1. The Impact of FORMOSAT-3/COSMIC GPS RO Data on Typhoon Prediction
UCAR: Y.-H. Kuo, T. Iwabuchi
NCAR: H. Liu, W. Wang, X. Fang, Z. Ma, Y.-R. Guo
CWB: C.-T. Terng, J.-S. Hong

2. COSMIC Accomplishments
Yes, it is nice that we are improving operational global model predictions, in terms of 100 mb temperature errors and 500 mb height anomaly correlations.
Yes, it is nice that COSMIC is shown to be a very valuable data set for monitoring climate change, and calibrating other satellite measurements.
Yes, it is nice that COSMIC data is shedding new lights on ionospheric features and helping with space weather forecasting.
BUT….

3. What can COSMIC do about this?

4. Taiwan’s societal needs: Hazard Mitigation
Taiwan is hit by typhoons 3.7 times a year (based on 20 years of statistics).
Almost every typhoon produces significant damage.
For hazard mitigation, evacuation, dam operation and rescue mission planning, Taiwan needs accurate prediction of typhoon in terms of track, intensity, rainfall, and wind gust forecasts at high temporal and spatial resolution.
The rain and wind distributions can be quite different depending on the track and intensity of the storm impinging Taiwan.
COSMIC is meant to be a “meteorological Satellite”. So, what can COSMIC do?

5. Challenges for Assessing the Impact of COSMIC data for Typhoons Affecting Taiwan
Large-scale global analysis does not provide a good description of typhoon vortex (in terms of intensity, size, and structure).
Little observations are available in the vicinity of typhoons.
Topography of Taiwan can significantly affects the track of typhoons, reducing the predictability, and complicating the impact assessment of COSMIC data.
Taiwan needs accurate rainfall and wind forecasts at high resolution (1 km, 1 hr)
COSMIC GPS RO is not mesoscale observations:
Along ray scale is 250~300 km
Taiwan’s dimension:
350 km (N-S), 150 km (E-W)

6. But, there are hopes:
COSMIC GPS RO soundings can provide valuable information about water vapor in the vicinity of typhoons (GPS RO measurements are not affected by clouds and precipitation).
COSMIC GPS RO can improve the analysis and prediction of western Pacific subtropical high (which is crucial for track forecast).
With advanced data assimilation techniques, COSMIC GPS RO data can be used to improve regional scale analysis and forecasts.
Radar data assimilation can be performed at cloud-resolving resolution, building upon improved regional analysis
Coupled with high-resolution mesoscale model, more accurate storm track, intensity, and precipitation forecast may be possible.

7. Factors affecting the impact of GPS RO assimilation on typhoon prediction
Assimilation systems:
3D-Var, 4D-Var, EnKF
Details of assimilation systems: assimilation windows, cycling frequency, grid resolution…
Bogus vortex implementation
Tuning of background and observation errors
Selection of observation operators for GPS RO soundings (e.g., local refractivity, nonlocal excess phase, 2D ray tracing, …etc)
Amount of GPS RO during the assimilation period
Assimilation of other observations
It is often difficult to isolate the impact of a specific data type (e.g., COSMIC) in operational setting with continuous data assimilation.

8. Impact of COSMIC Data on Typhoon
Genesis Stage:
Can COSMIC help improve the analysis and prediction of the genesis of typhoons?
Can COSMIC help improve the track and intensity forecast of typhoons, 1~5 day in advance?
Can COSMIC help improve short-range, high-resolution prediction of rainfall and wind gusts?
Here we show a few examples to illustrate what COSMIC could do to help typhoon predictions.

9. Impact of COSMIC Data on Hurricane Genesis
Example of Hurricane Ernesto (2006)

10. Impact of COSMIC on Hurricane Ernesto (2006) Forecast
With COSMIC
Without COSMIC
Effect of assimilation of COSMIC data on Hurricane prediction. Left with COSMIC, right w/o COSMIC. Integrated cloud liquid from model output is shown in grey shades.
6-h data assimilation at 0600 UTC 23 August 2006, followed by 66 h forecast
Results from Liu et al., NCAR

11. Impact of COSMIC on Hurricane Ernesto (2006) Forecast
With COSMIC
GOES Image
.. . And now compreed to the GOES image of the same storm
GOES Image from Tim Schmitt, SSEC

12. WRF/DART ensemble assimilation of COSMIC GPSRO soundings
WRF/DART ensemble Kalman filter data assimilation system
36-km, 36-members, 5-day assimilation
Assimilation of 171 COSMIC GPSRO soundings (with nonlocal obs operator, Sokolovskiey et al) plus satellite cloud-drift winds
Independent verification by ~100 dropsondes.
171 COSMIC GPSRO soundings during August 2006

13. Genesis of Hurricane Ernesto (2006)
No COSMIC
With COSMIC
06/8/21 12Z
06/8/22 12Z
06/8/23 12Z
06/8/24 12Z
06/8/25 12Z
Genesis of Hurricane Ernesto (2006)
Continuous data assimilation during genesis stage with WRF EnKF system

14. Verification of WRF/DART analysis by about 100 dropsondes during the Ernesto genesis stage.

15. 06/8/21 12Z
06/8/22 12Z
06/8/23 12Z
Analysis increment in Q (water vapor) due to the assimilation of COSMIC GPSRO data.
06/8/24 12Z
06/8/25 12Z

