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EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

EnKF Assimilation of Simulated HIWRAP Radial Velocity Data. Jason Sippel and Scott Braun - NASAs GSFC Yonghui Weng and Fuqing Zhang - PSU. Background. Motivation: NASA’s HS3 Campaign. Global Hawk flown for 2012-2014 hurricane seasons 26-h flight time; 19-km altitude

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EnKF Assimilation of Simulated HIWRAP Radial Velocity Data

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

  2. Background Motivation: NASA’s HS3 Campaign 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 Global Hawk at NASA’s Dryden hangar

  3. Background Motivation: Past WRF-EnKF success Humberto: Obs vs. EnKF analysis • 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

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

  5. Methods 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) Model domains

  6. Methods Selecting ‘truth’ realizations ‘Truth’ realizations and NODA forecasts • 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)

  7. Methods ‘Truth’ simulation flight tracks Observations every ~3 km on cone surface • 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 50° 19.0 km ~ 3 km ~ 3 km

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

  9. OSSE Results Results: Analysis error reduction • 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. OSSE Results Results: Deterministic forecasts • 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. OSSE Results Results: Ensemble forecasts • Similar to deterministic forecast results… Note huge benefit of cycling

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

  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. Methods (again) 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)

  15. OSSE Results Results: CTRL/VLEG comparison • 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. OSSE Results Results: CTRL/VLEG comparison • 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)

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