1. Kris Wargan & Natalya A. Kramarova(*)
Extending the GEOS ozone observing system with the OMPS Limb Profiler data
Kris Wargan
&
Natalya A. Kramarova(*)
(*) Atmospheric Chemistry and Dynamics Laboratory, NASA/GSFC

2. Outline The OMPS Limb Profiler Version 2.5 ozone data quality
Comparisons of OMPS-LP observations with MLS assimilation results
Assessment of an OMPS-LP assimilation experiment

3. Motivation
MLS: 2004 to present
OMPS-LP: 2012 to present. Future missions are planned on JPSS satellites
Potential for long-term studies of stratospheric ozone including trends but…
Can we maintain continuity between MLS and OMPS-LP-based analyses?
Objectives
Consistent representation of ozone variability
Identification and reduction of biases between MLS and OMPS-LP

4. The Ozone Mapping Profiler Suite (OMPS)
Three instruments currently on the Suomi NPP satellite
The Nadir Mapper: total ozone
The Nadir Profiler: an SBUV-like sensor measuring ozone partial columns
The Limb Profiler (OMPS-LP)
Data available from mid-2012 to present
The Nadir Mapper and Nadir Profiler provide data in near real time.
Near real time data are expected from the Limb Profiler; currently there is a ~4 day latency
Future missions
JPSS (Joint Polar Satellite System)-1 will have a Nadir Mapper and a Nadir Profiler
JPSS-2 will have all three instruments

5. The OMPS Limb Profiler
Measures scattered visible and UV radiation from the atmosphere’s limb
two-dimensional charge-coupled device allows simultaneous radiance acquisition from the entire profile
Separate retrievals in the UV ( km) and visible ( km) bands. The upper and lower stratospheric profiles are provided separately
1-D retrieval algorithm
The latest version 2.5 has a much reduced bias w.r.t. MLS thanks to an aerosol correction scheme
The instrument has three slits but data from the central slit only are distributed (stray light problem in slits 1 and 3)

6. Seasonal cycle from OMPS-LP and MLS

7. OMPS-LP vs. ozonesondes
within ±5% between 20 and 30 km, except for high NH latitudes;
~-15% – -20% differences in the SH mid-latitudes (20S- 60S) below 18 km;
In the tropical UTLS: positive bias in lower stratosphere ~5-20% and negative bias in upper troposphere ~-30%.

8. UV vs. visible retrievals
157W, 12.5S
143W, 34.5N
Sometimes the differences in the overlap layer can be large, posing a challenge for assimilation

9. UV vs. visible retrievals
Large Vis-UV differences at 10 hPa, 1-15 Jan 2016
The differences between UV and visible retrievals in the layer where they overlap can be large and frequent. Which one should we trust? Our approach: trust them if they agree.
One could also pick UV if they disagree; Chappuis bands retrievals less reliable

10. Assimilation strategy
Profiles are pre-screened for large differences between UV and Vis retrievals; if the difference exceeds the reported precision then both observations are removed
UV and visible profiles are assimilated separately, each with their own errors
Approximate vertical range: 15 – 0.5 hPa (UV), hPa (Vis)
All comparisons shown in this talk are done after applying these criteria and recommended quality data screening

11. Methodology of comparisons
Recall the guiding question:
Can we maintain continuity between MLS and OMPS-LP-based analyses?
Comparisons of OMPS-LP data against MLS+OMI analysis
Comparisons of OMPS-LP+OMI analysis against MLS+OMI analysis
Assimilation experiments were run for January-October 2016 using one of the latest versions of GEOS at the MERRA-2 resolution (0.625°× 0.5° longitude by latitude).
The two assimilation experiments use identical setup except that one uses MLS v4.2 ozone and the other assimilates OMPS-LP v2.5.

12. OMPS-LP data vs. analysis
How good is the agreement in the UTLS in the presence of complex ozone field morphologies?
Focus on the extratropics

13. Spatial variability compared to MLS analysis
30°N-90°N, Jan-March 2016
100 hPa
50 hPa
30 hPa
OMPS-LP [ppmv]
OMPS-LP [ppmv]
OMPS-LP [ppmv]
High latitudes
MLS analysis [ppmv]
MLS analysis [ppmv]
MLS analysis [ppmv]
Excellent agreement:
biases within 4% above 50 hPa and about 10% at 100 hPa
Difference standard deviation within 10% and 20%, respectively
Correlations of 0.9 and higher

