1. GPS radio occultation lecture 2 Extended applications
Sean Healy
NWP SAF lecture 2, April 5, 2017

2. Contributions from Chris Burrows Ian Culverwell Stig Syndergaard
Hans Gleisner
Torsten Schmidt
Paul Poli
Adrian Simmons
Uli Foelsche
Mark Ringer
Mohamed Dahoui

3. Some useful presentations on GPS-RO applications
See presentations at:
EG, Atmospheric Studies with GPS-RO
Bill Randel (NCAR)
Tropopause studies with GPS-RO
Torsten Schmidt (Helmholtz Centre Potsdam)
Gravity wave studies using GPS-RO
Feiqin Xie (Texas A&M University-Corpus Christi)
Planetary boundary layer studies with GPS-RO

4. Outline Aim: provide an overview of GPS-RO applications
Recap from lecture 1
GPS-RO information content, key characteristics core region, etc.
As you might expect, many applications are related to the GPS-RO measurement characteristics in the core region.
Key observation climate reanalyses (e.g., ERA-Interim)/climate monitoring.
Forecast verification with GPS-RO, testing NWP/climate models.
Outside the core region
Planetary boundary layer height estimates from GPS-RO.
Surface pressure information from GPS-RO.
Briefly Space weather/ionospheric applications.
Summary

5. Recap
GPS-RO measurements are useful because they complement satellite radiances.
Assimilation without bias correction
Good vertical resolution.

6. GPS-RO and IASI: 1DVAR simulations
Healy and Collard 2003, QJRMS:
Power to resolve a peak-shaped error in background: Averaging Kernel.
IASI
Expected retrieval error:
RO+IASI RO RO
Background
IASI

7. Recap
GPS-RO measurements are useful because they complement satellite radiances.
Assimilation without bias correction
Good vertical resolution
The information content is largest in the “core region”, between 7-35 km, and we see a large NWP impact on upper-tropospheric and lower/middle stratospheric temperatures.

8. Climate reanalysis applications

9. Climate reanalysis applications
We have only had large quantities of RO since It is likely to become more useful for climate monitoring as the time-series lengthens.
Claim: GPS-RO measurements should not be biased.
It should be possible to introduce data from new instruments without overlap periods for calibration.
No discontinuities in time-series as a result of interchange of GPS-RO instruments.
Bending angle time series derived from the ERA-Interim reanalysis can be used to investigate this claim.

10. Global bending angle (o-b)/b departure statistics from ECMWF operations for Aug.20 to Sept. 20, 2009
GRAS
COSMIC-6
COSMIC-4

11. I am always a bit uneasy about saying GPS-RO are bias free.
Global bending angle (o-b)/b departure statistics from ECMWF operations for Aug.20 to Sept. 20, 2009
I am always a bit uneasy about saying GPS-RO are bias free.
This difference in (o-b) statistics was traced to a smoothing of the COSMIC phase delays.
Corrected in 2009.
GRAS
COSMIC-6
COSMIC-4

12. Consistency of GPS-RO bending angles (ERA-Interim Reanalysis, Paul Poli)
Provided by Paul Poli. This is a very nice plot.
We already use the operational GPS-RO bending angles in ERA Interim and we have recently published a paper showing the impact (Poli et al, 2010).
GPS-RO cleaned up stratospheric biases in ERA Interim in the same way it did in operations.
One of the most important measurement GPS-RO measurement characteristics in this context, is that the measurements are unbiased and therefore different in instruments should have the same characteristics. Instruments should be interchangeable, without the need for overlap periods for calibration.
At the ECMWF/GRAS SAF seminar in June 2008, we were able to show that the COSMIC/GRAS bending angle (o-b) biases differed derived from operations. The difference was small, only ~ %, but it was noticeable in the bending angle statistics. Bending angles are not the raw measurements, they require some pre-processing, but this was a surprise.
EUMETSAT (Christian Marquardt) and UCAR (Sergey Sokolovskiy) identified the cause (how the phase delays were being smoothed in the processing) in early 2009, and a change to the operational COSMIC processing was made in October 12, We can see from the long ERA Interim time series how the COSMIC/GRAS bias (o-b) biases have converged since this change. ERA Interim is useful, because the NWP system is constant. The GRAS biases have changed because the mean state has changed as a result of the COSMIC changes.
Important for climate/reanalysis applications.
Value of workshops bringing NWP users and data providers together!!
Obviously, we will be using reprocessed COSMIC data for the next ERA CLIM reanalyses.
Generally,
As the time series of the GPS-RO measurements grows we can anticipate that GPS-RO observations will have an increasingly prominent role in climate monitoring and reanalysis applications.

