Transcript Commission for Instruments and Methods of Observation Fourteenth Session Geneva, 7 – 14 December 2006 INSTRUMENTS AND METHODS OF OBSERVATION FOR SURFACE MEASUREMENTS (OPAG Surface) surface technology.

Commission for Instruments
and Methods of Observation
Fourteenth Session
Geneva, 7 – 14 December 2006
INSTRUMENTS AND METHODS OF OBSERVATION
FOR SURFACE MEASUREMENTS
(OPAG Surface)
surface technology and measurement techniques
(ET-ST&MT)
Major topics
• Automation of visual and subjective
observations
• Information on available instrumentation and
instrument development
• Measurements in harsh environments
• Design, layout and representativeness of
weather stations
• Urban and road meteorological measurements
• EC: Cost reduction; environmental issue with
mercury
2006-12-07
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Major topics
• Automation of visual and subjective
observations
• Information on available instrumentation and
instrument development
• Measurements in harsh environments
• Design, layout and representativeness of
weather stations
• Urban and road meteorological measurements
• EC: Cost reduction; environmental issue with
mercury *
2006-12-07
3
Automation of (visual and
subjective) observations
• Automation of manned observations
– Low impact on instrument measurements
but: quality assurance & siting is critical
– Uniform and standardized determination of
Present/Past Weather (visual & subjective
observations) remains unsolved
“Observing the weather is more than
measuring a set of variables”
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Automation of (visual and
subjective) observations
Quality
assurance
Ref.: World Climate Data and Monitoring Programme,
WCDMP-52 (GUIDELINES ON CLIMATE OBSERVATION
NETWORKS AND SYSTEMS)
(Photo: Meteorological Service of Canada)
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Automation of (visual and
subjective) observations
• Lay-out of a station
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Manual on the GOS: Layout of an observing station in
the northern hemisphere showing minimum distances
between installations (Source: UK Meteorological
Office, Observer's Handbook, 4th edition, 1982)
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Automation of (visual and
subjective) observations
• Representativety
• Siting & exposure
• Intercomparing
MAN ↔ AUT
Ref.: World Climate Data and Monitoring Programme,
WCDMP-52 (GUIDELINES ON CLIMATE OBSERVATION
NETWORKS AND SYSTEMS)
(Photo: Meteorological Service of Canada)
2006-12-07
(Photo: Finnish Meteorological Institute, Finland)
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Automation of (visual and
subjective) observations
• Representativety
• Layout of a station
• Siting & exposure
Documented in
CIMO Guide,
IOM reports.
• Intercomparing
Like with instrument measurements to
provide the traditional physical variables, like
temperature, pressure, wind, etc.
In fact increased flexibility
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Automation of (visual and
subjective) observations
Variables
Atmospheric Pressure
Pressure tendency & characteristics
WMO MANUAL on the Global Observing System
(WMO-No. 544)
STANDARD
[Fixed] Ocean Aeronautical
Principle
SYNOP Land
Weather
meteorological climatological
Stations
Stations
station
station
1)
MA
MA
X
X
A
[M]
M
[A]
2)
3)
M A
MA
X
X
A
4)
MA
M
X
X
A
New developments (in collaboration with CBS
ET-AWS):
MA
MA
X
X
Surface
wind
A
M
M
X
X
Cloud Amount and Type
A
M [A]
M of a standard
X
X
Extinction
profile/Cloud-baseand description
A
• Definition
AWS
[M]
Direction of Cloud movement
M
M
X
X
Weather, Present & Past
A
•State Lists
of
basic
metadata
elements
[M]
n/a
X
of the Ground
[A]
[M] [A]
Special Phenomena
•Visibility
Quality monitoring
for data
fromA
M [A]procedures
M
X
X
[M] [A]
[A]
X
Amount of Precipitation
A
AWS
A
[A]
X
Precipitation Yes/No
A
[A]
Intensity of precipitation
• temperature
Standardized classification scheme ofX
Soil
A
X
Sunshine and/or Solar radiation
A
meteorological
stations,
taking
into
account
M [A]
Waves
A
MA
Sea temperature
the standards for siting and exposure of A
M = Required for manned stations, [M] = Based on a regional resolution, A = Required
meteorological instruments
for automatic stations, [A] = Optional for automatic stations, X = Required
Air temperature
Humidity5)
6)
7)
8)
8)
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Automation of (visual and
subjective) observations
2.1 Station information
New
developments (in collaboration with CBS
Basic station metadata include:
Type of ET-AWS):
metadata
Explanation
Examples
Station name
Official name of the station
Prievidza
Station index number(s)
Number(s) used by the National Meteorological
11867
• Definition and
description
of a standard
AWS
Service
to identify a station
Geographical co-ordinates
Latitude and longitude of the station reference point 18.7697
18.5939
•
Lists
of
basic
metadata
elements
Elevation above mean sea level
Vertical distance of a reference point of the station
260.25 m
measured from mean sea level
Types
ofQuality
soil, physical constants
Description of soil
type below the station, itsfor data
clay
•
monitoring
procedures
from
and profile of soil
characteristics
Types of vegetation and
Description of the station’s environment land
natural; grass, 7 Dec
AWS
condition,
2004
the date of the entry
Local
description
Description
of the station’s surroundings,
with
valley
• topography
Standardized
classification
scheme
of station
emphasis on topographic features that may
influence the weather at the station
meteorological
stations, taking into account
2.2 Individual instrument information
2.3 Data
processing
information
the
standards
for siting and exposure of
2.4 Data handling information
meteorological
instruments
2.5 Data
transmission information
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Automation of (visual and
subjective) observations
New developments (in collaboration with CBS
ET-AWS):
• Definition and description of a standard AWS
TECO-98 (Casablanca), IOM Report 70:
• Lists of basic
metadata elements
Meteorological
Measurement
• Quality monitoring
procedures
for data from
Representativety,
Nearby
Obstacles
AWS (Michel Leroy, France).
Influence
• Standardized classification scheme of
meteorological stations, taking into account
the standards for siting and exposure of
meteorological instruments
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Automation of visual and
subjective observations
However:
Assessment of the state and development of the
atmosphere, and of significant weather
Remains critical, i.e.
Subjective observations or qualitative data has
to be converted into quantitative data or
variables
To be able to generate requested
meteorological information
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Automation of visual and
subjective observations
• How to register quantitatively specific weather
phenomena on remote distance, like:
- significant phenomena (thunder, obscuration,
showers, fog patches or whirls in the vicinity)
- different mixtures of precipitation types and
intensities, inclusive freezing, blowing, drifting
- cloudiness: not only coverage and cloud base, but
also cloud type like cumulonimbus to indicate
convection (e.g. CB, CTU)
• How to encode all these phenomena
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Automation of visual and
subjective observations
Introducing
• appropriate models describing the present state of the
atmosphere
• sophisticated algorithms, linking various variables
data
‘easy’:
uniform
‘complex’:
divers
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physical
quantities
information

