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1、Satellite Winds Superobbing,Howard Berger Mary Forsythe John Eyre Sean Healy,Image Courtesy of UW - CIMSS,Hurricane Opal October 1995,Outline,Background/Problem Superob Methodology Method Observation Error Results Conclusions/Future Work,Problem:,High - Resolution satellite wind data sets showed neg
2、ative 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 x 2 x 100 hPa boxes,Bormann et al. (2002) compared wind data to co-located radiosondes showing statistically significant
3、 spatial error correlations up to 800 km.,Correlation,Met-7 W V NH Correlations,Graphic from Bormann et al.2002,Question:,Can we lower the data volume to reduce the effect of correlated error while making some use of the high-resolution data?,Proposed Solution:,Average the observation - background (
4、innovations) within a prescribed 3-d box to create a superobservation.,Advantages:,Data volume is reduced to same resolution that resulted from thinning. Averaging removes some of the random, uncorrelated error within the data.,Superobbing Method:,1) Sort observations into 2 x 2 x 100 hPa boxes.,28
5、N,16 W,26 N,18 W,2) Within each box: Average u and v component innovations, latitude, longitude and pressures.,28 N,26 N,16 W,18 W,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 W,18 W,Superob Obs
6、ervation Error,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,Superob Observation Error,Assume that within a box: Observation and background
7、 errors not correlated with each other. Background errors fully correlated. Background errors have the same magnitude.,Assumptions (cont): All of the innovations weighted equally. Constant observation error correlation.,Superob Observation Error,Token Evil Math Slide,Superob Observation Error,Vector
8、 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,00z 10 Ju
9、ne, 2003. (20 N - 40 N) (0E 30 E),Old Observation Error,Superob Error,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 h
10、Pa boxes,Trial Period: 24 Jan -17 Feb 2004 4 Analyses and 6-hr forecasts 00z,06z,12z,18z 1 analysis and 5-day forecast (12z),Experimental Design (cont),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 resol
11、ution) 38 levels hybrid-eta configuration 3D-Var Data Assimilation,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) Tropi
12、cs (TR) Southern Hemisphere (SH) For forecast periods of: T+24, T+48, T+72 ,T+96 , T+120,Trial Statistics,-2,-1,0,1,2,3,4,PMSL T+24,PMSL T+48,PMSL T+72,PMSL T+96,PMSL T+120,H500 T+24,H500 T+48,H500 T+72,W250 T+24,W850 T+24,W850 T+48,W850 T+72,W250 T+24,PMSL T+24,PMSL T+48,PMSL T+72,PMSL T+96,PMSL T+
13、120,H500 T+24,H500 T+48,H500 T+72,W250 T+24,Experiment Control RMS Error (%),Anomaly Correlations Vs. Forecast Range Compared to Analysis 500 hPa Height,NH,SH,TR,T+24 Forecast Sonde RMS Vector Error 250 hPa Wind,NH,TR,SH,250 hPa u-component Analysis Increments,250 hPa u-component Analysis Increments
14、,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,Implications,Mixed results suggest either: Random Error not
15、most significant error component of AMVs Superobbing set up not ideal to treat random error,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,Stripped down impact experiment (i.e no ATOVS radiances) Experiment using simulated AMVs in Met Office System Ideas from IWW!,Future Work (cont),