSFC for truth (model error) Methods Model domains

6. Selecting ‘truth’ realizations Realizations selected to test EnKF performance in face of: Small error of the prior How much improvement does the EnKF offer when the forecast is already pretty good? (NODA1) Large error of the prior How well can the EnKF correct when the truth is unlike most of the prior? (NODA2) Methods ‘Truth’ realizations and NODA forecasts

7. ‘Truth’ simulation flight tracks Instantaneous scans every ~28 km; observation cones slightly overlap at surface Data grouped into 1-h flight segments from same output time; ~1900 obs/hr Add 3 m/s random error, only assimilate when attenuated dBZ > 10 Methods 50° ~ 3 km 19.0 km Observations every ~3 km on cone surface

8. Results: CTRL analyses evolution OSSE Results Intensity metrics: Generally small corrections due to small initial error in NODA Position: more noticeable correction, particularly for CTRL2

9. Results: Analysis error reduction OSSE Results EnKF reduce RM-DTE > 80% after 13 cycles in both cases [DTE = 0.5 × (u`u` + v`v` + Cp/Tr × T`T`), prime is difference from truth] CTRL2 has larger and more widespread error reduction than CTRL1 Comparison of RM-DTE differences

10. Results: Deterministic forecasts OSSE Results Forecast error is reduced relative to NODA in both cases, particularly from 36- 48 h NODA2 needs more time to produce better analyses (i.e., that produce ‘good’ forecasts)

11. Results: Ensemble forecasts OSSE Results Similar to deterministic forecast results… Note huge benefit of cycling

12. Truth 13 HIWRAP cycles 12 HIWRAP cycles + 1 HIWRAP/HIRAD cycle Benefit of multiple data sources Improved analysis of inner-core winds and wavenumber 1 asymmetry with complement of simulated HIRAD data OSSE Results

13. Summary HIWRAP data appears to be useful for EnKF analyses and subsequent forecasts of a hurricane Strong analysis error reduction, particularly for a poor first guess Notable forecast improvements after just one assimilation cycle in CTRL1 A longer assimilation window (i.e., Global Hawk time scale) appears to benefit forecast more when the first guess is poor Future work will assess impact of multiple platforms at once as well as multiple instruments (e.g., HIRAD and dropsondes)

14. Results: Re-examining flight tracks Compare two different flight track philosophies: CTRL: 360-km legs that approximately span entire precipitation region VLEG: Single 360-km pattern followed by two complete patterns with 180-km legs, then repeat (i.e., focus more on center) Methods (again)

15. Results: CTRL/VLEG comparison OSSE Results Average CTRL RM-DTE near center is ~3-3.5 m/s VLEG systematically reduces near-center error more but error farther out less

16. Results: CTRL/VLEG comparison OSSE Results All CTRL forecasts better than NODA Differences between CTRL and VLEG forecasts on par with differences as a result of obs error (i.e., AERR)