Problems With Model Physics in Mesoscale Models Clifford F. Mass, University of Washington, Seattle,...
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Transcript of Problems With Model Physics in Mesoscale Models Clifford F. Mass, University of Washington, Seattle,...
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
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.
Cold spell
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
ObservedConditions
High-ResolutionModel 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 PrecipBias for
24-h
90% and 160% lines
are contoured
with dashed and solid
lines
For entireWinterseason
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
Bias: 100%-no bias
Windward slope
Lee
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.
Olympic Mts.
British Columbia
Washington
Ca
scad
e M
ts.
Cas
cade
Mts
.
Oregon
California
OrographicStudy Area
Washington
Oregon
Co
asta
l Mts
.
Co
asta
l Mts
.
S-Pol Radar Range
Santiam Pass
OSA ridge crest
Cas
cade
Mts
.
< 100 m
100-500 m
500-1000 m
1000-1500 m
1500-2000 m
2000-3000 m
> 3000 m
Terrain Heights
Portland
Salem
Newport
Medford
UW Convair-580
Airborne Doppler Radar
S-Pol Radar
BINET Antenna
NEXRAD Radar
Wind Profiler
Rawinsonde
Legend
Ground Observer
0 100 km
WSRP Dropsondes
Columbia R.
Rain Gauge Sites in OSA Vicinity
Santiam Pass
SNOTEL sites CO-OP rain gauge sites
50 km
Orographic Study Area
S-Pol Radar Range
Olympic Mts.
S-Pol Radar Range
Westport
90 nm(168 km)
Offshore FrontalStudy Area
Paine Field
Univ. of Washington
Area of Multi-Doppler
Coverage
Special Raingauges
PNNL RemoteSensing Site
TwoIMPROVE
observationalcampaigns:
I. Offshore Frontal Study (Wash. Coast, Jan-Feb 2001)
II. Orographic Study (Oregon Cascades, Nov-Dec 2001)
The NOAA P3 Research AircraftDual Doppler Tail Radar Surveillance RadarCloud Physics and Standard Met. Sensors
Convair 580Cloud Physics and Standard Met. Sensors
PARSLSite
Terr
ain
ht.
(m
)
Distance (km)0 50 100
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
S-POL Radar
SantiamJunction
SantiamPass
CampSherman
-50-100
20-40 inches/year40-60 inches/year60-80 inches/year80-100 inches/year> 100 inches/year
< 20inches/year60 km
100 km
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.
Total flight time: 3.4 h
Convair-580 Flight Strategy
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
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.
Real-time 12 h WRF Reflectivity Forecast
Composite NEXRAD Radar
4 km BAMEX forecast
Valid 6/10/03 12Z
10 km BAMEX forecast
22 km CONUS forecast
Example: Radar reflectivity,24 h fcst vs obs, valid 0000 UTC May 13, 2005
WRF 4km
WRF 2km
NMM 4.5km
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-2Dec13-14, 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.