various types   atmospheric 
of data
models
sources
 algorithms
temperature,
wind, etc.
weather
information
convert the data into information
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Automation of visual and
subjective observations
Conversion matrix (example):
INPUT: Data
ptu
Physical
Variables
fd
OUTPUT:
remote
atm.
prec.
Information (real
sensing models
time)

ptu

fd
(etc)
Weather


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







icing, slipperiness
cloud information

via database
phenomena
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Automation of visual and
subjective observations
ET/AWS-2006 (functional specifications)
VARIABLE
Maximum Effective
Minimum
Mode of
2)
3)
Range
Reported Resolution Observation
1)
4)
BUFR /
5)
CREX
CLOUDS
Maximum
Minimum I, V
Mode
of013 BUFR /
0 – 30 km
10 m
0 20
2)
3)
4)
5)
Effective Range
Reported Resolution
Observation
CREX
0 – 30 km
10 m
I, V
0 20 014
1)
Cloud base height
VARIABLE
Cloud top height
OBSCURATIONS
Cloud type, convective
vs.
up to 30 classes
BUFR Table
I
0 20 012
other types
Obscuration type
up to 30 types
BUFR Table
I, V
0 20 025
Cloud hydrometeor
1 – 700 hydrometeors
-3
1
hydrometeor
dm
I,
V
N
-3
up to 30 types
BUFR Table
I, V
0 20 025
concentration Hydrometeor type
dm
Maximum
Minimum
Mode of
BUFR /
1)
VARIABLE
2)
3)
Effective radius of
cloud
-5
Lithometeor type 2·10-5 – 32·10-5 up
I, V N Observation
0 20 025 4) CREX 5)
Effective
Reported
m to 30 types
2·10 Range
m BUFR Table
I, V Resolution
hydrometeors
-5
-5
-5
Hydrometeor radius -5
I, V
N
-2 2·10 -3– 32·10 m -5
-3 2·10 m
Cloud liquid water content PRECIPITATION
1·10 –1.4·?10 kg m
1·10 kg m
I, V
N
Horizontal - extinction
-1
-1
-2
0.001 m0.1 kg
I, Vm N
Accumulation
0 – 500 mm
TN
0 13 011
Optical depth within
each layer
Not specified yet 0 – 1 mNot specified
yet
I, m
V , 0.0001
coefficient
-1
-1
Duration
to 86 400
s
coefficient
0.001
m
Optical depth of Slant
fog - extinction
Not specified yet 0 – 1 mNotup
specified
yet
-3
Size
of 0
precipitating
– 0.5 m 1 m
Meteorological
Optical
Range
1 – 100 000 m1·10
Height of inversion
– 1 000 m element
10 m
0 26 020
Cloud cover
TN
I, V N
I, V60 s
-3
m I, V N
I, NV
I,1·10
V
-1
-2 -1
-1
h 010 0 20
I, V061
Runway visualIntensity
range 0- quantitative
I,0V20
– 100% 1 – 4 000 m0 – 2000
1% mm h 1 m0.1 kg m
I, Vs , 0.1 mm
0 13 055
Cloud amount
Other weatherType
type
0 20 021
30 types
I, V023
BUFR Table BUFR
0 – 8/8 up to 18 typesup to
1/8
I, V TableI,0V20 011 0 20
-2 -1
-3
-2 -1
Rate of ice accretion
0 – 1 kg dm h
1·10 kg dm h
I, V
N
N
I: Instantaneous – 1-minute value (instantaneous as defined in WMO-No.8, Part II, paragraph 1.3.2.4);
V: Variability – Average (mean), Standard Deviation, Maximum, Minimum, Range, Median, etc. of samples – those
reported depend upon meteorological variable;
T: Total – Integrated value during defined period (over a fixed period(s)); maximum 24 hours for all parameters except
radiation which requires a maximum of one hour.
A: Average (mean) value.·
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Automation of visual and
subjective observations
Quality evaluation and assurance of automated
subjective observations:
– ‘measurement uncertainty’ of a quantitative variable is
not applicable
– ‘performance indicators’, using a contingency matrix
detector
yes no
reality
yes
a
b
no
c
d
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ESS: Equitable Skill Score
POD: Probability of Detection
FAR: False Alarm Ratio
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Automation of visual and
subjective observations
Quality evaluation and assurance of automated
subjective observations:
– ‘measurement uncertainty’ of a quantitative variable is
not applicable
– ‘performance indicators’, using a contingency matrix
detector
yes
reality yes
no
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15%
no
5%
POD = 75%
FAR = 25%
ESS = 69%
acceptable?
5% 75%
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Automation of visual and
subjective observations
Items to be solved:
• How to calibrate (up to source) a “multiparameter followed by algorithm”?
• What is an appropriate (set of) reference(s)
(natural – artificial; human observations are
subjective)?
