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

Meteorological Observatory Lindenberg – Richard Assmann Observatory

The GCOS Reference

Upper Air Network: Assuring the 21st Century Climate

record?

Peter Thorne, CICS-NC

With thanks to GRUAN Lead Centre (DWD) and Working

Group on Atmospheric Reference Observations

What is GRUAN?

GCOS Reference Upper Air Network Network for ground-based reference observations for climate in

the free atmosphere under the auspices of GCOS

Initially 15 stations, envisaged to be a network of 30-40 sites across the globe

See www.gruan.org for more detail


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


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


Why is GRUAN required?

• Historical observations of the atmospheric column have been made primarily for operational monitoring purposes

• Change has been ubiquitous, poorly managed, and rarely adequately quantified

• Has led to substantial ambiguity in the rate and details of climatic changes

• Significant impediment to understanding climate change and its causes.


Ubiquitous change


Tropospheric temperature trend uncertainties


Implications

• Surface-troposphere trends issue has been ‘hot’ since 1990 paper in Science by Spencer and Christy using terms such as ‘precise’ to describe MSU.

• Since then 200+ papers and two dedicated reviews on the subject (NRC, CCSP)

• Several congressional hearings

• BUT …

• No resolution to the issue – simply a better understanding of the true degree of uncertainty

• Lesson 1: Never trust a single observational analysis. Structural uncertainty is key.

• Lesson 2: It doesn’t have to be this way going forwards. We need traceable measures in future to assure the record.

• Lesson 2 is where GRUAN comes in …


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


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:


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)

• Data streams reprocessed as necessary as new knowledge accrues


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


Future steps

• Bring in additional data streams

• Frostpoint hygrometer sondes (WV in UTLS)

• GNSS-PW

• Lidar, FTIR, MWR etc.

• Additional sites

• Workshop to be held summer 2012 (let me know if interested)

• Need to ascertain optimal mix of sites to meet the varied demands

• Building user base

• GRUAN will only be successful if the data are used on a regular basis.


Next challenge: How to use these measures to calibrate more globally complete networks

• Statistical and physical problem

• Geographical and temporal coincidence will be important.

• For satellite calibration use RTMs to convert the geophysical observations to radiance equivalents?

• Does sustained cal/val require launch coincident measurements? What is the cost/benefit? Who pays?

• Use of sites as opportunities to perform regular instrumentation suite intercomparisons?

• Could help in calibrating ground based remote sensing and in-situ sounding capabilities.


Summary of GRUAN


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

gruan reference observation

meteorological instruments

gruan tasks

gruan goals

uncertainty error

consistency gruan stations

gruan data products

uncertainty important