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Impact of EOS MLS ozone data on medium-extended range ensemble forecasts

Impact of EOS MLS ozone data on medium-extended range ensemble forecasts. 1 Imperial College London 2 Met Office. Jacob C. H. Cheung 1 , Joanna D. Haigh 1 , David R. Jackson 2. Overview. Motivation Methods Experimental period selected Impact on tropospheric forecasts

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Impact of EOS MLS ozone data on medium-extended range ensemble forecasts

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  1. Impact of EOS MLS ozone data on medium-extended range ensemble forecasts 1Imperial College London 2Met Office Jacob C. H. Cheung 1, Joanna D. Haigh1, David R. Jackson2

  2. Overview • Motivation • Methods • Experimental period selected • Impact on tropospheric forecasts • Is there significant improvement?

  3. Motivation Decrease in forecast skill Increase in forecast skill Source: Mathison et al. 2007

  4. Motivation • Full tropospheric response to stratospheric thermal forcing is a two-stage process • Improving representation of ozone will possibly improve medium-extended forecasts Forecast range considered by Mathison et al. Source: Simpson et al. 2009

  5. Aim • Is the representation of stratospheric ozone important in medium-extended range tropospheric forecasts?

  6. Methods • Met Office Global and Regional Ensemble Prediction System (MOGREPS) • Resolution: N216L85 • 31-day free running forecast • 24 ensemble member

  7. Experimental period selected – March 2011 Source: NASA/Goddard

  8. Ozone profiles

  9. Results – Stratospheric temperature LiShine forecast error MLS forecast error • General reduction in temperature forecast errors with MLS ozone • Temperature anomaly between runs is significant in stratosphere • Not much change in temperature in troposphere MLS-LiShine

  10. Results – Tropospheric zonal wind LiShine forecast error MLS forecast error • Tropospheric zonal wind anomaly between runs is weak compared to individual forecast errors • Response is statistically significant in some area MLS-LiShine

  11. Results - SLP NH SH [hPa] [hPa] MLS-LiShine

  12. NH TR SH Temperature RMSE (10hPa)

  13. Horizontal wind RMSE (250hPa) GPH RMSE (500hPa) NH TR SH

  14. NH TR SH Horizontal wind RMSE (250hPa) GPH RMSE (500hPa)

  15. Summary • Performed a case study in which the MLS ozone profile is much superior that of LiShine • Zonal wind and temperature response is sensitive to the ozone climatology in current NWP systems (in agreement with other ST coupling studies) • Tropospheric forecast errors are dominated by ensemble spread in medium-extended range forecasts • > In our experiments, using monthly mean zonal mean EOS MLS ozone data does not significantly improve medium-extended tropospheric forecasts

  16. Questions?

  17. Horizontal wind RMSE (50hPa) Temperature RMSE (50hPa) NH TR SH

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