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Analysis scheme for HIRDLS/Aura retrievals Valery Yudin and HIRDLS Science Team

Analysis scheme for HIRDLS/Aura retrievals Valery Yudin and HIRDLS Science Team. Value of Aura data for data fusion studies; Resolution Kernels and Scale-consistent DA schemes First UTLS ozone analysis results with HIRDLS data (V13); Quality Control of HIRDLS O3 data.

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Analysis scheme for HIRDLS/Aura retrievals Valery Yudin and HIRDLS Science Team

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  1. Analysis scheme for HIRDLS/Aura retrievalsValery Yudin and HIRDLS Science Team Value of Aura data for data fusion studies; Resolution Kernels and Scale-consistent DA schemes First UTLS ozone analysis results with HIRDLS data (V13); Quality Control of HIRDLS O3 data. Cross isentropic filaments in the middle and upper stratosphere Current studies and future plans

  2. 01.23.2006: OMI data and GEOS-5.01 ozone columns

  3. HIRDLS and MLS capability to observe the UTLS ozone structures at ~20N, 155 W Dashed lines are vertical resolutions: HIRDLS MLS GEOS-5 24-01-2006

  4. GEOS-5.01 PV: ozonesonde location and HIRDLS orbit for 24.01.2006

  5. MLS and HIRDLS orbits HIRDLS spatial sampling along the orbits (dx~1 deg) is consistent to the HIRDLS vertical resolution of retrievals (dz~0.7 km spacing). For extra-tropics: dx/dz ~ N/f Scale-consistent sampling gives opportunity to study baroclinic disturbances and low-frequency waves in the UT and stratosphere.

  6. UTLS observed and analyzed Ozone and PV structures along satellite orbits: 01/23/2006

  7. Resolution Ozone Kernels of N-V instruments /DFS ~ 0.7 – 3.5/ and L-V (DFS ~N vertical levels) OMI-TOMS Layer Efficiency Factors TES Kernels

  8. HIRDLS O3streamers and some “GW” O3signatures cannot be fully supported by analyzed winds Vertical Mapping of data: HzXo=> Projecting HIRDLS Data => GEOS5 grid e.g. GW –control (averaging or digital filters......) Horizontal Mapping of forecast: HsXf => Binning “high-resolution” forecast to HIRDLS footprint Joint Observation-Forecast Space: Jobs = (HzXo – HsX)Wdd(HzXo – HsX)T

  9. Specifics of trial HIRDLS ozone data analysis • Model –> Tracer version of MOZART models, 3D daily mean ozone production and loss terms; IC –January monthly averaged WACCM ozone. • GEOS-5.01 – Transport (72 levels with degraded to 2ox2o hor. resolution, “LAPTOP” version). • DA –scheme: sub-optimal Kalman Filter with state-dependent stochastic model error growth to improve data weight in large spatial gradients of O3 • Quality Control: PV-O3 correlations + Large OmF are discarded Tropical UTLS ozone (P > 100 hPa) is not assimilated Additional Control using 12.1 mk aerosol cloud flag Only data with relative accuracy of ~25% or better => to analysis • Experimental Data Version of HIRDLS retrievals: V2.04.13 (with 2 km-altitude shift.....) processed only for Jan 2006 /for UTLS, released V2.04.09 => high O3, experimental versions V2.04.13-16 can fix some issues for ozone DA studies./

  10. 01.23.2006:Orbital (HIRDLS) PV-structures GEOS-5.01, GEOS-4, GFS and GEOS-5.1.0 (not available)

  11. PV-O3 correlations along MLS and HIRDLS orbits (20o-70o N)

  12. Jan 23 2006: Two versions of HIRDLS O3

  13. UTLS O3 : Analysis and CTM forecast driven by GEOS-5.01 winds Good News, CTM-forecast supports HIRDLS/MLS ozone streamers

  14. Data and DA results: linear (unbiased) correction of O3-forecast by data is performed Quality control Issue ?

  15. Data assimilation of HIRDLS O3 retrievals in the UTLS • Current GEOS-5.01 O3 analyses of column/sub-column ozone satellite data have some issues with resolution of vertical structures e.g. representation of ozone laminaes, and intrusions of air masses across and along the tropopause. • HIRDLS UTLS ozone retrievals can be scale-consistently assimilated in the CTM driven by GEOS-5 transport. • Consistency in the horizontal (along the orbit) and vertical resolution of UTLS retrievals is unique feature of HIRDLS for data assimilation studies. • Some filtering of GW signatures in the retrieved O3 unsupported by GEOS-5.01 dynamics should be performed to achieve optimal constrain of ozone forecast by HIRDLS data. Revision of retrieval errors are also expected.

