Impact of FORMOSAT 3/COSMIC Radio Occultation. near Taiwan
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1 Impact of FORMOSAT 3/COSMIC Radio Occultation Data on Predictions i of Typhoon and Mei yu Fronts near Taiwan CHING YUANG HUANG 1,2, CHAN SENG WONG 1, CHIEN JU WANG 2, and CHIN CHENG TSAI 2 1 Department of Atmospheric Sciences, National Central University 2 Taiwan Typhoon and Flood Research Institute, Taichung, Taiwan NCAR CG1, Oct. 2009
2 OUTLINE Recent weather (typhoon and front) predictions with assimilation of FORMOSAT 3/COSMIC RO data (Huang et al. 2009, GPS Solutions) Observing Systems Simulation Experiments (OSSE) on Typhoon Prediction for FORMOSAT 3 follow on (COSMIC II) Krosa (2007) Sinlaku (2008) revised raytracing Kalmaegi (2008) Morakot(2009) Conclusions
3 Morakot (real time run) WRF V3.1 at TTFRI Input Data : NCEP Global Forecast System (GFS) 1.0 deg in GRIB2 format Domain : D01 : (45 km) D02 : (15 km) D03 : ( 5km) Starting Time : 2009/08/06 00UTC Ending Time : 2009/08/09 00UTC Physics Options in WRF : Microphysics scheme Goddard GCE scheme Physics Options in MM5 : Longwave radiation scheme Explicit Moisture Schemes Shortwave radiation scheme Boundary layer parameterization RRTM scheme simple ice Goddard short wave YSU scheme Cumulus Parameterizations Grell (D01 & D02) Cumulus parameterization PBL Schemes Surface layer scheme Radiation Schemes Land surface parameterization Grell Devenyi ensemble scheme MRF PBL Monin Obukhov similarity scheme cloud(d01) Unified Noah land surface model
4 Morakot Track from 2009/08/06 00 UTC 13 GPS RO Track from 2009/08/06 12 UTC 27 GPS RO
5 Total Rainfall (3days) nogps Max 2733 mm No. of GPS : 13 Max 2632 mm GPS
6 Typhoon LUPIT Initial ii Time : 2009/ UTC NOGPS GPS
7 Typhoon LUPIT Initial ii Time : 2009/ UTC NOGPS GPS
8 2009 Cases (post runs) 2009/0525 Mei-yu front 2009/0602 Mei-yu front Experiments GTS Assimilated data Assimilated with the conventional data fromcwb GTS+REF Assimilated il with the conventional data and FORMOSAT-3 3GPS RO (REF) Molave Typhoon Parma Typhoon Melor Typhoon Experiments NOGPS GPS Assimilated data No assimilation Assimilated with FORMOSAT-3 GPS RO (REF)
9 2009/0525 Mei-yu front Initial time: :00 GTS(sea level pressure and wind vector) GTS+REF( sea level pressure and wind vector)
10 18-h accumulated rainfall (mm) 2009/5/ UTC-1800UTC 24-h accumulated rainfall (mm) 2009/5/ UTC- 5/ UTC GTS max value: 162.9mm max value: 158.5mm GTS+REF max value: 79.63mm max value: 124.2mm
11 2009/0602 Mei-yu front Initial time: :00 GTS(sea level pressure and GTS+REF( sea level pressure wind vector) and wind vector)
12 24-h accumulated rainfall (mm) 2009/6/ UTC- 6/ UTC 24-h accumulated rainfall (mm) 2009/6/ UTC- 6/ UTC max value: 134.1mm GTS max value: 165.6mm GTS+REF max value: 54.5mm max value: 76.2mm
13 Molave Typhoon Initial time: :00 Track Error Nogps gps 400 Error (km) Time (hr)
14 Parma Typhoon Initial time: :00 -n- NO GPS -g- GPS 250 NOgps gps Track Error km hr
15 Melor Typhoon Initial time: :00 -n- NO GPS -g- GPS 160 NOgps gps Track Error km hr
16 Kalmaegi Typhoon Initial time: :00 Pressure -N- None -E- EPH -D- Drop -C- CGTS -S- SSMI -Q- QSCAT P ressure (hpa) Error (km) Obs None EPH Drop CGTS SSMI QSCAT Time (h)( ) Track Error None EPH Drop CGTS SSMI QSCAT Time (h)
17 Fung Wong Typhoon Initial time: :00 -N- None -E- EPH -D- Drop -C- CGTS -S- SSMI -Q- QSCAT Pressure(hPa) Pressure OBS None EPH Drop CGTS SSMI QSCAT Time(h) Track error None EPH Drop CGTS SSMI QSCAT km) error( Time(h)
18 Jangmi Typhoon Initial time: :00 Pressure -N- None -E- EPH -D- Drop -C- CGTS -S- SSMI Pres sure (hpa) Obs None EPH Drop CGTS SSMI Time (h) Track Error None EPH Drop CGTS SSMI Error (km) Time (h)
