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Ozone Data Assimilation

Ozone Data Assimilation. K. Wargan. S. Pawson M. Olsen A. Douglass P.K. Bhartia J. Witte. Global Modeling and Assimilation Office. Motivation and challenges. Ozone is important Controls temperature in the stratosphere – long range forecasts should start from realistic ozone

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Ozone Data Assimilation

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  1. Ozone Data Assimilation K. Wargan S. Pawson M. Olsen • A. Douglass • P.K. Bhartia • J. Witte Global Modeling and Assimilation Office

  2. Motivation and challenges • Ozone is important • Controls temperature in the stratosphere – long range forecasts should start from realistic ozone • Pollution in the troposphere, greenhouse gas • Modern reanalyses have ozone • Not easy to assimilate • Complex, variable structure • Limited data coverage • Limited vertical resolution • What we do to make it work • Fine tuning of background errors • More data sources (MLS, radiances)

  3. Plan of talk • Description of the ozone data and the GEOS-5 data assimilation system • The impact of assimilating averaging kernels • Background error covariance modeling • Assimilation of the Microwave Limb Sounder (MLS) ozone • Towards direct assimilation of MLS radiances

  4. Ozone Data • Solar Backscatter Ultraviolet Radiometer 2 (SBUV/2) • Ozone Monitoring Instrument on EOS Aura (OMI) • Microwave Limb Sounder on EOS Aura (MLS); currently only in research analyses

  5. Solar Backscatter Ultraviolet Radiometer 2 (SBUV/2) 12 Z 0.1 Partial columns / Dobson Units 1 NOAA 17, 18, 19 10 100 700 90S 60S 30S EQ 30N 60N [DU] SBUV measures backscattered UV radiation. Ozone is retrieved in 21 layers, ~3 km thick, compare to 72 GEOS-5 layers. Footprint: 170 × km 340 km Very good agreement with independent data. Long data record (1970s to present) Small structures are not resolved – poor resolution below the ozone maximum.

  6. Ozone Monitoring Instrument (OMI) Observation locations and ozone total column, 10/08/2012 12 Z • On EOS Aura • Nadir viewing geometry • Measures radiance in visible and ultraviolet • Footprint 13 km × 24 km • Retrieved species: ozone, NO2, SO2, BrO, OClO, aerosols

  7. Data coverage, 12Z All ozone data SBUV and OMI observe sunlit atmosphere SBUV – vertical information OMI Total ozone column

  8. Data coverage, 12Z All ozone data SBUV and OMI observe sunlit atmosphere SBUV – vertical information OMI Total ozone column Radiance data - meteorology

  9. The GEOS-5 Data Assimilation System • Atmospheric General Circulation Model: • Horizontal resolution: flexible - 2.5° to ¼° • 72 layers from the surface to 0.01 hPa • Parameterized ozone chemistry (stratospheric P&L; dry deposition) • 3D-Var analysis: • GridpointStatistical Interpolation • developed in collaboration with NCEP • Observations: • Conventional (surface, sondes, radar, aircraft, MODIS-derived winds,…) • Satellite radiance data (TOVS/ATOVS, AIRS, IASI, SSM/I, GOES, GPS) • Ozone data

  10. OMI – efficiency factors (averaging kernels) 1 1 10 10 efficiency factor Pressure [hPa] 100 100 8.4S, 130.5W Reduced sensitivity near the surface A priori profile 1000 1000 0 20 40 60 80 0 1.0 2.0 3.0 [DU] The retrieved ozone column is a combination of the true signal and a priori climatology. The a priori is removed in the assimilation

  11. OMI – efficiency factors (averaging kernels) The retrieved ozone column is a combination of the true signal and a priori climatology. The a priori is removed in the assimilation

  12. Generally reduced sensitivity near the surface. Efficiency factors can take values >1 due to multiple scattering

  13. The system’s response to efficiency factors Zonal mean analysis tendency near 900 hPa 0.8 Analysis without efficiency factors Analysis with efficiency factors 0.6 [ppbv/day] 0.4 0.2 Tendencies introduced by analysis Reduced impact of ozone data near the surface 0.0 -0.2 90S 60S 30S EQ 30N 60N 90N Zonal mean analysis tendency near 1000 hPa 0.5 [ppbv/day] 0.0 -0.5 90S 60S 30S EQ 30N 60N 90N

  14. OMI efficiency factors – the impact on assimilated tropospheric column Tropospheric column relative difference: With minus without efficiency factors February 2007 [%]

  15. The model – ozone chemistry • Parameterized ozone production and loss rates in the stratosphere • No chemistry is implemented below the tropopause – observations are the only source of information on ozone distribution in the free troposphere • Dry deposition mechanism removes surface ozone Accurate representation of ozone sources near the surface (e.g. anthropogenic ozone precursors) is needed to compensate for limited sensitivity of observations

