impact of eos mls ozone data on medium-extended range ensemble forecasts jacob c. h. cheung 1,...
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Impact of EOS MLS ozone data on medium-extended range ensemble forecasts
Jacob C. H. Cheung 1, Joanna D. Haigh1, David R. Jackson2
1Imperial College London 2Met Office
Overview
• Motivation
• Methods
• Experimental period selected
• Impact on tropospheric forecasts
• Is there significant improvement?
Motivation
Comparison of Forecast RMS differences: Forecast against analysis for extended global index
-2
-1.5
-1
-0.5
0
0.5
1
1.5
NH Tropics SH
Location
FC
RM
S (
%)
dif
fere
nces
SPARC climatology
ECMWF Ozone Field
Assimilation of SBUV only
Assimilation of SBUV andEOSMLS
Source: Mathison et al. 2007
Increase in forecast skill
Decrease in forecast skill
Motivation
• Full tropospheric response to stratospheric thermal forcing is a two-stage process
• Improving representation of ozone will possibly improve medium-extended forecasts
Source: Simpson et al. 2009
Forecast range considered by Mathison et al.
Aim
Is the representation of stratospheric ozone important in medium-extended range tropospheric forecasts?
Methods
• Met Office Global and Regional Ensemble Prediction System (MOGREPS)
• Resolution: N216L85• 31-day free running forecast• 24 ensemble member
Run Id LiShine (Control) MLS (Experiment)
Dataset Li and Shine 95 EOS MLS
Ozone 5-year monthly mean zonal mean
Monthly mean zonal mean correspond to the chosen forecast date
Case Studies Northern winter; Southern winter, spring
Northern spring (March 2011)
Experimental period selected – March 2011
Source: NASA/Goddard
Ozone profiles
Results – Stratospheric temperature
MLS-LiShine
MLS forecast errorLiShine 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
Results – Tropospheric zonal wind
MLS-LiShine
MLS forecast errorLiShine forecast error
- Tropospheric zonal wind anomaly between runs is weak compared to individual forecast errors
- Response is statistically significant in some area
Results - SLP
[hPa] [hPa]
MLS-LiShine
NH SH
Temperature RMSE (10hPa)
NH
TR
SH
Horizontal wind RMSE (250hPa) GPH RMSE (500hPa)
NH
TR
SH
Horizontal wind RMSE (250hPa) GPH RMSE (500hPa)
NH
TR
SH
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
Questions?
Horizontal wind RMSE (50hPa) Temperature RMSE (50hPa)
NH
TR
SH