results of the measurement strategy of the gcos reference upper air network (gruan) holger vömel,

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


Water vapor trends in the troposphere?

Motivation 1:Long term „trend“ humidity


Water vapor trends in the troposphere?

Dessler and Davis, JGR 2010

Specific humidity at 300 hPa


Water vapor trends in the upper troposphere?

e.g.: Lindenberg 8km (0:00 UT)


Freiberg RKS-2 RKS-5 MARZ RS80 RS92




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




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


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, …


The GCOS Reference Upper-Air Network:

What constitutes a “reference” observation?


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


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


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.


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 u1 (“agreement within

errorbars”)

22

2121 uukmm


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


Determining the Uncertainty: Daytime temperature Vaisala RS92


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



Sources of measurement uncertainty (in order of importance):

Sensor orientation Ventilation Unknown radiation field Lab measurements of the

radiative heating Ground check Calibration


GRUAN Data Product: RH uncertainty

RH Uncertainty:



Good example:28 Mar 2012:


Redundant Observations: CFH and GRUAN RS92



Cooperation withmanufacturers needed to address these issues

Bad example:15 Mar 2012:


Traceability

Manufacturer Independent Ground Checks


Additional ground checks



Production Year:



Lindenberg Ground Check 30 May 2012, 0:00

GC25 (0%) SHC (100%)

U1 0.33 106.00

U2 0.36 100.98


U1

switc

hed

onU

2 switched on


Change Management


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


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

results of the measurement strategy of the gcos reference upper air network (gruan) holger vömel,

Documents

upper troposphere

utwater vapor trends

hpawater vapor trends

managedmeta data

data point

gruan data products

measurement gum

measurement strategy