Northwest AIRQUEST12/4/06

Cliff Mass

University of Washington

Topics

• PBL Parameterizations

• The Current and Future State of the System

• The Data Assimilation Revolution

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

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

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?)

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.

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.

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.

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

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.

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.

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.

Current State of the System

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.

The Switch to WRF

• Testing the current version of WRF and some components of the new system.

• MM5 is verifying better still.

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.

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.

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.

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.

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.

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.

NewAOR

Cluster