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

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<ul><li>Slide 1</li></ul> <p>Meteorological Observatory Lindenberg Richard Assmann Observatory The GCOS Reference Upper Air Network Slide 2 Meteorological Observatory Lindenberg Richard Assmann Observatory 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 Slide 3 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 Slide 4 Meteorological Observatory Lindenberg Richard Assmann Observatory 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, Slide 5 Meteorological Observatory Lindenberg Richard Assmann Observatory 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 Slide 6 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 Slide 7 Meteorological Observatory Lindenberg Richard Assmann Observatory Establishing reference quality Slide 8 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 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. Slide 9 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) Slide 10 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 Slide 11 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 Slide 12 Meteorological Observatory Lindenberg Richard Assmann Observatory 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 Slide 13 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 Slide 14 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) Slide 15 Meteorological Observatory Lindenberg Richard Assmann Observatory Distributed data processing Slide 16 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 </p>