GCOS Reference Upper Air Network
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Transcript GCOS Reference Upper Air Network
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
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 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
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GRUAN tasks
Provide long-term high-quality upper-air climate records
Constrain and calibrate data from more spatiallycomprehensive global observing systems (including
satellites and current radiosonde networks)
Fully characterize the properties of the atmospheric
column
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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, …
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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
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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.
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Ubiquitous change
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Tropospheric temperature trend uncertainties
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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 …
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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
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Establishing reference quality
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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.
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Uncertainty, Redundancy and Consistency
GRUAN stations should provide redundant measurements
Redundant measurements should be consistent:
m1 m2 k u12 u22
No meaningful consistency analysis possible without uncertainties
if m2 has no uncertainties use u2 = 0 (“agreement within errorbars”)
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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
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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
m1 m 2 k 2 u12 u 22
This test is only meaningful, i.e. observations are co-located
or co-incident if:
u12 u22
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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
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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
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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
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GRUAN data flow
Distributed data processing
DATA dissemination (at NCDC)
documentation
GRUAN
Meta-database
(at GRUAN
lead center)
data
Data
processing
center
raw
data
archive
GRUAN sites
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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.
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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.
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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
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