Results of the Measurement Strategy of the GCOS Reference Upper Air Network (GRUAN) Holger Vömel,

Results of the Measurement Strategy of the GCOS Reference Upper Air Network (GRUAN) • Holger Vömel, • GRUAN Lead Center • DWD Meteorological Observatory Lindenberg • CIMO TECO 2012

Water vapor trends in the troposphere? • Motivation 1:Long term „trend“ humidity

Water vapor trends in the troposphere? Specific humidity at 300 hPa Dessler and Davis, JGR 2010

Water vapor trends in the upper troposphere? e.g.: Lindenberg 8km (0:00 UT)

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

Water vapor trends in the upper troposphere? e.g.: Lindenberg 8km (0:00 UT) • No trend estimate possible: Trend signals dominated by instrumental change • Observations have been done for numerical weather prediction, not for long term climate • Measurements are not traceable (Instrumental uncertainties not well characterized and systematic errors disregarded ) • Instrumental change instantaneous not managed • Meta data are incomplete • Note: Even the Vaisala RS92 data record is inconsistent

GCOS Reference Upper Air Network • GRUAN in response to the need of WMO and the Global Climate Observing System for highest accuracy data possible • Ground based network for reference upper air observations for climate under GCOS and integrated into WIGOS • Currently 16 sites, with aim to expand to 30 to 40 sites worldwide • Check out www.gruan.org

GRUAN goals • Maintain observations over decades • Validation of satellite systems • Characterize observational uncertainties • Traceability to SI units or accepted standards • Comprehensive metadata collection and documentation • Validate observations through deliberate measurement redundancy • Long-term stability through managed change Priority 1: Water vapor, temperature, (pressure and wind) Priority 2: Ozone, …

The GCOS Reference Upper-Air Network: • What constitutes a “reference” observation?

Referenceobservation • A GRUAN reference observation: • Is traceable to an SI unit or an accepted standard • Provides a comprehensive uncertainty analysis • Maintains all raw data • Is documented in accessible literature • Is validated (e.g. by intercomparison or redundant observations) • Includes complete meta data description

Establishing reference quality Uncertainty of input data Traceable sensor calibration Transparent processing algorithm Best estimate + Uncertainty GRUAN Measurement Black box software Proprietary methods Disregarded systematic effects

Establishing Uncertainty • Error is replaced by uncertainty • Important to distinguish contributions from systematic error and random error • A measurement is described by a range of values • A measurement is expressed as m ± u • m is corrected for systematic errors • u is random uncertainty Literature: • Guide 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.

Uncertainty, Redundancy and Consistency • GRUAN stations provide redundant measurements • Redundant measurements should be consistent: • No meaningful consistency analysis possible without uncertainties • if m2 has no uncertainties use only u1 (“agreement within errorbars”)

Uncertainty and Raw Data • Analyze sources of uncertainty: systematic: calibration, radiation errors, … random: noise, production variability, … Document this! • Synthesize best uncertainty estimate: Uncertainties for every data point, i.e. vertically resolved • Raw data: Store all raw and all meta data to allow future reprocessing

Determining the Uncertainty: Daytime temperature Vaisala RS92

Uncertainty example: Daytime temperature Vaisala RS92

Uncertainty example: Daytime temperature Vaisala RS92 Assumptions: • On average 50% of maximum insolation • Downward direct, downward and upward diffuse radiation • Radiative streamer model for radiation field, with climatological clouds

Uncertainty example: Daytime temperature Vaisala RS92 Sources of measurement uncertainty (in order of importance): • Sensor orientation • Ventilation • Unknown radiation field • Lab measurements of the radiative heating • Ground check • Calibration

GRUAN Data Product: RH uncertainty • RH Uncertainty:

GRUAN Data Product: RH uncertainty Good example: 28 Mar 2012:

Redundant Observations: CFH and GRUAN RS92

GRUAN Data Product: RH uncertainty Cooperation withmanufacturers needed to address these issues Bad example: 15 Mar 2012:

Traceability • Manufacturer Independent Ground Checks

Additional ground checks

Additional ground checks Production Year:

Additional ground checks GC25 (0%) SHC (100%) U1 0.33 106.00 U2 0.36 100.98 Lindenberg Ground Check 30 May 2012, 0:00

U1 switched on U2 switched on

Change Management

Change Management • Change management is mandatory to maintain traceability, homogeneity in a time series and consistency within a network • A new system, software, or procedure must be evaluated prior to implementation • Systematic and random errors must be quantified for the new system in the same manner as for the old system • Redundant observations verify that the new system are of equal quality as the old system. • If transfer functions are required, old data will be reprocessed based on the stored raw data

Summary • GRUAN is an approach to long term observations of upper air essential climate variables • Focus on priority 1 variables to start: Water vapor and temperature(starting to bring in other variables) • Reference observation means: • quantified uncertainties • traceable • well documented • Understand the uncertainties: • analyze sources • synthesize best estimate • verify in redundant observations • Management of change utilizes measurement uncertainty

Results of the Measurement Strategy of the GCOS Reference Upper Air Network (GRUAN) Holger Vömel,