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  • 1. Creating an icing climatology WeatherTech using downscaling techniques Results from Vindforsk project V-313 Hans Bergström and Petra Thorsson, Uppsala universitet Esbjörn Olsson and Per Undén, SMHI Stefan Söderberg, Magnus Baltscheffsky, Weathertech Scandinavia Hans Bergström – Department of Earth Sciences 1Winterwind 2013
  • 2. What do we mean by an ”icing climatology”? A mapping of icing should give answers to questions as: WeatherTech • How often icing occurs • How long the icing persists • How large the amounts of ice are The most important questions may be: • How often do active icing occur? • How is the production affected? Hans Bergström – Department of Earth Sciences 2Winterwind 2013
  • 3. An icing climatology could be based upon todays NWP models (Numerical Weather Prediction) or upon meteorological observations. Data from weather Data from meteorological models (NWP) or observations WeatherTech Model of ice load dM  E  w V  D  Q (Makkonen) dt dM/dt=ice growth (M = ice mass, t = time) E = accretion efficiency Modelled ice load w = liquid water content Comparisons with V = wind speed observations of ice load D = diameter Q = melting, sublimation Gives ice growth on a 0.5 m long cylinder with 30 mm diameter. Hans Bergström – Department of Earth Sciences 3Winterwind 2013
  • 4. Icing modelling - what do we know today? • Models catch the icing events quite well • But sometimes large differences between modelled and WeatherTech measured ice amounts • Measuring ice loads very difficult Measured Modelled Hans Bergström – Department of Earth Sciences 4Winterwind 2013
  • 5. Icing climatology: Two problems to balance: • The need for period length to get a representative estimate of the climate WeatherTech • The need for resolution to get a result accurately reproducing the geographical variations Our options • A long period (30 years) is dynamically modelled using low resolution (9 km) – the results are then dowscaled to desired resolution (1 km). • A limited number of periods is modelled with high resolution – the periods are chosen to be representative for the icing cimate. Hans Bergström – Department of Earth Sciences 5Winterwind 2013
  • 6. Icing climatology: Is 30 years really needed? This example shows variability between seasons. Monthly numbers of icing hours according to measurements at a site in northern Sweden How representative is a single year? Not at all! WeatherTech Or 10 or 30 years? Hans Bergström – Department of Earth Sciences 6Winterwind 2013
  • 7. Icing climatology: Downscaling Step 1: Downscale to 9 km resolution from reanalysis data (ERA-Interim, NCEP/NCAR, GFS) using a mesoscale NWP. WeatherTech Here WRF & COAMPS have been used. Result is a 30 year climatology. Step 2: Statistical downscaling from 9 km to 1 km resolution. What is needed? Icing is dependent on wind speed, temperature and liquid water content (also precipitation). Thus all must be included in the downscaling. To take account of seasonal (and annual) variations, the statistical downscaling is made on a monthly basis. Hans Bergström – Department of Earth Sciences 7Winterwind 2013
  • 8. Model domains – COAMPS/WRF: Outer mesh forced by GFS Grid resolutions: 27x27, 9x9, 3x3, 1x1 km2 WeatherTech Winter seasons 2010/2011 and 2011/2012. Hans Bergström – Department of Earth Sciences 8Winterwind 2013
  • 9. Statistical downscaling: 1) Interpolate 9 km resolution topography data to the 1 km resolution grid. WeatherTech 1 km resolution topography 9 km resolution topography Hans Bergström – Department of Earth Sciences 9Winterwind 2013
  • 10. Statistical downscaling: 2) Interpolate 9 km resolution wind speed data to the 1 km resolution grid. Wind speed versus height – monthly averages at each 1 km grid point. WeatherTech Hans Bergström – Department of Earth Sciences 10Winterwind 2013
  • 11. Statistical downscaling: 2) Interpolate 9 km resolution wind speed data to the 1 km resolution grid. Downscaled to 1 km resolution WeatherTech Modelled at 1 km resolution Modelled at 9 km resolution Hans Bergström – Department of Earth Sciences 11Winterwind 2013
  • 12. Statistical downscaling: 3) Interpolate 9 km resolution temperature data to the 1 km resolution grid. Temperature versus height – monthly averages at each 1 km grid point. A month with typical decrease of temperature with height. WeatherTech Hans Bergström – Department of Earth Sciences 12Winterwind 2013