• Can a reference be made traceable to any
standard?
• Is regional climate relevant (arctic, tropic,
mountainous, deserts)?
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Information on available
instrumentation and instrument
development
1. Instrument Development Inquiry
(IDI-7 published, IDI- 8 to be issued)
2. World Meteorological Instrument
Catalogue (CMA) on CD
3. HMEI* Members Product Catalogue
via the Web (see INF. 9)
4. Web Portal on Development,
Maintenance and Operation of
Instruments, Observing Methods and
AWS (CIMO homepage)
5. Other (CIMO Guide, IOM reports)
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OPAG CB
issues
* HMEI = Association of Hydro-Meteorological Equipment Industry
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Information on available
instrumentation and instrument
development
• Instrument Development (only) Inquiry
(now: every 4 years)
(IDI- 7 published, IDI- 8 to be issued):
• IDI-reports published
– Like IDI-7 (IOM Report No. 93, WMO/TD No. 1352)
Or / and
– As Web Portal, updated regularly, to be up-to-date.
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Measurements in harsh
environments
•
•
Most instruments are designed for use in moderate
climate zones, although requirements are valid for all
climate zones.
Special attention shall be given to
– Harsh environments (arctic, tropic, desert, mountains)
– Severe weather (able to survive)
Necessary actions:
1. Extend of definitions and requirements on measurements
in severe weather conditions.
2. To provide recommendations for instrument development
3. HMEI members are encouraged to develop ..
4. Intercomparisons have to be organized for further
evaluation
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Measurements in harsh
environments
Source: Eumetnet Severe Weather Sensors Project no. 2
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Measurements in harsh
environments
• Extend of definitions and requirements on
measurements in severe weather conditions:
Rec. 4.1/1:
The CIMO Guide be expanded to include:
a. A definition of the siting characteristics of the
Automatic Weather Station in terms of local icing
conditions, and
b. The requirements for measurements in severe icing
conditions.
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Urban and road meteorological
measurements
• Urban meteorology: new chapter in CIMO
Guide (Urban Observations) [all scales of
urban climates (micro-, local- and meso-scale)
considered] + IOM rep. 81
• Road meteorology: publication of IOM rep. 71:
– Need to review the use of Roadway Environmental
Stations (R-ESS),
– To provide a comparison, between R-ESS and
standard synoptic meteorological stations
– To examine differences between the existing and
proposed R-ESS standards
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surface technology and measurement
techniques (ET-ST&MT)
1. Progress in development of new technologies
2. Additional siting standards for Synoptical meteorology, Climate, Marine,
Agrometeorology, Hydrology + Urban and Roadway sensor locations
3. Standard observing methods for the automatic measurement of present
weather, clouds and weather phenomena. Optimize methods for reporting
present weather, clouds and weather phenomena (in cooperation with the
HMEI)
4. Evaluate the performance of AWOSs in tropics and consult manufacturers on
relevant findings to propose improved designs. Advise Members on use of
AWOS in extreme climatological conditions;
5. Available algorithms used in AWSs - possible standardization;
6. Support to Natural Disaster Prevention and Mitigation (NDPM) in identifying
how surface-based technologies can support monitoring of natural hazards;
7. Extreme weather events: encourage instrument manufacturers and others to
develop more robust instruments with greater resilience to extreme weather
conditions and with increased measuring range;
8. Taking into account the environmental concerns of Members using mercurybased instruments investigate alternative solutions and advise Members;
9. Develop guidelines and procedures for the transition from manual to
automatic surface observing stations.
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