EnKF Assimilation of Simulated HIWRAP Radial Velocity Data
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EnKF Assimilation of Simulated HIWRAP Radial Velocity Data
Jason Sippel and Scott Braun - NASAs GSFC
Yonghui Weng and Fuqing Zhang - PSU
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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 CampaignBackground
Global Hawk at NASA’s Dryden hangar
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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
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Objectives1. 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
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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
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Selecting ‘truth’ realizations
Realizations selected to test EnKF performance in face of:
• Small error of the priorHow much improvement does the EnKF offer when the forecast is already pretty good? (NODA1)
• Large error of the priorHow well can the EnKF correct when the truth is unlike most of the prior? (NODA2)
Methods
‘Truth’ realizations and NODA forecasts
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‘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
~ 3 km
19.0 km
Observations every ~3 km on cone surface
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Results: CTRL analyses evolutionOSSE Results
• Intensity metrics: Generally small corrections due to small initial error in NODA
• Position: more noticeable correction, particularly for CTRL2
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Results: Analysis error reductionOSSE 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
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Results: Deterministic forecastsOSSE 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)
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Results: Ensemble forecastsOSSE Results
Similar to deterministic forecast results…
Note huge benefit of cycling
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Truth
13 HIWRAP cycles12 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
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SummaryHIWRAP 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)
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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)
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Results: CTRL/VLEG comparisonOSSE 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
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Results: CTRL/VLEG comparisonOSSE 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)