Transcript Mountain Meteorology - Home

The Planetary Boundary Layer in
Complex Terrain
John Horel
NOAA Cooperative Institute for
Regional Prediction
Department of Meteorology
University of Utah
[email protected]
Photo: J. Horel
References
•Barry, R., 1992: Mountain Weather and Climate. Rutledge
•Blumen, W., 1990: Atmospheric Processes Over Complex Terrain. American Meteorological Society,
Boston, MA.
•Clements, C., D. Whiteman, J. Horel, 2003: Cold pool evolution and dynamics in a mountain basin. J.
Appl. Meteor., 42, 752-768.
•Garratt, J., 1992: The Atmospheric Boundary Layer. Cambridge
•Horel, J., M. Splitt, L. Dunn, J. Pechmann, B. White, C. Ciliberti, S. Lazarus, J. Slemmer, D. Zaff, J.
Burks, 2002: MesoWest: Cooperative Mesonets in the Western United States. Bull. Amer. Meteor. Soc.,
83, 211-226.
•Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge
•Kossmann, M., and A. Sturman, 2003: Pressure-driven channeling effects in bent valleys. J. Appl.
Meteor., 42, 151-1158.
•Lazarus, S., C. Ciliberti, J. Horel, K. Brewster, 2002: Near-real-time Applications of a Mesoscale
Analysis System to Complex Terrain. Wea. Forecasting. 17, 971-1000.
•Stull, R. B., 1999: An Introduction to Boundary Layer Meteorology. Kluwer
•Whiteman, C. D., 2000: Mountain Meteorology. Oxford
•Zhong, S. and J. Fast, 2003: An evaluation of the MM5, RAMS, and Meso-Eta Models at Subkilometer
resolution using VTMX field campaign data in the Salt Lake Valley. Mon. Wea. Rev., 131, 1301-1322.
•Zumpfe, D., 2004: A Case Study of a Strong Lake-Breeze Front in the Salt Lake Valley. M.S. Thesis.
University of Utah. 75pp.
Notes: Summer School on Mountain Meteorology 2003. http://www.unitn.it/convegni/ssmm.htm
Outline
 Part
I
 Characteristics/impacts
of complex terrain
 Resources for observing surface weather
 Part
II
 Basin
boundary layer
 Mountain-valley and lake breezes
Field Programs
 CASES-99
Cooperative Atmosphere-Surface
Exchange Study. Kansas. Poulos et al., 2002:
BAMS, 83, 555-581.
 MAP Mesocale Alpine Program. Alps. Bougeault
et al., 2002, BAMS, 82, 433-462.
 VTMX Vertical Transport and Mixing Experiment.
Salt Lake Valley. Doran et al. 2002, BAMS, 83,
537-551.
PBL Issues
www.pnnl.gov/vtmx
VTMX Science Plan:
 Measurement and modeling of vertical transport and mixing
processes in the lowest few kilometers of the atmosphere are
problems of fundamental importance for which a fully satisfactory
treatment has yet to be achieved
 Although a general theoretical understanding of many of the
physical phenomena relevant to vertical transport and mixing
processes exists, that understanding is incomplete, the
representation of various phenomena in models is often poor, and
the data needed to test those models are lacking.
 The upward and downward movements of air parcels in stable and
residual layers of the atmosphere and the interactions between
adjacent layers are particularly difficult processes to characterize,
and significant difficulties also exist in describing the behavior of
the atmosphere during morning and evening transition periods.
 Complications due to heterogeneous land surfaces and complex
terrain further compromise our ability to treat vertical transport and
mixing processes properly.
VTMX Science Questions
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What are the fundamental processes that control vertical transport for stable and
transition boundary layers?
How can momentum, heat, and moisture fluxes be modeled and predicted in a
stratified atmosphere with multiple layers?
What improvements in numerical simulations and forecasts of vertical transport
and mixing during stable and transition periods are feasible and how can they be
implemented?
What formulations are most appropriate for the description of vertical diffusion in
stable air? For example, how rapidly will an elevated layer of pollutants mix
towards the ground in a stable pool trapped within a basin, and how can that
mixing be modeled?
What is the sensitivity of current local weather forecast and dispersion model
predictions to variations in the treatment of vertical diffusivity and turbulence?
What limits our ability to forecast vertical transport in current numerical
prediction models?
