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
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
Automatic Weather Systems • AWOS • AWS and ASOS • Integrated Suites (Custom) TUSCON which is an AWS • Tac. Met
Automatic Weather Systems (2)
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) • Tac. Met • 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 • • • • • • • 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 5 cm 10 cm 20 cm 30 cm 50 cm 100 cm All in an A and a B suite (for back-up/comparision) HMP 45 D and a HMP 243 or HMP 337 (heated humidity sensors) Wind Speed and Direction Solar radiation Barometer – PTB 220 Rainfall – 0. 1 mm & 0. 2 mm 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; ▫ ▫ ▫ ▫ 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 -5 km 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-processing-archiving); • 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 ▫ Temperature ▫ Humidity ▫ Wind Speed ▫ Wind Direction ▫ Pressure ▫ Rainfall • Comms Infrastructure • Control Software • GUI
AWS Components at Valentia (2) • Data Logger • CR 23 X • CR 10 X
AWS Components at Valentia (3) • Temperature ▫ 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 Logger. Net Create custom data logger programs • Convert Edlog programs for the data loggers to CRBasic programs for the CR 3000 • 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 • • • Developed using Edlog Sample from Code outlining an averaging sample Code is converted for machine readable code for uploading to Data Logger • • 14: P 92 1: 0000 2: 0001 3: 10 • • • 15: P 80 1: 2 2: 99 • • 16: P 77 1: 1110 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 2. 14 • • • • • ; Sample one minute average of dry temp, wet temp and grass temp into Array 1 9: If time is (P 92) 1: 0000 Minutes (Seconds --) into a 2: 0001 Interval (same units as above) 3: 10 Set Output Flag High (Flag 0) 10: Set Active Storage Area (P 80)^28446 1: 2 Final Storage Area 2 2: 1 Array ID 11: Real Time (P 77)^18956 1: 1110 Year, Day, Hour/Minute (midnight = 0000) 12: Sample (P 70)^23843 1: 2 Reps 2: 7 Loc [ avg_dry ]
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 WXT 510 • 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 IP 66 • 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 ?
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