vertical velocity at 5km (colored) and surface cold pool (black lines, every 2k)
DESCRIPTION
Towards Assimilating Clear-Air Radar Observations with an WRF-Based EnKF Yonghui Weng, Fuqing Zhang, Larry Carey Zhiyong Meng and Veronica McNeal Texas A&M University. Vertical velocity at 5km (colored) and surface cold pool (black lines, every 2K). - PowerPoint PPT PresentationTRANSCRIPT
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Towards Assimilating Clear-Air Radar Observations with an WRF-Based EnKF
Yonghui Weng, Fuqing Zhang, Larry Carey Zhiyong Meng and Veronica McNeal
Texas A&M University
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Vertical velocity at 5km (colored) and surface cold pool (black lines, every 2K)
Storm-scale EnKF with Simulated Radar OBS (Snyder and Zhang 2003, MWR; Zhang, Snyder and Sun 2004, MWR)
Assimilating Vr if dBZ>12 every 5 minutes; no storm in initial ensemble
Truth
EnKF
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Mesoscale EnKF (SND and SFC obs): Perfect Model(Zhang, Meng and Aksoy 2006, MWR, 722-736)
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Mesoscale EnKF (SND and SFC obs): Imperfect Model(Meng and Zhang 2006, MWR, revised)
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CNTL KFens KF2ensBmens KUOensMulti1 Multi2 KF3ensMulti3 Multi4
(m/s) RM-DTE Forecast/Analysis errors after Final Assimilation
Pure forecast Perfect-model OSSE Single-wrong-scheme Multi-wrong-scheme
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Mesoscale EnKF with WRF: Real Data Experiments(Meng and Zhang, this workshop)
Assimilating profiler (ev 3h), surface (ev 6h) and sounding (ev 12h) obs verified with soundings
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SHV
HGX
FWS
94W96W98W100W 92W 90W
26N
28N
30N
32N
34N
GRK
EWX
CRP
LCH
POE
WSR-88D Network: for mesoscale NWP, clear-air obs
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Clear-Air Radar Observations: What are measured?(Wilson et al. 1994)
Clear-air echoes mixed Boundary layer primarily come from insects/birds are most commonly observed over land from spring to autumn. Doppler radar velocities measured the winds if the insects and birds are not migrating.
Typical clear-air data for WSR-88D: 0-3km, 50-70km radius, ev4-10minTypical clear-air data for SMART Radar: 0-3km, 40-60km radius, ev3-10min
Background of the Current Study
As part of the TXAQS II air quality field campaign, one SMART radar (C-band) is deployed 22 km away from the KHGX WSR-88D radar (S-band) which collected data nearly continuously from July 11 to August 31, 2005
Dual Doppler analysis is feasible with Vr observations from both radars
A WRF-based EnKF to simulate clear-air observations from one or both radars
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KHGX WSR-88D and SMART Radar (SR1)
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WSR-88D of KHGX at 2105UTC and Vr EditingTop Left: DZ, Top Right, Unedited VR, Bottom Left: AutoVR, Bottom Right: AutoManualVR
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SMART Radar at 2110UTC and Vr EditingTop Left: DZ, Top Right: Unedited VR, Bottom Left: AutoVR, Bottom Right: AutoManualVR
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Dual Doppler Analysis Winds at 400 m at 2100 UTC
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Experimental Design: OSSE with EnKF
• Forecast model: WRF, 12-km (and 4-km) grids of 51x51 centered over Houston
• Case in study: TXAQS II of 2 August 2005
• A 30-member ensemble: initiated at 00Z 2 August with random but balanced
perturbations using WRF 3Dvar background error statistics (Barker et al. 2003)
• Perfect-model OSSE: truth as one of the ensemble members; no model error
• OBS type: boundary layer (0-4km) clear-air radar obs of Vr from truth run
centered on KHGX radar site and at (24 km)2 spacing, every 1 h
• OBS error: 3 m/s for Vr; uncorrelated
• Square-root sequential EnKF: OBS assimilated one by one; OBS not perturbed
• Radius of influence: 12 grid points each direction (Gaspari and Cohn 1999)
• Variance relaxation: mixing prior and posterior variances (Zhang, Snyder and Sun 2004)
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WRF Model Domains and Configurations
D1 (12km)
AssimilationDomain (D2)
Model Physics: Grell CPS, 6-class WSM microphysics & YSU PBLEnsemble generation: WRF/3DVAR covariance; size 30
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EnKF Performance: Analysis vs. Forecast
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Horizontal RMS Error Distribution at 12h: u,vPure EF EnKF Difference
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Horizontal RMS Error Distribution at 12h: T,QPure EF EnKF Difference
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Horizontal RMS Error Distribution at 24h: u,vPure EF EnKF Difference
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Horizontal RMS Error Distribution at 24h: T,QPure EF EnKF Difference
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Time Evolution of Vertical RMS Error : u
Pure EF
EnKF
EF-EnKF
Update
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Pure EF
EnKF
EF-EnKF
Update
Time Evolution of Vertical RMS Error : v
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Pure EF
EnKF
EF-EnKF
Update
Time Evolution of Vertical RMS Error : T
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Pure EF
EnKF
EF-EnKF
Update
Time Evolution of Vertical RMS Error : Q
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EnKF Performance: Analysis vs. Forecast
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Concluding Remarks
• ISSUES:
– Quality control of clear-air observations is harder
– Resolution and frequency of the obs to be assimilated
– Observational error covariance; correlated obs error
– Model error: boundary layer parameterizations and surface (external) forcing
– Lack of error growth could mean slow redevelopment of meaningful covariance
• Promises
– Abundant WSR-88D clear-air observations
– Good for boundary layer profiling; convective initiation
– Complementary to (and potentially better) than Vr than in precipitation mode for mesoscale
NWP because of the larger characteristic spatial scales in clear-air mode vs. precipitation mode
• Ongoing and future work
– Ready to assimilate the real data of clear-air Vr
– Compared to Dual Doppler and subsequent nudging
– Assimilated obs from multiple radars over large domains; both clear-air and precip modes