  • 13. Statistical downscaling: 3) Interpolate 9 km resolution temperature data to the 1 km resolution grid. Temperature versus height – monthly averages at each 1 km grid point. A month with temperature inversions. WeatherTech Hans Bergström – Department of Earth Sciences 13Winterwind 2013
  • 14. Statistical downscaling: 3) Interpolate 9 km resolution temperature data to the 1 km resolution grid. Downscaled to 1 km resolution WeatherTech Modelled at 1 km resolution Modelled at 9 km resolution Hans Bergström – Department of Earth Sciences 14Winterwind 2013
  • 15. Statistical downscaling: 4) Interpolate 9 km resolution liquid water content (LWC) data to the 1 km resolution grid. LWC versus height – monthly averages at each 1 km grid point. Poor relation – NOT USED! WeatherTech Hans Bergström – Department of Earth Sciences 15Winterwind 2013
  • 16. Statistical downscaling: 4) Interpolate 9 km resolution liquid water content (LWC) data to the 1 km resolution grid. Alternative: Use 9 km temperature and specific humidity. Assume specific humidity is preserved: WeatherTech Lift temperature from 9 km terrain height to 1 km height ↓ Gives new temperatures at corrected height ↓ Determine specific humidity at saturation ↓ If super-saturated, increase LWC until 100 % rel.humidity ↓ Use Makkonen’s equation to estimate icing ↓ If ice accretion > 10 g/h count as ”icing hour” Hans Bergström – Department of Earth Sciences 16Winterwind 2013
  • 17. Statistical downscaling: 4) Number of icing hours interpolate from 9 km resolution to 1 km resolution grid. Downscaled to 1 km resolution WeatherTech Modelled at 1 km resolution Modelled at 9 km resolution Hans Bergström – Department of Earth Sciences 17Winterwind 2013
  • 18. Statistical downscaling: Domain averages of seasonal number of icing hours. Larger differences between models than from using statistical downscaling. WeatherTech Hans Bergström – Department of Earth Sciences 18Winterwind 2013
  • 19. Statistical downscaling: Domain maximum of seasonal number of icing hours. Differences between models not so large. 9 km resolution results underestimate the maximum. Statistical downscaling in reasonable agreement with 1 km WeatherTech resolution results. Hans Bergström – Department of Earth Sciences 19Winterwind 2013
  • 20. Summary and conclusions WeatherTech Statistical downscaling possible to use. Comparing 1 km resolution model results to statistically downscaled results: • Advantage: We gain accuracy as regards climatological representativity – possible to use long enough periods • Drawback: We may loose accuracy as regards local terrain response. 20Winterwind 2013
  • 21. Summary and conclusions WeatherTech Alternative to statistical downscaling: Model representative periods with high resolution? • Advantage: We directly model with 1 km resolution – no downscaling needed. • Drawback: We loose some accuracy as regards climatological representativity. How representative are the chosen periods? Hans Bergström – Department of Earth Sciences 21Winterwind 2013
  • 22. Future work What method to use for a climatology: Statistical downscaling or representative periods? • Statistical downscaling: Needs less computer time but a larger amount of man months. WeatherTech • Representative periods: Not so many man months needed but large amounts of computer time as at least five years are needed for a reasonable accurate climatology. Regarding the final accuracy of the results it is difficult to say which method is to be recommended – uncertainties due to a shorter period length has to be weighed against statistical uncertainties with the downscaling, AND – the differences are sometimes large between the models used – maybe this is the largest uncertainty. MORE RESEARCH IS NEEDED to increase the accuracy! Hans Bergström – Department of Earth Sciences 22Winterwind 2013
  • 23. Thank you for yout attention WeatherTech Hans Bergström hans.bergstrom@met.uu.se V-313 Windpower in cold climates Report will be published here (likely in March 2013): http://www.elforsk.se/Programomraden/El-- Varme/Vindforsk/reports/reports_area_1_2/ Hans Bergström – Department of Earth Sciences 23Winterwind 2013