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Assimilation of GPS refractivity data from CHAMP with WRF 3D-Var system

Assimilation of GPS refractivity data from CHAMP with WRF 3D-Var system. 1 Y.-R.Guo, 1 X. X. Ma, 1 H.-C. Lin, 1 Y.-H. Kuo, and 2 C. Terng 1 National Center for Atmospheric Research (NCAR) 2 Central Weather Bureau, Taiwan Present at FORMOSAT-3 Science Workshop 7 October 2005.

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Assimilation of GPS refractivity data from CHAMP with WRF 3D-Var system

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  1. Assimilation of GPS refractivity data from CHAMP with WRF 3D-Var system 1Y.-R.Guo, 1X. X. Ma, 1H.-C. Lin, 1Y.-H. Kuo, and 2C. Terng 1National Center for Atmospheric Research (NCAR) 2Central Weather Bureau, Taiwan Present at FORMOSAT-3 Science Workshop 7 October 2005

  2. Objectives • Establish WRF 3D-Var system for operational NWP at CWB Retrospective experiments with CWB NFS numerical model Typhoon bogus assimilation in WRF 3D-Var Global bogus assimilation in WRF 3D-Var Background Error Statistics calculation and tuning • Assimilation of GPS RO data in WRF 3D-Var system with the local OBS operator and assessing its impact on Typhoon Dujuan forecast Ingest the GPS RO data to WRF 3D-Var system Observation error estimate and quality control for GPS RO data Impact study of the vertical variation of the GPS RO ray perigee points Impact of GPS RO assimilation on Typhoon Dujuan forecast

  3. Establish WRF 3D-Var system for operational NWP at CWB Interface between CWB NFS model and WRF 3D-VarObservation decoder program: CWB FGGE to LITTLE_r ASCII First guess converter program: CWB NFS DMS format forecast at  level to WRF netCDF format at  level 3D-Var analysis converter program: WRF 3D-Var netCDF at  level to CWB NFS DMS format at  levelCompleted the retrospective experiments for summer month of July 2004 and winter month of January 2005 with WRF 3D-Var+CWB NFS in CWBTyphoon bogus assimilation and Global bogus assimilation in WRF 3D-VarCWB Typhoon and Global bogus data decoder program Different observation errors specifications for Typhoon bogus and global bogus in 3D-Var OBS preprocessor Typhoon bogus (Sea level pressure and wind) and global bogus (wind, temperature, and moisture) data assimilation code included in WRF 3D-Var Tuned the background error statistics (cv_options=3) for CWB NFS 45-km resolution domain, and derived and tested the new background error statistics (cv_options=5)

  4. Typhoon bogus assimilation and Global bogus assimilation in WRF 3D-Var The difference of the assimilation with and without bogus data at 0000 UTC 31 August 2003 40 Typhoon bogus Sea Level Pressure difference field 134 Global bogus The maximum difference at Typhoon center is 11.1 hPa The global bogus data assimilation only made small differences. The maximum differences of 1.54 hPa located over the Tibetan plateau.

  5. Background error statistics tuning for GPS refractivity For Single OBS, B is the background value, O is the observation value, and the sb2is the background variance and so2 is the observation variance. Then, the analysis A should be When the (O-B), (O-A), and so2 of GPS Ref. are known, the background error for GPS Ref. Can be computed. Single GPS Ref.: (O-B) = 1.0 so = 1.0

  6. Assimilation of GPS RO data in WRF 3D-Var system with the local OBS operator and assessing its impact on Typhoon Dujuan forecast Ingest the GPS RO data to WRF 3D-Var system Developed the shell script to download the CHAMP GPS RO data from COSMIC/CDAAC or from TACC in CWB when it is available Decoder program for CHAMP GPS data (wetPrf and atmPrf): CDAAC netCDF data to LITTLE_r ASCII file This is the domain for numerical experiment with WRF 3D-Var and WRF model for Typhoon Dujuan

  7. Observation error estimate and quality control for GPS RO data Observation error estimate: Three types of GPS refractivity observation error estimates are available • GPS Ref. Observation error based on Huang et al (2004) where N_bottom = 10 and N_top = 3 N-units are N errors at the bottom (1000 hPa) and top (27o hPa) of the atmosphere. The observation error is a function of pressure. • Modified error based on Kuo et al (2004): Data --- Month of December 2001 h = 1000 hPa = 0o Error percentage = 3.0%  = 90o Error percentage = 1.0% h >= 270 hPa Error percentage = 0.3% The observation error is a function of the pressure and latitude. The linear interpolation is applied to the specific point (p,). • Modified error based on Chen and Kuo (2005): Data --- 15 Aug to 15 Sep. 2003 (Dujuan) For = 0o, 2.5 km > h >= 0 km Error percentage = 2.5% h = 5.5 km Error percentage = 1.3% h >= 12 km Error percentage = 0.3% For  = 90o, h = 0 km Error percentage = 1.5% h = 12 km Error percentage = 0.3% The observation error is a function of height and latitude. The linear interpolation is applied to the specific point (h,).

