Transcript Automated Weather Observing

Automated Weather Observing
Welcome
Introduction
Michael Gill M.Sc. MIET
Technical Officer ICT/Meteorological Systems
20 + Years Systems Experience
Worked as a Project Engineer (Sabbatical) for
Climatronics Corp, Long Is NY developing
AWOS software and designing integrated
AWS/AWOS and commissioning those systems
world wide.
• Developed in house AWS for Met Eireann
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Agenda
Types of AWS
Why automation?
Limitations and differences
Consequences of automation
Strategy of automation and
design at Valentia and
Nationwide
• Associated Automation
activities
• Future Trends
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Automatic Weather Systems
• AWOS
• AWS and ASOS
• Integrated Suites (Custom)
TUSCON which is an AWS
• TacMet
Automatic Weather Systems (2)
TEN METER WEATHER STATION
Lightning surge protection.
WM-III wind speed and direction sensor.
Ten meter self-supporting, fold over,
heavy-duty aluminum instrument
tower hinged at ground level
Environmental enclosure with data
logger, battery back-up power
supply, RS-232 interface plus
optional data storage module and
baromertic pressure sensor
Solar panel (OPTIONAL)
Solar radiation sensor
(OPTIONAL)
Naturally aspirated temperature
shield with temperature and
OPTIONAL relative humidity sensor
Rain Gauge
(OPTIONAL)
110Vac/60Hz
RS-232 Data line
Automatic Weather Systems (3)
• AWS
• Limited array of sensors
• Collects data over given time
period
• Stores data in memory or sent
to post processor.
• Used primarily in weather
observing
• AWOS
• Calculates aviation
meteorological data such as
pressure values (QFE, QNH),
Runway Visual Range, and
generates METAR, SPECI and
SYNOP reports.
• Available by phone or radio
frequency
• Used primarily in aviation
• Intelligent sensors
• Built in sensor fault analysis
Automatic Weather Systems (4)
• TacMet
• Field-deployable, compact
tactical meteorological
observation system offering
full support for various field
operations
• Used in military, chemical
emergencies, construction.
• Often uses PDA’s for
parameter display
TUSCON
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Met Eireann developed AWS
(The Unified Climatological and Synoptic
Observing Network)
Replacing phased out manual stations and filling
in areas of poor coverage
Measures
Dry
Grass
5cm
10cm
20cm
30cm
50cm
100cm
All in an A and a B suite (for back-up/comparision)
HMP45D and a HMP243 or HMP337 (heated humidity
sensors)
Wind Speed and Direction
Solar radiation
Barometer – PTB220
Rainfall – 0.1mm & 0.2mm Tipping Bucket Rain gauge.
And of course - a logger to gather the data and a modem to
transmit the data to HQ.
Why Automation ?
• Continuous increase of
demands for regular,
timely and on-line data
with the increased time
resolution.
• Increased time
resolution (10 min)
becomes a basic
requirement to cope
with the severe weather
forecasting and
warnings.
WHY AUTOMATION ? (2)
• Higher density of observations available in real
time;
• Continuous measurement of the atmosphere
(each minute up-to-date observations);
• Data from AWS can be integrated more
effectively with the data from other systems;
• AWS’ data can be more effectively archived;
• Lesser cost per data piece.
WHY AUTOMATION ? (3)
• The observation consistency (site-to-site and
day-to-night);
• Objective and uniform measurements;
• High frequency of data provision;
• Higher accuracy and quality of data;
• Better timeliness and data availability;
• More frequent special observations;
Automation Limitations and Differences
• AWS does not provide a horizon-to-horizon evaluation of the weather, only of weather that
has passed through the sampling volume of the sensor array (measurements made at a
fixed location);
• Some elements are difficult to automate;
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shallow or patchy fog
blowing dust
smoke
falling ash
volcanic eruptions
tornadoes
precipitation that is not in the form of rain or snow, such as hail, ice pellets and snow grains
multiple forms of precipitation falling at the same time
depth of new snowfall
total snow depth
in-cloud and cloud-to-cloud lightning
clouds that are not directly above the station
clouds that are more than twelve thousand feet above ground level
cloud type
• AWS requires initial capital investment.
Automation Limitations and Differences (2)
• AWS and observer differ in their methods of
sampling and processing the various weather
elements:
• A human observer estimates weather
phenomena at a fixed location by integrating in
space.
• An automatic system estimates weather
phenomena at a fixed location by integrating in
time.
Automation Limitations and Differences (3)
• AWS applies procedures and algorithms to the
collected data in order to extrapolate the
weather over a wider area.
• AWS provides objective and consistent
information while human observations show
significant subjectivity and uncertainty.
Automation Limitations and Differences (4)
• AWS
• Fixed location (time averaged);
• Representation for 3-5km of
sensor site;
• Continuous observation;
• Consistent observation;
• Report everything detected by
sensors.
• HUMAN
• Fixed time (spatial-averaged);
• Representation horizon-tohorizon;
• Time constraints;
• Affected by lights, building,
human perception;
• Intelligent filtering.
CONSEQUENCES OF AUTOMATION
• Introduces more technological complexity to the
observation process;
• Influences all phases of data flow
(measurement-transmission-processingarchiving);
• Introduces data in homogeneity (comparing to
old data series);
• Influences maintenance system (replaces
observers by technicians for maintenance);
• Requires refreshment courses at all levels.
AWS Strategies and Design
• Chose site for AWS based on
guidelines.
• Sensors should be positioned
at the same height (and place)
to those of classic instruments
• Temperature & Humidity
Measurement inside classic
Stevenson Screen
• Wind on standard 10 M towers
• Rainfall Gauges in shielded
pits.
