1. Radio occultation (RO) and its use in NWP
Chris Burrows
Sean Healy (ECMWF), Jordis Tradowsky (Bodeker Scientific), John Eyre (Met Office),
András Horányi (ECMWF), James Cotton (Met Office)
10 May 2016

2. Contents Introduction to radio occultation (RO)
Impact of assimilating RO data
Bias correction of radiosondes using RO
Future instruments
Summary

3. Introduction to radio occultation (RO)

4. Radio occultation geometry
Bending angle
Fig from ROPP
user guide.
Impact parameter
During a rising/setting occultation event, the tangent point (TP) sweeps up/down through the atmosphere/ionosphere.
This results in a profile of bending angle (α) as a function of impact parameter (a).
These observations are “unbiased”.

5. Distribution and number of obs
Coverage of RO from Metop-A for one month.
Distribution is uniform, and multiple instruments minimise swath and LST effects for any model cycle.
Total observations (profiles) received in a month.
All instruments sum to for this period.

6. Impact of assimilating RO data

7. RO data denial experiment
Removing RO from a global NWP system degrades forecasts against a number of metrics.
The following slides contain a small selection of results.
(Both the experiment and control used VarBC.)

8. 500 hPa height compared to ECMWFNH SH
Removing RO increases RMSE

9. T+72 temperatures compared to ECMWFNH SH
Removing RO increases RMSE

10. T+72 wind speed compared to ECMWFNH SH
Removing RO increases RMSE

11. Data denial experiments (incomplete OSE)
Data denial experiments (incomplete OSE). Metric is change in Met Office “NWP Index”
Verified against obs
Verified against “own” analyses
Verified against ECMWF analyses

12. FSOI (plots from James Cotton)
Impact of observations on T+24h forecast skill. (moist energy norm integrated from surface to 150hPa).
Impact of RO is relatively small compared to ECMWF. This is partly due to the norm cutoff.
After sondes, RO has the largest impact per sounding (i.e. per profile).

13. Where does RO influence the NWP forecast?
This is the vertically-integrated moist energy norm of the difference between a 1hr forecast with all observations in the analysis and the 1hr forecast with no RO.
Effectively this is the difference between analysis increments with/without RO.
Each blob corresponds to a radio occultation observation.

15. Indirect impact VarBC was implemented at the Met Office in March 2016.
In VarBC, “anchor” measurements (RO, radiosondes, etc.) attempt to constrain the biases of the system.
This allows RO to have an indirect impact.
[For a quantitative comparison of direct vs indirect impact see Aparicio and Laroche, 2015, Mon. Wea. Rev., 143, ]

16. O-B statistics No VarBC VarBC
Backgrounds pull closer to RO with VarBC applied. Likely due to the RO itself, and perhaps radiosondes.

17. T+6 Bias against refractivity (COSMIC-1)
No VarBC VarBC

18. Bias correction of radiosondes (for greater consistency between anchor observations)

19. Method of diagnosing radiosonde temperature biases
ROM SAF visiting scientist Jordis Tradowsky from Bodeker Scientific spent two months at the Met Office investigating radiosonde biases using RO. The result was a vertical bias correction profile for each radiosonde site for four different solar elevation angles.
Sun et al 2011 (and others) have used co-locations to achieve this.
We use model backgrounds to avoid co-location errors
A linear temperature retrieval was used to provide additional control (mainly to remove dependence on prior information). α denotes bending angle.
Jordis’s report: Characterisation of radiosonde temperature biases and errors using radio occultation measurements.
Submitted to JAMC (Tradowsky, Burrows, Healy, Eyre).

20. A few results
First guess mean temperature departures for RO (blue), radiosonde (red) and correction (black). The green line is an extension of the black, to cover all standard pressure levels.
Site in Norway. Type: RS92
Site in Russia. Type: MARZ 2-2

21. Solar elevation angle > 22.5°
There is “clustering” of particular bias corrections in various regions. To first order, this effect is likely to be due to the radiosonde types.

22. Solar elevation angle > 22.5°
An assimilation experiment with these corrections is underway
There is “clustering” of particular bias corrections in various regions. To first order, this effect is likely to be due to the radiosonde types.

23. Future instruments (EDA study)
Slides from:
András Horányi (ECMWF),
Sean Healy (ECMWF),
Axel von Engeln (EUMETSAT),
Yago Andres (EUMETSAT)
In brief, the spread in the ensemble of data assimilations (EDA) is a proxy for the analysis and forecast error statistics. This is used to assess the impact of future observations by assimilating simulated observations. It is an alternative to OSSEs.

24. Scaling of GNSS RO impact (F. Harnisch)
Temperature analysis at 100 hPa
~ 50 % of the impact
of profiles
~ 25 million
bending
angles
per day
today
Large improvements up to profiles per day
Even with – profiles still improvements possible
→ no evidence of saturated impact up to profiles
(although the additional impact per observation is decreasing)

25. COSMIC-2 Polar + (EPS-SG +COS2-EQ, extra- tropics) - TEMPERATURE
COSMIC-2 EQ +
COSMIC-2 POLAR
No RO
EPS-SG
EPS-SG ~2800 occs per day (2 sats measuring GPS + Galileo)
+COSMIC2-EQ
~10500 (total)
+COSMIC2-POLAR
~18000 (total)
(EPS-SG+ COSMIC-2 EQ)

26. Summary RO data perform well in forecast impact experiments and FSOI
With VarBC, the mean RO forecast departures are reduced
RO may act as a reference in a hierarchical bias correction system
Increasing data volumes can be expected to improve analyses and forecasts

27. Questions