Problems With Model Physics in Mesoscale Models Clifford F Problems With Model Physics in Mesoscale Models Clifford F. Mass, University of Washington, Seattle, WA

Major Improvements in Mesoscale Prediction Major improvements in the skill of mesoscale models as resolution has increased to 3-15 km. Since mesoscale predictability is highly dependent on synoptic predictability, advances in synoptic observations and data assimilation have produced substantial forecast skill benefits. Although model physics has improved there are still major weaknesses that need to be overcome.

Important to Know the Strengths and Weaknesses of Our Tools

Very Complex Because Model Physics Interaction With Each Other—AND Model Dynamics

Some Physics Issues with the WRF Model that Are Shared With Virtually All Other Mesoscale Models

Overmixing in Mesoscale Models Most mesoscale models have problems in maintaining shallow, stable cool layers near the surface. Excessive mixing in the vertical results in excessive temperatures at the surface and excessive winds under stable conditions. Such periods are traditionally ones in which weather forecasters can greatly improve over the models or models/statistical post-processing

Cold spell Time series of bias in MAX-T over the U.S., 1 August 2003 – 1 August 2004. Mean temperature over all stations is shown with a dotted line. 3-day smoothing is performed on the data.

Shallow Fog…Nov 19, 2005 Held in at low levels for days. Associated with a shallow cold, moist layer with an inversion above. MM5 and WRF predicted the inversion…generally without the shallow mixed layer of cold air a few hundred meters deep MM5 or WRF could not maintain the moisture at low levels

Observed Conditions

High-Resolution Model Output

So What is the Problem? We are using the Yonsei University (YSU) scheme in most work. We have tried all available WRF PBL schemes…no obvious solution in any of them. Same behavior obvious in other models and PBL parameterizations. Doesn’t improve going from 36 to 12 km resolution, 1.3 km slightly better. There appears to be common flaws in most boundary layer schemes especially under stable conditions.

Problems with WRF surface winds WRF generally has a substantial overprediction bias for all but the lightest winds. Not enough light winds. Winds are generally too geostrophic over land. Not enough contrast between winds over land and water. This problem is evident virtually everywhere and appears to occur in all PBL schemes available with WRF. Worst in stable conditions.

10-m wind bias, 00 UTC, 24-h forecast, Jan 1-Feb 8, 2010

10-m wind bias, 12 UTC, 12-h forecast, Jan 1-Feb 8, 2010

The Problem

Insufficient Contrast Between Land and Water

This Problem is Evident in Many Locations

Northeast U.S. from SUNY Stony Brook (Courtesy of Brian Colle): 12-36 hr wind bias for NE US: additive bias (F-O)

SUNY Stony Brook: Wind Bias over Extended Period for One Ensemble Member

U.S. Army WRF over Utah

Cheng and Steenburgh 2005 (circles are WRF)

UW WRF 36-12-4km: Positive Bias Change in System July 2006 Now

Wind Direction Bias: Too Geostrophic

MAE is something we like to forget…

Surface Wind Problems Clearly, there are flaws in current planetary boundary layer schemes. But there also be another problem?—the inability to consider sub-grid scale variability in terrain and land use.

The 12-km grid versus terrain

A new drag surface drag parameterization Determine the subgrid terrain variance and make surface drag or roughness used in model dependent on it. Consulting with Jimy Dudhia of NCAR came up with an approach—enhancing u* and only in the boundary layer scheme (YSU). For our 12-km and 36-km runs used the variance of 1-km grid spacing terrain.

38 Different Experiments: Multi-month evaluation winter and summer

Some Results for Experiment “71” Ran the modeling system over a five-week test period (Jan 1- Feb 8, 2010)

10-m wind speed bias: Winter Original

With Parameterization

MAE 10m wind speed

With Parameterization

Case Study: Original

New Parameterization

Old New

During the 1990’s it became clear that there were problems with the simulated precipitation and microphysical distributions Apparent in the MM5 forecasts at 12 and 4-km Also obvious in research simulations of major storm events.

Early Work-1995-2000 (mainly MM5, but results are more general) Relatively simple microphysics: water, ice/snow, no supercooled water, no graupel Tendency for overprediction on the windward slopes of mountain barriers. Only for heaviest observed amounts was there no overprediction. Tendency for underprediction to the lee of mountains

MM5 Precip Bias for 24-h 90% and 160% lines are contoured with dashed and solid lines For entire Winter season

Testing more sophisticated schemes and higher resolution ~2000 Testing of ultra-high resolution (~1 km) and better microphysics schemes (e.g., with supercooled water and graupel), showed some improvements but fundamental problems remained: e.g., lee dry bias, overprediction for light to moderate events, but not the heaviest. Example: simulations of the 5-9 February 1996 flood of Colle and Mass 2000.

5-9 February 1996 Flooding Event

MM5: Little Windward Bias, Too Dry in Lee Windward slope Lee Bias: 100%-no bias

Flying Blind

IMPROVE Clearly, progress in improving the simulation of precipitation and clouds demanded better observations: High quality insitu observations aloft of cloud and precipitation species. Comprehensive radar coverage High quality basic state information (e.g., wind, humidity, temperature) The IMPROVE field experiment (2001) was designed and to a significant degree achieved this.