13. GPS-RO and the bias correction of radiances
“Bias correction schemes for satellite radiances need to be grounded by a reference.” The reference measurements are often called “anchor” measurements.
“Recommendation to NWP Centres to identify part of 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.”

14. VarBC is used at ECMWF 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.

15. Experiment removing GPS-RO from ERA-Interim (Dec. 08, Jan-Feb 09)
Impact on bias correction. E.g., globally averaged MetOP-A, AMSU-A channel 9 bias correction.
GPS-RO
assimilated
We have numerous examples, this example is taken from ERA Interim.
The black line is the bias we apply with and without GPSRO assimilated.
The difference in the bias correction is comparable to the standard deviation of the first guess.
In fact, Gabor’s latest work also suggest sthat GPS-RO the most important anchor measurement in the stratosphere. Ie, more important than radiosondes for anchoring radiances. Presumably this is because of numbers/globally distributed etc.
No GPS-RO
Bias correction
applied to radiance

16. GPS-RO have improved the consistency between climate reanalyses in the upper-troposphere and lower/middle stratosphere Compare ERA-Interim, JRA-55, MERRA2, reanalysis

17. Recent time-series computed by Craig Long (NOAA)
Twelve month running average of tropical-mean temperature (K) at 100 hPa

18. Recent time-series computed by Craig Long (NOAA)
Twelve month running average of tropical-mean temperature (K) at 100 hPa

19. Recent time-series computed by Craig Long (NOAA)
GPS-RO has become a key observation type for climate reanalyses in the stratosphere in recent years.
Twelve month running average of tropical-mean temperature (K) at 100 hPa

20. Climate monitoring applications GPS-RO likely to become more important for climate monitoring But which variables should we monitor? Bending angles or more geophysical quantities?

21. Recall basic GPS-RO processing chain:
Excess phase delays.
Doppler shift.
Bending angle.
Refractivity.
Pressure/Temp. Geopotential height.

22. Bending angle for climate monitoring Simulation study using the Hadley Centre climate model
Simulation studies to assess:
potential of GPS-RO for detecting climate trends
what variable should we monitor
information content of GPS-RO in relation to other sensors
Simulations use:
Met Office Hadley Centre coupled climate model (HadGEM1)
Climate change scenario (A1B) for 2000 – 2100
Forward modelling of the GPS-RO bending angles
Provided by Mark Ringer (Hadley Centre)

23. Initial comparison with observations
Bending angle trends 2001 – Courtesy of Torsten Schmidt, GFZ, Potsdam, Germany.

24. Trends in the tropics may be detectable in about ~15 years
Detection times
(95% confidence intervals)
26 km: 9.4 – 11.7 years
20 km: 13.6 – 18.7 years
12 km: 14.6 – 18.2 years

25. Problem with monitoring bending angles
More difficult to interpret that geophysical quantities.
Most climate related work looks at temperature/geopotential heights.