  16. Some results

  17. The annual cycle of total ozone Springtime maximum (2011) 2011 Antarctic ozone hole (2010) 2010 Springtime maximum (2010) Antarctic ozone hole (2009) 2009 Springtime maximum (2009) south north [DU]

  18. Comparisons with ozone sondes Ozone integrated between the tropopause and 50 hPa 250 Very good agreement with ozone sonde data in the lower stratosphere. Important for climate forcing by ozone and stratosphere – troposphere exchange y=0.94x+4.44 R = 0.98 RMS diff = 12.78 % Bias = 0.5 % 200 150 Assimilation [DU] 100 50 0 0 50 100 150 200 150 Ozone sondes [DU]

  19. Transport using assimilated winds leads to realistic ozone profile structure in the UTLS Assimilation of SBUV in GEOS-5 does not show this vertical structure: smoothing from the assimilation process Pressure [hPa] PV contours (white) and ozone (shaded) from the GEOS-5-driven CTM Pressure [hPa] Pressure [hPa] HIRDLS retrievals CTM SBUV analysis Assimilated ozone from GEOS-5 using SBUV/2 data [ppmv] Ozone [mPa]

  20. Background errors GEOS-5/MERRA • NMC Method • 2-D lookup table of variances and correlation length scales • High ozone gradients in the UTLS are not resolved • Statistics-based; real-time dynamics not accounted for tropopause Altitude Background Mean O3 Ozone mixing ratio

  21. Background errors GEOS-5/MERRA • NMC Method • 2-D lookup table of variances and correlation length scales • High ozone gradients in the UTLS are not resolved • Statistics-based; real-time dynamics not accounted for tropopause Altitude Background Mean O3 Observed Mean O3 Ozone mixing ratio

  22. Background errors Background error correlation, vertical extent GEOS-5/MERRA • NMC Method • 2-D lookup table of variances and correlation length scales • High ozone gradients in the UTLS are not resolved • Statistics-based; real-time dynamics not accounted for tropopause Altitude Background Mean O3 Observed Mean O3 Ozone mixing ratio

  23. Background errors Background error correlation, vertical extent GEOS-5/MERRA • NMC Method • 2-D lookup table of variances and correlation length scales • High ozone gradients in the UTLS are not resolved • Statistics-based; real-time dynamics not accounted for tropopause Altitude Background Mean O3 Observed Mean O3 Ozone mixing ratio After assimilation: the analysis increment “leaks” through the tropopause, vertical gradient is reduced

  24. How can we take advantage of data without damaging transport-induced small-scale structures? So...

  25. Background errors GEOS-5/MERRA Alternate approach Proportional method A possible candidate for σ2: • NMC Method • 2-D lookup table of variances and correlation length scales • High ozone gradients in the UTLS are not resolved • Statistics-based; real-time dynamics not accounted for [O3] – background ozone α – specified parameter • Process-based

  26. New vs. old background errors 2007 May 9th, SBUV/2 ozone assimilated 10 Stony Plain 53.5N, 114W Bratt’s Lake 51N, 104W 100 Pressure [hPa] Sonde profile NMC errors New errors Sonde ozone Analysis, new errors 1000 0 5 10 15 20 25 0 5 10 15 20 25 Ozone [mPa] Ozone [mPa]

  27. Ozone field in the Upper Troposphere – Lower Stratosphere. Fine structures Theta [K] The High Resolution Dynamics Limb Sounder (HIRDLS) detects small scale structures in the ozone field near the tropopause. These structures are often absent in SBUV/2 & OMI analysis but do get captured with the new background error model Theta [K] Theta [K] Latitude

  28. Lamina Identification • Interpolate to theta (only above 260 hPa) • Average profiles in 2° latitude bands • Determine lamina bottom and top • Apply thickness and magnitude criteria • Lamina must be coherent across 3 mean profiles

  29. Counting the laminae Total # 1131 Number of profiles with Laminae April 2007 Theta [K] HIRDLS data Latitude Total # 283 Total # 12 Theta [K] Latitude Latitude SBUV assim, NMC errors SBUV assim., New errors The new background errors lead to large improvements in the representation of the UTLS ozone

  30. Conclusions so far • SBUV and OMI based ozone analyses produce ozone fields which are in a reasonable agreement with ozone sonde data in terms of vertically integrated partial columns • Small vertical features near the tropopause are not always represented • Replacing NMC, static background error variances with a process dependent error model leads to a better representation of these features (as compared with independent data)

