Transcript Assimilation of GPS Radio Occultation Refractivity with an Ensemble Filter [Liu] 2004 PowerPoint

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)
GPS Radio occultation (RO)
GPS satellite
GPS receiver on
Low Earth Orbit
(LEO) 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.
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.
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.
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.
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.
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.
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.
3. 300 profiles are simulated and 1% obs error of N is assumed.
Initialized from
free integration
06z
obs
12z
obs
18z
00z
obs
obs
4. 6-h forecasts/guess and analyses of moisture are verified to
the truth at observation locations
Initial results are available.
Latitude
Background error correlation of T and Q
Correlation
Background error variances of T and Q
Refractivity bias (.vs. obs, 7-day average)
NH
Tropics
Guess = before assimilation; analysis = after assimilation
Refractivity RMSE (.vs. obs, 7-day average)
NH
Tropics
Guess = before assimilation; analysis = after assimilation
Q bias (.vs. truth, 7-day average)
NH
Tropics
Guess = before assimilation; analysis = after assimilation
Q RMSE (.vs. truth, 7-day average)
NH
Tropics
Guess = before assimilation; analysis = after assimilation
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.
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.