26. ROM SAF work (Gleisner et al) RO mean tropospheric temperatures ––
Measured (retrieved!!) geopotential height z(p) and mean virtual temperature:
where
For standard values of the constants, and at standard surface pressure (pS), a
1 degree mean temperature increase of the atmospheric column raises the
100, 200, and 300 hPa pressure surfaces by 68, 47, and 36 meters, respectively.
Issues to consider:
is the atmosphere “dry” down to the selected isobar: difference between p and pdry
surface pressure variability (1 hPa in surf. Pressure => 7 meter in geopot height)
use of virtual temperature instead of physical temperature

27. Geopotential height at 300 hPa – CHAMP & COSMIC, global –

28. RO (gph at 300 hPa) and MSU/AMSU (TLT) – bulk tropospheric temperatures –

29. The RoTrends Project

30. ROtrends collaboration
RO community started comparison of different processing centres in 2007 (ROtrends). Main aim is to validate RO as a climate benchmark, identifying the impact of processing assumptions (structural uncertainty).
ROtrends partners: DMI, JPL, GFZ, UCAR, WEGC, and EUMETSAT
Common focus on CHAMP data, Aug 2001 to Sep 2008
Aiming at improved understanding of structural uncertainty
while still keeping the algorithm/software development independent
1st Round: profile-to-profile comparison between processing centres main results described in Ho et al. [2012]
2nd Round: comparison of monthly mean climatologies main results described in Steiner et al. [2013]

31. Initial RO-CLIM data set
CHAMP zonal monthly mean data: 5 deg x 200 m, 8-30 km, global coverage
To be provided with error characteristics and sampling-error corrected means
To be provided as an ensemble of 5 or 6 data sets
Currently no single community RO data set – discussions ongoing
Planned to be released during 2015 (following re-formatting, documentation, etc)
Current focus: a) multi-mission inter-comparisons, b) high-altitude initialization

32. Structural uncertainty: upper-level initialization
Good agreement between ROM SAF and UCAR raw bending angles.
Upper level bias between optimized and raw bending angles, leading to biases in refractivity and dry temperature.
Blue lines: mean, st. dev. Red lines: median, MAD

33. Structural uncertainty: upper-level initialization
Same as previous slide, but only including high-latitude data.
Blue lines: mean, st. dev. Red lines: median, MAD

34. Structural uncertainty: upper-level initialization
Same as previous slide, but only including high-latitude data.
Blue lines: mean, st. dev. Red lines: median, MAD

35. Aim: Produce GPS-RO datasets and software for testing climate models
CFMIP Observation Simulator Package (COSP) (see A. Bodas-Salcedo et al, 2011: COSP: Satellite simulation software for model assessment. Bull. Amer. Meteor. Soc., 92, 1023–1043.
doi:
Satellite simulator package that enables testing climate models in measurement space using forward models.
We have recently introduced the 1D bending angle operator into this package.
Work in progress, but the aim is to enable GCM developers to test their changes against robust GPS-RO climatologies.

36. One month of comparisons against COSMIC measurements.
Provided by
Alejandro Bodas-Salcedo.

37. NWP forecast verification in the stratosphere

38. Verification with GPS-RO in the stratosphere
Stratospheric forecasts are usually verified against analyses or radiosondes.
Some model changes can lead to big analysis differences, so having the option of verifying against obs. is important.
BUT, in the southern hemisphere, ECMWF only verifies against ~40 radiosonde sites, over land.
GPS-RO data are globally distributed, and reasonably good quality.
We can now verify a “day-n” forecast against GPS-RO bending angle profiles or the corresponding “classical” retrievals, assuming it is valid below ~5 hPa.

