Progress and Problems with
Forecasting Orographic Precipitation
over the Pacific Northwest and
Southwest Canada
Clifford F. Mass, University of
Washington, Seattle, WA
AMS Mountain Meteorology Conference, August 2008
Orographic Precipitation is
an essential part of the
regional meteorology
Few Areas of North America Experience Such
Large Amounts and Gradients of Precipitation
Northwest Orographic Precipitation
Has Major Societal Impacts
Flood Control on Dozens of Dams (Wynochee Dam shown)
Billion-Dollar Storms Are All Associated
with Orographic Precipitation
Mount Rainier National Park
18 inches in 36 hr (Nov 8, 2006)
Dec. 3, 2007
20 inches in two
days over coastal
terrain of SW
Washington
The results:
massive
landslides
and river
flooding
And, of course,
the 2010
Olympics will
depend on our
understanding
and predictive
capabilities for
orographic
precipitation
Northwest U.S. and S.W. Canada an
excellent testbed for studying
orographic precipitation
• Relatively simple terrain of various configurations
– Olympics—an orographic island
– Vancouver Island and portions of Cascades (linear
• Undisturbed flow approaching the barriers
• Accessible with a large number of surface observing
stations
• Major high resolution real-time simulation efforts at
the UW and University of British Columbia.
• Lack of deep convection.
There have been major progress in
understanding and predicting
orographic precipitation over this region
during the past several decades
• A number of regional field
experiments have led to substantial
advances in understanding.
Major Regional Orographic
Precipitation Field Experiments
•
•
•
•
•
•
CYCLES (1970s)
COAST (Dec. 1993, Dec. 1995)
IMPROVE 1 (Jan.-Feb. 2001)
IMPROVE 2 (Nov.-Dec. 2001)
COASTAL OLYMPICS (2003-2004)
Proposed: OLYMPEX 2010
Progress
• Long-term real-time NWP and case-specific
numerical experiments have examined the
strengths and weaknesses of orographic
NWP in the region.
• Prior to roughly 1995-2000 operational
center models lacked the resolution and
physics to even begin to handle the regional
precipitation.
• NWP is now resolving major orographic
precipitation features of the region.
NGM,
80 km,
1995
NGM, 1995
2001: Eta Model, 22 km
2007-2008
12-km
UW MM5
Real-time
12-km WRF-ARW
and WRF-NMM
are similar
December 3, 2007
0000 UTC Initial
12-h forecast
3-hr precip.
2007-2008
4-km MM5
Real-time
NWS WRF-NMM 12-km
NWS WRF-NMM (12-km)
UW Real-Time Prediction
System
• Running the MM5 and WRF-ARW at 36-12-4
km since 1996
• Thompson Microphysics
• NOAH LSM
• Run twice a day to 72h
• Verified with thousands of stations from over 70
networks. Long record of model biases and
issues over terrain.
Domains
A Few Major Lessons
• There are several key horizontal scales that
influence orographic precipitation. The first is
the scale of the major mesoscale barriers (e.g.,
west slopes of Cascades, mountains of
Vancouver Island).
• In order to resolve the influence of the these
features, one needs grid spacing of 12-15 km.
100 km
36-km
12-km
Major Lessons
• Then there are smaller scale features, produced
by the corrugations in the terrain associated
with the river valleys, and smaller-scale
features forced by terrain such as the Puget
Sound convergence zone.
• Such features require 4-km or better grid
spacing to get a reasonable handle on the
precipitation distributions.
10-km
12-km
4-km
Small-Scale Spatial Gradients in Climatological Precipitation on the Olympic Peninsula
Alison M. Anders, Gerard H. Roe, Dale R. Durran, and Justin R. Minder
Journal of Hydrometeorology
Volume 8, Issue 5 (October 2007) pp. 1068–1081
Annual Climatologies of MM5 4km domain
Verification of Small-Scale
Orographic Effects
But not so perfect for individual events
(issues of resolution, model physics, and
initialization, among others)
Perhaps the most detailed look at this scale
separation of orographic flows was presented by
Garvert, Smull and Mass, 2007 (IMPROVE-2
paper)
Garvert et al.
