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 • TacMet
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 • 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; • 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-to-horizon; • 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 long-term 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 10X
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 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 • Developed using Edlog • Sample from Code outlining an averaging sample • Code is converted for machine readable code for uploading to Data Logger • 14:P92 • 1:0000 • 2:0001 • 3:10 • 15:P80 • 1:2 • 2:99 • 16:P77 • 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,.2,14.4,11.97,12.14 • ;Sample one minute average of dry temp, wet temp and grass temp into Array 1 • 9: If time is (P92) • 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 (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 ]
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 ?