AWS Platform Requirements
• Scalable
• Ease of sensor
integration
• Availability of
sensors and longterm parts not
locked into one
vendor rapid
obselence
• Development
software and tools
AWS Components at Valentia
• Based on Campbell Scientific
Data loggers
• Measuring
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Temperature
Humidity
Wind Speed
Wind Direction
Pressure
Rainfall
• Comms Infrastructure
• Control Software
• GUI
AWS Components at Valentia (2)
• Data Logger
• CR 23 X
• CR 10X
AWS Components at Valentia (3)
• Temperature
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Dry
Wet Bulb
Grass Temp
Humidity using calculation
from Dry and Wet
• Measurement Platinum
resistance thermometers
(PRTs) offer excellent
accuracy over a wide
temperature range (from -200
to +850 °C).
AWS Components at Valentia (4)
• Wind Speed and Direction
• Measurement using Vector
wind speed and direction
sensors which interface with
the data loggers.
AWS Components at Valentia (5)
• Rainfall Measurement
• Measurement using Casella
tipping bucket rain gauge.
• 0.1 mm and 0.2 mm for light
and moderate rainfall
performance
AWS Components at Valentia (6)
• Pressure
• Polled every minute from
operational and backup PTB
220 for inclusion in GUI and
for downstream operational
data.
• Incorporates 3 pressure
sensors for accuracy and
redundancy.
Tying it all together
Middleware LoggerNet
Create custom data logger programs
• Convert Edlog programs for the data
loggers to CRBasic programs for the
CR3000
• Display or graph data
• Build a custom display screen to view
data or control flags/ports
• Collect data on demand or schedule
• Retrieve data using any of the
included telecommunications
options
• Post process data files
• Export data to third-party analysis
packages
• Communicate with storage modules
• Download new data logger
operatingsystems and configure
devices
Programming
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Developed using Edlog
Sample from Code outlining an averaging sample
Code is converted for machine readable code for
uploading to Data Logger
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14:P92
1:0000
2:0001
3:10
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15:P80
1:2
2:99
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16:P77
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Output is in array format.
2,2006,288,2340,12.15
151,2006,288,2340,70.1
20,2006,288,2340,13.33,14.29
1,2006,288,2341,14.42,11.89
99,2006,288,2341,2006,288,23,41,.2,14.4,11.97,1
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;Sample one minute average of dry
and grass temp into Array 1
9: If time is (P92)
1: 0000
Minutes (Seconds --)
2: 0001
Interval (same units
3: 10
Set Output Flag High
temp, wet temp
into a
as above)
(Flag 0)
10: Set Active Storage Area (P80)^28446
1: 2
Final Storage Area 2
2: 1
Array ID
11: Real Time (P77)^18956
1: 1110
Year,Day,Hour/Minute (midnight =
0000)
12: Sample (P70)^23843
1: 2
Reps
2: 7
Loc [ avg_dry
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GUI Development
• Developed using RTDM
• Designed to tie together all
data from multiple sources
• Reads from arrays.
ASSOCIATED ACTIVITIES (1) Quality Control
• Detailed performance monitoring of the
functionality of the whole system is a
precondition of the successful automated
weather monitoring network;
• It should allow for prompt remedial actions
(pulling the data from AWS, filling the gaps,
correction of errors);
• It should go deep enough into the AWS so that
long-term drift of sensors can be detected.
ASSOCIATED ACTIVITIES (2) Calibration
• To guarantee data quality and validity there is a
need to enhance all levels:
• Initial calibration
• Field calibration
• Laboratory calibration, this involves comparison
against a known standard to determine how
closely instrument output matches the standard
over the expected range of operation.
ASSOCIATED ACTIVITIES (3) Maintenance
• Preventive (cleaning);
• Corrective (AWS component failures);
• Adaptive (changed requirements or
obsolescence of components);
• Part of a broader performance monitoring:
• To ensure rapid response time for periodic
transmission of self-checking diagnostic
information by the AWS is needed.
ASSOCIATED ACTIVITIES (4)
In addition to standard documentation, such as:
• Documentation of initial siting of the system,
sensors (maps, photographs);
• Ongoing documentation of equipment and siting
(metadata) and all changes;
• Metadata showing changes in the station’s
immediate surroundings or sensors;
Documentation of the procedures and algorithms
used and all changes to them.
Future Trends
• Integration of AWS
• Helsinki Testbed project goals broadly consist of
mesoscale weather research, forecast and
dispersion models development and verification,
demonstration of integration of modern
technologies with complete weather observation
systems, end-user product development and
demonstration and data distribution for public
and research community
Future Trends (2)
• http://testbed.fmi.fi/Current_weather.en.html
• Part of what makes this possible is the WXT
transmitter
• Measures 6 most essential weather parameters as
WXT510
• Accurate and stable
• Low power consumption - works also with solar
panels
• Compact, light-weight
• Easy to install
• No moving parts
• Vaisala Configuration Tool for PC
• USB connection
• Housing with mounting kit IP66
• Applications: weather stations, dense networks,
harbors, marinas
Future Trends (3)
• AWS data and databases exposed as web services
• National Digital Forecast Database (NDFD)
Extensible Markup Language (XML) is a service
providing the public, government agencies, and
commercial enterprises with data from the National
Weather Service’s (NWS) US digital forecast
database. This service, which is defined in a Service
Description Document, provides NWS customers
and partners the ability to request NDFD data over
the internet and receive the information back in an
XML format
Thank You
Questions ?