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Development of Convective-Scale Data Assimilation Techniques for 0-12h High Impact Weather Forecasting

This talk discusses the unique aspects of convective-scale data assimilation (DA) techniques and their application in forecasting high impact weather events within a 0-12 hour time frame. The success, issues, and future challenges of these techniques are also explored. The talk is focused on warm-season quantitative precipitation forecasting (QPF) and the use of radar observations.

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Development of Convective-Scale Data Assimilation Techniques for 0-12h High Impact Weather Forecasting

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  1. Development of convective-scale data assimilation techniques for 0-12h high impact weather forecasting JuanzhenSun NCAR, Boulder, Colorado Oct 25, 2011

  2. Outline • Introduction - Unique aspects of convective-scale DA - Overview of techniques • Success and Issues • Future challenges • This talk is in the context of • Warm-season QPF • Radar observations • NCAR experiences Oct 25, 2011

  3. What makes convective-scale DA different? • Objective - QPF, high-impact weather nowcast/forecast - Forecast accuracy: county/city scale • Predictability of high-impact weather systems - Rapid error growth - Small-scale with multiple scale interaction • Observations - Limited high-resolution in-situ observations - Remote sensing: high resolution, but limited coverage, limited and indirect variables

  4. Convective-scale DA strategies • Place storms at right locations - Warm Start: Cloud analysis, latent heating insertion, saturation adjustment, updraft profiling • Use frequent update - Sub-hourly; 10-15 min window for 4DVAR - Take advantage of high temporal frequency obs. - Forced by predictability limitation • Consider cloud-scale balance - Temporal derivative terms should not be neglected - Different balance from the large-scale • Use different error statistics - Large-scale error statistics is not applicable - Research is still lacking

  5. Overview of techniques • Techniques based on reflectivity or precipitation - DFI, nudging, cloud analysis - Simple and efficient - No or limited multivariant balance • 3D techniques assimilating both RV and RF from radar - 3DVAR - Efficient - Balance is mostly large-scale • 4D techniques assimilating both RV and RF - 4DVAR, EnKF (and its variants) - Computationally expensive - Full model balance, but compromised in practice (limited ensemble members, limited assimilation window)

  6. Latent Heat Nudging Mei Xu • ingest radar reflectivity observations (converted to QR/QS) • add tendency terms to model variables QR/QS and T based on the model state and observations • result in thermodynamic and microphysical adjustment • Hydrometeor increment per Δt • (dQR/dt)obs * Δt if QRmod< QRobs& (dQR/dt)obs>0 • ΔQR = g *(QRobs- QRmod) if QRmod> QRobs • 0 otherwise • Temperature increment • ΔT = CLS/CPM * ΔQR • where CLS is the latent heat of condensation (or fusion) • CPM=CP*(1.+0.8*QV) is the specific heat for moist air

  7. Impact of radar data LHN case 200906 1206 analysis observation no radar with radar LHN

  8. Impact of radar data LHN case 200906 1206 1 h forecast observation no radar with radar LHN

  9. Impact of radar data LHN case 200906 1206 2 h forecast observation no radar with radar LHN

  10. Impact of radar data LHN case 200906 1206 3 h forecast observation no radar with radar LHN

  11. Skills for June 11-17, 2009 Front Range Domain FSS Evaluation Averaged over 24 forecasts Analysis period

  12. Hongli Wang WRF 3DVAR Radar DA • Cost function • Reflectivity data assimilation - Assimilate rainwater - Cloud analysis (optional) - Assimilate saturation water vapor within cloud (optional) • Control variables - stream function - unbalanced velocity potential - unbalanced temperature - unbalanced surface pressure - pseudo relative humidity For radar DA

  13. 6-h Forecasts after four 3DVAR cycles IHOP one-week runs • NORD: Control with no radar DA • RV: Assimilate radial velocity • RF: Assimilate reflectivity • RVRF: Assimilate both One-week FSS skill (5mm) RF Both RV Cycled 3DVAR