How do traveling weather systems remove stable stagnant air out of a basin, and
under what conditions do these removal mechanisms fail?
What is the nature of the interaction of terrain-induced flows (e.g., drainage winds
at night, upslope winds during the day, and waves) with cold air pools in basins,
and how do such flows affect the formation and erosion of those pools and the
dispersion of pollutants in them?
What are the effects of complex terrain?
Substantial modification of synoptic or meso scale
weather systems by dynamical and thermodynamical
processes through a considerable depth of the atmosphere
 Recurrent generation of distinctive weather conditions,
involving dynamically and thermally induced wind
systems, cloudiness, and precipitation regimes
 Slope and aspect variations on scales of 10-100 m form
mosaic of local climates
(Barry 1992)

Effects of Complex Terrain
Carruthers and Hunt 1990
Energetic Considerations
Since the atmosphere is heated mainly from the
ground, cooling effect upon earth’s surface of
latent and sensible heat fluxes is nearly double that
of radiative fluxes
 Since much of the land surface is hilly, thermally
driven circulations play important role in global
energy balance

F. Fiedler. Summer School Trento
Billiard ball analogy
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“If the earth were greatly reduced in size while maintaining its shape, it would
be smoother than a billiard ball”. (Earth radius = 6371 km; Everest = 8.850
km)
Nonetheless, mountains have a large effect on weather. Why is this, if they
are so insignificant in size?
Answer: the atmosphere, like the mountains, is also shallow (scale height 8.5
km) so mountains are a significant fraction of atmos depth.
But, this answer underestimates mountain effect for two reasons:
 Stability gives the atmosphere a resistance to vertical displacements
 The lower atmosphere is rich in water vapor so that slight adiabatic ascent
brings the air to saturation.
Example: flow around a 500-m mountain (<< 8.5 km) could
include 1) broad horizontal excursions, 2) downslope windstorm
on lee side, and 3) torrential orographic rain on windward side.
Smith (1979)
Distribution of mountains on the globe
(Barry 1992)
Elevation range
Mountains (106 km2)
Plateau (106 km2)
Mountains/Land
Surface (%)
>3000 m
6
---
4.0
2000-3000 m
4
6
2.7
1000-2000 m
5
19
3.4
0-1000 m
15
94
10.1
Total
30
119
20.2
Total land surface is about 149 million km2. Oceanic islands covering 2 million km2 are not included
in the listed areas. Plateau & mountains are both included in the table’s 1st line.
Louis (1975)
Western
U.S. Terrain
(high- dark;
low-light)
High terrain
(dark)
Flat (tan)
Mtn. Valleys
(light)
A. Reinecke
Diurnal Temperature Range
A. Reinecke
Planetary
boundary
layer
1 km
Energy and mass exchanges near ground
---interactions among soil science, hydrological cycles
(ground and air), ecosystems, and atmosphere.
•Canopy
•Terrain
•Heterogeneous surfaces
•Clouds/fog
•Urban environment, air pollution
D. Lenschow
Shallow Drainage Flows – Mahrt, Vickers, Nakamura, Soler, Sun,
Burns, & Lenschow – BLM, 101, 2001.
Schematic cross-section of prevailing southerly synoptic flow, northerly surface flow down
The gully, and easterly flow likely drainage flow from Flint Hills. Numbers identify the
Sonic anemometers on the E-W transect. E is to the right and N into the paper.
