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  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) 2

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 3

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 4

Solar Backscatter Ultraviolet Radiometer 2 (SBUV/2) 5 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. NOAA 17, 18, S 60S 30S EQ 30N 60N [DU] Partial columns / Dobson Units 12 Z

Ozone Monitoring Instrument (OMI) 6  On EOS Aura  Nadir viewing geometry  Measures radiance in visible and ultraviolet  Footprint 13 km × 24 km  Retrieved species: ozone, NO 2, SO 2, BrO, OClO, aerosols Observation locations and ozone total column, 10/08/ Z

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

All ozone data Radiance data - meteorology All observations within a 6h window ~2,500,000 Ozone observations ~20,000 8 Data coverage, 12Z OMI Total ozone column SBUV – vertical information SBUV and OMI observe sunlit atmosphere

The GEOS-5 Data Assimilation System 9 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: Gridpoint Statistical 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

A priori profile efficiency factor [DU] Pressure [hPa] S, 130.5W OMI – efficiency factors (averaging kernels) Reduced sensitivity near the surface 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 Analysis without efficiency factors Analysis with efficiency factors 90S 60S 30S EQ 30N 60N 90N [ppbv/day] Tendencies introduced by analysis Reduced impact of ozone data near the surface The system’s response to efficiency factors Zonal mean analysis tendency near 900 hPa Zonal mean analysis tendency near 1000 hPa

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

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 15 Accurate representation of ozone sources near the surface (e.g. anthropogenic ozone precursors) is needed to compensate for limited sensitivity of observations

SOME RESULTS 16

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

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

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 [ppmv] Pressure [hPa] PV contours (white) and ozone (shaded) from the GEOS-5-driven CTM HIRDLS retrievals CTM SBUV analysis Pressure [hPa] Ozone [mPa] Assimilated ozone from GEOS-5 using SBUV/2 data 19

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 20 Background Mean O 3 tropopause Ozone mixing ratio Altitude

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 21 Observed Mean O 3 Background Mean O 3 tropopause Ozone mixing ratio Altitude

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 22 Observed Mean O 3 Background Mean O 3 tropopause Ozone mixing ratio Altitude Background error correlation, vertical extent

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 23 Observed Mean O 3 Background Mean O 3 tropopause Ozone mixing ratio Altitude After assimilation: the analysis increment “leaks” through the tropopause, vertical gradient is reduced Background error correlation, vertical extent

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

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 Alternate approach Proportional method A possible candidate for σ 2 : 25 [O 3 ] – background ozone α – specified parameter Process-based

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

27 Latitude Theta [K] Ozone field in the Upper Troposphere – Lower Stratosphere. Fine structures 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

 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 Lamina Identification 28

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

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) 30

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

Microwave Limb Sounder on EOS Aura 32  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, SO 2, cloud ice,...  Ozone profiles are retrieved at relatively high vertical resolution (varies with version) between ~260 hPa and top of the atmosphere

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) 33 Legionowo, 52.4N, 21E Apr 6 th 2005 Sonde profile SBUV analysis MLS analysis Ozone [mPa] Pressure [hPa] We can assimilate:  Retrieved data – already implemented in GSI  Radiance data

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

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

MERRA (SBUV/2) MLS+SBUV, GEOS Station data MERRA (SBUV/2) MLS+SBUV, GEOS Station data Total ozone column [DU] Sep Oct Nov 2010 The 2009 Antarctic ozone hole Oct 18 th 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 36

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

ASSIMILATION OF MLS RADIANCES 38

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 39

The MLS viewing geometry 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

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 41 Observed minus simulated radiances

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

Equator 60S 60N 30S 30N Relative RMS difference [%] Relative RMS difference: Assimilation of MLS v3.3 minus radiance assimilation 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 43

Ozone [mPa] MLS v3.3 analysis Radiance analysis 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 Lower stratospheric zonal mean profile at the equator 44

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. 45