www.gruan.org meteorological observatory lindenberg results of the measurement strategy of the gcos...
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www.gruan.org Meteorological Observatory Lindenberg
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
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Water vapor trends in the troposphere?
Motivation 1:Long term „trend“ humidity
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Water vapor trends in the troposphere?
Dessler and Davis, JGR 2010
Specific humidity at 300 hPa
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Water vapor trends in the upper troposphere?
e.g.: Lindenberg 8km (0:00 UT)
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Freiberg RKS-2 RKS-5 MARZ RS80 RS92
Water vapor trends in the upper troposphere?
e.g.: Lindenberg 8km (0:00 UT)
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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
Water vapor trends in the upper troposphere?
e.g.: Lindenberg 8km (0:00 UT)
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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
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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, …
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The GCOS Reference Upper-Air Network:
What constitutes a “reference” observation?
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Reference observation
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
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Establishing reference quality
Best estimateBest estimate++
UncertaintyUncertainty
Uncertainty of Uncertainty of input datainput data
Traceable Traceable sensor sensor
calibrationcalibration
Transparent Transparent processing processing algorithmalgorithm
Disregarded Disregarded systematic effectssystematic effects
Black box Black box softwaresoftware
Proprietary Proprietary methodsmethods
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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.
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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 u
1 (“agreement within
errorbars”)
22
2121 uukmm
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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
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Determining the Uncertainty: Daytime temperature Vaisala RS92
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Uncertainty example: Daytime temperature Vaisala RS92
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Uncertainty example: Daytime temperature Vaisala RS92
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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
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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
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GRUAN Data Product: RH uncertainty
RH Uncertainty:
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GRUAN Data Product: RH uncertainty
Good example:28 Mar 2012:
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GRUAN Data Product: RH uncertainty
Good example:28 Mar 2012:
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GRUAN Data Product: RH uncertainty
Good example:28 Mar 2012:
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Redundant Observations: CFH and GRUAN RS92
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GRUAN Data Product: RH uncertainty
Cooperation withmanufacturers needed to address these issues
Bad example:15 Mar 2012:
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Traceability
Manufacturer Independent Ground Checks
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Additional ground checks
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Additional ground checks
Production Year:
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Additional ground checks
Lindenberg Ground Check 30 May 2012, 0:00
GC25 (0%) SHC (100%)
U1 0.33 106.00
U2 0.36 100.98
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U1
switc
hed
onU
2 switched on
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Change Management
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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
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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