Pollutant Transport in Valleys
Nighttime
Stable Layer
in Valley
After Breakup
of Nighttime
Stable Layer
in Valley
Savov et al. (2002; JAM)
Daytime vertical mixing processes
Jerome Fast
Diurnal mountain wind systems
Whiteman (2000)
Mountain-plain circulation, Rocky Mountains
US radar profiler network, 1991-1995, Jun-Aug, 500 m gate, max=3.5 m/s
Whiteman and Bian (1998)
Alpine pumping
Mountain venting, anti-slope flow
25 July 2001
1000
Vertical cross-section
of slope flow (upslope
to the right)
Height Above Ground (m)
900
800
CBL Height
600 from Lidar
700
500
400
300
3 m/s
200
100
0
0900
Local Time (PDT)
Reuten et al.( 2002
Valley cross sections
temperature
and wind
structure
layers at a
time midway
through the
transition
Whiteman (2000)
Whiteman (1980)
Channeling of synoptic/mesoscale winds
Forced Channeling
Whiteman (2000)
Pressure Driven Channeling
Dynamic Channeling
Kossman
and
Sturman
2003
Diurnal fair weather evolution of bl over a plain
Whiteman (2000)
free →
troposphere
mixed →
layer
surface →
layer
D. Lenschow
D. Lenschow
D. Lenschow
Atmospheric structure evolution in valley terrain
Whiteman (2000)
Effects of horizontal heterogeneity in
surface properties
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Changes in surface roughness
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Changes in surface energy fluxes
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Rough to smooth
Smooth to rough
Sensible heat flux
Latent heat flux
Changes in incoming solar radiation
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Cloudiness
Slope
Roughness Effects
 For
well-mixed conditions (near neutral lapse rate)
 U2 = u1 ln (z2/zo)/ln(z1/z0)
 Roughness length zo=.5 h A/S where h height of
obstacle, A- silhouette area, S surface area A/S< .1
 Zo- height where wind approaches 0
Roughness lengths zo for different natural surfaces (from M. de
Franceschi, 2002, derived from Wieringa, 1993).
zo (m)
Landscape Description
________________________________________________________________
0.0002
Open sea or lake, tidal flat, snow-covered plain, featureless desert,
tarmac, concrete with a fetch of several km.
0.005
Featureless land surface without any noticeable obstacles; snow
covered or fallow open country
0.03
Level country with low vegetation and isolated obstacles with
separations of at least 50 obstacle heights
0.10
Cultivated area with regular cover of low crops; moderately open
country with occasional obstacles with separations of at least 20
obstacle heights
0.25
Recently developed “young” landscape with high crops or crops of
varying height and scattered obstacles at relative distances of about
15 obstacle heights
0.50
Old cultivated landscape with many rather large obstacle groups
separated by open spaces of about 10 obstacle heights; low large
vegetation with with small interstices
1.0
Landscape totally and regularly covered with similar sized obstacles
with interstices comparable to the obstacle heights; e.g., homogeneous
cities
D. Lenschow
(Kaimal & Finnigan, 1994).
Surface Wind and Vorticity Around Isolated Mountain:
Interaction with Large-scale flow
Chen, C.-C., D. Durran and G. Hakim
(2003) ICAM
Potential Temp, Vertical Velocity, and Turbulent
Mixing
Chen, C.-C., D. Durran and G. Hakim (2003) ICAM
Summary- Impacts of Complex Terrain
 Terrain
affects atmospheric circulation on local to
planetary scales
 Terrain induced eddies modify and contribute to
the vertical and horizontal exchange of mass,
temperature, and moisture in a much stronger
manner than turbulent eddies over flat terrain
Photo: J. Horel
Problems and possible future directions
Most theoretical, modeling and observational results are
applicable to a horizontally homogeneous PBL and
underlying surface.
 Non-uniform surfaces predominate over land.
 New tools are needed and are becoming available to
address PBL structure over heterogeneous terrain.

D. Lenschow
Resources for Observing Surface Weather
 Access
to Surface Weather Conditions:
 MesoWest
 Surface
& ROMAN
Data Objective Analysis:
 ADAS
 NDFD
Forecast Verification
What is MesoWest/ROMAN?
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MesoWest: A cooperative program to collect and distribute
environmental obs across the western US and Nation
 150+ participating agencies/groups
 3500+ stations in western US (2000+ in real time)
 Cost-effective supplement to ASOS network
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ROMAN: Real-time Observation Monitor and Analysis Network
 Tailored distribution/access of MesoWest data for the wildland
fire community throughout the nation
 8000+ active stations in U.S. and Canada
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Primary support: NWS and BLM
2003 Fire Locations (Red); MesoWest/ROMAN stations (Grey)
10,000 stations
In database;
8000+ reported
data during past
month
Fire locations provided by Remote Sensing
Applications Center from MODIS imagery
Sample stations
Ben Lomond Peak Snotel, UT (8000’)
Mt. Allen, UT (9400’)
Beacon Light RAWS, NV (4800’)
Portable fire RAWS, USFS
Gunnison Is, UT (4242’)
Are All Observations Equally Bad?
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All measurements have
errors (random and
systematic)
Errors arise from many
factors:
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Siting (obstacles, surface
characteristics)
Exposure to environmental
conditions (e.g., temperature
sensor heating/cooling by
radiation, conduction or
reflection)
Sampling strategies
Maintenance standards
Metadata errors (incorrect
location, elevation)
SNZ
Are All Observations Equally Good?
