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Update: AFWA ensemble development and findings

Update: AFWA ensemble development and findings. J. Hacker / E. Kuchera Collaborators: C. Snyder, J. Berner , S.-Y. Ha, M. Pocernich , J. Schramm, WRF developers. Guiding interests. Primarily lower atmosphere (winds, shear) Multiple time scales (0-60 h) and fine spatial scales

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Update: AFWA ensemble development and findings

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  1. Update: AFWA ensemble development and findings J. Hacker / E. Kuchera Collaborators: C. Snyder, J. Berner, S.-Y. Ha, M. Pocernich, J. Schramm, WRF developers

  2. Guiding interests • Primarily lower atmosphere (winds, shear) • Multiple time scales (0-60 h) and fine spatial scales • Desire to run decision support algorithms using the output • Limited in-house NWP model development • Complement and augment global NWP ensembles

  3. Development and testing • Multi-parameters in a single set of physics (Param) • Stochastic backscatter in a single set of physics (Berner) • Multi-scheme/parameterization WRF (Phys) • Up to 20 configurations; typically run 10 members • Began with WRFv2.2 and ongoing with WRFv3.2 • Limited (3) multiple physics configurations, chosen for independence and low individual errors • Perturbations to land-use tables • Perturbed observations in independently cycling WRF-3DVar (PO) • Ensemble Transform Kalman Filter (ETKF) • Ensemble Kalman Filter (EnKF; Snyder/Ha) Model uncertainty IC uncertainty All runs use the GEFS ensemble (Ensemble Transform) for lateral boundary conditions.

  4. Physics Configurations AFWA Operational configuration

  5. Testing/verification over two domains CONUS: Nov 2008 – Feb 2009, 00/12Z initialization every other day (cycling as needed). Korea: Oct 2006, 00/12Z initialization every other day (cycling as needed).

  6. Should we include obs error? • Rank histograms of 24-h, 10-m wind speed • Obs errors from N (0,so) • NCEP so is 1.1-1.3 ms-1 (dependent on pressure) Chose not to include observation error because it makes interpretation ambiguous.

  7. Effect of land-use perturbations • Land use perturbations (Eckel and Mass 2005) have a small positive impact on mean error in the PBL; effect is smaller for probabilistic metrics. Temperature Wind speed Solid Includes LU perturbations

  8. Score against direct dynamical downscaling • Baseline is AFWA control on GEFS • 1 – difference; positive is better • Clear advantage of multiple physics 2-m T 700-mb T 10-m WS 700-mb WS Multiple PhysicsMultiple Physics / Perturbed Obs (3DVar) Perturbed Parameters

  9. Score against solely multiple physics 2-m T 700-mb T • Baseline is multi-physics on GEFS • 1 – difference; positive is better • Typically an advantage for obs use in very short range 10-m WS 700-mb WS Multiple Physics / Perturbed Obs (3DVar) ETKF (GEFS mean) Hybrid (3DVar on mean) - All ensemble use same multiple physics configuration

  10. Score against solely multiple physics • Baseline is multi-physics on GEFS • 1 – difference; positive is better • Fewer physics schemes with stochastic streamfunction perturbations is promising StochasticThree Physics+Perturbed ParametersStochastic+LimitedPhysics+Parameters - More recent results (Berner) show advantages of Stochastic+Multi-physics

  11. Concurrent to R&D: • AFWA proceeded with deploying a simple multi-physics ensemble • Uses more LBC sources than used in R&D • Pushed to higher resolution • Deployed in sparsely observed locations • Developed operational products aimed at USAF users • Include back-end models

  12. 4 km SWA ensemble12 Apr 2010 Iraq IR satellite loop from 09-18Z 50 knot wind gust probability at 19Z 58 knots observed at 1911Z Black contour=where individual ensemble member forecasted 40 knots sustained Lightning probability loop from 09-18Z 00Z 12 Apr 2010 ensemble run Black contours=where individual ensemble member forecasted intense lightning

  13. 4 km SWA ensemble27 Apr 2010 Iraq “One thing to take away from this was the success of the Ensembles” 28 OWS storm review for 27 April thunderstorm event • Keys to forecast success • Convective scale ensemble members (4 km) • Direct diagnosis of supercells in WRF (updraft helicity) • Good ensemble agreement (high forecast confidence) 15Z satellite and radar 15 hour supercell forecast

  14. 10 June 2010 Wake low from MCS—24 hour forecast

  15. Closing thoughts • Logistical need to get away from multiple models and physics schemes • User needs often introduce constraints that are not recognized in current research; relocatable/rapidly deployable domains is an example • Observation error desirable to include in verification, but generally applicable values are not available • Multiple physics shows a clear advantage over doing nothing else • Using obs in perturbations and/or via data assimilation shows advantage in very short ranges; LBC a strong constraint • Stochastic perturbations (backscatter) showing promise; may gain from combining with other methods for PBL forecasting

  16. Ongoing: C&V probability • Ceiling and Visibility are key forecast parameters for USAF ops • Evaluating potential for probabilistic predictions from current ensembles • Evaluating potential for ensemble augmentation techniques to fill out pdf of visibility

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