meteorological observatory lindenberg – richard assmann observatory the gcos reference upper air...

Meteorological Observatory Lindenberg – Richard Assmann Observatory

The

GCOS Reference

Upper Air Network


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


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


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, …


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


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


Establishing reference quality


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.


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


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


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


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


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


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)


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


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

meteorological observatory lindenberg – richard assmann observatory the gcos reference upper air...

Documents

meteorological instruments

uncertainty error

reference quality slide

gruan reference observation

expression of uncertainty

uncertainty important

errorbars slide

random uncertainty literature