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Problems of HIRLAM in wintertime stable boundary layer - analysis of forecasts and observations

Problems of HIRLAM in wintertime stable boundary layer - analysis of forecasts and observations. Timo Vihma, Evgeni Atlaskin, and Laura Rontu. Model validation against Sodankylä sounding data Periods: January and March 2005

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Problems of HIRLAM in wintertime stable boundary layer - analysis of forecasts and observations

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  1. Problems of HIRLAM in wintertime stable boundary layer - analysis of forecasts and observations Timo Vihma, Evgeni Atlaskin, and Laura Rontu

  2. Model validation against Sodankylä sounding data Periods: January and March 2005 Model versions: H635E (Gollvik-Rodriques soil-snow-forest schema), H637 and H640 Model product validated: 24 h forecasts

  3. In 2005, January was very mild in Sodankylä but March was colder than usually

  4. Results for January 2005: focus on the errors in the air temperature • - At the heigths of 30 and 90 m, HIRLAM has a large positive bias in cold conditions. • In warm conditions, the bias is often negative but much smaller in magnitude • H635N with the snow schema does not produce better results

  5. - The largest errors in the lowermost 100 m occur under conditions of a strong inversion, as estimated from the temperature difference between 30 and 1100 m

  6. - The temperature error at the heights of 30 and 90 m depends much more on T(170-30m) than on T(1100-170m), i.e., large errors are related to near-surface-based inversions, but not so much to clearly elevated inversions.

  7. The largest temperature errors at the height of 30 are not associated with saturation (neither observed nor modelled).

  8. Errors in the air specific humidity • Near the surface, HIRLAM has a positive bias in cold conditions and a negative bias in warm conditions. This bias in q is qualitatively similar to that in T, but now magnitudes of the positive and negative bias are approximately equal.

  9. Errors in q occur also without any temperature inversion, but in conditions of a large T(170-30m), the bias in q(30m) is always positive

  10. The largest errors in q(30m) typically occur when the observed RH(30m) = 0.85-0.95. H335N yields much lower values of RH than H640

  11. March 2005 • Everything told before holds also for March 2005, except: • - the error in specific humidity at the heights of 30 and 90 m depends neither on T nor on RH • considering the spoecific humidity, H635N performs better than H637

  12. Conclusions • largest temperature errors occur in cold conditions with a large T(170-30m) • - in conditions of a large T(170-30m), the bias in q(30m) is always large • - the largest errors in temperature and humidity were typically not related to saturation • the presence of solar radiation does not have a large effect on the temperature error in HIRLAM • Although January 2005 was mild and March 2005 was cold, the differences in the model performance with respect to the air temperature were small • - H635N with the new snow-forest scheme does not show improvement, but this may also be related to problems in snow analysis and the digital filter initialization • much more analyses are needed: next application of the tower data, • then analysis on the relative importance of factors controlling T2m (a) in reality and (b) in HIRLAM.

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