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Y.-R.Guo 1 , X. X. Ma 1 , H.-C. Lin 2 , C.-T. Terng 2 , Y.-H. Kuo 1

Assimilation of GPS refractivity data from CHAMP with WRF 3D-Var system for Typhoon Dujuan. Y.-R.Guo 1 , X. X. Ma 1 , H.-C. Lin 2 , C.-T. Terng 2 , Y.-H. Kuo 1 1 National Center for Atmospheric Research (NCAR) 2 Central Weather Bureau, 64 Kung-Yuan Road, Taipei, Taiwan

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Y.-R.Guo 1 , X. X. Ma 1 , H.-C. Lin 2 , C.-T. Terng 2 , Y.-H. Kuo 1

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  1. Assimilation of GPS refractivity data from CHAMP with WRF 3D-Var system for Typhoon Dujuan Y.-R.Guo1, X. X. Ma1, H.-C. Lin2, C.-T. Terng2, Y.-H. Kuo1 1National Center for Atmospheric Research (NCAR) 2 Central Weather Bureau, 64 Kung-Yuan Road, Taipei, Taiwan Present at Second GPS RO Data User’s Workshop, 22 August 2005 60 CHAMP GPS sounds during the period of 1200 UTC 28 to 1200 UTC 31 August 2003 Downloaded from COSMIC/CDAAC Typhoon track from 1800 UTC 29 August to 0000 UTC 3 September 2003 with 6 hourly positions 6 of these sounds are marked with “bad” flag by CDAAC

  2. Three methods for observation error specification for GPS Refractivity Chen-Kuo 2005 Kuo et al. 2004 Huang et al. 2004 Percentage of GPS REF error Percentage of GPS REF error

  3. Quality control for GPS Refractivity in WRFVAR (3D-Var) • Step 1. Background check: |(O-B)| > 5o • Step 2. Relative Error check: h =< 7 km, R.E. (Relative error) > 5.0% 7 km < h < 25 km, R.E. (Relative error) > 4.0% h >= 25 km, R.E. (Relative error) > 10.0% • Step 3. Low level check: if the data at certain level is failed to pass relative error check, all the data below that level will be discarded. Chen-Kuo 2005 Reject rate = 5.29% (O-A)/(O-B) = 0.344 Kuo et al. 2004 Reject rate = 18.62% (O-A)/(O-B) = 0.285 Huang et al. 2004 Reject rate = 1.32% (O-A)/(O-B) = 0.703 Chen-Kuo 2005, variable perigee Reject rate = 5.78% (O-A)/(O-B) = 0.341

  4. Experiment design Exp1: none no 3dvar Exp2: cv conventional CWB obs Exp3: cvgps conventional CWB obs + GPS Exp4: cvcold conventional CWB obs , initiated at 2003083012Z Exp5: cvgpscold conventional CWB obs + GPS, initiated at 2003083012Z Note: Exp1 72-h forecasts initiated with NCEP AVN analysis every 12-h interval. Exp2 and 3 used the cycling mode with wrfvar(3D-Var) and wrf, which means the first guess fields in 3D-Var are from the previous 6-h forecasts (warm-start). Exp4 and 5 used the NCEP AVN analysis as the first guess in 3D-Var (cold-start). Experiment design Exp1: none no 3dvar Exp2: cv conventional CWB obs Exp3: cvgps conventional CWB obs + GPS Exp4: cvcold conventional CWB obs , initiated at 2003083012Z Exp5: cvgpscold conventional CWB obs + GPS, initiated at 2003083012Z Note: Exp1 72-h forecasts initiated with NCEP AVN analysis every 12-h interval. Exp2 and 3 used the cycling mode with wrfvar(3D-Var) and wrf, which means the first guess fields in 3D-Var are from the previous 6-h forecasts (warm-start). Exp4 and 5 used the NCEP AVN analysis as the first guess in 3D-Var (cold-start). 45-km/15-km nested model forecast with WRF model initiated at 1200 UTC 30 August 2003 The initial condition from 45-km domain cycling run (seven 6-h cycles)starting from 1200 UTC 28 August 2003 Typhoon Dujuan track forecast Track forecast error Typhoon Dujuan intensit forecast

  5. Summary and conclusions 1, GPS RO assimilation has been implemented in WRFVAR (3D-Var) system with the local observation operator and most recent estimate of the observation errors, and the quality control procedure.2, With the CHAMP data during Typhoon Dujuan period, the observation error specification and quality control procedure works properly in terms of the reject rate of the GPS RO data, innovations (O-B), and the ratio of analysis error reduction (O-A)/(O-B), etc.3, From the WRF model forecast experiments with GPS refractivity assimilation by WRFVR (3D-Var), assimilation of the CHAMP data, in addition to the conventional data, showed the positive impact on the Typhoon Dujuan’s track and intensity forecast when the two domains (45-km/15-km) nested WRF model was used.4, The forecast with the cycling mode with WRFVAR (3D-Var) and WRF model gave better results than the cold-start mode. This may be because more observations, including more CHAMP data, were used, and more mesoscale features were generated by the fine resolution model.

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