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: Multimonth 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.
British Columbia
Legend
Washington
UW Convair-580
Airborne Doppler
Radar
Two
IMPROVE
observational
campaigns:
S-Pol Radar
Offshore Frontal
Study Area
BINET Antenna
Olympic Mts.
Olympic
Mts.
Paine Field
Univ. of Washington
NEXRAD Radar
Area of MultiDoppler
Coverage
Wind Profiler
Rawinsonde
Westport
WSRP Dropsondes
Special Raingauges
Columbia R.
PNNL Remote
Sensing Site
90 nm
(168 km)
Washington
Ground Observer
0
S-Pol
Radar
Range
S-Pol
Radar
Range
100 km
Portland
I. Offshore
Frontal
Study
(Wash. Coast,
Jan-Feb 2001)
Oregon
Terrain Heights
Salem
< 100 m
100-500 m
500-1000 m
1000-1500 m
1500-2000 m
2000-3000 m
Orographic
Study Area
Newport
> 3000 m
Rain Gauge Sites in OSA Vicinity
Santiam Pass
OSA ridge crest
Santiam Pass
Orographic Study Area
S-Pol Radar Range
SNOTEL sites
CO-OP rain gauge sites
50 km
Oregon
Medford
California
II. Orographic
Study
(Oregon
Cascades,
Nov-Dec 2001)
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
> 100 inches/year
80-100 inches/year
60-80 inches/year
40-60 inches/year
20-40 inches/year
< 20inches/year
8000
60 km
7000
6000
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.
Terrain ht. (m)
5000
4000
3000
100 km
2000
Total flight time: 3.4 h
1000
0
S-POL
Radar
-100
-50
0
Santiam
Junction
Distance (km)
50
Santiam
Pass
Camp
Sherman
100
PARSL
Site
The S-Pol
Doppler Radar
Pacific Northwest National Lab
(PNNL)
Atmospheric Remote Sensing
Laboratory (PARSL)
S-Band Vertically Pointing Radar
•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
Microphysics Differences ETA - MRF
Graupel-diff
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
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.