  14. Shuiyong Fan Beijing Results 2-hour forecasts • NORD: Control with no radar DA • RV: Assimilate radial velocity • RF: Assimilate reflectivity • RVRF: Assimilate both FSS skill for four 2009 summer cases OBS No Radar RV RF RV RF

  15. Diurnal variation of Radar DA impact • Radar DA has longer • positive impact for late • evening initializations • The positive impact • only lasted 4 hours for • morning initializations • It suggests • that the radar DA works • more effectively for • growing storms than • dissipation storms Dashed lines: Warm start Solid lines: Cold start 00Z 12Z

  16. Cold start analysis An example of failed forecast Cold start 3DVAR cycled analysis Cycled 3DVAR RF

  17. Can 3DVAR retrieve the tangential wind? Radial component Tangential component Radars with overlap Corr: 0.724 Analysis Single radars Corr: 0.402 Truth From Sugimoto et al. (2009)

  18. Study of a supercell storm using a 4DVAR system VDRASSun (2004) Rainwater correlation Radial velocity only Color contour: qr qv Observation w RVand RF Observation Forecast RV only RF only Reflectivity only qv w • Without radial velocity, the rain falls • out quickly. • Radial velocity assimilation results • in slantwise updraft and moisture, but • not the reflectivity assimilation • Assimilating both RV and RF • consistently outperforms RV or • RF only

  19. 4DVAR systems: VDRAS and WRF 4DVAR VDRAS • Developed for a cloud model • Trajectory is modeled by the nonlinear model • Full adjoint of the cloud model is used to calculate the gradient in the minimization • Control variables are model prognostic variables WRF 4DVAR • Developed for WRF model • Trajectory is modeled by the tangent linear model of WRF with reduced physics • Adjoint of the reduced tangent linear model • Control variables follow those in WRF 3DVAR

  20. VDRAS 3km Inserting VDRAS analysis into WRF inner domain 19 UTC 15 June 2002 WRF 9 km

  21. 2-h WRF forecasts valid at 061302 Observation (061302) No VDRAS With VDRAS

  22. 5-h WRF forecasts valid at 061302 No VDRAS Observation(061305) With VDRAS

  23. WRF 4DVAR Radar Data Assimilation4-hour forecasts from a case study (13 June 2002) OBS 3DVAR 4D_RV 4D_RF

  24. ETS of 0-6 hour forecast 4D_RF 4D_RV 1 mm 3DVAR 5 mm

  25. WRF/DART EnKF Convective scale data assimilation Glen Romine Observations 00Z 01Z 02Z Mem 1 w/ radar assim Mem 1 control

  26. Future Challenges • DA for nowcasting application requires different configurations • - Frequent updating • - Radar DA crucial for minimizing spinup time • - Different background error statistics • - Multiple pass for observations with different resolutions • - Different DA schemes • - Make better use of surface observations • - Different physics options? • Rapid cycling with/without radar DA can have negative impact • on convective initiation • - Will more frequent updating with radar DA help? • - Diurnal variation of radar DA impact • - The impact also depends on convection type

  27. Opportunities and Challenges • Radar DA still a great challenge • - Reflectivity assimilation • > Improve the accuracy of the latent heating and relative • humidity specification in the simple techniques • > Balance with dynamics • > Error statistics • - Radial velocity assimilation • > Retrieval of the tangential component in 3DVAR • > Clear air returns • > Balance with thermodynamics and microphysics

  28. Opportunities and Challenges • Challenges for the 4D techniques • - Computation cost • - Large resource required for developing a full 4DVAR • - Choice of control variables for the convective scale in 4DVAR • - Sample issues and maintenance of ensemble • spread for EnKF • - Model errors

  29. VDRAS radar data assimilation reveals how cold pools trigger storms0611 2046 UTC - 0612 1250 UTC Pert. Temp. (color) Shear vector (black arrow) Wind vector at 0.1875km (brown arrow) Contour (35 dBZ reflectivity)

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