  31. Microwave Limb Sounder on EOS Aura The EOS Aura satellite was launched on July 15th 2004

  32. Microwave Limb Sounder on EOS Aura • MLS measures atmospheric limb emissions in five spectral regions centered around 118 GHz , 190 GHz, 240 GHz, 640 GHz, and 2.5 THz • Designed to measure atmospheric temperature and composition including ozone, moisture, CO, ClO, SO2, cloud ice, ... • Ozone profiles are retrieved at relatively high vertical resolution (varies with version) between • ~260 hPa and top of the atmosphere

  33. Why assimilate MLS ozone • Assimilation of limb sounder data improves the representation of fine scale vertical structures • Global day and night coverage • Possibility to do it in NRT if either NRT MLS retrievals or MLS radiances are used • A template to assimilate other point measurements (e.g. NPP OMPS-Limb Profiler) 10 Sonde profile SBUV analysis MLS analysis 100 Pressure [hPa] Legionowo, 52.4N, 21E Apr 6th 2005 • We can assimilate: • Retrieved data – already implemented in GSI • Radiance data 1000 0 5 10 15 20 25 Ozone [mPa]

  34. Vertical structure near the tropopause Theta [K] High resolution data combined with state-dependent background error covariance model reproduces the structure of the UTLS ozone field Theta [K] Theta [K] Latitude

  35. UTLS ozone laminae in GEOS-5 – comparison with High Resolution Dynamics Limb Sounder Number Profiles with Laminae April 2007 Total # 1131 Total # 821 Theta [K] MLS assim. New errors HIRDLS data Total # 283 Total # 934 Theta [K] MLS assim. old errors SBUV assim., New errors Latitude Latitude • MLS assimilation has the most faithful representation of ozone structure

  36. The 2009 Antarctic ozone hole MERRA (SBUV/2) MLS+SBUV, GEOS-5.6.1 Station data MLS+ SBUV analysis agrees well with data from the South Pole balloon sondes. SBUV analysis (without MLS) overestimates ozone over the South Pole by almost 100 DU in September. Good agreement in October and November 400 300 200 100 0 Oct 18th Total ozone column [DU] Sep Oct Nov 2010

  37. MLS+SBUV analysis Total Ozone, Sep 3rd, 6Z And SBUV coverage MLS+SBUV analysis Total Ozone, Oct 15th, 6Z And SBUV coverage The polar night region is not constrained by SBUV observations until October. The polar ozone is overestimated in September MLS provides near global coverage SBUV analysis Total Ozone, Sep 3rd, 6Z SBUV analysis Total Ozone, Oct 15th, 6Z

  38. Assimilation of MLS radiances

  39. Why radiances? • Retrieved product is always affected by priors • In case of MLS temperature the priors come from GEOS-5 analysis – self-contamination of the system if these data are assimilated • By assimilating radiances we avoid additional errors resulting from the retrieval process

  40. The MLS viewing geometry 125 vertical scans per 25 s in the direction of motion Each scan records emissions at a number of distinct frequencies Band 7 radiances, centered at 240 GHz are sensitive to ozone, 25 channels

  41. Information from MLS radiances • Contrast: ozone concentration • Breadth: tangent pressure • Position: baseline/extinction We need all three pieces. In the current implementation only the contrast is assimilated. We use previously retrieved tangent pressure data. Implementation of online baseline retrieval is underway Observed minus simulated radiances

  42. Some results Comparison with MLS V3.3 retrieved ozone. A single profile Pressure [hPa] Pressure [hPa] Mean MLS, 60N-90N Radiance assimilation MLS profile v3.3, 82S Radiance assimilation Ozone [ppmv] Ozone [ppmv] Reasonable agreement in mid- and upper stratosphere – sanity check passed

  43. Relative RMS difference: Assimilation of MLS v3.3 minus radiance assimilation Equator 60S 60N 30S 30N Relative RMS difference [%] Agreement within 5% in mid to upper stratosphere except southern high latitudes. The lower stratosphere is expected to improve once the extinction retrieval is implemented

  44. Lower stratospheric zonal mean profile at the equator The MLS v3.3 analysis (assimilation of retrieved MLS ozone) exhibits oscillations in the tropical lower stratosphere – even in the zonal mean. These are not seen in radiance assimilation MLS v3.3 analysis Radiance analysis Ozone [mPa]

  45. Summary • New process-based background error covariance model leads to significantly improved representation of fine scale structures in assimilated ozone • High vertical resolution ozone data (MLS) brings further improvements to the assimilated product – the structure of the ozone field in the Upper Troposphere – Lower Stratosphere layer are well represented • The impact of assimilating OMI efficiency factor has yet to be fully assessed • Direct assimilation of MLS radiance data (in ozone and temperature bands) is underway.

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