Fundamentals of Mountain
Weather
by Robert Hahn
November 28, 2007
Mountain Weather
• Overview of Talk:
– Motivation
– Fundamental Interactions between atmosphere and
topography
– Mid-Lattitude Winter Storms
– Weather prediction, weather models, data access, and
trip planning
– In the field: Constant re-assessment of weather
situation.
Motivation
• Climbers:
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
– PRECIP > Visibility > temperature
• Skiers:
– Precip., visibility, Snowpack stability, rain/snow line.
• Mountaineers:
– Depends upon the climb.
“If only the governor of Georgia
were here to pray for it to clear…”
Mt. Olympus???
Is mountain weather harder to
forecast?
• Yes
– Models are our best predictors for the future state of the
atmosphere and grid resolutions currently available fail
to capture many important terrain features.
– Mountains can be a focus for convection, which occurs
at a resolution smaller than the scale of model grids.
– Topography-->sharper gradients, so a bad forecast
becomes a disaster.
• Contrary to popular belief, mountains don’t “create their own
weather”, but rather alter the flow patterns of the atmosphere,
leading to this apparent effect (glaciers are an exception).
• No
– Mountains become a focal point for mid-latitude
wintertime precipitation.
Atmospheric
Pressure
• Warmer air is less dense
– > expansion
Mountain Weather
• Key Concepts
– Atmospheric Stability
• Warm over Cold = stable
– Think highly stratified/layered atmosphere.
• Cold over Warm = unstable
– Think convection!! (eg. Boiling)
Stable Stratus Clouds
Stable Tropical Clouds (AM)
Stable Pre-Frontal Clouds
Clouds lower and moisture increases at middle and
upper levels as storm approaches
Mountain Wave Cloud
Stable Orographic Ascent
Inversion
Usually temperature decreases
with height by approximately
5.5 C per km
• But with high pressure, clear or near clear
skies, and light winds, radiational cooling at
the surface can produce INVERSIONS.
• Inversions are when temperature increases
with height.
• Surface-based inversion range from meters
to hundreds of meters in depth
Satellite photo of the Pacific Northwest at 12:45 PM on 20 November 2005. (b) Sea-level pressure map valid at 10 AM on 20 November 2005
Temperatures associated with the
Inversion
• Observations that day from aircraft and at
mountain stations indicated that the fog and low
clouds over western Washington had bases
between 100 and 300 feet and tops around 1200
feet. A layer of cool air, with temperatures of
approximately 45F was found in the lowest 700800 ft, above which the temperature warmed
rapidly with elevation (the inversion). By 2000 ft,
temperatures had reached 58F! At Paradise
Ranger Station (elevation 5500 ft) and other
mountain locations temperatures reached the midsixties that day.
Unstable
Mountains Focus convection
Solar Heating
Mountain Weather
• Another Key Concept
– Air wants to flow from High to Low pressure.
• Gap Flows
• Barrier Jet (in front of a linear mountain range)
• Valley Winds (confined by topography)
Pattern
Corresponding to
Wintertime
Climatology
Barrier Jet
Approaching Storm
L
H
Storm passes to north
-Cuts off easterly flow at passes --> westerly flow at passes and ridges
-Barrier Jet possible on lower windward slopes.
L
L
Mountain Weather
• Key Local Feature
– Proximity to Ocean
• Frequent development of marine boundary layer (top of cloud
layer 1-2 km usually-->good day to climb high or head to East
side depending upon thickness of layer).
– Temperature can drop rapidly (5-10C) in a matter of hours.
– These forecasts, particularly the timing of onset and dissipation
is tricky (and I’m not an expert in this area).
• This is also our moisture source both for wintertime and
summertime precip.
Summertime T-storm Setup
L
Moisture
source
The Norwegian Cyclone Model
• 1920--Illustrates the idealized storm system.
Dynamics & Microphysics
of Cool-Season Orographic
Storms
Presented by University of Utah
Professor, former UW grad student,
and outdoor enthusiast, Jim
Steenburg.
http://www.meted.ucar.edu/norlat/orographic/
Numerical Weather Prediction
•Observations give the distribution of mass
and allows us to calculate the various forces.
•Then… we can solve for the acceleration
using F=ma
•But this gives us the future…. With the
acceleration we can calculate the velocities in
the future.
•Similar idea with temperature and humidity.
Numerical Weather Prediction
• These equations can be solved on a threedimensional grid.
• As computer speed increased, the number of grid
points could be increased.
• More (and thus) closer grid points means we can
simulate (forecast) smaller and smaller scale features.
