COSMIC GPS Radio Occultation and

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Laurence Pearson
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1 An Impact Study of FORMOSAT-3/ COSMIC GPS Radio Occultation and Dropsonde Data on WRF Simulations 27 Mei-yu season Fang-Ching g Chien Department of Earth Sciences Chien National and Taiwan Kuo (29), Normal GPS University Solutions 28 Mei-yu season, Taipei, SoWMEX TAIWAN Southwesterly Ying-Hwa Monsoon Kuo Experiment Mesoscale & Microscale Meteorology (MMM) Division National Center for Atmospheric Research Boulder, CO

2 -h rainfall average over Taiwan (May 1-June 30 27) Pre-SoWMEX FORMOSAT-3/COSMIC GPS Dropsonde obs Simulation period

3 Flight track and dropsonde obs

4 Case Review UTC Radar Accumulated rain IR satellite picture UTC

5 UTC Radar Accumulated rain IR satellite picture UTC

6 WRF Domain settings D1: 45 km; D2: 15 km

7 WRF settings Model settings for the control experiment (CON) WRF V2.2: WSM 5-class microphysics, Kain-Fritsch cumulus parameterization scheme, and YSU PBL scheme WRF Var v2.2 beta (surface obs and soundings) Contains 22 runs of 72-h simulation that are initialized twice daily from UTC 5 June to UTC 15 June 27. The initial data of the first run at UTC 5 June 27 are obtained from the NCEP GFS + WRF Var. The initial data of the other 21 runs are obtained from the -h update cycle of the previous WRF run + WRF Var. The GPS experiment (GPS) CON + GPS RO data assimilation The DRP experiment (DRP) CON + dropsonde data assimilation The ALL experiment (ALL) CON + GPS RO + dropsonde data assimilation

8 Amounts of OBS used in WRF Var Init time (dd/hh) 05/ 05/ 06/ 06/ 07/ 07/ 08/ 08/ 09/ 09/ 10/ 10/ SYNOP SOUND GPS Dropsonde Init time (dd/hh) 11/ 11/ / / 13/ 13/ 14/ 14/ 15/ 15/ SYNOP SOUND GPS Dropsonde GPS RO:,, T, and q Dropsonde:,, T, q, and wind

9 UTC Sounding Dropsondes + Synop GPS RO

10 Verification on pressure levels Verification method: Root-mean-square error (RMSE) Mean error (ME) Correlation coefficient (CC) SS Skill score (SS) RMSECON RMSE RMSE CON Averaged on (1) grid points of the 15-km domain against NCEP GFS analyses + WRF Var with sounding and surface obs, (2) traditional sounding stations inside the 15-km domain. Averaged for all the 22 runs of each experiment. Variables: H, T, RH, U, V Pressure levels: 850 hpa, 5 hpa, 3 hpa Times: 0,, 24, 36, 48, 60, 72 h GPS 1%

11 RMSE averaged on grid points of D2 850hPa 5hPa 3hPa H (m) T ( o C ) RH (%) Forecast hours

12 RMSE 850hPa 5hPa 3hPa U (m/s) V (m/s ) Forecast hours

13 Skill Score of GPS against CON SS RMSECON RMSE RMSE CON 1% GPS GPS 27 Forecast hours

14 Skill Score of DRP against CON SS RMSECON RMSE RMSE CON 1% DRP DRP 27 Forecast hours

15 Skill Score of ALL against CON GPS 27 ALL 27 Forecast hours

16 ETS and bias, verified against rain gauges -h accumulated rainfall -24 h ETS Bias 1:CON 2:GPS 3:DRP 4: ALL mm h

17 ETS and bias, verified against rain gauges -h accumulated rainfall h ETS Bias 1:CON 2:GPS 3:DRP 4: ALL mm h

18 UTC UTC The -h accumulated rainfall observation in Taiwan(May 1-June 30 28) Black: (UTC-UTC) Dropsonde obs Simulation period Gray: (UTC-UTC)

19 Data amounts used in WRF Var (6-h) Init time (dd/hh) 6/3 6/3 18 6/4 6/4 06 6/4 6/4 18 6/5 6/5 06 6/5 6/5 18 6/6 6/6 06 SYNOP SOUND GPS Dropsonde Init time (dd/hh) 6/6 6/6 18 6/7 6/7 06 6/7 6/7 18 6/8 6/8 06 6/8 6/8 18 6/9 6/9 06 SYNOP SOUND GPS Dropsonde GPS RO:,, T, and q Dropsonde:,, T, q, and wind

20 Data amounts used in WRF Var Init time (dd/hh) 6/9 6/9 18 6/10 6/ /10 6/ /11 6/ /11 6/ / 6/ 06 SYNOP SOUND GPS Dropsonde Init time (dd/hh) 6/ 6/ 18 6/13 6/ /13 6/ /14 SYNOP SOUND GPS Dropsonde GPS RO:,, T, and q Dropsonde:,, T, q, and wind

21 Skill Score of GPS against CON GPS 27 GPS 28 Forecast hours

22 Skill Score of DRP against CON DRP 27 DRP 28 Forecast hours

23 Skill Score of ALL against CON ALL 27 ALL 28 Forecast hours

24 ETS and bias, verified against rain gauges -h accumulated rainfall -24 h ETS Bias 1:CON 2:GPS 3:DRP 4: ALL mm h

25 Conclusions 27 (-h update cycle): The assimilation of the GPS data can help to improve the simulation for longer integration. The dropsonde data has smaller positive impact than the GPS data, and the impact decreases over time. There are few GPS RO observations in the fine domain. The large-scale simulation is first improved using the GPS RO observations, and the resulting changes can have a positive impact on the mesoscale at the later time. The dropwindsonde observations were taken inside the fine domain such that their impact can be detected early in the simulation. With both the GPS and the dropsonde d data assimilated il together, the simulation shows even greater improvement. At early time, there is no impact of GPS and dropsonde data on rainfall forecasts. However, when the integration time getting g longer, the GPS and dropsonde data start to help the rainfall simulation. 28 (6-h update cycle): A positive impact of GPS and dropsonde d data is still found, but the influence of individual variable is slightly different to that of 27. More investigation is needed.

Impact of FORMOSAT-3/COSMIC GPS radio occultation and dropwindsonde data on regional model predictions during the 2007 Mei-yu season

GPS Solut (2010) 14:51 63 DOI 10.1007/s10291-009-0143-2 ORIGINAL ARTICLE Impact of FORMOSAT-3/COSMIC GPS radio occultation and dropwindsonde data on regional model predictions during the 2007 Mei-yu season