Northwest AIRQUEST 12/4/06

1. Northwest AIRQUEST12/4/06 Cliff Mass University of Washington

2. Topics • PBL Parameterizations • The Current and Future State of the System • The Data Assimilation Revolution

3. Boundary Layer Parameterizations • It is now recognized both locally and nationally that the boundary layer parameterizations in current mesoscale models (e.g., WRF, MM5) have substantial weaknesses when run at typical resolutions of 4-15 km horizontal grid spacing. • These problems are most profound for stable boundary conditions, which are unfortunately important for the air quality community. Example, maintaining a shallow (few hundred meter thick cold layer near the surface). • Another issue is what type of parameterization is appropriate for grid spacings below 1 or 2-km, where we start to explicitly model large BL circulations

4. Shallow Fog…Nov 19, 2005 • Held in at low levels for days • MM5 could produce and maintain the inversion…but generally without the shallow mixed layer of cold air a few hundred m deep • MM5 could not maintain the moisture at low levels

11. Where are the problems? • PBL parameterizations themselves? • Land surface models or surface specifications? • Model dynamical cores (too much diffusion, etc)? • Other physics (such as radiation?)

12. Who is working on PBL parameterizations and evaluating current ones? • Generally a hollowing out of the U.S. efforts (e.g., UW Energy Transfer Group) • For MM5/WRF mainly Korean effort (Yonsei University) • Still activity in Europe…mainly Dutch? • Eric Grimit’s activities and Chris Bretherton’s group • No one at NCEP actively working on PBL parameterizations for mesoscale models.

13. Important Note • At the WRF annual workshop at NCAR, PBL was recognized as number one physics problem. • I chaired the Physics working group and several individuals wanted to organize a PBL effort.

14. Eric Grimit Research Before Leaving • He generated two months of output (November-December 2005) using 3 model configurations in WRF: • Default WRF with YSU-PBL • YSU-PBL with Garratt SL • UW-PBL with Garratt SL Note: YSU is Yonsei PBL Scheme…an improved version of the current MRF scheme • Did initial comparison/verification using tower data at Hanford. • Some encouraging results.

15. the average theta difference from the surface value (2-m actually) at 12 Z for each configuration from the model column closest to Hanford, WA

16. Where do we go from here? • Chris Bretherton is now supervising the further analysis by an undergraduate student (Alfredo Arroyo) of these comparison runs. • Eric Grimit has left for 3-tier, but would like to continue working on the problem at some (small) level. • Chris is willing to lead a renewed PBL effort if there was some hope of funding. • There was a meeting last January at Hanford to talk about a regional boundary layer initiative.

17. The NW has many of the pieces • Considerable intellectual resources in NW (UW, WSU) • Real-time modeling system infrastructure • Substantial observational assets (e.g. PNNL and wonderful mesoscale network) • Engaged user community (e.g., NW modeling consortium) • Active partners north of the border • Developing regional data assimilation system that could be a major tool.

18. Computer forecast models were nearly perfect for the onset and amount of snow over Puget Sound. The Missing Element: Leadership and around Portland…. and we know why.

19. Current State of the System

20. High-Resolution Forecasts • MM5-GFS 36, 12 and 4-km runs have been highly stable without any significant down times. • The MM5-NAM 36-12 km has been moved from the old Tahoma to Linux machines..earlier availability • Did very well for both the snow storm and flooding events. • Grid-based bias correction for temperature and dew point are now online.

21. The Switch to WRF • Testing the current version of WRF and some components of the new system. • MM5 is verifying better still.

22. WRF • Waiting on new version of WRF with nudging and better radiation schemes. • Will evaluate for an extended period. If equal or superior will switch..after the consortium provides approval. Probable switch..next summer.

23. Ensemble Probabilistic Prediction • Our 36-12 km mesoscale ensemble system is highly stable. • Running on new Linux processors and includes extended physics ensemble. • Substantial effort is going into development of post processing (bias correction and Bayesian model averaging)--which greatly enhances the probabilistic skill. • The ensembles and many products are online.

26. The New Revolution: Mesoscale Analysis using Ensemble Techniques • New mesoscale data assimilation approaches based on ensembles should allow great improvement in using all observational assets to produce a highly realistic 3D description of the regional atmosphere. • Will be a boon for weather and air quality prediction, both for verification and for initializing our models. • Will allow us to get the most out of the rapidly increasing number of surface observations and aircraft reports. • We are building the system right now at the UW to do it.

27. How it works… • You have a collection of ensemble members, each a little different. • You can correlate an observed parameter with 3D atmospheric structures produced by the model. • Example: for the current synoptic/mesoscale configuration…more precip at Sea Tac would be associated with more convergence in wind field • Using the model as a key part of the data assimilation cycle.

28. The Start • We have purchased and installed a new cluster…the AOR cluster…to be the computational resources (13 nodes with 4 processors each) • Have purchased a new RAID array to serve it. • Have hired a very good post-doc..Brian Ancel…to do the work…and he has begun to build the system. • This will be the highest resolution attempt to do this in the country. • Based on our successful coarse (45 km) resolution system. • Probably 60- 90 members.

29. The Impact • If this works, we will have extraordinarily high quality mesoscale analyses suitable for air quality and NWS needs • If will produce high quality forecasts and will greatly enhance the 0-12 hr forecasts of the real-time system. Maybe much longer in some cases. • Can overcome some of the PBL problems by pushing the model hard with observational assets.

30. NewAOR Cluster