19 Typhoon track error ratio = EXP track error/none track error Kalmaegi Fung-Wong Jangmi EPH Drop CGTS SSMI QSCAT EPH Drop CGTS SSMI QSCAT EPH Drop CGTS SSMI 0h h h h h h h h h h h h h
20 MM5 4DVAR (BDA in domain 01) MM5 V374(45 V3.7.4 (45 km, 15=km, 5 km) Physics: Simple ice, Grell Cumulus scheme, MRF PBL Typhoon Kalmaegi (2008) COSMIC REF and Bogus SSM/I OWS and Bogus
21 Kalmaegi track prediction 2008/07/16 06Z 07/19 06Z
22 Experiment Framework Experiment (3 days Mean Track Error) Num of obs Time(HR) Mean Track Error (km) 24 h 48 h 72 h NONE (292) D (136) D+IWV (198) D+OWS (147) 11139,10426, 4457, ,9774, 4153,5954 1,2,3, ,2,3, D+REF (113) 6,2,7,4,4 0,1,2,3, D+REF+OWS (99)
23 Intensity and Track error NONE OBS D+OWS D+REF D+IWV D D+OWS+REF CSLP(hPa) SIMULATION TIME (HOUR) 600 1st day 2nd day 3rd day Track error (k km) NONE D+OWS D+REF D+IWV D D+OWS+REF Experiments
24 Equivalent Threat Score Kalmaegi 2008/07/ /1806 UTC Kalmaegi 2008/07/ /1806 UTC ETS D+REF Bias 1.5 D 1 D+OWS D+OWS+REF 0.5 D+REF D D+OWS D+OWS+REF Threshold Threshold
25 OSSE Nature run generate RO soundings representative of true state Control run (no RO data assimilation) Assimilation run (with ithdifferent observations)
26 Revised raytracing OSSE (Sinlaku) for COSMIC II GPS GALILEO GLONASS 4 LEOs at 72 o 4 LEOs at 48 o 4 LEOs at 24 o Hypothetical RO data provided by Vicky Chu of NSPO
27 OSSE with GPS RO soundings produced by MM5 nature run 120 km Z CIRA MM5 ~ 23 km 0 km CIRA: dry 10 deg. resolution longitudinally varying Z CIRA EC Data 120 km 40 km MM5 ~ 23 km 0 km
28 Sinlaku OSSE MM5 V3.7 Input Data : ECMWF Reanalysis (TOGA) Domain : D01 : (45km) D02 : (15km) D03 : (5km) Model Top : 10hPa Starting Time : 2008/09/11 00UTC Ending Time : 2008/09/15 00UTC Physics Options in MM5 : Explicit Moisture Schemes Goddard microphysics Cumulus Parameterizations Kain Fritsch 2(D01 & D02) PBL Schemes MRF PBL Radiation Schemes Simple cooling (D01) Surface Schemes Five Layer Soil model
29 MM5 (BDA) BDA Lat 21.0 Lon PC 935 hpa R km ROUT 500 km no BDA BDA
30 Nature Run vs. OBS - CWB Best Track - Nature Run
31 WRF V3.1 Input Data : NCEP/NCAR Reanalysis Domain : D01 : (45km) D02 : (15km) D03 : (5km) Model Top : 30hPa Starting Time : 2008/09/11 12UTC Ending Time : 2008/09/15 00UTC Physics Options in MM5 : Explicit Moisture Schemes Goddard microphysics Cumulus Parameterizations Grell Devenyi ensemble scheme(d01 & D02) PBL Schemes Yonsei University scheme Radiation Schemes RRTM scheme Surface Schemes Noah Land Surface Model
32 RO data in 2 h in the regional domain
33 Nature Run 2008/09/ UTC 2008/09/ UTC 12hr Ctrl. Run 2008/09/ UTC 2008/09/ UTC Non-Cycling Run /09/ UTC RO data (2-h assimilation window) Cycling Run /09/ UTC RO data (2-h assimilation window)
34 GPS GAL GLO GPS : 45 GAL : 35 GLO : 19
35 Nature Run none GPS GAL. GLO. all
36 GPS GAL GLO GPS : 44 GAL : 26 GLO : 25
37 Nature Run none GPS GAL GLO all
38 GPS GAL GLO all Nature Run GPS GAL GLO all
39 CONCLUSIONS Some real case forecast/simulation results indicate that the GPS RO data have some positive impacts on typhoon track prediction, which in turns improve the rainfall prediction over local regions. Some marginally positive impacts are also found for Mei-yu front rainfall simulation. GPS RO data may have positive impact on top of assimilation with other data (including bogus vortex). The OSSE studies show positive impacts of GPS RO dt data on typhoon track prediction, diti but btdepending di on specific RO distributions.
40 ONGOING WORKS Conduct a series of OSSE studies for COSMIC-II II to further understand the impacts of different RO deployments on regional weather prediction, to better quantify the performances of different assimilation operators for GPS RO data. N i i ray N r ray r GPS N r GPS r ENKF
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