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Surface Measurement Techniques and Automation for Weather Observation

This session focuses on the automation of visual and subjective observations, information on available instrumentation and measurements in harsh environments. It also covers the design, layout, and representativeness of weather stations, urban and road meteorological measurements, and cost reduction and environmental issues.

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Surface Measurement Techniques and Automation for Weather Observation

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  1. Commission for Instruments and Methods of ObservationFourteenth SessionGeneva, 7 – 14 December 2006 INSTRUMENTS AND METHODS OF OBSERVATION FOR SURFACE MEASUREMENTS (OPAG Surface) surface technology and measurement techniques (ET-ST&MT)

  2. 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

  3. 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 *

  4. Automation of (visual and subjective) observations • Automation of manned observations • Low impact on instrument measurementsbut: 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”

  5. Automation of (visual and subjective) observations Qualityassurance Ref.: World Climate Data and Monitoring Programme, WCDMP-52 (GUIDELINES ON CLIMATE OBSERVATION NETWORKS AND SYSTEMS) (Photo: Meteorological Service of Canada)

  6. Automation of (visual and subjective) observations • Lay-out of a station 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)

  7. Automation of (visual and subjective) observations • Siting & exposure • IntercomparingMAN ↔ AUT • Representativety Ref.: World Climate Data and Monitoring Programme, WCDMP-52 (GUIDELINES ON CLIMATE OBSERVATION NETWORKS AND SYSTEMS) (Photo: Meteorological Service of Canada) (Photo: Finnish Meteorological Institute, Finland)

  8. Automation of (visual and subjective) observations • Representativety • Layout of a station • Siting & exposure • Intercomparing Documented inCIMO Guide, IOM reports. Like with instrument measurements to provide the traditional physical variables, like temperature, pressure, wind, etc. In fact increased flexibility

  9. Automation of (visual and subjective) observations New developments (in collaboration with CBS ET-AWS): • Definition and description of a standard AWS • Lists of basic metadata elements • Quality monitoring procedures for data from AWS • Standardized classification scheme of meteorological stations, taking into account the standards for siting and exposure of meteorological instruments M = Required for manned stations, [M] = Based on a regional resolution, A = Required for automatic stations, [A] = Optional for automatic stations, X = Required

  10. Automation of (visual and subjective) observations New developments (in collaboration with CBS ET-AWS): • Definition and description of a standard AWS • Lists of basic metadata elements • Quality monitoring procedures for data from AWS • Standardized classification scheme of meteorological stations, taking into account the standards for siting and exposure of meteorological instruments

  11. Automation of (visual and subjective) observations New developments (in collaboration with CBS ET-AWS): • Definition and description of a standard AWS • Lists of basic metadata elements • Quality monitoring procedures for data from AWS • Standardized classification scheme of meteorological stations, taking into account the standards for siting and exposure of meteorological instruments TECO-98 (Casablanca), IOM Report 70: Meteorological Measurement Representativety, Nearby Obstacles Influence (Michel Leroy, France).

  12. 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 requestedmeteorological information

  13. 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

  14. Automation of visual and subjective observations Introducing • appropriate models describing the present state of the atmosphere • sophisticated algorithms, linking various variables ‘easy’:uniform ‘complex’:divers convert the data into information

  15. Automation of visual and subjective observations Conversion matrix (example):INPUT: Data PhysicalVariables Weather via database

  16. Automation of visual and subjective observations ET/AWS-2006 (functional specifications) 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.·

  17. 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 noreality yes ab no cd ESS: Equitable Skill Score POD: Probability of Detection FAR: False Alarm Ratio

  18. 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 15%5% no 5%75% POD= 75% FAR = 25% acceptable? ESS = 69%

  19. Automation of visual and subjective observations Items to be solved: • How to calibrate (up to source) a “multi-parameter 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)?

  20. Information on available instrumentation and instrument development • Instrument Development Inquiry(IDI-7 published, IDI- 8 to be issued) • World Meteorological Instrument Catalogue (CMA) on CD • HMEI* Members Product Catalogue via the Web (see INF. 9) • Web Portal on Development, Maintenance and Operation of Instruments, Observing Methods and AWS (CIMO homepage) • Other (CIMO Guide, IOM reports) OPAG CBissues * HMEI = Association of Hydro-Meteorological Equipment Industry

  21. 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.

  22. 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: • Extend of definitions and requirements on measurements in severe weather conditions. • To provide recommendations for instrument development • HMEI members are encouraged to develop .. • Intercomparisons have to be organized for further evaluation

  23. Measurements in harsh environments Source: Eumetnet Severe Weather Sensors Project no. 2

  24. 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.

  25. 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

  26. surface technology and measurement techniques (ET-ST&MT) • Progress in development of new technologies • Additional siting standards for Synoptical meteorology, Climate, Marine, Agrometeorology, Hydrology + Urban and Roadway sensor locations • 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) • 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; • Available algorithms used in AWSs - possible standardization; • Support to Natural Disaster Prevention and Mitigation (NDPM) in identifying how surface-based technologies can support monitoring of natural hazards; • 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; • Taking into account the environmental concerns of Members using mercury-based instruments investigate alternative solutions and advise Members; • Develop guidelines and procedures for the transition from manual to automatic surface observing stations.

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