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Part II
Access to Surface Weather Conditions:MesoWest & ROMAN
Surface Data Assimilation:ADAS
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
http://www.met.utah.edu/mesowest
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
Horel et al. 2002: Bull Amer. Meteor. Soc.
MesoWest User Interface Redesign
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
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
State Map
Station Interface
Weather Near Fires
Weather Near Biscuit Fire
Location Search
Current Weather Summary
Trend Monitor
MODIS Interface
Plan for 2004 Fire Season
CIRPData Sources
Fire WxUser Community
DedicatedComms
Web ServerBoise WFO/
NIFC
LDM
AWIPS/FX-NET/
GFE
RAWS
RAWS
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
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
Use of MesoWest in Data AnalysisUse 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
Maximum Temperature: Monday. April 15. 2002
Tax Day Storm:
April 15, 2002
Tax Day Storm:April 15, 2002
Bagley. Salt Lake Tribune
Maximum Sustained Wind Speed (mph)
ADAS Graphical Interface
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
What is a Good Analysis?
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
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
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 InversionsO
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OO ?
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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
What added value does ADAS provide?
Part II: Summary MesoWest/ROMAN/ADAS under development
for use by weather professionalsGovernment server with 24/7 support by next summer
Tools can be adjusted to meet needs for office and field use
Feedback: [email protected]
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?
