The GNSS-RO Data Impact on the Typhoon Predictions by MPAS-GSI Model
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1 The GNSS-RO Data Impact on the Typhoon Predictions by MPAS-GSI Model 1 Shu-Ya Chen, 1,2 Cheng-Peng Shih, 2,3 Ching-Yuang Huang, 2 Wen-Hsin Teng, and 1 Yang-Cheng Huang 1 GPS Science and Application Research Center, National Central University, Jhongli, Taiwan 2 Department of Atmospheric Sciences, National Central University, Jhongli, Taiwan 3 Taiwan Typhoon and Flood Research Institute, Taipei, Taiwan
2 Introduction Currently, the data assimilation system for MPAS model is the DART system (Data Assimilation Research Testbed), which is an EnKF (Ensemble Kalman Filter) method. The global GSI (Gridpoint Statistical Interpolation) system is an operational and community system, that can use for the variational or hybrid data assimilation. GSI is more flexible, and can ingest the flow-dependence when using a hybrid. Therefore, we intend to connect the GSI with MPAS. Currently, a preliminary version has been built up. In this study, we will show the results of assimilations with GNSS- RO data by the MPAS-GSI system (one-way link) in a typhoon prediction.
3 MPAS-GSI flowchart Under testing GSI output (spectral format) GSI GSI input (spectral format) Convert to gaussian grid on sigma level CWB DMS key (spectral data on sigma level) Convert to pressure level MPAS-GSI flowchart CWB DMS key WPS intermediate file Convert to gaussian grid on sigma level MPAS input (netcdf format) MPAS MPAS output (netcdf format) DMS: Data Management System in the Central Weather Bureau (CWB) in Taiwan
4 Typhoon Nepartak (2016) The track predictions from six NWP centers 2016/07/ UTC~ 2016/07/ UTC 07/09 07/08 07/07 07/06 Courtesy of CWB) (courtesy of TTFRI TAPEX s report) 07/05 JTWC: 2016/07/03 00UTC: Tropical depression 2016/07/03 12UTC: Tropical storm 2016/07/04 18UTC: Typhoon
5 Model Settings km km Variable reso. meshes: km / km Vertical levels of 41 and model top of 30 km Physics suites : mesoscale reference Convection: Tiedtke ; Microphysics: WSM6 ; Land surface: Noah ; Boundary layer: YSU ; Surface layer: Monin-Obukhovi ; Radiation, LW/SW: RRTMG
6 MSLP & SSP after 2-days data assimilation Without TC bogus With TC bogus weak strom
7 Experimental Design GSI+GFS during cycling DA NODA Hybrid weighting: 3DVAR (0.25) + Ensemble (0.75) CASE DA obs. GPS variable TC bogus GTS GTS (con. & rad.) x Y(the last DA) REF GTS + GPSRO Refractivity Y(the last DA) BND GTS + GPSRO Bending angle Y(the last DA) BNDH Same as BND, but use km mesh for MPAS
8 GPS RO soundings (2016/07/02 00UTC~2016/07/04 00UTC) Around 2000 soundings 240 GPS RO
9 Time-averaged differences during DA period REF-GTS BND-GTS 500 hpa T 850 hpa Q
10 Verify against ERA-Interim (final ana.) 850hPa specific humidity (g kg -1 ) GTS (w/o RO) REF (with RO) BND (with RO) H T q GTS REF BND GTS REF BND GTS REF BND ERA-Interim (T255, ~80km)
11 5-days simulation NODA REF BND GTS MPAS v5.2 with km reso. Physics suite: Mesoscale_reference Best NODA REF BND GTS Northwestward simulated track in NCEP With GPSRO data assimilation have a better performance than that without.
12 Streamline & Wind (1-day fcst.) GTS REF BND Wind
13 Streamline & Wind (2-day fcst.) GTS REF BND Wind
14 Streamline & Wind (3-day fcst.) GTS REF BND Wind
15 Verify against ECMWF Analysis in Geop. H 04 Jul 05 Jul 06 Jul GTS REF BND 07 Jul 08 Jul 09 Jul unit: meter
16 Verify against ECMWF Analysis in Temperature 04 Jul 05 Jul 06 Jul GTS REF BND 07 Jul 08 Jul 09 Jul unit: K
17 Resolution comparison (BND)
18 Verify against ECMWF (After 3-days forecast) T q Z U V
19 Time-Averaged Track Error (before landfall: 2016/07/ /07/07) Ave. Tr. Err (group 1) (group 2) NODA GTS REF BND
20 Summary and Future Work An one-way link between GSI and MPAS system has been built-up and performed for the simulations of typhoon Nepartak (2016). The results show that the assimilations with GNSS-RO data make improvements on simulated tracks and intensities than the RO data denied (GTS). From the simulation, assimilating with bending angle shows a further improvement in typhoon track before landfall. A preliminary two-way link of MPAS-GSI has been developed and is under testing. More experiments will be carried out to assess the RO data (e.g., FORMOSAT-7/COSMIC-2) impact on severe weather events in the future. Thank you for your attention!
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