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Why was the sensor installed?
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Station siting results from pragmatic tradeoffs:
power, communication, obstacles, access
Use common sense
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Wind sensor in the base of a mountain pass
will likely blow from only two directions
Errors depend upon conditions (e.g.,
temperature spikes common with calm winds)
Use available metadata
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Observing needs and sampling strategies vary
(air quality, fire weather, road weather)
Topography
Land use, soil, and vegetation type
Photos
Monitor quality control information
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Basic consistency checks
Comparison to other stations
UT9
MesoWest/ROMAN: http://www.met.utah.edu/mesowest
How can I access the data?
Search by placename, zipcode, lat/lon
What has changed since yesterday?
24-Hour Trend Monitor
Where are conditions extreme/severe?
Fire Weather Monitor
Objective Analysis
Analysis value = Background value + observation Correction
- A good analysis requires a good background field
- Background fields are supplied by a model forecast
- A good analysis requires a good previous model forecast
- Observation correction depends upon weighted differences between
observations & background values at observation locations
- Weights typically depend upon:
- distance of observations from analysis grid point
- Expected error of observations
- Expected error of background field
ADAS: ARPS Data Assimilation System
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ADAS is run in near-real time to create analyses of temperature, relative humidity, and wind over
the western U. S. (Lazarus et al. 2002 WAF)
Analyses on NWS GFE grid at 5 km spacing in the West
Test runs made for lower 48 state NDFD grid at 5 km spacing
Typically > 2000 surface temperature and wind observations available via MesoWest for analysis
(5500 for lower 48)
The 20km Rapid Update Cycle (RUC; Benjamin et al. 2002) is used for the background field
Background and terrain fields help to build spatial & temporal consistency in the surface fields
Current ADAS analyses are a compromise solution; suffer from many fundamental problems due to
nature of optimum interpolation approach
Limitations:
 Analysis depends strongly upon the background field
 Hour-to-hour consistency only through background field
 Analysis sensitive to choice of background error decorrelation length scale
 Wind field not adjusted to local terrain
 Manual effort required to maintain station blacklist
Difficult to assess independently the quality of the analysis: analysis can be constrained to match
observations, which typically leads to spurious analysis in data sparse regions
Arctic Outbreak: 21-25 November 2003
NDFD 48 h forecast
ADAS Analysis
Arctic Outbreak: 21-25 November 2003
NDFD and ADAS DJF 2003-2004 seasonal means removed
NDFD 48 h forecast
ADAS Analysis
Upper Level Ridging and Surface Cold Pools:
14 January 2004
NDFD 48 h forecast
Analysis
Surface Cold Pool Event: 14 January 2004
NDFD and ADAS DJF 2003-2004 seasonal means removed
NDFD 48 h forecast
ADAS Analysis
Bias and RMS for Temperature
as a function of forecast length: DJF 2003-2004
Cold Pools
Arctic Outbreak
Front
Anomaly Pattern Correlation for 48h Temperature Forecasts
Anomalies Relative to Winter Season Mean
DJF 2003-2004 Anomaly Pattern Correlations
Summary
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At the present time, verification of NDFD forecasts is relatively
insensitive to methodology. The errors of the NDFD forecasts are
larger than uncertainty in the verification data sets.
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Differences between analyses (e.g., RUC vs. ADAS) and differences between
analyses and observations are smaller than differences between NDFD
forecast grids and analyses or NDFD forecast grids and observations
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Difference between ADAS temperature analysis on 5 km grid and station
observations is order 1.5-2C
Difference between NDFD temperature forecast and ADAS temperature analysis is
order 3-6C
Systematic NDFD forecast errors are evident that may be correctable at WFOs
and through improved coordination between WFOs
Skill of NDFD forecast grids, when the seasonal average is removed to focus
upon synoptic and mesoscale variations, depends strongly on the parameter
and the synoptic situation:
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Anomaly pattern correlations between NDFD and ADAS temperature grids over
the western United States suggest forecasts are most skillful out to 72 h
Dew point temperature skill evident out to 36 h and wind speed out to 24 h
Little difference in NDFD skill when evaluated over areas where analysis
confidence is higher
Some strongly forced synoptic situations are well forecast over the West as a whole
Persistence forecasts were hard to beat during cold pool events