1. 1 MODIS winds assimilation experiments and impact studies to date at the Met Office Howard Berger, Mary Forsythe, Met Office, Bracknell/Exeter, UK UW-CIMSS Madison, WI

2. MODIS imagery from Terra and Aqua used to generate winds. IR(11  m) and WV (6.7  m) channels 100 min between overlapping images. Time delay of 5-6 hours after valid time before winds are available. Still experimental. Met Office obtains them via ECMWF. Picture courtesy of CIMSS MODIS polar winds at the Met Office

3. 3 Two Runs Global Forecast Run Global Update Run

4. Met Office Operational Schedule T 0z,6z,12z,18z T+3T-3 assimilation window Run Begins at T+2 Assimilate observations valid for 6 hour window surrounding valid analysis time Only obs valid in window that arrive before T + 2 are used Produces 144 hour forecast Global Forecast Run:

5. Met Office Operational Schedule Observations valid for same time as forecast run, but given extra time to arrive. Produces 6 hour forecast that is used as background for successive forecast run. Global Update Run: T+3T-3 assimilation window Run Begins at T+7 T 0z,6z,12z,18z

6. Met Office operational schedule impact on MODIS wind assimilation T 0z,6z,12z,18z T+3T-3 assimilation window Update Run Begins Most MODIS data arrives in time for the update run.

7. 7 Distribution of winds from Terra and Aqua AQUA TERRA QU06 QU00 QU12QU18 Aqua 12,562 Terra 8,661 Total 21,223 Aqua 16,973 Terra 27,535 Total 44,508 Aqua 23,206 Terra 21,640 Total 44,846 Aqua 29,229 Terra 7,804 Total 37,033

8. 8 Incentive MODIS polar winds provide information on the wind field in data sparse regions. This should benefit polar wind analyses. Investigations at ECMWF with 3DVAR and 4DVAR show benefit from assimilating the Terra MODIS polar winds.

9. 9 Trial Set-up Trial period: 12th May - 15th June 2003 Control: low resolution global model run in real time Experiment: Same as control, but use winds from Aqua and Terra –thinned to one wind per 140 km x 140 km x 100 hPa box –blacklisting the following regions: –altitudes below 700 hPa for IR winds over sea –altitudes below 550 hPa for WV winds over sea –altitudes below 400 hPa for IR and WV winds over land

10. 06Z July 03, 2003

12. 12 Token Model Info Slide Low-res Trial Model Characteristics: Grid-point model (288 E-W x 217 N-S) Staggered Arakawa C Grid Analysis resolution (216 x 163) Approx 100 km horizontal resolution (one-half operational resolution) 38 vertical levels, hybrid-eta configuration Run times: 00, 06, 12, 18 Z 3-D VAR Data Assimilation

13. 13 Results

14. TROP EU NH SH Anomaly Correlation 500 hPa Geo Height compared to their own analysis

15. % normalized root mean square (rms) error against control rms error calculated for: Mean sea-level pressure (PMSL) 500 hPa height (H500) 850 hPa wind (W850) 250 hPa wind (W250) In regions: Northern Hemisphere (NH) Tropics (TR) Southern Hemisphere (SH) For forecast periods of: T+24, T+48, T+72,T+96, T+120 Further verification:

16. Verfication against observations Verfication against analysis against analysis Met Office Forecast verification RMS change (Trial – Control) (%)

17. Verfication against observations Verfication against analysis against analysis Met Office Forecast verification RMS change (Trial – Control) (%)

18. Verfication against observations Verfication against analysis against analysis Met Office Forecast verification RMS change (Trial – Control) (%)

22. 22 Why such different results than ECMWF? This study used Terra and Aqua, ECMWF used Terra Different season Cloud height detection difficult in polar winter Inappropriate Observation Errors

23. 23 Further Work ECMWF ’ s positive results justify further study! Plan to: Complete trial verification Produce routine O-B statistics Other tests Retrial

24. 24 Future Trial Options Increase Thinning box to 200 km Use Quality Indicators for threshold/thinning Only use Northern Hemisphere winds Modify observation errors Change blacklisting criteria Different trial season

25. 25 Satellite Winds Superobbing Howard Berger Mary Forsythe John Eyre Sean Healy Image Courtesy of UW-CIMSS Hurricane Opal October 1995

26. 26 Outline Background/Problem Superob Methodology Conclusions/Future Work

27. Problem: Met Office preliminary impact studies using high resolution satellite wind data sets showed negative impact (Butterworth and Ingleby, 2000) It was suspected that the observation errors were spatially correlated, violating an assumption in the data assimilation system. To account for this negative impact, wind data were/are thinned to 2 º x 2 º x 100 hPa boxes

28. 28 Bormann et al. (2002) compared wind data to co-located radiosondes showing statistically significant spatial error correlations up to 800 km. Correlation Met-7 W V NH Correlations Graphic from Bormann et al.2002

29. 29 Question: Can we lower the data volume to reduce the correlated error while making some use of the high-resolution data?

30. 30 Proposed Solution : Average the observation - background (innovations) within a prescribed 3-d box to create a superobservation.

31. 31 Advantages: Data volume is reduced to same resolution that resulted from thinning. Averaging removes some of the random, uncorrelated error within the data.

32. 32 Superobbing Method:

33. 33 1) Sort observations into 2 º x 2 º x 100 hPa boxes. 28 N 16 W 26 N 18 W

34. 34 2) Within each box: Average u and v component innovations, latitude, longitude and pressures. 28 N 26 N 16 W18 W

35. 35 3) Find observation that is closest to average position and add averaged innovation to the background value at that observation location. 26 N 28 N 16 W18 W

36. 36 Superobbing removes some of the random observation error. This new error can be approximated by making a few assumptions about the errors within the background and the observation. Superob Observation Error

37. 37 Superob Observation Error Assume that within a box: Observation and background errors not correlated with each other. Background errors fully correlated. Background errors have the same magnitude.

38. 38 Assumptions (cont): All of the innovations weighted equally. Constant observation error correlation. Superob Observation Error

39. 39 00z 10 June, 2003. (20 N - 40 N) (0E 30 E)

40. Conclusions Reducing data volume lowers the effect of correlated error in satellite winds. Superobbing does this reduction and reduces the random error within the observations. The new error can be approximated by making assumptions about the structure of observation and background errors

41. 41 Future Work Model forecast impact studies are underway Develop quantitative “ test ” to evaluate where superobs will work well and where they will not.

42. 42 Thanks, Any Questions?