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. Cross isentropic filaments in the middle and upper stratosphere Current studies and future plans : OMI data and GEOS-5.01 ozone columns HIRDLS and MLS capability to observe the UTLS ozone structures at ~20N, 155 W Dashed lines are vertical resolutions: HIRDLS MLS GEOS-5 GEOS-5.01 PV: ozonesonde location and HIRDLS orbit for 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. UTLS observed and analyzed Ozone and PV structures along satellite orbits: 01/23/2006 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 HIRDLS O 3 streamers and some GW O 3 signatures cannot be fully supported by analyzed winds Vertical Mapping of data: H z X o => Projecting HIRDLS Data => GEOS5 grid e.g. GW control (averaging or digital filters......) Horizontal Mapping of forecast: H s X f => Binning high-resolution forecast to HIRDLS footprint Joint Observation-Forecast Space: J obs = (H z X o H s X)W dd (H z X o H s X) T 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 2 o x2 o 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 O 3 Quality Control: PV-O3 correlations + Large OmF are discarded Tropical UTLS ozone (P > 100 hPa) is not assimilated Additional Control using 12.1 k aerosol cloud flag Only data with relative accuracy of ~25% or better => to analysis Experimental Data Version of HIRDLS retrievals: V (with 2 km- altitude shift.....) processed only for Jan 2006 /for UTLS, released V => high O3, experimental versions V can fix some issues for ozone DA studies./ :Orbital (HIRDLS) PV-structures GEOS-5.01, GEOS-4, GFS and GEOS (not available) PV-O 3 correlations along MLS and HIRDLS orbits (20 o -70 o N) Jan : Two versions of HIRDLS O3 UTLS O 3 : Analysis and CTM forecast driven by GEOS-5.01 winds Good News, CTM- forecast supports HIRDLS/MLS ozone streamers Data and DA results: linear (unbiased) correction of O 3 -forecast by data is performed Quality control Issue ? Data assimilation of HIRDLS O3 retrievals in the UTLS Current GEOS-5.01 O 3 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 O 3 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. 23/01/2006: Stratospheric O 3, HIRDLS and MLS (relatively small orbital data-data differences) Examples of stratospheric O3-analyses Weighted with density PV-field (MPV) and HIRDLS O 3 Discussions on the MPV conservation laws in Lait 1994, Muller & Gunther, 2003 Stratospheric vertical filaments seen by MLS O 3 and N 2 O retrievals: 01/23/2006 Current Studies & Future Plans We plan to proceed in O 3 N 2 O and HNO 3 multi-instrumental (MLS/HIRDLS) DA studies in WACCM-GEOS5 stressing on UTLS- region. Move DA studies to 1 o x1 o (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 O 3 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. CO across the tropopause: MOPITT and MLS, model and assimilation of MOPITT in CTM IR CO Retrievals in the tropopshere => Assimilating Profiles or Partial Columns MOZART CO-MODEL MOPITT CO, May 2000 Profiles Assim Assimilating Partial CO Sub-columns: 1)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 Orbital plots: GEOS-5.01 O 3 analysis and NV TES O 3 retrievals Possible explanations why O 3 -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 20 o - 40 o 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 Biases in DA and inverse estimation studies /example of wavy T-biases in the stratosphere/ 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. Dee, 2005 NP SPSTRAT: AMSU-A rad-es Scatter plots Trop. O3 column estimations with various definitions of the tropopause boundaries Total and Trop. Columns Estimations: DAS vs OMI Math summary for scale-consistent and rank-deficient computations of analysis increments for D w /L c >> 1 Scale-consistent analysis For deep layer sensitive channels D w /L c >>1 V-shapes W-shapes, e.g. Gaussian shapes. -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 =WC ff [WC ff W+C bb ] -1 = K, 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: [C an ] -1 = [C ff ] -1 + W[C bb ] -1 W T Mixture 2km 10 km of scales Cor. Length Width of W Wavy structure of analysis initiates spurious DA temperature waves SVD of W provides natural tapering of vertical correlations and fine structures in T-variances invisible for AMSU radiances. 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 (D w ) and vertical correlation lengths (L c ): D w /L c >>1. In rank-deficient schemes( D w /L c >>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. W-Jacobian T-corr. T-Var dT-anal DwDw LcLc DA wave D w /L c >>1 DFS