14. Spatial variability compared to MLS analysis
90°S-30°S, October 2016
100 hPa
50 hPa
30 hPa
OMPS-LP [ppmv]
OMPS-LP [ppmv]
OMPS-LP [ppmv]
The ozone hole
MLS analysis [ppmv]
MLS analysis [ppmv]
MLS analysis [ppmv]
Excellent agreement:
biases within 2% above 50 hPa and about 10% at 100 hPa
Difference standard deviation within 10%
Correlations of 0.9 and much higher above 50 hPa

15. Horizontal and vertical variations
600nm horizontal smoothing functions
‘Horizontal Smoothing functions apply to variations in log of ozone density in the horizontal with respect to  the tangent point.’
‘All horizontal smoothing show larger contributions from the LOS towards the satellite.  This implies that if the ozone density has S-N gradient the retrieved ozone would have a bias. ‘
Towards satellite
Away from satellite
PK Bhartia, personal communication
Does this affect the retrievals near the sharp ozone gradients across the edge of a polar vortex?

16. Spatial variability compared to MLS analysis
29 February 2016, 12UTC
OMPS-LP reproduces ozone variability along the track, including the sharp gradients across the edge of the polar vortex
Colors: MLS+OMI assimilation
Circles: OMPS-LP observations color coded by ppmv

17. Spatial variability compared to MLS analysis
28 September 2016, 12UTC
OMPS-LP reproduces ozone variability along the track, including the sharp gradients across the edge of the polar vortex (the ozone hole)
Colors: MLS+OMI assimilation
Circles: OMPS-LP observations color coded by ppmv

18. Spatial variability compared to MLS analysis
Depressed values inside the vortex
“Ozone collar”
MLS analysis
OMPS-LP data
A typical example: the ozone gradient across the edge of the polar vortex is less sharp in OMPS-LP and the sharp maximum at the edge is less pronounced.
Ozone hole is the only known example where this is an issue

19. The model transport sharpens the gradient
MLS analysis
OMPS-LP data
10-30 Sep 2016
OMPS-LP analysis
OMPS-LP data
Ozone hole
midlatitudes
“collar”
Ozone at 480 K in the southern hemisphere as a function of potential vorticity
The cross-edge ozone gradient is slightly underestimated in OMPS-LP compared to MLS analysis but also it is underestimated compared to OMPS-LP analysis:
The model transport sharpens the gradient

20. OMPS-LP+OMI analysis vs. MLS+OMI analysis
We will compare the two experiments
Variability
Mean differences
Comparisons with ozonesondes
This is very preliminary

21. MLS and OMPS-LP analysis: a global picture
Zonal mean difference within ~5% in most of the stratosphere.
Negative bias of up to 35% below 70 hPa but MLS is biased high there.
Positive bias centered at 70 hPa in the tropics
Zonal RMS difference within 10 % above 70 %
Up to 70% in the upper troposphere where ozone mixing ratio is small

22. MLS and OMPS-LP analysis: a global picture
Difference at the upper boundary of “the middle world”:
Mostly negative (OMPS-LP is low)
The largest difference of up to 50% around 30°S
Positive difference in the tropics may have a seasonal dependence

23. Global ozonesondes, January-October 2016
Mean and st. dev
Relative difference
Correlation
Sonde mean
Analysis mean
Sonde st. dev.
St. dev. of difference
MLS analysis
OMPS-LP analysis
The comparisons are done in tropopause-based vertical coordinate
Both analyses: high bias at the tropopause
OMPS-LP analysis: ~10% negative bias at 5 km above the tropopause (compensated in the UT)
Very similar representation of variability (difference standard deviations and correlations)

24. 1 km above the tropopause
MLS
OMPS-LP
MLS
OMPS-LP
Ozonesondes
Ozonesondes
3 km above the tropopause
Remarkably good agreement with ozonesondes, except very close to the tropopause where ozone variability is high.
Very similar performance of MLS and OMPS-LP analyses in terms of variability.
MLS
OMPS-LP
Ozonesondes
Ozonesondes

25. Very promising results, overall!
Summary
OMPS Limb Profiler is a UV and visible limb sounder currently flying on Suomi NPP and planned for JPSS-2
Version 2.5 ozone data have been released recently: Aerosol correction, central slit data only, UV and visible retrievals are provided separately
Much better agreement wit MLS than in previous versions
Some biases remain in the lower stratosphere
Main conclusions from assimilation
OMPS-LP analysis reproduces ozone variability as well as MLS analyses
Remaining biases (LS) require attention when combining the two data records
There’s some evidence of underestimated gradients across the ozone hole boundary. Using horizontal smoothing functions may be considered
Very promising results, overall!