MesoWest Quality Checking

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MesoWest Quality Checking

• John Horel
• Mountain Meteorology Group
• Department of Meteorology
• University of Utah
• jhorel_at_met.utah.edu

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The Mix of Observing Assets
http//www.met.utah.edu/mesowest
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Google API User Interface
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ADAS

• Near-real time surface
• analysis of T, RH, V
• (Lazarus et al. 2002 WAF
• Myrick et al. 2005 WAF
• Myrick Horel 2006 WAF)
• Analyses on NWS GFE
• grid at 5 km spacing
• Background field RUC
• Horizontal, vertical anisotropic weighting

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MesoWest Data Flow
MesoWest Database _at_WR
MADIS/LDM Delivery
Preprocessing _at_ WR
Data Streams
Other WR/WFO Apps
MesoWest Database _at_UU
Web Server _at_ UU
Metadata/QC/ ADAS _at_ UU
ROMAN Database _at_WR
Preprocessing _at_ WR
ROMAN Web Server _at_ WR
RAWS In ASCADS
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MesoWest Quality Checking

• See Splitt et al. (WMO 2001), Horel et al. (BAMS
  2002)
• Focus on temperature, wind, pressure, and
  relative humidity
• Current automated MesoWest QC is crude
• Designed to identify quickly provisional
  observations that have egregious problems or are
  inconsistent with surrounding observations
• One QC flag reported for each observation (not
  specific variable)
• Suspect (Red/-1) fails simple gross checks
• Unknown (0) QC flag not available
• Caution (Orange/1) significant departure from 3D
  multivariate linear regression estimate or fails
  wind persistence check
• OK (Green/2) passes all checks
• Current manual MesoWest QC is cumbersome
• Designed to identify stations with consistently
  poor observations prior to use in ADAS data
  assimilation (ADAS blacklist file)
• One QC flag reported for each station manually
  (not specific observation)
• Uncertainty regarding metadata
• Manual identification of problem with 1 or more
  variables over extended period of time
• Stations that frequently report observations that
  differ extremely from ADAS are flagged

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3D Regression Check
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RUC/ADAS Temp. Analysis vs. Observations
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Qv (specific humidity)
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Wind Speed
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Probability of 1-hour Temp Change (F) at UT16
(2000-2006)
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Quality Checking Issues

• Applications of RTMA as background field
• http//www.emc.ncep.noaa.gov/mmb/rtma
• Hourly analysis at 5 km resolution available 30
  minutes past hour (T,RH,V,precip,cloud)
• Taking full advantage of nearby observations
• Representativeness of nearby observations for
  buddy checking
• Impacts of differences in elevation, terrain
  blocking, etc. on Barnes analysis

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RTMA
NCEP Real Time Mesoscale Analysis in development
testing 2DVar approach with RUC as background
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Sub-5km Variability in Terrain Height
Dark gt 200m
Myrick and Horel (2006)
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Observational (Measurement and Representativeness)
Temperature Error (oC) as a Function of Mesonet
estimated from covariance of observation
background differences (Myrick and Horel 2006,
WAF)
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Small Observation Error
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Joint Probability
4062 pairs observations (2005-06)
UTDOG HOHU1(C)
UTDOG Temperature (C)
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Final Random Thoughts

• QC Information along with original data must be
  accessible to the end user
• Incomplete metadata affects application of QC
  algorithms (low resolution lat/lon, incorrect
  elevation)
• Flexibility required as new mesonet stations
  added frequently
• QC work underway to
• improve automated methods (including use of
  Kalman Filter)
• Use climatological approaches as 10-year database
  for some stations
• integrate more completely QC from ADAS into
  MesoWest
• use MADIS and other (Clarus?) QC sources

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Inaccurate Metadata
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Large Observation Error
2438m
1829m