• Used aircraft radar and in situ data from the
IMPROVE-2 field experiment, as well as high
resolution (1.3 km grid spacing) MM5 output.
• Documented and simulated small scale
mountain waves and their
microphysical/precipitation implications.
Proposed Olympex 2010-2011
will hopefully continue this work
During the 1990’s it became
clear that there were problems
with the simulated precipitation
and microphysical distributions
over Northwest terrain
• Apparent in the daily UW real-time 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)
Colle and Mass, 1999;Colle, Mass and Westrick ,2000
• Relatively simple microphysics: water, ice/snow, no
supercooled water, no graupel. (explicit moisture scheme of
Hsie et al. 1984, with ice-phase microphysics below 0°C
Dudhia 1989) was applied in for 36, 12, and 4-km domains.
• Tendency for overprediction on the windward slopes, even
after considering undercatchment. Only for heaviest observed
amounts was there no overprediction.
• Tendency for underprediction to the lee of the barrier and in
major gaps.
MM5
Precip
Bias
for
24-h
90% and
160% lines
are
contoured
with dashed
and solid
lines
For entire
Winter
season
Problems Were Obvious in the Lee of
the Olympics
• Lack of clouds and precipitation in model on
the lee side in light to moderate events.
• Too much precipitation moving over
mountains under strong winds.
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
Colle and Mass, 2000
Little Windward Bias, Too Dry in Lee
Windward slope
Lee
Bias: 100%-no bias
Higher
Resolution:
changes lee
precipitation, but
lee bulls eyes of
heavy precip
develop
mountain
waves too
strong?
Varying Microphysics
• Modest changes, with graupel causing high
intensity areas in the immediate lee.
Most sophisticated microphysics did not necessarily produce the best verification
Flying Blind
IMPROVE
• Clearly, progress in improving the simulation
of orographic precipitation demanded better
observations:
– High quality insitu observations aloft of cloud and
precipitation species.
– Comprehensive radar coverage above the barrier
– 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
An IMPROVE-2 Sample:
Dec. 13-14, 2001
• Strong, extremely well
sampled event on the
Oregon Cascades
• Varied biases on the
windward slopes, and now
overprediction over the lee.
1.3 km
• Overprediction at valley
stations on windward side
• Little bias on windward crest
stations
Garvert et al., 2005a
4 -km
But now, we 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!
In Comparison: The Weaker
Dec. 4-5, 2001 Event
Based on WRF
Model
•Overprediction
over windward
slopes
•Too much precip in
the immediate lee of
the crest
•Underprediction to
the east of the
Cascades
Yanluan and Colle 2008
•Excessive
generation of snow
aloft
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 (work of Woods, Hafen, and
Another Major Question
• What is the importance of unresolved small
scale orographic features and sub-grid scale
motions on mesoscale orographic
precipitation?
• Do these features enhance precipitation? Do
they need to be parameterized for coarser
simulations? Or do we need ultra high
resolution to get the orographic precipitation
right?
The Influence of Small Scale Ridges
(Colle 2008)
Small net windward enhancement by small scale features
The Influence of Shear-Induced
Turbulence on Microphysics
Houze and Medina, JAS, 2005
The problems with the simulation of
orographic precipitation are not limited to
microphysics and resolution
• The MM5 and WRF V1-2.1 lacked positive
definite advection schemes for moisture
variables.
• The result of such numerics is a lack of
conservation of moisture, producing
essentially an unphysical source of water.
Thus, lack of PD advection explains part of the
overprediction problem in MM5/WRF
• COAMPS and CSU RAMS have PD schemes.
Recent Work of Robert Hahn, UW, for Dec.
13-14, 2001 IMPROVE 2 event
PD-NOPD
Domain
36km
12km
4km
1.33km
Coast Water
-4.0%
-2.5%
-6.5%
-6.6%
Coast Mountains
-4.1%
-4.4%
-7.9%
-9.8%
Willamette Valley
-3.5%
-3.9%
-13.0%
-15.6%
Cascade Windward
-4.1%
-5.0%
-13.5%
-17.2%
Cascade Leeward
-4.3%
-8.0%
-10.2%
-11.4%
DOMAIN TOTAL
-3.9%
-4.4%
-10.9%
-13.4%
Benefits Appear to Be Apparent
In UW Real-Time Prediction
MM5 and WRF
have similar bias
WRF has lesser
bias
Positive Definite
Advection Initiated
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 (as noted at recent WRF users group meeting
in Boulder).
