1. Hui Liu, Jeff Anderson, and Bill Kuo
Application of Radio Occultation Data in Analyses and Forecasts of Tropical Cyclones Using an Ensemble Assimilation System
Hui Liu, Jeff Anderson, and Bill Kuo
NCAR
Acknowledgment: C. Snyder, Y. Chen, T. Hoar, K. Raeder, N. Collins

2. Introduction
• Operational analyses and forecasts of tropical cyclones, especially their intensity, have large errors
• Lack of accurate observations of atmospheric water vapor and temperature near the cyclones is one of the reasons
• New type of satellite observations has room to improve forecasts of tropical cyclones

3. GPS Radio Occultation (RO)
Basic measurement principle:
Deduce atmospheric water vapor and temperature
based on measurement of GPS signal phase delay.

4. Limb sounding of atmosphere as LEO satellite receivers rise or
set with respect to GPS satellites
Global observations of:
Temperature, Humidity,
Refractivity.
(~2500 profiles per day)
Upper left panel shows the LEO setting with respect to the GPS satellite, obtaining a series of scans through the atmosphere.
The lower right panel on this slide shows a birds-eye view of the CHAMP satellite in LEO. The white line indicates the ray to the GPS satellite, which is not shown and the white dot shows the location of the tangent point introduced in the top left panel.
As the tangent point descends through the atmosphere, the temperature profile is shown in the little panel to the right of the globe.
The temperature profiles are obtained in post processing.

5. COSMIC GPS RO Research Mission (2006 - 2011)
A set of six mini-satellites in Low Earth Orbit (LEOs) with GPS receivers were launched on
15 April 2006.
15 April 2006
Vandenberg AFB
COSMIC launch picture provided by Orbital Sciences Corporation

6. Dec 7, 2007 Global coverage including over oceans and polar areas 1878 soundings

7. GPS Radio Occultation Refractivity
Has accurate measurements of both water vapor and temperature with high vertical resolution
Minimally affected by clouds and precipitation
Has great potential to improve weather analyses and forecasts over data-sparse and cloudy areas like tropical oceans
So, RO is especially useful for tropical cyclone forecasts

8. Challenges for Assimilation of RO Refractivity
• RO refractivity is a function of both water vapor and temperature
• Retrieval of water vapor and temperature requires accurate estimate of covariance between RO data, temperature, and moisture
• These covariances are highly time-varying and not well known

9. Ensemble Kalman Filter Assimilation
• Covariance of RO refractivity with water vapor and temperature is computed from online ensemble forecasts
• The error covariance is time-varying, related to weather patterns

10. Typhoon Shanshan Case (Sept 10-17, 2006)
Operational forecasts
using variational assimilation failed to predict the curving of the typhoon
Central SLP pressure

11. COSMIC RO soundings (September 13, 2006)
RO soundings, randomly distributed over the domain, provide large-scale information.

12. Assimilation experiments
 WRF/DART ensemble assimilation at 45km resolution for 8-14 September 2006.
 32 ensemble members.
 Control/NoGPS run:
Assimilate operational datasets including radiosonde, cloud winds, land and ocean surface observations, SATEM thickness, and QuikScat surface winds.
• GPS run:
Assimilate the above observations + RO refractivity.

13. Typhoon central pressure in analyses (8-14, Sept.)
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Observed
Ensemble mean
NOGPS
GPS
Intensity of the typhoon is enhanced with RO data.

14. Typhoon Maximum surface wind in analyses (8-14, Sept.)
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Observed
Ensemble mean
NOGPS
GPS
Intensity of the typhoon is enhanced with RO data.

15. Impact of RO refractivity on Ensemble forecasts (16 members, with a finer nested grid of 15km) initialized at 00UTC 13 and 14 Sept 2006.
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds

16. 1. Forecast from 00UTC 13 Sept 2006
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds

17. Ensemble Forecasts of Central Sea Level Pressure
NoGPS GPS
Ensemble Mean
Observed
Ensemble mean
Observed
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Intensity of the typhoon is increased with RO data

18. Ensemble Forecasts of Maximum surface wind
NoGPS GPS
Ensemble Mean
Observed
Ensemble mean
Observed
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Intensity of the typhoon is increased with RO data

19. Forecast Probability of Rainfall >60mm/24h, 12Z 14-15 Sept.
NoGPS GPS
Ensemble mean
Observed
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
OBS
Probability = Rainy members/total members
Rainfall probability is increased with RO data

20. Ensemble Forecasts of Typhoon Track
NoGPS GPS
Ensemble mean
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Observed
Ensemble mean
Observed
NOGPS
GPS
Curving of the Typhoon is well predicted in both cases.

21. Ensemble Forecasts of Typhoon Track
NoGPS GPS
Ensemble mean
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Observed
Ensemble mean
Observed
NOGPS
GPS
Curving of the Typhoon is well predicted in both cases.

22. 2. Forecast from 00UTC 14 Sept 2006
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds

23. Ensemble Forecasts of Minimum Sea Level Pressure
NoGPS GPS
Ensemble mean
Observed
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Intensity of the typhoon is increased with RO data

24. Ensemble Forecasts of Maximum surface wind
NoGPS GPS
Ensemble mean
Observed
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Intensity of the typhoon is increased with RO data

25. Forecast Probability of Rainfall >60mm/24h, 12Z 14-15 Sept.
NoGPS GPS
Ensemble mean
Observed
Shading: PV (in PVU) at low-level (9th model level ~ 800mb over ocean)
Contour: Sea level pressure (in hPa)
Hurricane symbol (white) is the observed cyclone position at that time (only after Aug. 25)
EXC: is the experiment with GPS and satellite winds, CTL: is the experiment with only satellite winds
Rainfall probability is increased with RO data

26. Summary
• Forecasts of the typhoon intensity and rainfall probability are improved by using RO refractivity observations with the WRF/DART ensemble system.
• The curving path of the typhoon is well predicted.

27. Next Efforts • Weather analysis with use of RO data for:
• More hurricane cases
• Quasi-operational testing at Taiwan Central Weather Bureau, etc
• Genesis of tropical cyclones
• Combination of other satellite observations with RO data to further improve analyses and forecasts of tropical cyclones
• Weather analysis with use of RO data for:
• Tropical convection study
• Forecasts over Antarctic/Arctic, etc.