1. ECMWF reanalysis using GPS RO data Sean Healy Shinya Kobayashi, Saki Uppala, Mark Ringer and Mike Rennie

2. Outline Background – why are GPS RO measurements useful given that we already have millions (ATOVS, AIRS, IASI) of satellite radiance measurements available per day. Assimilation options for NWP and reanalysis: Bending angle or refractivity. Reanalysis applications: Some results from reanalysis experiments and the use of CHAMP GPSRO measurements in ERA Interim. Impact on bias correction of other sensors. Summary.

3. Background Why are GPSRO measurements useful for NWP and reanalysis, given that we already have millions of satellite radiances available? –Globally distributed –Good vertical resolution –All-weather capability –Can be assimilated without bias correction –Quite simple to forward model (in comparison with radiances)

4. Information content studies: GPSRO/IASI Collard and Healy (2003)

5. Vertical resolution

6. Assimilation Options NWP centres tend to assimilate quantities that are as close to the raw measurement as possible. We do not assimilate temperature and humidity retrievals derived from GPSRO measurements. Experience with satellite radiances suggests that the error characteristics of retrievals are difficult to model.

7. Assimilation Options (2) Operational NWP centres assimilate either bending angle or refractivity. At ECMWF we assimilate bending angle profiles with a 1D operator. It evaluates: The bending angles can be evaluated efficiently in terms of standard integrals (Gaussian Error function). Extrapolating NWP model above the model top. ECMWF model goes up to 80 km. The bending above the model top for a ray with a tangent height at 40 km is ~ 0.05 microradians. Impact parameter Refractive index (x=nr)

8. Assimilating refractivity Assimilating refractivity requires an additional step in the processing of the observation. Refractivity is derived with an Abel transform of the bending angle profile One of the problems of this approach is that the Abel transform requires bending angles to infinity. Processing centres have their own methods for extrapolating/smoothing the noisy bending angle profile, but most adopt what is called “statistical optimization”.

9. Statistical Optimization The bending angles used in the Abel transform are the weighted average of the observed values and bending angle values simulated with a climatology or NWP model (eg, MSIS, CIRA or ECMWF!). Simulated BA’s Observed BA’s Error cov. matrix for simulated BA’s Error cov. matrix for observed BA’s statistically optimized bending angle.

10. Produced by Michael Rennie (Met Office) Comparing CHAMP measurements processed at UCAR and GFZ. REFRACTIVITY BENDING ANGLE

11. Statistical optimization (cont) The statistical optimization step introduces number of new degrees of freedom. How do we specify the error covariance matrices, chose the climatology/NWP information etc? This may be a significant factor in the refractivity differences above 25 km from different processing centres. Ultimately, we do not want to assimilate information from MSIS or CIRA into the ECMWF analysis/reanalysis, so bending angle assimilation is the preferred option.

12. Operational implementation of GPSRO at ECMWF ECMWF started assimilating GPSRO data operationally on December 12, 2006. Initial implementation neutral in the troposphere, but good improvement in the stratospheric temperature scores. Clear improvement in the bias in operational fit to radiosonde temperature measurements.

13. Reanalysis applications: ERA Interim After operation assimilation of GPSRO measurements we started considering use in ERA-Interim. ERA-Interim will cover the period 1989 to present day. The reanalysis group have reprocessed up to late 2002 so far. (A public website will be launched in March and there will be article in the next ECMWF newsletter.) Improvements since ERA-40 include: –Use of 4D-Var –Variational Bias correction (VarBC) of satellite radiances –Improved moisture analysis.

14. Reanalysis applications (Shinya Kobayashi) The first reanalysis experiments ran from July 2002 to February 2003 using CHAMP measurements. Shinya investigated how GPSRO modifies the analyses produced with and without a recent correction he had introduced to the RTTOV Zeeman splitting (AMSU-A Channel 14 weighting function too high). The error in the RTTOV Zeeman splitting appears to have been the main source the wave-like bias in the mean temperature analyses over the poles in the operational and ERA analyses.

15. Use of GPSRO in ERA experiments (Shinya Kobayashi) GPSRO passiveGPSRO active Red: old RTTOV coefficients for AMSU-A Black: new RTTOV coefficients for AMSU-A Normalised (observation – background) bending angle departures in Polar regions. (normalised with ob error)

16. 1228 = New RTTOV, no GPSRO 1230 = New RTTOV + GPSRO 1231 = Old RTTOV, no GPSRO 1236 = Old RTTOV + GPSRO

17. Value of GPSRO – a satellite radiance bias correction perspective The importance of GPSRO measurements was noted in the report by working group 3 at the ECMWF/EUMETSAT NWP-SAF workshop on bias correction in data assimilation (2005) (Proceedings available from ECMWF). The GPSRO measurements have clearly improved biases in the operational ECMWF stratospheric temperature analyses. Why - good vertical resolution and assimilated without bias correction. All satellite radiance measurements (AIRS, IASI) are bias corrected to the model. In fact, a good bias correction scheme is a key component for obtaining positive impact from these radiance data. ECMWF has an adaptive, variational bias correction (VarBC) model. VarBC can only work if other data (e.g., radiosondes, GPSRO) are assimilated without bias correction. They are “anchor points” and they stabilise the VarBC scheme. VarBC assumes the NWP model is unbiased. GPSRO should improve the assimilation of satellite radiances by reducing model biases.

18. ECMWF/EUMETSAT NWP SAF Bias correction workshop Working Group 3: Operational implementation of bias correction Reference network –“Bias correction schemes need to be grounded by a reference. This is particularly important for adaptive schemes that may have the potential to drift. Therefore a network of observations approximating the “true” state of the atmosphere is required. The data must be compared to the NWP models, either passively to monitor any drift or actively to anchor the data assimilation system.” –“Recommendation to the NWP centres to identify a part of the global observing system (e.g. high quality Radio-sondes, GPS Radio- Occultation) as reference network which is actively assimilated but NOT bias corrected against an NWP system.”

19. VarBC Dee, QJRMS (2007), 131, pp 3323-3343 Bias corrected radiances are assimilated. VarBC assumes an unbiased model. In the 4D-Var, we minimize an Augmented cost function, where the bias coefficients are estimated.

20. Experiment in a simplified NWP system Period: June 15 th to August 31 st, 2007. CONTROL: Assimilates all conventional measurements + AMSU-A and MHS measurements from the METOP-A satellite. COSMIC: As control, but with all COSMIC measurements assimilated. How do the COSMIC measurements modify the evolution of the bias correction of AMSU-A radiances?

21. How the bias correction evolves for AMSU-A, channel 9 (SH) CONTROL (NO COSMIC MEASUREMENTS) COSMIC MEASUREMENTS ASSIMILATED

22. Fit to radiosonde and COSMIC measurements (SH) Radiosonde temperature Bending angle COSMIC-4 (normalised O-B Departures) CONTROL: red COSMIC: black

23. Summary Tried to highlight the value of GPSRO measurements for NWP. Assimilate bending angle or refractivity. I would argue that bending angle is better because it circumvents the statistical optimization step. Reanalysis experiments: Recent improvement/correction to the RTTOV model has improved the fit to CHAMP bending angle measurements. CHAMP is now being used in ERA-Interim. Assimilation experiments in a reduced NWP system have shown that the COSMIC bending angles “anchor” the bias correction for the AMSU-A upper tropospheric and lower stratospheric channels.

24. Temperature bias caused by aircraft (NH)

28. q=0

29. Bias in Dry temperature Specific humidity (Kg/Kg)

30. 5 km 8 km 12 km 20 km

31. Fig 3, Foelsche et al (2008), JGR