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Clarus Quality Checking

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Nine QCh procedures being implemented as part of the Clarus Proof of Concept ... Seven supplemental QCh algorithms that were included in the Clarus Detailed ... – PowerPoint PPT presentation

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Title: Clarus Quality Checking


1
Clarus Quality Checking
Kevin R. Petty National Center for Atmospheric
Research ICC 4 Falls Church, Virginia August
8, 2006
2
Clarus Quality Checking
  • GOALS
  • Document the scientific merit of the proposed QCh
    tests
  • Identify Gaps in current Algorithms
  • Propose supplemental QCh processes that would
    improve the QCh service
  • Recommend the most robust combination of QCh
    tests for the Clarus system

3
Clarus Quality Checking Proposed Algorithms
  • Nine QCh procedures being implemented as part of
    the Clarus Proof of Concept
  • Manually Change QCh Flag
  • Sensor Range Test
  • Climate Test
  • Barnes Spatial Test
  • Step Test
  • Persistence Test
  • Like Instrument Test
  • Dew Point Temperature Test
  • Sea Level Pressure Test

4
Clarus Quality Checking Proposed Algorithms
  • Seven supplemental QCh algorithms that were
    included in the Clarus Detailed Requirements
    Document
  • Optimal Interpolation Spatial Test
  • Potential Temperature Test
  • Theoretical Solar Radiation Test
  • Precipitation Amount Test
  • Wind Direction Test
  • Soil Moisture Change Test
  • Soil Moisture Freeze Test

5
Clarus Quality Checking Scientific Merit
  • Algorithms have be used for decades
  • Proven
  • Fast and efficient
  • Original constraints/limitations have changed
  • Spatial distribution of data
  • Computational power
  • Data types
  • Data availability and acquisition

6
Clarus Quality Checking Gaps
  • Topographic Features
  • Mountains
  • Coastal Areas
  • Oceans
  • Lakes
  • Rivers
  • Data sparse regions
  • Beyond state variables (e.g. precipitation, cloud
    cover)
  • Low temporal resolution data
  • Mobile data

7
Clarus Quality Checking Supplemental Algorithms
  • Algorithms based on ancillary data
  • Radar (NEXRAD, TDWR, ARSs, Etc.)
  • Satellite (GOES, A-TRAIN CONST., Etc.)
  • Cameras
  • Cosmic
  • Numerical Model
  • Fuzzy Logic based algorithms
  • Alleviate the need to threshold
  • Ability to provide one flag
  • Computationally efficient

8
Clarus Quality Checking
User Perspectives
  • Diverse End User Community
  • Data Contributors
  • Academic Institutions
  • State, County, Local Maintenance Managers
  • Traffic Operations Center
  • Transit
  • Emergency Managers
  • Etc.
  • Diverse Requirements
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