16. Genesis of Hurricane Ernesto (2006)
No COSMIC
With COSMIC
06/8/21 12Z
06/8/22 12Z
06/8/23 12Z
06/8/24 12Z
06/8/25 12Z
Genesis of Hurricane Ernesto (2006)
Cloud and Rain water
Continuous data assimilation during genesis stage with WRF EnKF system

17. NCEP GSI/NMM system Typhoon Sinlaku (2008) 36km 38L Ptop=50hPa;
Two cycling experiments were performed;
Ctrl: operational data without COSMIC GPS
GPS: Ctrl + COSMIC GPS
Cycling period: 00UTC 5——18UTC 10 September 2008

18. Continuous Assimilation over Four Days
With COSMIC GPS
Without COSMIC GPS

19. With COSMIC GPS
GPS – No GPS

20. Relative Vorticity 850 mb
With COSMIC GPS
GPS – No GPS

21. Impact of COSMIC GPS RO Data on Typhoon Track and Intensity Prediction
Examples of Kalmaegi (2008)

22. Typhoon Kalmaegi (2008)
July 13-20, 2008

23. Ensemble Forecasts of Tracks (initialized at 00UTC 17 July)
NoGPS GPS
Ensemble mean
Observed
Ensemble mean
Observed
45-km WRF/DART system
Forecast performed with 15-km WRF – 16 ensemble members
NOGPS
GPS
Left turning of the Typhoon is predicted with COSMIC GPSRO data.

24. Ensemble Forecasts of Tracks (initialized at 00UTC 17 July)
NoGPS GPS
Ensemble mean
Observed
Ensemble mean
Observed
NOGPS
GPS
Track error is smaller with GPSRO data.

25. Ensemble Forecasts of accumulated Rainfall (00UTC 17-18 July)
NoGPS GPS OBS
Ensemble mean
Observed
Ensemble mean
Observed
NOGPS
Precipitation is enhanced with GPSRO data.

26. Prediction of Typhoon with a High-Resolution Mesoscale Model
Typhoon Jangmi (2008)

27. Super Typhoon Jangmi (2008)

30. Radar and Rainfall Obervations

31. WRF 1.33 km Movable Grid Initialized with NCEP GFS analysis
Initial Condition:
1200 UTC
27 September
2008

32. WRF 1.33 km Movable Grid Initialized with NCEP GSF analysis
Accumulated Rain + SLP
Surface wind speed and directions

34. COSMIC Profile Availability 00
00z
00z
Free forecast
NCEP GFS for IC and LBC
Availale conventional data
(+1d)
DA time window
COSMIC data is available continuously
Not like radiosonde data available only at 00 and 12Z
Cycling 3DVAR for long data assimilation time window is one of the optimal assimilation scheme

35. Cold 3DVAR vs Cycling 3DVAR
Significant improvement for trackforecast in cycling 3DVAR

36. Track Forecast Errors
Cycling 3DVAR forecast is much better than cold 3DVAR forecast
Forecast with COSMIC profile is better than that without COSMIC profile

37. Intensity Forecast Comparison
Gaps between best track from JMA and WRF forecast
Trend of intensity changes is more similar in cycling 3DVAR forecast than those in cold 3DVAR forecast

38. Typhoon Real-time Forecast with COSMIC
Real-time and continuous ingestion of COSMIC profile helped to forecast typhoon Jangmi
We need more test case studies for statistical discussions
We need to mprove intensity forecasts

39. A COSMIC Sounding during Jangmi

40. A COSMIC Sounding during Jangmi

41. WRF Experiments: Some Conclusions
WRF Model with 1.33 km movable grid initialized with NCEP GFS analysis (which already assimilated COSMIC data) does show skills in track and rainfall forecasts.
Vortex is too weak: Need careful work on vortex initialization.
Need additional experiments to isolate the value of COSMIC data.
It would be interesting to have a close collaboration between operational and research centers to study a few cases.

42. FORMOSAT-3/COSMIC Follow-On Mission: Why do we need it?
Typhoon Prediction:
COSMIC GPS RO data has been shown to be valuable to improve:
Analysis and prediction of hurricane genesis
Short-range prediction of typhoon track and intensity
Coupled with high-resolution models offer chances to improve rainfall and wind prediction
2,000 soundings per day is good, but not great. It is far from saturation.
Operational analysis already incorporated COSMIC data, and already “implicitly” helping with typhoon prediction (e.g., NCEP, ECMWF forecasts)

43. Data Distribution During Sinlaku (2008)
24-h
12-h
Most global models perform assimilation with 6-h windows, regional models with 3-h windows.
3-h
6-h

44. Suggestions
Continue to improve the assimilation of COSMIC data with advanced data assimilation systems (e.g., EnKF, 4D-Var).
Improve vortex initialization procedure.
Improve high-resolution forecast models suitable for operational use in Taiwan.
Conduct collaborative studies (with operational centers) to more carefully assess the value of COSMIC in typhoon prediction.
We need to work hard to make sure COSMIC continues, and to ensure GPS RO data are available for operations and research for many more years to come.