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Detected Inhomogeneities In Wind Direction And Speed Data From Ireland

Detected Inhomogeneities In Wind Direction And Speed Data From Ireland. P redrag Petrovi ć Republic H y drometeorological Service of Serbia Mary Curley Met Éi reann Ivana T ošić Institute for Meteorology , University of Belgrade.

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Detected Inhomogeneities In Wind Direction And Speed Data From Ireland

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  1. Detected Inhomogeneities In Wind Direction And Speed Data From Ireland Predrag Petrović Republic Hydrometeorological Service of Serbia Mary Curley Met Éireann Ivana Tošić Institute for Meteorology, University of Belgrade

  2. Detected Inhomogeneities In Wind Direction And Speed Data From Ireland • Inhomogeneities of wind data • Available dataset • The procedure for detection of inhomogeneities • Types of break points according to possible causes • Homogenisation possibilities • Results • Comparison with known methods (SNHT) • Plans for the future • Conclusions, comments, remarks…

  3. Inhomogeneities of wind data • Coupled series problem • Present methods can deal with one series at the time • Impossibility of dealing with direction (azimuth, u- and v- wind components are of no use) • Distribution of values rather than averages or extremes • The ReDistribution Method as a solution • Small gaps problem does not disturb results (no need to fill in the data and thus bias the series)

  4. Available dataset • Wind data from Ireland • 13 stations countrywide • Wind direction and speed series • Hourly temporal resolution • Series start from 1939 to 1964 up-to-date • Metadata available

  5. The procedure for detection of inhomogeneities • Summary information about wind data (range, resolution…) Wind direction range 0 to 360°, resolution 10° Wind speed range 0 to 63 knots, resolution 1 knot • Setting number of categories for calculations Best results are obtained for 10 to 20 categories 19 categories of wind direction: 18 subranges by 20° + 1 for calms 18 categories of wind speed: 16 subranges by 3 knots, 1 for speed over 51 knots + 1 for calms

  6. The procedure for detection of inhomogeneities Tne ReDistribution Method calculations • Runs of both direction and speed • Initial moving window length – 2 years • Additional moving window lengths – 4 and 6 years • Daily distribution values in percentage

  7. The procedure for detection of inhomogeneities Selection of RDI peak values as break points Simultaneous peaks of both series over noise level Peaks of series over 0.125 – certain breaks Peaks of series over 0.100 – possible breaks Peaks of series below 0.100, but over noise level

  8. Types of break pointsaccording to possible causes Distinguishing types of inhomogeneities at selected break points – main types both wind direction and speed (i.e. relocation) wind direction only (i.e. misorientation) wind speed only (i.e. recalibration)

  9. Types of break pointsaccording to possible causes Types of inhomogeneities – direction • shifting (Valentia, 1950) - surroundings widening / narrowing (Shannon, 1991) – instrument replacement

  10. Types of break pointsaccording to possible causes Types of inhomogeneities – direction • spreading / contracting (Kilkenny 1997, instrument) moving of prevailing wind (Clones 1997, instrument)

  11. Types of break pointsaccording to possible causes Other types of inhomogeneities – direction (not present in Irish wind data) • rotation (instrument misorientation) • starry (change in number of directions, i.e. 8 to 16) • irregular redistribution (heavy distortion due to change in surroundings) Examples in other wind data sets (see references)

  12. Types of break pointsaccording to possible causes Types of inhomogeneities – speed • shift up (Malin Head 1962, anemometer rise) shift down (Claremorris 1968, instrument change)

  13. Types of break pointsaccording to possible causes Types of inhomogeneities – speed • calms up (Claremorris 1958, anemometer friction) calms down (Birr 1992, instrument)

  14. Types of break pointsaccording to possible causes Types of inhomogeneities – speed • calms out (Mulingar 1996, introduction of AWS) measurement unit (Dublin 1944, estimates to measurements) irregular redistribution (not present in Irish wind data)

  15. Homogenisation possibilities • One correction value is not valid for the whole range • Some breaks can not be homogenised (at least not easily) • Investigation of correction function (in plans for the future)

  16. Results Number of detected breaks • 27 breaks of wind direction 8 breaks of wind direction only • 8 verified by metadata (so far…) • 47 break of wind speed 28 breaks of wind speed only • 12 verified by metadata (so far…) • 19 simultaneous breaks • 5 verified by metadata (so far) • total 55 breaks found • 15 verified by metadata (so far…)

  17. Results Accuracy of break detection • The lowest RDI value of verified break point is 0.087 • The largest difference - 6 months (Mullingar: detected 11.5.1974, metadata 7.11.1973) • Higher RDI value returns better accuracy (generally shorter difference) • The shortest difference - 2 days (introduction of AWS) Claremorris: RDI 0.146 detected 10.09.1995, metadata 12.09.1995 Birr: RDI 0.186 detected 20.06.1998, metadata 24.06.1998

  18. Results Metadata • A lot of good metadata available, but still incomplete • Few metadata seem not to be quite certain • Some metadata will be available later (i.e. calibration lists) Uncertainties • Low RDI peaks return many uncertain break points If existing, such breaks are of minor importance • Gradual changes (trends) are not certainly detected If existing, they might produce high noise of RDI

  19. Comparison with known methods (SNHT) • Comparison made for speed only • SNHT run on monthly and annual averages • 20 breaks out of 47 detected by SNHT • Undetected breaks, discovered by SNHT – none! • SNHT detected some minor breaks • SNHT skipped some major breaks (RDI up to 0.162, Mullingar, June 1996) • The ReDistribution Method returned better results than SNHT and it is highly recommended!

  20. Plans for the future • More experience with existing wind datasets • Construction of surrogate sub-daily wind datasets • Introduction of wind gusts • Application to other weather elements • Investigation of correction method (redistribution matrix, correction function)

  21. Conclusions • The ReDistribution Method returns very good results • More experience with the method will be available in wider use (to consider STSMs for that purpose) • Accuracy of the method might be determined after more processed data verified by metadata • Correction method should be considered and developed • Metadata should be as complete as possible

  22. Comments, remarks… … are welcome! Thank you for being aware of which way the wind blows!

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