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Researchers improved OPTRAN model by reducing coefficients, adding predictors, and automating validation, resulting in better spectral sounder prediction accuracy.
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Developments of the fast RT model ‘OPTRAN’for High Spectral Sounder Yoshihiko Tahara (UCAR, JMA, NCEP) Paul V. Delst (CIMSS, NCEP) John C. Derber (NCEP) Larry M. McMillin (NESDIS) Xiaozhen Xiong (NESDIS) Thomas J. Kleespies (NESDIS)
OPTRAN Meets Difficulty • Absorption coefficient prediction • A huge number of equations • 900,000 equations for 1000 channels • Difficult to validate all • Numerous coefficients • 41 Mbytes for 1000 channels • A lot of computer resources • Difficult to introduce high spectral sensors • Difficult to keep updated • Difficult to be more developed • More absorbers, more predictors, more layers, etc
New Prediction Form Coefficients traced over 300 layers by polynomial • Coefficients are reduced more than 95%. • 1 additional atmospheric predictor introduced • No vertical interpolation
Weight in Generating Trans Coef • Impossible to transfer all information of 300 equations to one • Weight transfers and preserves the essential information. NOAA/HIRS Ch 3 Error in Abs Coef Error in Trans Weight Trans
Atmospheric Predictors • 17 atmospheric predictors • 2 new predictors • Q/T2 for wet continuum • P1/4 for Ozone
Choice of Predictors P* P Predictand • 21778 predictor set possibilities for each gas and channel • Known unstable sets removed in advance • Stability is monitored by a stability index • Transmittance prediction error is monitored to select the best set. Validated sets automatically Less than 24 hrs to find best sets for 500 chs using 10 CPUs on IBM SP4
LBL Transmittances • UMBC atmospheric profiles (for generating coefficients) • 48 profiles • 101 levels (0.005 -- 1100 hPa) • CIMSS atmospheric profiles (for independent verification) • 32 profiles • 101 levels (0.005 -- 1100 hPa) • Transmittances (Infrared) • LBLRTM Ver 6.12 with HITRAN2000 + AER updates • 6 angles for each atmos profile • Sep 2002 AIRS SRFs for AIRS • Central frequencies generated based upon the SRFs
Comparison to Dependent S.D. Bias
Comparison to Independent S.D. Bias
Comparison to Real-time Data Ch 28 656.1 SD 0.76 MN –0.16 Ch 253 722.1 SD 0.39 MN +0.29 (clr sky) Ch 1082 1036.5 SD 0.63 MN +0.33 (clr sky) Ch 1756 1524.4 SD 1.22 MN +0.44 (clr sky)
Comparison to Real-time Data S.D. Bias Num
Impacts of New OPTRAN on Global Fcst (No AIRS, Preliminary) NH 1000 Z SH 1000 Z NH 500 Z SH 500 Z
Impacts of New OPTRAN on Global Fcst (No AIRS, Preliminary) Trop 850 Vct Wind Trop 200 Vct Wind
Conclusion • Equations and coefficients are reduced significantly. • Predictor selection and inspection is automated. • Offline validation shows • Around 0.1 deg or less S.D. for most channels • 0.2 – 0.3 deg S.D. for ozone channels • Small bias except ozone and WV channels in independent val. • Real-time data comparison shows • Stable calculation for AIRS 281 subset channels except 1 • 0.5 – 1 deg S.D. for dry and ozone, 0.5 – 1.3 deg S.D. for WV • Within plus minus 1 deg bias • Impacts of the new OPTRAN (without AIRS) on the NCEP global model are small positive or neutral over the extra tropics and neutral over the tropics.