  8. Observation error specifications for GPS Refractivity pressure Chen-Kuo 2005 Kuo et al. 2004 Huang et al. 2004 Percentage of GPS REF error Percentage of GPS REF error GPS REF error ( N-UNIT )

  9. Observation operator (Local) The refractivity N is where p is the pressure in hPa, T is the temperature in K, and q is the specific humidity in kg/kg (Zou et al 1995). Zou, X., Y.-H. Kuo, and Y.-R. Guo, 1995: Assimilation of Atmospheric Radio Refractivity Using a Nonhydrostatic Adjoint Model. Mon. Wea. Rev. 123, 2229-2249. This is an one-step formula for refractivity calculation because the p, T, and q are the analysis variables in wrf3dvar.

  10. Quality control in WRF 3D-Var • Step 1. Background check: |(O-B)| > 5o • Step 2. Relative Error check: h =< 7 km, R.E. > 5.0% 7 km < h < 25 km, R.E > 4.0% h >= 25 km, R.E> 10.0% • Step 3. Low level check: if the data at certain level fails to pass relative error check, all the data below that level will be discarded.

  11. The number of the data points assimilated after quality Control procedure The percentages of the points relative to ingested points are shown in parenthesis Innovations (O-B) before the quality control Innovations (O-B) after quality control for the different OBS error specifications Huang et al 2004Kuo et al 2004Chen-Kuo 2005

  12. The averaged (O-B) and (O-A) of the GPS Refractivity from thirteen 3D-Var experiments with 6-h time window assimilation for the different observation error specificationsThe RMS reduction percentages (O-A)/(O-B) are shown in parenthesis Chen-Kuo 2005, variable perigee Huang et al 2004 Kuo et al 2004 Chen-Kuo 2005

  13. Remarks • Rejection rate by quality control with Chen-Kuo observation error estimate is more reasonable. • With Chen-Kuo observation error specification, it gave a reduction of the analysis error close to e-1. • Considering the vertical variation of the ray-path perigee points did not make a significant difference for experiments on a domain with resolution of 45-km. • Conclusion • Chen-Kuo (2005) observation error estimate will be used in all numerical experiments, and CWB retrospective runs.

  14. Experiment design Exp1: non no 3dvar Exp2: cvn assimilation of conventional CWB obs Exp3: cvb assimilation of conventional CWB obs + Bogus Exp4: all assimilation of conventional CWB obs + Bogus + GPS Exp5: cold-all cold-start with assimilation of conventional CWB obs + Bogus + GPS Note: Exp1 72-h forecasts initiated with NCEP AVN analysis every 12-h interval. Exp2,3 and 4 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). The domain size is 222x128x31 with resolution of 45-km. The tuned NCEP background error statistics (cv_options=3) are used in WRF 3D-Var. The physics used in WRF model are listed in the table below.

  15. Cycling design 08/28/12 28/18 31/12 3dvar 3dvar 29/00 3dvar 29/06 09/01/00 3dvar …… 31/12 09/03/12 3dvar 2003/08/28_12 ——2003/08/31_12 7 forecasts of 72h : 2812 、2900、2912、3000、3012、3100、3112 6 forecasts of 6h : 2818 、2906、2918、3006、3018、3106 { total

  16. Results of the Typhoon track forecast for Exp2 (no Bog), Exp3 (no GPS) and Exp4 (GPS) 72-h tack forecast initiated at 0000 UTC 31 August 2003 OBS Exp2 Exp3 Exp4 Exp2 Exp3 Exp4 0 9 18 27 36 45 54 63 72 Definition of the 24 hours average increment of track errors InER: Error(Exp.?) = position(Exp.?) - position(OBS)

  17. * CWB Typhoon bogus data only available at 3000Z, 3012Z, 3100Z, and 3112Z for this experiments

  18. Results of the Typhoon track forecast for Exp1 (no 3dvar), Exp4 (cyc), and Exp5 (cold-start) 72-h tack forecast initiated at 0000 UTC 31 August 2003 OBS Exp1 Exp5 Exp4 Exp1 Exp5 Exp4 0 9 18 27 36 45 54 63 72

  19. Remarks for this preliminary experiments • The track forecasts with the WRF 3D-Var assimilation are much better than the forecasts initiated directly from NCEP AVN analysis; • The GPS RO assimilation gave the positive impact on the Typhoon Dujuan track forecast except the forecast initiated at 2003083012Z; • When the CWB Typhoon bogus data are assimilated, the track forecasts are improved significantly; • From initial time at 3100Z, the cycling mode run started at 2812Z gives better results than the cold start run.

  20. Summary • 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. • With the CHAMP data during the Typhoon Dujuan period, the observation error specification and quality control procedure works properly in terms of the rejection rate of the GPS Refractivity data, innovations (O-B), and the analysis reduction ratio , etc. • 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 and CWB bogus data, showed the positive impact on the Typhoon Dujuan’s track forecast when the 45-km WRF model used. • Forecast with the cycling mode by using WRFVAR(3D-Var) and WRF model gives better results than the cold start run that starts at 3100 Z.

  21. Plan for future work • For Typhoon prediction, increase of model resolution (15-km, 5-km, etc.) is the first priority for improving the skill of forecast. • For the cycling mode, the frequent cycles, such as 3-h or 1-h, should be explored to account for the asynoptic observations, especially for GPS RO data. • When other approaches of assimilating GPS RO data, such as non-local observation operator, bending angel assimilation, etc., are ready, more comparison study should be conducted between the local observation operator and other operators. • New type (cv_options=5) of the background error statistics need to be derived and tested.

  22. END THANK YOU !

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