We call this improved resolution.
A Steady Improvement
• Faster computers and better understanding of the
atmosphere, allowed a better representation of
important physical processes in the models.
• More and more data became available for
initialization.
• As a result there has been a steady increase in forecast
skill from 1960 to now.
• The improvement is such that models generally
predict mountain weather better than humans,
however human experience can still be valuable.
P
Forecast Skill Improvement
NCEP operational S1 scores at 36 and 72 hr
over North America (500 hPa)
National Weather Service
75
S1 score
65
"useless forecast"
55
36 hr forecast
72 hr forecast
45
Forecast
Error 35
10-20 years
Better
"perfect forecast"
25
15
1950
1960
1970
Year
1980
Year
1990
2000
Satellite and Weather Radars Give Us a More
Comprehensive View of the Atmosphere
Camano
Island
Weather
Radar
Problems with the Models
• Some forecasts fail due to inadequacies in
model physics…. How the model handles
precipitation, friction, and other processes.
Example: too much precipitation on
mountain slopes
• My research is to identify and fix problems
such as these.
Some forecasts fail due to
poor initialization, i.e., a
poor starting description of
the atmosphere.
This is particularly a
problem for the
Pacific Northwest,
because we are
downstream of a
relatively data poor
region…the Pacific
Ocean.
Radar
Blockage
3 March 1999: Forecast a snowstorm
… got a windstorm instead
Eta 48 hr SLP Forecast valid 00 UTC 3
March 1999
Eta Model Sea Level Pressure: 12 UTC 2 March 99
Major
Initialization
Errors
Pacific Analysis
At 4 PM
18 November
2003
Bad Observation
If the model gets the large-scale
pattern wrong, the regional
weather model don’t have a
prayer (ie. Storms have to be in
the correct location--the governor
of Georgia even knows this).
Ensemble Prediction
• Instead of making one forecast…make
many…each with a slightly different
starting state, representing the uncertainty
associated with initialization.
The Thanksgiving Forecast 2001
42h forecast (valid Thu 10AM)
SLP and winds
1: cent
Verification
- Reveals high uncertainty in storm track and intensity
- Indicates low probability of Puget Sound wind event
2: eta
5: ngps
8: eta*
11: ngps*
3: ukmo
6: cmcg
9: ukmo*
12: cmcg*
4: tcwb
7: avn
10: tcwb*
13: avn*
Ensemble Prediction
•Can use ensembles to provide a new generation
of products that give the probabilities that some
weather feature will occur.
•Can also predict forecast skill
•When forecasts are similar, forecast skill is higher.
•When forecasts differ greatly, forecast skill is less.
http://probcast.com/
Likely amount of Precipitation (also: Quantitative
Precipitation Forecast or QPF) - This figure
represents the most likely, or best guess, at the
amount of precipitation predicted to fall during the
corresponding time period for the specified location.
The figure is typically displayed in inches or
hundredths of an inch.
Unlikely but possible upper extreme for amount of
precipitation ('As much as') - An unlikely but possible
upper extreme for the amount of precipitation; 9 times in
10 the actual amount of precipitation is predicted to be
less than this amount.
Olympic Mtns Windward Slopes:
An Orographic Precipitation
Mecca
(Courtesy of climber and Atmospheric Science Grad Student
magnet Justin Minder)
What does the precip climatology look like over the Olympics?
Olympic Precip Climatology (MM5):
4km-MM5 Annual Precipitation: (7 year mean)
Water years 2000-2006
10km
Pcp
(mm/yr)
Contours: elevation (100m)
Anders 2005
What does the precip climatology look like over the Olympics?
Where the winds come
from
(raw wind roses)
850 hPa
BKBW Wind
direction climatology:
Wyrs 2003-2006
Where the rain comes
from
(precip weighted wind
roses)
850
hPa
BKBW1
Near Surface (10m)
Near Surface (10m)
What shapes the precip pattern?
Nov 28 event: cross-section analysis (fhr 27)
Precipitation Rate
w and Cloud
LowLevel
Flow
Contours: w (cm/s)
Precip rate: Rain
Colors: Cloud water mixing ratio (g/kg)
Precip rate: Graupel (mm/hr)
Precip rate: Snow
(mm/hr)
(mm/hr)
Dashed Red: Freezing level
Know your climatology and consider the
factors creating differences from climatology
Wind Climatology
Weather Info
• http://www.atmos.washington.edu
– http://www.atmos.washington.edu/mm5rt/
• http://www.noaa.gov
• http://www.avalanche.org
• http://probcast.com/
The End