Update: AFWA ensemble development and findings

J. Hacker / E. KucheraCollaborators: 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• Desire to run decision support algorithms

using the output• Limited in-house NWP model development• Complement and augment global NWP

ensembles

Development and testing1. Multi-parameters in a single set of physics (Param)2. Stochastic backscatter in a single set of physics (Berner)3. Multi-scheme/parameterization WRF (Phys)

– Up to 20 configurations; typically run 10 members– Began with WRFv2.2 and ongoing with WRFv3.2

4. Limited (3) multiple physics configurations, chosen for independence and low individual errors

5. Perturbations to land-use tables

6. Perturbed observations in independently cycling WRF-3DVar (PO)7. Ensemble Transform Kalman Filter (ETKF)8. Ensemble Kalman Filter (EnKF; Snyder/Ha)

All runs use the GEFS ensemble (Ensemble Transform) for lateral boundary conditions.

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Physics ConfigurationsMember

(JME mem)Physical parameterizations

Surface Microphysics PBL Cumulus LW_RA SW_RA

1 (1) Thermal Kessler YSU KF RRTM Dudhia

2 Thermal Lin MYJ BM RRTM Goddard

3 Thermal WSM3 MRF Grell CAM Dudhia

4 Thermal Eta YSU Grell CAM CAM

5 (3) Thermal WSM6 MYJ KF RRTM CAM

6 Thermal Thompson YSU BM CAM Goddard

7 (4) Noah Kessler MYJ BM CAM Dudhia

8 (5) Noah Lin MYJ Grell CAM CAM

9 Noah Lin MRF KF RRTM CAM

10 (6) Noah WSM5 YSU KF RRTM Dudhia

11 (7) Noah WSM5 MYJ Grell RRTM Dudhia

12 Noah Eta MRF BM CAM Goddard

13 Noah Thompson YSU KF RRTM Dudhia

14 RUC Kessler MRF Grell CAM Goddard

15 (8) RUC Lin YSU BM CAM Dudhia

16 (2) RUC Eta MYJ KF RRTM Dudhia

17 (9) RUC Eta YSU BM RRTM CAM

18 RUC WSM6 YSU Grell CAM CAM

19 (10) RUC Thompson MYJ Grell CAM CAM

20 RUC Thompson YSU BM RRTM CAM

AFWA Operational configuration

Testing/verification over two domains

Korea: Oct 2006, 00/12Z initialization every other day (cycling as needed).

CONUS: Nov 2008 – Feb 2009, 00/12Z initialization every other day (cycling as needed).

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.

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.

Wind speed Temperature

Solid Includes LU perturbations

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

700-mb WS10-m WS

Multiple Physics Multiple Physics / Perturbed Obs (3DVar) Perturbed Parameters

Score against solely multiple physics• Baseline is

multi-physics on GEFS

• 1 – difference; positive is better

• Typically an advantage for obs use in very short range

Multiple Physics / Perturbed Obs (3DVar) ETKF (GEFS mean) Hybrid (3DVar on mean)- All ensemble use same multiple physics configuration

2-m T 700-mb T

700-mb WS10-m WS

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

Stochastic Three Physics+Perturbed Parameters Stochastic+Limited Physics+Parameters- More recent results (Berner) show advantages of Stochastic+Multi-physics

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

Fly - Fight - Win 12

IR satellite loop from 09-18Z

4 km SWA ensemble12 Apr 2010 Iraq

Lightning probability loop from 09-18Z00Z 12 Apr 2010 ensemble run

Black contours=where individual ensemble member forecasted intense lightning

50 knot wind gust probability at 19Z58 knots observed at 1911Z

Black contour=where individual ensemble member forecasted 40 knots sustained

Fly - Fight - Win 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

oConvective scale ensemble members (4 km)oDirect diagnosis of supercells in WRF (updraft helicity)oGood ensemble agreement (high forecast confidence)

15Z satellite and radar

15 hour supercell forecast

Fly - Fight - Win 14

10 June 2010Wake low from MCS—24 hour forecast

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

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