1. Assimilation of Global Positioning System Radio Occultation Observations Using an Ensemble Filter in Atmospheric Prediction Models
Hui Liu, Jefferey Anderson, Bill Kuo, Chris Snyder, Alain Caya
National Center for Atmospheric Research (NCAR)
Boulder, Colorado

2. Outline 1. Motivation 2. Brief introduction of ensemble filter
3. Preliminary results

3. Motivation
• Over oceans, traditional radiosondes (balloon soundings) are sparse and current global estimates of T and Q (moisture) rely heavily on satellite radiances and cloud motion derived winds.

4. Motivation (cont.)
• Significant areas of cloud-cover may exist over oceans, e.g., in case of hurricanes. Radiances are not yet routinely used in cloud-covered areas.

5. Motivation (cont.)
• In such cases, satellite cloud motion derived winds are the major data resource and the estimates of T and Q may have large uncertainty.
• Initialization of hurricane forecasts from such estimates may also have large uncertainty.
• Study of climate in the tropics also needs more reliable estimates of T and Q.

6. Radio Occultation (RO) measurements
Atmosphere
Radio raypath
GPS receiver on Low Earth Orbit (LEO) satellites
GPS satellite
Earth
• Bending angles of radio raypaths and retrieved refractivity contain integrated (non-local) T and Q information along the raypaths (~500km) with high vertical resolutions.
• Not affected by clouds and has good coverage over oceans.
• Their observation operators are simple and accurate.

7. Radio Occultation Measurements (cont.)
• UCAR/COSMIC RO data: ~2500 profiles/day globally
• Upcoming EQUARS RO data: ~ 500 profiles/day in tropics
• Traditional radiosonde (balloon): ~1500 profiles/day over land

8. Goal of our work
Assimilation of RO data in the NCAR regional Weather Research and Forecast (WRF) model and the global climate Community Atmospheric Model (CAM) to improve:
 Estimates of T and Q over oceans, especially in the tropics.
Our work will improve:
 Hurricane forecasts.
• Understanding and prediction of El Nino etc.

9. Use of Ensemble Data Assimilation
• Use of RO non-local operators are important in the tropics, where there is a lot of variations of refractivity along radio raypaths.
• Various non-local RO operators can be easily implemented (requiring only forward models).
• Time varying forecast error correlation of T and Q is included and this may significantly improve retrieval of T and Q from RO data.
The NCAR Data Assimilation Research Testbed (DART) is used:
• The Ensemble Adjustment Filter (Anderson, 2001) is used.
• WRF and CAM are implemented.

10. Ensemble Data Assimilation
• Integrate an ensemble of NWP models to a time at which next observation is available.
• By using an ensemble filter, we can use the observation to constrain or adjust the prior ensemble of model state variables, so the ensemble may have less uncertainty (filter out noise).
observation

11. Ensemble Adjustment Filter
1st step: Update the prior ensemble estimates of the observation. * * * * * * * * * * * * * * * * * * * *
N (refractivity)
N1
N10
• With the observation, we can reduce spread of the prior ensemble estimates of the observation; and shift their mean closer to the observed value.
• Get analysis increments by differencing the updated and prior ensemble.

12. Ensemble Adjustment Filter (cont.)
2nd step Update prior ensemble of model variables at nearby grid points using prior ensemble joint statistics of the observation and the variables. qj
(moisture) * *
qj,10 * * * * * * * * * * * * * * * * * * *
qj,1 * * * * * * * * * * o
N (refractivity)
N1
N 10

13. A preliminary study
We are interested in exploring impact of GPS RO data on improving estimates of T and Q in ocean areas where no radiosondes exist and clouds may prevent the use of satellite radiances.
To simulate this, we first assimilated only cloud motion derived winds. (experiment #1)
Then we added RO (CHAMP) refractivity to improve estimates of T and Q. (experiment #2)
The radiosonde observations were left out of the assimilation and are used to verify the results.
The assimilation is done over North America because many radiosonde observations are available for verification.

14. Experiment Details
The WRF regional model at 50km resolution was used to assimilate the observations.
• Sat Experiment: Assimilate satellite winds only.
 Sat+Ref experiment: Assimilate satellite winds + RO refractivity.
• A total of ~500 RO refractivity profiles during Jan 1-31, 2003 are assimilated.
• The estimates of T and Q are verified to 104 profiles of co-located (< 200km and +/- 3 hours) radiosonde observations.

15. RO profile locations (Jan 1-31, 2003)

16. Co-located radiosondes used for Verification
(Jan 1-31, 2003)

17. Impact of RO Refractivity on T estimate
(Vertical distribution of mean and RMS error, averaged over the domain)
mean error
RMS error
OBS number

18. Impact of RO Refractivity on Q estimate
(Vertical distribution of mean and RMS error, averaged over the domain)
mean error
RMS error
OBS number

19. Impact of RO Refractivity on T estimate
(Latitudinal distribution of mean and RMS error, hPa )

20. Impact of RO Refractivity on Q estimate
(Latitudinal distribution of mean and RMS error, hPa)

21. Summary
The preliminary results suggest that RO data may improve the estimates of T and Q over oceans in cloudy situations.
Further study to explore the impact of RO data on tropical estimates of T and Q and forecasts of hurricanes is underway.
(DART is available on