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

2 Outline Background/Problem Superob Methodology Method Observation Error Results Conclusions/Future Work

Problem: High - Resolution satellite wind data sets showed negative impact (Butterworth and Ingleby, 2000) Why? Suspected that observations errors were spatially correlated To account for this negative impact, wind data were/are thinned to 2 º x 2 º x 100 hPa boxes

4 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

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

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

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

8 Superobbing Method:

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

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

11 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

12 Superob Observation Error

13 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

14 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.

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

Token Evil Math Slide Superob Observation Error Vector of Weights (1xN) Diagonal matrix of component observation errors (N x N) Observation Error Correlation Matrix (NxN)

Observation Correlation Matrix Correlation within box. Value calculated from correlation function in Bormann et al., 2002 Correlation of ith observation with jth observation

18 00z 10 June, (20 N - 40 N) (0E 30 E) Old Observation Error Superob Error

19 Experimental Design Control Run: Operational Set up plus GOES BUFR VIS/IR/WV winds GOES-9 is still Satob format Thinning to 2˚ x 2˚ x 100 hPa boxes Superob Experiment Same as control run, except winds are superobbed to 2˚ x 2˚ x 100 hPa boxes

20 Trial Period: 24 Jan -17 Feb Analyses and 6-hr forecasts 00z,06z,12z,18z 1 analysis and 5-day forecast (12z) Experimental Design (cont)

21 Token Model Info Slide Grid – point model (288 E-W x 217 N-S) Staggered Arakawa C-Grid Approx 100 km horizontal resolution (one- half operational resolution) 38 levels hybrid-eta configuration 3D-Var Data Assimilation

22 Results

% normalized root mean square (rms) error against control rms differences 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 Trial Statistics

PMSL T+24PMSL T+48PMSL T+72PMSL T+96 PMSL T+120 H500 T+24H500 T+48H500 T+72 W250 T+24W850 T+24W850 T+48W850 T+72W250 T+24 PMSL T+24PMSL T+48PMSL T+72PMSL T+96 PMSL T+120 H500 T+24H500 T+48H500 T+72 W250 T+24 Experiment – Control RMS Error (%) TP – Observations TP – Analysis NH – Observations NH – Analysis SH – Observations SH – Analysis

25 Anomaly Correlations Vs. Forecast Range Compared to Analysis 500 hPa Height NH SH TR

26 T+24 Forecast – Sonde RMS Vector Error 250 hPa Wind NH TR SH

hPa u-component Analysis Increments

hPa u-component Analysis Increments

29 Results Summary Superobbing experiment results are small and mixed Generally more positive in the northern hemisphere than in the southern hemisphere or tropics Time series results are mixed: Some forecasts better than control, some worse

30 Implications Mixed results suggest either: Random Error not most significant error component of AMVs Superobbing set up not ideal to treat random error

31 Future Work Back to basics approach Re-calculate observation errors from innovation statistics Experiment with “ model independent ” quality indicators and “ model independent ” components in Bufr file

32 Stripped down impact experiment (i.e no ATOVS radiances) Experiment using simulated AMV ’ s in Met Office System Ideas from IWW!!!!! Future Work (cont)