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 30-40 sites across the globe

See www.gruan.org 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 www.gruan.org 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 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

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 m2 has no uncertainties use u

2 = 0 (“agreement within errorbars”)

22

2121 uukmm

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

22

21

221 uukmm

22

21 uu

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 processingGRUAN data flow

GRUAN sites

Data processing center

GRUAN Meta-data-base(at GRUAN lead center) raw

data archive

DATA dissemination (at NCDC)

datadocumentation

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