  16. 23/01/2006: Stratospheric O3, HIRDLS and MLS (relatively small orbital data-data differences)

  17. Examples of stratospheric O3-analyses

  18. Weighted with density PV-field (MPV) and HIRDLS O3 Discussions on the MPV conservation laws in Lait 1994, Muller & Gunther, 2003

  19. Stratospheric vertical filaments seen by MLS O3 and N2O retrievals: 01/23/2006

  20. Current Studies & Future Plans • We plan to proceed in O3 N2O and HNO3 multi-instrumental (MLS/HIRDLS) DA studies in WACCM-GEOS5 stressing on UTLS-region. • Move DA studies to 1ox1o (0.5x.05) CTM resolutions. • Optimize data analysis scheme for chemically-active regions (include diurnal cycles in P-D terms) • Plans to look at the residual tropospheric O3 columns using OMI data. • CO: radiance data assimilation schemes for CO (MOPITT) and bias-corrected MLS CO retrievals • Demonstrate a power of Aura scale-consistent chemical observations to constrain transport ? • Participate in campaigns to provide the UTLS tracer forecast constrained by Aura chemicals.

  21. CO across the tropopause: MOPITT and MLS, model and assimilation of MOPITT in CTM

  22. IR CO Retrievals in the tropopshere =>Assimilating Profiles or Partial Columns MOPITT CO, May 2000 MOZART CO-MODEL Profiles Assim • Assimilating Partial CO Sub-columns: • Use the Total Column Kernel Vector to evaluate Data minus Forecast CO column deficit • 2) Update Partial Model Columns according to standard statistical estimation • 3) The Vertical Structure of CO analysis is less damaged by extra-smoothing from retrieved profiles

  23. Orbital plots: GEOS-5.01 O3 analysis and NV TES O3 retrievals • Possible explanations why O3-analysis cannot resemble PV-structures may be addressed to the analysis schemes of column-based data that can degrade thin low-ozone streamers. • For example, TES O3 retrievals tend to produce the low-O3 values between 20o-40o N. However, assimilating TES “smoothed” profiles can degrade ozone streamers seen by HIRDLS and MLS. • To prevent ozone streamers algorithms should adjust only observable scales. Additional tracer forecast, PV-O3 correlations may serve to identify shortcomings of analysis schemes that work with the sub-column ozone data

  24. Attractive feature of these explorative for DA: Before assimilation of data they diagnose and attempt to suppress the large model biases operating with model physics and persistent OmF differences. Biases in DA and inverse estimation studies /example of wavy T-biases in the stratosphere/ Dee, 2005 STRAT: AMSU-A rad-es SP NP

  25. Scatter plots Trop. O3 column estimations with various definitions of the tropopause boundaries

  26. Total and Trop. Columns Estimations: DAS vs OMI

  27. Scale-consistent analysis For deep layer sensitive channels Dw/Lc >>1 V-shapes  W-shapes, e.g. Gaussian shapes. <dT>-increment is not affected by “wavy” vertical correlations. Rank-deficient analysis schemes are close to direct use of OE formulae or linear filters that ignore consistency of scales => extra-sensitvity K =WCff’[WCffW’+Cbb]-1 <dT> = K<dTb>, DFS = tr(KW) ~.5-2 For DFS~[0.5-2]. K-vector can be modulated by the forecast errors on scales invisible to the instrument. Adjustment of fine-scale structures and errors by deep-layer sensitive channels is a signature of extra-sensitivity of the inverse projection from data space => forecast. Scale-inconsistent Error Analysis: [Can]-1= [Cff]-1 + W[Cbb]-1WT Mixture2km10 km ofscalesCor. LengthWidth of W Wavy structure of analysis <dT> initiates spurious “DA” temperature waves SVD of W provides natural tapering of vertical correlations and fine structures in T-variances invisible for AMSU radiances. Math summary for scale-consistent and rank-deficient computations of analysis increments for Dw/Lc >> 1

  28. Challenges in the MA data assimilation • DA of radiances from deep-layer sensitive channels (AMSU-10:14) in SMLT /Dee, Polavarapu et al., 2005/. • Two scales of inverse solution: vertical width of Jacobians (Dw) and vertical correlation lengths (Lc): Dw/Lc >>1. • In rank-deficient schemes( Dw/Lc >>1) initiates “wavy” T-increments that are not bounded by W, AMSU Jacobians; • In areas of high-density data insertion, analysis can be damaged by persistent errors related to scale-inconsistent projections of radiance misfits onto model levels (polar DA waves). • In DA of AMSU data dT-analysis increments adjust layer averaged values rather than T-profiles. • dT-anal spreads between model levels due to wide width of W-Jacobian and should be insensitive to short-scale T-correlations and variances. dT-anal W-Jacobian Dw DA wave T-corr. T-Var Lc Dw/Lc >>1 <=> DFS << N-levels

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