Access to Surface Weather Conditions:

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Part II

• Access to Surface Weather Conditions
• MesoWest ROMAN
• Surface Data Assimilation
• ADAS

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• MesoWest and ROMAN (Real-time Observation Monitor
  and Analysis Network)
• MesoWest/ROMAN Development Team
• John Horel
• Mike Splitt
• Judy Pechmann
• Brian Olsen
• http//www.met.utah.edu/mesowest
• http//www.met.utah.edu/roman
• mesowest_at_met.utah.edu

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http//www.met.utah.edu/mesowest
Horel et al. 2002 Bull Amer. Meteor. Soc.

• Real-time collection of weather observations from
  over 5000 stations and 120 participating
  organizations
• Data processing, QC, and graphics generation
  every 15 min
• Observations in areas not sampled by NWS/FAA or
  RAWS networks
• Improved analysis/diagnosis of local and regional
  wind systems
• Specialized interfaces for fire weather, RWIS,
  wind power applications
• Distributed to WFOs by LDM

MesoWest
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MesoWest User Interface Redesign
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ROMAN

• Software developed at CIRP to assist entire fire
  weather community, including NWS forecasters at
  WFOs and IMETs, to obtain access to current
  surface weather information
• Support for development of ROMAN from NWS through
  CIRP base funding and from fire agencies in
  support of NIFC and GACC meteorologists
• Builds upon MesoWest database to store and
  display observations nationwide
• Tools designed for fire weather applications can
  be used for many other purposes

Geographic Area Coordination Centers
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MesoWest/ROMAN

• Designed for quick access to data from variety of
  networks
• Tabular and graphical formats geared to
  operational fire weather needs
• Structured by
• GACCs
• NWS CWAs
• NWS Fire Weather Zones
• States
• Intuitive, easily navigable interface
• Clickable maps
• Station Weather
• Weather Summary
• Trend Monitor
• Weather Monitor
• 5 Day Temp/RH Summary
• Precip Summary/Monitor
• Weather Near Fires
• Search by zip code, geographic location

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State Map
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Station Interface
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Weather Near Fires
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Weather Near Biscuit Fire
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Location Search
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Current Weather Summary
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Trend Monitor
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MODIS Interface
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Plan for 2004 Fire Season
RAWS
RAWS
CIRP
Data Sources
LDM
AWIPS/ FX-NET/ GFE
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Local Data Assimilation ADAS

• Utah ARPS Data Assimilation System (ADAS)
• Mesoscale analyses require different assimilation
  techniques than those on a national scale,
  especially in complex terrain
• Local analysis serves as a visual and numerical
  integrator of the MesoWest surface observations
• Background and terrain fields help to build
  spatial temporal consistency in the surface
  fields
• Analyses serve as an additional quality control
  step to the MesoWest observations

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What is ADAS?

• ADAS is short for the Advanced Regional
  Prediction System (ARPS) Data Assimilation System
  (Xue et al. 2000, 2001a,b)
• At CIRP, ADAS is run in near-real time to create
  analyses of meteorological variables over the
  complex topography of the western U. S.
• 10km analysis every 15 minutes 2.5 km analysis
  once per hour
• ADAS employs the Bratseth method of successive
  corrections (Bratseth 1986) to complete the
  objective analysis
• The 20km Rapid Update Cycle (RUC Benjamin et al.
  2002) is used for the background field
• ADAS can be used for nowcasting and as a
  verification tool by National Weather Forecast
  offices

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Use of MesoWest in Data Analysis

• Integration of weather resources into single
  analysis product
• Many local data sources are not used in
  national-scale data assimilation systems
• Local analysis graphics serve as a visual
  integrator of the MesoWest surface observations
• Weather over complex terrain of Intermountain
  West depicted more accurately

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Tax Day Storm April 15, 2002
Maximum Temperature Monday. April 15. 2002
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ADAS Graphical Interface
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What is a Good Analysis?

• Depends on
• the application
• Initializing numerical forecast?
• Specifying atmospheric state for verification?
• the dominant scales of motion
• data spacing
• Mesonet observations
• Radar/satellite observations
• available computational resources
• Successive corrections, OI, 3/4-D Var
• See Kalnay (2003) and Lazarus et al. (2002) for
  more details

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Data 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

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An analysis is more than spatial interpolation

• Background field provides
• Information where few observations
• Avoids extrapolation far from observations
• Provides detail between observations
• Introduces dynamical consistency
• Typical errors of observations and background
  field are considered

• Data used in analysis are not limited to
  analysis/ forecast variables
• Knowledge of atmospheric behavior can be used to
  relate 1 variable to another
• Scales of motion too small to be resolved by
  forecast model can be removed

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Data Assimilation in Complex Terrain

• Data Assimilation in complex terrain must be
  able to handle a wide range of scale interactions

Strongly forced
Weakly forced
Elevated Valley Inversions
O
?
O
?
?
O
O
O
O
z
T
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Key Points

• High resolution analysis based upon coarse
  background field and sparse data is simply
  downscaling to specified grid terrain
• High resolution analysis adds value IFF
• high resolution data sources are available
• OR the background field is at high resolution
• Spatial scales specified by weighting functions
  determine degree to which observed local weather
  variations can be resolved by the analysis

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What added value does ADAS provide?
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Part II Summary

• MesoWest/ROMAN/ADAS under development for use by
  weather professionals
• Government server with 24/7 support by next
  summer
• Tools can be adjusted to meet needs for office
  and field use
• Feedback mesowest_at_met.utah.edu

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Mini-Lab

• Goal- increase familiarity with
  MesoWest/ROMAN/ADAS tools
• Evaluate and apply tools to your CWA
• What observations do you have access to at your
  WFO that are not available in MesoWest/ROMAN?