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  • GCOS Reference Upper Air Network

    Holger VmelMeteorological Observatory LindenbergDWD

  • What is GRUAN?GCOS Reference Upper Air NetworkNetwork for ground-based reference observations for climate in the free atmosphere in the frame of GCOS Initially 15 stations, envisaged to be a network of 30-40 sites across the globeLead Center at DWD Meteorological Observatory Lindenberg

    See WWW.GRUAN.ORG for more detail

  • The GCOS Reference Upper-Air Network is tasked to:Provide long-term high-quality upper-air climate recordsConstrain and calibrate data from more spatially-comprehensive global observing systems (including satellites and current radiosonde networks)Fully characterize the properties of the atmospheric column

  • Initial GRUAN stations

  • Focus on reference observationsA GRUAN reference observation:

    Is traceable to an SI unit or an accepted standardProvides a comprehensive uncertainty analysisIs documented in accessible literatureIs validated (e.g. by intercomparison or redundant observations)Includes complete meta data description

  • Select GRUAN requirementsPriority 1: Water vapor, temperature, (pressure and wind)

  • Stratospheric water vapor over BoulderFrom Hurst et al. JGR, 2011

  • Water vapor trends in upper troposphere? Freiberg RKS-2 RKS-5 MARZ RS80 RS92 e.g.: Lindenberg 8km (0:00 UT)

  • Water vapor trends in upper troposphere? Freiberg RKS-2 RKS-5 MARZ RS80 RS92 e.g.: Lindenberg 8km (0:00 UT)

  • Water vapor trends in upper troposphere? No trend estimate possible: Trend signals caused by instrumental changeObservations have been done for numerical weather prediction, not for long term climateInstrumental change has not been managedObservational biases have not been fed back to improve observationsInstrumental uncertainties and biases have not been (well) characterized or documentedMeta data are incompletee.g.: Lindenberg 8km (0:00 UT)

  • These deficiencies are some of the motivators to establish the GCOS Reference Upper Air Network (GRUAN)

  • Establishing UncertaintyGUM concept:The "true value" of a physical quantity is no longer usedError is replaced by uncertaintyA measurement = a range of valuesgenerally expressed by m um is corrected for systematic effectsu is (random) uncertainty

  • Establishing UncertaintyGuide to the expression of uncertainty in measurement (GUM, 1980)

    Guide to Meteorological Instruments and Methods of Observation, WMO 2006, (CIMO Guide)

    Reference Quality Upper-Air Measurements: Guidance for developing GRUAN data products, Immler et al. (2010), Atmos. Meas. Techn.

  • Establishing reference quality

  • Uncertainty example: Daytime temperature Vaisala RS92

  • Validation: Redundancy and ConsistencyGRUAN stations should provide redundant measurementsRedundant measurements should be consistent:

    No meaningful consistency analysis possible without uncertaintiesif m2 has no uncertainties use u2 = 0 (agreement within errorbars)

  • Consistency in a finite atmospheric regionCo-location / co-incidence:Determine the variability () of a variable (m) in time and space from measurement or modelTwo observations on different platforms are consistent if

    This test is only meaningful, i.e .observations are co-located or co-incident if:

  • Redundant observationsUse uncertainty formalism to make use of redundant observations:Redundant observations continuously validate the understanding of instrumental performanceRedundant observations in intensive campaigns place GRUAN observations in larger contextRedundant observations maintain homogeneity across the networkProvide a feedback that identifies deficiencies in order to improve the measurements (instrumental upgrade, reprocessing)

  • Issues affecting long term trendsUse uncertainty formalism to improve long term trends:Identify, which sources of measurement uncertainty are systematic (calibration, radiation errors, ), and which are random (noise, production variability )Develop and verify tools to identify and adjust systematic biasesMaintain raw data and document every step in the data collection and processing chain

  • Managed changeUse uncertainty formalism to manage change:Determine instrumental uncertainties and biases of new systemRemove systematic biases in new instrument and quantify random uncertaintyVerify uncertainty estimate of new instrument in simultaneous (dual) observations

  • Distributed data processing

  • Principles of GRUAN data managementArchiving of raw data (at site or lead center) is mandatoryAll relevent meta-data is collected and stored in a meta-data base (at the lead centre)For each measuring system just one data processing centerVersion control of data products. Algorithms need to be traceable and well documented.Data levels for archiving: level 0: raw datalevel 1: raw data in unified data format (pref. NetCDF)level 2: processed data product dissemination (NCDC)

  • SummaryGRUAN is a completely new approach to long term observations of upper air essential climate variables: High-quality upper-air climate records Constrain data from satellites and current radiosonde networks Characterize the properties of the atmospheric columnFocus on reference observation: quantified uncertainties traceable well documentedGRUAN requires a new data processing and data storage approachFocus on priority 1 variables to start: Water vapor and temperatureData started in March 2011 and slowly growingExpansion to other instruments is planned

    The only long term climate record for stratospheric water vapor has been done in Boulder, CORedundant observations in agreement is an essential part of operational quality assurance

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