Meteorological Observatory Lindenberg – Richard Assmann Observatory The GCOS Reference Upper Air Network

Meteorological Observatory Lindenberg – Richard Assmann Observatory  GCOS Reference Upper Air Network  Network for ground-based reference observations for climate in the free atmosphere in the frame of GCOS  Currently 16 stations, envisaged to be a network of sites across the globe See for more detail What is GRUAN?

Meteorological Observatory Lindenberg – Richard Assmann Observatory  Provide long-term high-quality upper-air climate records  Constrain and calibrate data from more spatially- comprehensive global observing systems (including satellites and current radiosonde networks)  Fully characterize the properties of the atmospheric column GRUAN tasks

Meteorological Observatory Lindenberg – Richard Assmann Observatory 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  Long-term stability through managed change  Validate observations through deliberate measurement redundancy Priority 1: Water vapor, temperature, (pressure and wind) Priority 2: Ozone, clouds, …

Meteorological Observatory Lindenberg – Richard Assmann Observatory GRUAN structure See for further information

Meteorological Observatory Lindenberg – Richard Assmann Observatory Focus on reference observations A GRUAN reference observation: Is traceable to an SI unit or an accepted standard Provides a comprehensive uncertainty analysis Is documented in accessible literature Is validated (e.g. by intercomparison or redundant observations) Includes complete meta data description

Meteorological Observatory Lindenberg – Richard Assmann Observatory Establishing reference quality Best estimate +Uncertainty Uncertainty of input data Traceable sensor calibration Transparent processing algorithm Disregarded systematic effects Black box software Proprietary methods Literature:  Guide to the expression of uncertainty in measurement (GUM, 1980)  Reference Quality Upper-Air Measurements: Guidance for developing GRUAN data products, Immler et al. (2010), Atmos. Meas. Techn.

Meteorological Observatory Lindenberg – Richard Assmann Observatory 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  m is corrected for systematic errors  u is random uncertainty  generally expressed by m ± u 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.

Meteorological Observatory Lindenberg – Richard Assmann Observatory Uncertainty, Redundancy and Consistency  GRUAN stations should provide redundant measurements  Redundant measurements should be consistent: No meaningful consistency analysis possible without uncertainties if m 2 has no uncertainties use u 2 = 0 (“agreement within errorbars”)

Meteorological Observatory Lindenberg – Richard Assmann Observatory Uncertainty, Redundancy and Consistency Understand the uncertainties:  Analyze sources: Identify, which sources of measurement uncertainty are systematic (calibration, radiation errors, …), and which are random (noise, production variability …). Document this. Synthesize best uncertainty estimate:  Uncertainties for every data point, i.e. vertically resolved Use redundant observations:  to manage change  to maintain homogeneity of observations across network  to continuously identify deficiencies

Meteorological Observatory Lindenberg – Richard Assmann Observatory Co-location / co-incidence:  Determine the variability (  ) of a variable (m) in time and space from measurement or model  Two observations on different platforms are consistent if This test is only meaningful, i.e. observations are co-located or co-incident if: Consistency in a finite atmospheric region

Meteorological Observatory Lindenberg – Richard Assmann Observatory Uncertainty example: Daytime temperature Vaisala RS92 Sources of measurement uncertainty (in order of importance):  Sensor orientation  Unknown radiation field  Lab measurements of the radiative heating  Ventilation  Ground check  Calibration  Time lag

Meteorological Observatory Lindenberg – Richard Assmann Observatory Uncertainty example: Comparison Vaisala RS92 with Multithermistor  Minor systematic difference at night  Significant systematic difference during the day  But observations are consistent with the understanding of the uncertainties in the Vaisala temperature measurements  Lack of uncertainties in Multithermistor measurements precludes further conclusions

Meteorological Observatory Lindenberg – Richard Assmann Observatory Principles of GRUAN data management  Archiving of raw data is mandatory  All relevant meta-data is collected and stored in a meta-data base (at the lead centre)  For each measuring system just one data processing center  Version control of data products. Algorithms need to be traceable and well documented.  Data levels for archiving: level 0: raw data level 1: raw data in unified data format (pref. NetCDF) level 2: processed data product → dissemination (NCDC)

Meteorological Observatory Lindenberg – Richard Assmann Observatory Distributed data processing

Meteorological Observatory Lindenberg – Richard Assmann Observatory Summary  GRUAN is a new approach to long term observations of upper air essential climate variables  Focus on priority 1 variables to start: Water vapor and temperature  Focus on reference observation: quantified uncertainties traceable well documented  Understand the uncertainties: analyze sources synthesize best estimate verify in redundant observations  GRUAN requires a new data processing and data storage approach