The GCOS Reference Upper Air Network

1. The • GCOS Reference • Upper Air Network

2. What is GRUAN? • GCOS Reference Upper Air Network • Network 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 globe • See www.gruan.org for more detail

3. GRUAN tasks • 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

4. GRUAN goals • Maintain observations over several decades for accurately estimating climate variability and change • Focus on characterizing observational biases, including complete estimates of measurement uncertainty • Ensure traceability of measurements by comprehensive metadata collection and documentation • Ensure long-term stability by managing instrumental changes • Tie measurements to SI units or internationally accepted standards • Measure a large suite of co-related climate variables with deliberate measurement redundancy Priority 1: Water vapor, temperature, (pressure and wind) Priority 2: Ozone, clouds, …

5. GRUAN structure • GCOS/WCRP AOPC Working Group on Atmospheric Reference Observations (WG-ARO) • GRUAN Lead Centre at the Lindenberg Meteorological Observatory (DWD) • GRUAN sites world wide (currently 15 to be expanded to 30-40) • GRUAN task teams for • Radiosondes • GNSS-Precipitable Water • Measurement schedules and associated site requirements • Ancillary measurements • Site representation • GRUAN Analysis Team for Network Design and Operations Research (GATNDOR) • See www.gruan.org for more detail

6. Focus on referenceobservations • 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

7. Establishing reference quality

8. 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 • generally expressed by 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.

9. Uncertainty, Redundancy and Consistency • GRUAN stations should provide redundant measurements • Redundant measurements should be consistent: • No meaningful consistency analysis possible without uncertainties • if m2 has no uncertainties use u2 = 0 (“agreement within errorbars”)

10. 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

11. Consistency in a finite atmospheric region • 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:

12. Uncertainty example: Daytime temperature Vaisala RS92 Sources of measurement uncertainty (in order of importance): • Sensor orientation • Radiative heating of sensor • Unknown radiation field • Ventilation • Ground check • Calibration • Time lag

13. 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

14. 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)

15. Distributed data processing

16. 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