39. An example
A recent operational upgrade improved the stratospheric forecasts, with BIG improvements for sudden stratospheric warming events.
North pole day-6 forecast departures statistics for all GPS-RO data.
Statistics for period Dec 20-30, 2014.
(ie first forecast valid Dec 26)

40. Forecast step, day-5 (O-F)/σ_o

41. GPS-RO retrieval with a-priori coming from a 6hr forecast
The agreement between GPS-RO and 6hr forecast in the upper stratosphere / stratopause region is because the 6 hr fc is used in the GPS-RO retrieval

42. GPS-RO retrieval with a-priori coming from a 6hr forecast
The agreement between GPS-RO and 6hr forecast in the upper stratosphere / stratopause region is because the 6 hr fc is used in the GPS-RO retrieval

43. EXAMPLE
2.5 months of statistics
Mapping the bending angle information to temperature space.
We’ve integrated the temperature retrieval into the statistics package.
See both the bending angle and temp. statistics
Confident in the quality of the temp. retrievals from the tropopause up to ~10 hPa.

44. Novel (surprising) applications: PBL height estimates, surface pressure information

45. Novel applications: Deriving planetary boundary layer information from GPSRO measurement
Some papers have suggested that we should be able to derive information on the height of the planetary boundary layer from the GPSRO bending angle and refractivity profiles.
Sokolovskiy et al, 2007, GRL, 34,L18802,doi /2007GL030458
VonEngeln et al, 2005, GRL, 32, L06815,doi /2004GL022168
The central idea is that you see big changes in the bending angle and refractivity profile gradient across the top of the PBL.

46. Sokolovskiy et al, (2007)
It is a very interesting idea, which still needs to be investigated further. Horizontal gradient errors. Meaning of PBL height averaged over km?

47. Horizontal gradient error simulations Gradients in the ionosphere!
Red=L1
Green=L2
Blue=corrected
Black no-iono simulation
From Zeng et al, Atmos. Meas. Tech., 9, , 2016.
Recall:
Ionospheric gradients causing a
PBL height error!

48. Surface pressure information from GPS-RO
Measuring or retrieving surface pressure information from satellite radiances has been discussed for many years (Smith et al, 1972).
The GPS-RO measurements have a sensitivity to surface pressure because they are given as a function of height.
Hydrostatic integration is part of the GPS-RO forward model. If we increase the surface pressure the bending angle values increase.
Can GPS-RO constrain the surface pressure analysis when all conventional surface pressure measurements are removed?

49. NH 12 hour PMSL forecast scores
Mean
GPS-RO included. The GPS-RO measurements manage to stabilise the bias.
Standard deviation

50. Space weather/ionospheric applications

51. Retrieval methods are similar to the classical retrieval
Bending is small and is usually neglected
The excess phase delay:
These can be directly assimilated into ionospheric data assimilation systems
neutral
electron density

54. Why is the ionospheric work of interest to us?
If we want to improve upon the standard ionospheric correction for neutral applications: we need to include some a-priori information about the ionospheric state. One idea is the direct assimilation of L1, L2 bending angles by estimating the ionospheric model parameters as part of the 1D-Var retrieval.

55. Model ionosphere: electron density
Single Chapman Layer (Chapman 1931)
ne(r) = TEC/√(2πeH2) · exp(½(1 – u – e-u)),
where u = (r – r0) / H.
3 parameters:
TEC = ∫ ne dr
r0 = peak height
H = ionospheric scale height
Not much hope of retrieving an electron density profile, so model ion with 3 params: {TEC, R0, H}.
Explain why long tail and short nose.
(Chapman’s paper largely connected with impact of variation of sec(zenith) term in front of the exponentiated exp. This formula for normal incidence.)

56. First application
The ionospheric correction becomes part of the forward modelling.
The retrieved ionospheric parameters can be discarded.
L1, L2 bending angles look reasonable but more work required here.
Background: unifrom T & q  exponential N and α.

57. Summary
A comprehensive set of talks on the applications of GPS-RO data can be found at:
Many current applications are related to the GPS-RO measurement characteristics in the core-region. Impact in reanalysis established.
We expect the GPS-RO climate applications to increase as time-series lengthens.
Novel applications PBL heights, surface pressure.
Use of GPS-RO for ionospheric applications, and we are expecting increased collaboration between the ionospheric and neutral at. Communities.