1. Data Assimilation Initiative, NCAR
Assimilation of GPS Radio Occultation refractivity with an ensemble filter
Liu H.
Data Assimilation Initiative, NCAR
Collaborators: J. Anderson (DAI)
B. Kuo and S. Sokolovsky (UCAR/COSMIC)

2. GPS Radio occultation (RO)
GPS receiver on Low Earth Orbit (LEO) satellite
GPS satellite
• Radio signal is delayed due to refractivity structure of atmosphere.
• Atmospheric refractivity profiles can be obtained with high VERTICAL resolution (~100m) and global coverage.
• Temperature and moisture can then be retrieved.

3. GPS Radio Occultation Missions
• In 1995, the pioneering UCAR GPS/MET satellite mission was launched.
It suggested the GPS data is likely to have positive impact on global analyses and forecast.
• In 2000, German CHAMP and Argentine SAC-C satellites were launched.
• Next year, UCAR/COSMIC mission will be launched (6 satellites) and will provide 4000 profiles globally per day.
• In 2006, European GARS and ACE+ missions will also be launched.
Many GPS radio occultation data will be available in next few years and this gives us an opportunity and challenge.

4. Challenge of assimilating GPS data
After several years investigation of assimilation of GPS refractivity by 3D-Var, some problems still exist, e.g.:
• Improvement on moisture analysis in troposphere is not significant, where the GPS data is supposed to have large impacts.

5. Possible causes for poor moisture analysis:
1. Observations obtained so far were immature and had large error and their number was very limited.
2. Current 3D-Var may have problems extracting moisture from GPS refractivity, e.g.:
• Background error is specified by long time-average and not flow dependent.
.vs. Moisture has very large time/spatial variations.
• Moisture analysis is independent of other variables.
.vs. Refractivity is related to both T and Q.
Background errors of Q and T are strongly correlated.

6. Motivation • Since ensemble filter has the advantages of:
1. Flow-dependent forecast/background error covariance.
2. Analysis of moisture is related to other variables.
• So, we are interested in exploring the capability of ensemble filters to improve moisture retrieval from GPS refractivity.
No similar work has been done yet.

7. This work
• As a first step, we will do an assimilation of simulated GPS refractivity with the ensemble filter of DART.
The reason to do this experiment first is that we will have truth (e.g., moisture) for verification to help our understanding.

8. Design of the experiment
1. CAM T42 as assimilation model.
2. A control run of 7-day started from long free integration is used as “truth”. Refractivity (N) calculated from the control run plus noise is used as only observations.
profiles are simulated and 1% obs error of N is assumed.
4. 6-h forecasts/guess and analyses of moisture are verified to the truth at observation locations
Initial results are available.
06z
12z
18z
00z
Initialized from free integration
obs
obs
obs
obs

9. Background error correlation of T and Q
Latitude
Correlation

10. Background error variances of T and Q

11. Refractivity bias (.vs. obs, 7-day average)NH
Tropics
Guess = before assimilation; analysis = after assimilation

12. Refractivity RMSE (.vs. obs, 7-day average)NH
Tropics
Guess = before assimilation; analysis = after assimilation

13. Q bias (.vs. truth, 7-day average)NH
Tropics
Guess = before assimilation; analysis = after assimilation

14. Q RMSE (.vs. truth, 7-day average)NH
Tropics
Guess = before assimilation; analysis = after assimilation

15. Brief summary
These initial results show that the ensemble filter is able to retrieve moisture from GPS refractivity observations with high accuracy in the ‘perfect model’ situation.

16. Next steps
• Continue the synthetic observation experiment to further demonstrate advantages of ensemble filter in retrieving moisture from GPS refractivity.
• Explore the potential of the ensemble filter to retrieve moisture from realistic GPS radio occultation data.