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Computational Weather Forecasting for Research and Operational Activities (and other Department of Geography interests)

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Title: Computational Weather Forecasting for Research and Operational Activities (and other Department of Geography interests)


1
Computational Weather Forecasting for Research
and Operational Activities (and other Department
of Geography interests)
  • Anna Klene
  • Don Morton

2
Stuff Were Doing
  • Geography
  • Analysis and modeling of meteorological events in
    the Northern Rockies and Alaska
  • Operational and research numerical weather
    predictions for WFO MSO and FAI
  • Beaufort Sea Mesoscale Meteorology Modeling Study
    for Department of Interiors Minerals Management
    Service

3
Geography
  • Statistics
  • Field Applications
  • Ulli Kamp, Anna Klene Dave Shively
  • GIS
  • Klene, Shively, Christiane von Reichert, Paul
    Wilson
  • Remote sensing
  • Ulli Kamp Anna Klene

4
Geography
  • Field Applications
  • Remote data-loggers and weather stations
  • Montana Alaska
  • Distributed networks
  • Live Weather Stations
  • Hellgate Canyon

5
Geography
  • GIS
  • Transportation modeling
  • Land-change modeling
  • Hydrologic applications
  • Energy-balance approaches
  • Student projects???

Spatio-temporal GIS in Migration Research
Migration Histories as Space-Time Paths
6
Geography
  • Remote sensing
  • DEM generation
  • Classification (pattern recognition machine
    learners)
  • MontanaView
  • Looking for contributions of raw and analyzed
    imagery outreach ?

Slope Aspect, Nanga Parbat, Pakistan
7
Analysis and Modeling in Northern Rockies and
Alaska
  • Met stations
  • Case Studies
  • MSO Great Inversion of November 2005
  • Western Montana Arctic Outbreak of February
    2006
  • Tanana Valley Wind event of October 2006

8
The Great Inversion of November 2005
  • 18 26 November 2005

9
The Great Inversion of November 2005
10
Conditions Elsewhere in Western Montana
  • Most valleys west of Divide appear to be
    saturated, while high country looks to be above
    it all

11
Some Local Observations
12
Dewpoint Depression2005-11-18_18Z - 75- vs.
119-level
13
Synoptic Overview
14
Synoptic Overview
15
Synoptic Overview
16
Synoptic Overview
No graphics available for rest of event
17
2006-02-15_00Z
18
2006-02-16_00Z
19
2006-02-17_00Z
The Ninepipe Eddy
20
2006-02-16_00Z
21
2006-02-16_21Z
22
Tanana Valley Wind Event October 2006
23
Tanana Valley Wind Event October 2006
24
Operational WRF Runs
  • 48-hour operational runs, 00Z and 12Z
  • Postprocessed for NWS AWIPS and web products

25
http//weather.arsc.edu/
26
http//weather.arsc.edu/
27
PASC (Deadhorse)
28
Beaufort Sea Mesoscale Meteorology Modeling Study
  • Department of Interiors Minerals Management
    Service funding for UAF and UM

29
Data Collection and Analysis
Data Sources National Climatic Data Center MMS
(RESDAT) UAF - WERC SHEBA others
AtigunAnaktuvuk PassBarrowBarter
IslandDeadhorseFranklin BluffsGalbraith
LakeHappy ValleyIguigigKuparukNuiqsutOliktok
Prudhoe BayUmiat
Various periods of record Hourly wind data
www. ncdc.noaa.gov/oa/ncdc.html
30
  • SAR wind
  • Canadian RADARSAT-1 European ENVISAT satellites
  • Processed at UAF
  • 250 m resolution
  • Daily Arctic coverage
  • MODIS wind
  • NASA EOS satellites, available at NOAA NESDIS
  • Derived by tracking water vapor and cloud
    features
  • Low, middle, and high levels
  • Daily coverage

SAR-derived surface wind speed with NOGAPS (Navy
Operational Global Atmospheric Prediction System)
model wind direction at 258 UTC 25 July 2006
over the Beaufort Sea coastal region.
MODIS polar wind product at 1420 UTC 06 Aug.
2006 low (below 700 hPa, yellow), middle (700 to
400 hPa, cyan), and high (above 400 hPa, magenta)
level wind vectors are displayed.
31
Domain Setup Cases Selection Data
Preparation Model Configuration Resolution
Assimilation
Complex Region
Nesting design One-way nesting Two-way
nesting
Physics Options in MM5 7 cumulus
parameterizations 8 explicit moisture schemes
4 radiation schemes 7 planetary boundary layer
schemes 3 land surface schemes
32
Summary
  • Most of this work has huge network, data storage,
    and computational requirements
  • Numerous interdisciplinary opportunities
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