1. Brian Ancell, Cliff Mass, Gregory J. Hakim University of Washington
The University of Washington Pacific Northwest Mesoscale Analysis System
Brian Ancell, Cliff Mass, Gregory J. Hakim
University of Washington

2. Motivation High-resolution analyses are important for:
Operational forecasting (fire weather, air quality..)

3. Motivation High-resolution analyses are important for:
Operational forecasting (fire weather, air quality..)
Studying the mesoscale effects of climate change

4. Motivation High-resolution analyses are important for:
Operational forecasting (fire weather, air quality..)
Studying the mesoscale effects of climate change
Alternative energy development

5. Motivation High-resolution analyses are important for:
Operational forecasting (fire weather, air quality..)
Studying the mesoscale effects of climate change
Alternative energy development
Pacific Northwest complex terrain presents a challenge to creating good analyses
Flow-dependence during data assimilation may be vital

6. An Attractive Option: EnKF
An ensemble Kalman filter (EnKF) has strong potential for mesoscale analysis:
Observational information is spread spatially using flow-dependent statistics

7. An Attractive Option: EnKF
Temperature observation
3DVAR
EnKF

8. An Attractive Option: EnKF
An ensemble Kalman filter (EnKF) has strong potential for mesoscale analysis:
Observational information is spread spatially using flow-dependent statistics
Analysis and forecast uncertainty is easily calculated and is also flow-dependent

9. An Attractive Option: EnKF
An ensemble Kalman filter (EnKF) has strong potential for mesoscale analysis:
Observational information is spread spatially using flow-dependent statistics
Analysis and forecast uncertainty is easily calculated and is also flow-dependent
Computational resources can handle EnKF demand

10. How the EnKF Works An analysis is created from:
1) An ensemble of short-term forecasts (Background)
2) Observations
For a single observation:
Observation (T1)
Mean Forecast (T2)
Observation Variance (V1)
Forecast Variance (V2)
Analysis (T3,V3)

11. How the EnKF Works An analysis is created from:
1) An ensemble of short-term forecasts (Background)
2) Observations
For a single observation:
Observation (T1)
Mean Forecast (T2)
Observation Variance (V1)
Forecast Variance (V2)
Analysis (T3,V3)
Analysis increment then spread spatially using covariance statistics of ensemble

12. EnKF Configuration
Large, coarse domain EnKF already tested (Torn and Hakim 2008)
- EnKF competitive with global models

13. EnKF Configuration
D3 (4km)
D2 (12km)
D1 (36km)

14. EnKF Configuration WRF model V2.1.2 38 vertical levels
80 ensemble members
6-hour update cycle
Observations:
Surface temperature, wind, altimeter
ACARS aircraft winds, temperature
Cloud-track winds
Radiosonde wind, temperature, relative humidity
Half of surface obs used for assimilation, other half for verification

15. 36-km vs. 12-km EnKF
36-km
12-km
SLP, 925-mb temperature, surface winds

16. 36-km vs. 12-km EnKF
36-km
12-km
SLP, 925-mb temperature, surface winds

17. 36-km vs. 12-km EnKF
36-km
12-km
SLP, 925-mb temperature, surface winds

18. EnKF 36-km vs. 12-km Wind Temperature Improvement of 12-km EnKF
Analysis
10%
13%
Forecast
10%
10%

19. Issue #1 – Representative Error
Model terrain = Actual terrain
at and near observation sites
Model terrain
Actual terrain

20. Surface Observations Model grid points (12-km resolution)

21. Surface Observations Model grid points (12-km resolution)
Observation location
Model grid points (12-km resolution)

22. Surface Observations Model grid points (12-km resolution)
High-resolution terrain data
(1.33 km resolution)
Observation location
Model grid points (12-km resolution)

23. Issue #1 – Representative Error
Using representative observations only, we can reduce observation uncertainty:
Observation Standard Deviations
Temp: K (36-km) K (12-km)
Wind: m/s (36-km) m/s (12-km)

24. Issue #1 – Representative Error
Using representative observations only, we can reduce observation uncertainty:
Observation Standard Deviations
Temp: K (36-km) K (12-km)
Wind: m/s (36-km) m/s (12-km)
Drawback: Lose ~75% of available surface obs

25. Issue #1 – Representative Error
Wind
Temperature
Improvement using reduced observation uncertainty
Analysis 5%
10%

26. Issue #2 – Lack of Background Surface Variance
Too little background variance exists in model surface fields

27. Issue #2 – Lack of Background Surface Variance
Too little background variance exists in model surface fields
Solution: Inflate surface variance with variance aloft

28. Issue #3 – Model Surface Bias
Significant biases exist in the model surface wind and temperature fields
Temperature Bias
Light Wind Speed (<3 knots) Bias

29. Further Improvement After Variance Inflation, Bias Removal
Wind
Temperature
Improvement using inflation and bias removal
Analysis 9% 3%

30. EnKF 12-km vs. GFS, NAM, RUC Wind Temperature RMS analysis errors GFS

31. 12-km vs 4-km EnKF
12-km
4-km
SLP, 925-mb temperature, surface winds

32. Summary
A multi-scale, nested WRF EnKF (36km, 12km, 4km) is being tested over the Pacific Northwest to produce quality analyses and short-term forecasts
Three obstacles to accurate surface analyses were discovered and dealt with using the 12-km EnKF:
Poor model terrain height profile (representative check)
Lack of model surface forecast variance (variance inflation from aloft)
Model surface wind and temperature bias (pre-assimilation bias removal)
Resulting WRF 12-km EnKF surface analyses were better than the WRF 36-km EnKF, GFS, NAM, and RUC
Future direction:
Better bias removal techniques
Tuning of data assimilation parameters
Testing of 4-km nested domain
Evaluation of analysis fields aloft
Short-range forecast verification
Comparison with current NWS mesoscale analysis techniques (RTMA, MOA)