Two IMPROVE observational campaigns: I. Offshore Frontal Study Legend British Columbia Washington UW Convair-580 Airborne Doppler Radar Cascade Mts. Two IMPROVE observational campaigns: I. Offshore Frontal Study (Wash. Coast, Jan-Feb 2001) II. Orographic (Oregon Cascades, Nov-Dec 2001) S-Pol Radar BINET Antenna Offshore Frontal Study Area Olympic Mts. Olympic Mts. Paine Field NEXRAD Radar Univ. of Washington Wind Profiler Area of Multi-Doppler Coverage Rawinsonde Westport WSRP Dropsondes Cascade Mts. Special Raingauges PNNL Remote Sensing Site Columbia R. 90 nm (168 km) Ground Observer Washington S-Pol Radar Range 100 km S-Pol Radar Range Portland Oregon Terrain Heights Coastal Mts. < 100 m Salem Orographic Study Area 100-500 m 500-1000 m 1000-1500 m 1500-2000 m Newport 2000-3000 m > 3000 m Rain Gauge Sites in OSA Vicinity Santiam Pass OSA ridge crest Santiam Pass Orographic Study Area S-Pol Radar Range Cascade Mts. Coastal Mts. Oregon SNOTEL sites CO-OP rain gauge sites Medford 50 km California

The NOAA P3 Research Aircraft Dual Doppler Tail Radar Surveillance Radar Cloud Physics and Standard Met. Sensors Convair 580 Cloud Physics and Standard Met. Sensors

Convair-580 Flight Strategy 9000 20-40 inches/year 40-60 inches/year 60-80 inches/year 80-100 inches/year > 100 inches/year < 20inches/year 8000 60 km 7000 Slope matches that of an ice crystal falling at 0.5 m/s in a mean cross-barrier flow of 10 m/s, which takes ~3 h. 6000 5000 Terrain ht. (m) 4000 3000 100 km Total flight time: 3.4 h 2000 1000 S-POL Radar Santiam Junction Santiam Pass Camp Sherman PARSL Site -100 -50 50 100 Distance (km)

The S-Pol Doppler Radar

S-Band Vertically Pointing Radar Pacific Northwest National Lab (PNNL) Atmospheric Remote Sensing Laboratory (PARSL) 94 GHz Cloud Radar 35 GHz Scanning Cloud Radar Micropulse LIDAR Microwave Radiometer Broadband radiometers  Multi-Filter Rotating Shadowband Radiometer (MFRSR) Infrared Thermometer (IRT) Ceilometer Surface MET Total Sky Imager S-Band Vertically Pointing Radar

We now had the microphysical data aloft to determine what was happening Model Observations

The Diagnosis Too much snow being produced aloft Too much snow blowing over the mountains, providing overprediction in the lee Too much cloud liquid water on the lower windward slopes Too little cloud liquid water near crest level. Problems with the snow size distribution (too few small particles) Several others!

Problems and deficiencies of boundary layer and diffusion schemes can significantly affect precipitation and microphysics Boundary layer parameterizations are generally considered one of the major weaknesses of mesoscale models Deficiencies in the PBL structures were noted during IMPROVE. Errors in boundary layer structure can substantially alter mountain waves and resultant precipitation.

Impacts of Boundary Layer Parameterization on Microphysics Snow-diff CLW-diff Graupel-diff Microphysics Differences ETA - MRF

Lots of activity in improving microphysical parameterizations New Thompson Scheme for WRF that includes a number of significant improvements. Higher moment schemes are being tested. (e.g., new Morrison two-moment scheme) Microphysical schemes are being modified to consider the different density and fall speed characteristics of varying ice habits and degrees of riming.

Convective Parameterization The need for convective parameterization declines at models gain enough resolution to explicitly model convection. Appears that one starts getting useful explicit convective predictions at 4-km grid spacing. In the future, they is one problem that will go away as we move to sub-4km grid spacing.

Composite NEXRAD Radar Real-time 12 h WRF Reflectivity Forecast Valid 6/10/03 12Z 4 km BAMEX forecast 10 km BAMEX forecast 22 km CONUS forecast Composite NEXRAD Radar

Example: Radar reflectivity, 24 h fcst vs obs, valid 0000 UTC May 13, 2005 WRF 4km NMM 4.5km WRF 2km observed http:// www.spc.noaa.gov/exper/Spring_2005

Hurricane Rainbands Ultra high resolution (< 2 km grid spacing) result in better structures and intensity predictions. 15-km grid spacing 1.67 km grid spacing

More Physics Issues Serious deficiencies in many land surface modeling schemes, particularly in the areas of snow physics and soil moisture Need to characterize uncertainties in physics schemes and the development of stochastic physics. Require physics schemes applicable to a wide range of resolutions for the next generation of unified models.

Resolution Was Easy We have had a lot of fun increasing resolution over the past few decades. Now we have to put much more emphasis on doing the research and operational testing required to improve model physics and describing the uncertainties in our schemes. This work is made more difficult by the interactions among the physics parameterizations.

The End

Garvert, Mass, and Smull, 2007 Improve-2 Dec13-14, 2001 Changes in PBL schemes substantially change PBL structures, with none bein correct.

An Issue Our method appears to hurt slightly during strong wind speeds and near maximum temperatures in summer.

Summer-0000 TC-Original

With Sub-grid drag

Summer

Improvement? Next step—could have the parameterizaton fade out for higher winds speeds and lower stability, possibility by depending on Richardson number. Actually, this makes some sense…sometimes the atmosphere is well-mixed, and at these times variations in sub-grid roughness would be less important.