• Deficiencies in the PBL structures were noted during
IMPROVE.
• Errors in boundary layer structure can substantially
alter mountain waves and resultant precipitation.
Garvert, Mass,
and Smull, 2007
Improve-2
Dec13-14, 2001
Changes in PBL
Schemes
substantially
change PBL
structures, with
none being
correct.
Impacts of Boundary Layer Parameterization on
Microphysics
Snow-diff
CLW-diff
Microphysics Differences ETA - MRF
Graupel-diff
The Next Major Challenge: Probabilistic
Orographic Precipitation Prediction
• The atmosphere is not deterministic and there
are substantial uncertainties in initial
conditions and physics parameterizations, and
continued approximations in the numerics.
• Over the next several years, we need to perfect
approaches for probabilistic prediction of
orographic precip that produce sharp and
reliable probability density functions.
Special Challenges and
Advantages of Probabilistic
Prediction Over Terrain
• Less observations that over flatland, making
calibration more difficult. (disadvantage)
• More frequent precipitation (an advantage).
• Less of a phase space, since orography does
constrain possible atmospheric states.
Orographic flow often controlled by
interaction of synoptic scale flow with
mesoscale terrain. (advantage).
Probabilistic NWP over NW
terrain is already well along
Current Operational Systems
– University of Washington UWME system (36-12
km)
– University of Washington EnKF System (3612km)
– NWS Multi-Model SREF System (32 km)
UWME
– Core : 8 members, 00 and 12Z
• Each uses different synoptic
scale initial and boundary
conditions
• All use same physics
– Physics : 8 members, 00Z only
• Each uses different synoptic
scale initial and boundary
conditions
• Each uses different physics
• Each uses different SST
perturbations
• Each uses different land surface
characteristic perturbations
– Centroid, 00 and 12Z
• Average of 8 core members used
for initial and boundary
conditions
Current International Multi-Analysis Collection
Resolution (~ @ 45 N )
Objective
Analysis
Abbreviation/Model/Source
Type
gfs, Global Forecast System,
Spectral T254 / L64
~55km
1.0 / L14
~80km
SSI
3D Var
Finite
Diff.
0.9 / L28
~70km
1.25 / L11
~100km
3D Var
Finite
Diff.
12km / L60
90km / L37
SSI
3D Var
Spectral T239 / L29
~60km
1.0 / L11
~80km
3D Var
Spectral T106 / L21
~135km
1.25 / L13
~100km
OI
Spectral T239 / L30
Fleet Numerical Meteorological & Oceanographic Cntr.
~60km
1.0 / L14
~80km
OI
tcwb, Global Forecast System,
1.0 / L11
~80km
OI
National Centers for Environmental Prediction
cmcg, Global Environmental Multi-scale (GEM),
Canadian Meteorological Centre
eta, Eta limited-area mesoscale model,
National Centers for Environmental Prediction
gasp, Global AnalysiS and Prediction model,
Australian Bureau of Meteorology
jma, Global Spectral Model (GSM),
Japan Meteorological Agency
Computational
ngps, Navy Operational Global Atmos. Pred. System,
Taiwan Central Weather Bureau
ukmo, Unified Model,
United Kingdom Meteorological Office
Spectral T79 / L18
~180km
Finite
Diff.
Distributed
5/65/9/L30 same / L12
~60km
3D Var
Ensemble domain
Post-Processing of Ensembles
• Uses Bayesian Model Averaging to optimally
combine the various ensemble members to
produce reliable and sharp probabilistic
forecasts.
• The output provides spatially varying PDFs of
precipitation and other parameters.
Probability Density Function
at one point
Ensemble-Based
Probabilistic Products
Work Cut Out for Us
• Large amount of work yet to be done to perfect
ensemble-based probabilistic prediction of
orographic precipitation.
• Quantification of uncertainty in parameterizations
• Higher resolution
• Many others.
The End
High (4-km or higher) resolution
also need for some small scale
orographically forced
precipitation features