Bias correction - SSEC偏置校正-中国石化.pptVIP

Experimental Design Experimental Design (cont) Trial Period: 24 Jan -17 Feb 2004 4 Analyses and 6-hr forecasts 00z,06z,12z,18z 1 analysis and 5-day forecast (12z) 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 Stripped down impact experiment (i.e no ATOVS radiances) Experiment using simulated AMV’s in Met Office System Ideas from IWW!!!!! Future Work (cont) * 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 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 2o x 2o x 100 hPa boxes 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 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 (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 2o x 2o x 100 hPa boxes. 28 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 back