Resolution-dependent data analysis of UTLS ozone : value and impact of Aura data (HIRDLS-MLS)
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Resolution-dependent data analysis of UTLS ozone : value and impact of Aura data (HIRDLS-MLS) Valery Yudin, NCAR/ACD. Acknowledgements to Aura Instrument Science Teams and, GFS/NOAA, GEOS/GMAO, and GMI/GSFC-UMBC, SHADOZ groups for data and simulations.

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Resolution-dependent data analysis of UTLS ozone : value and impact of Aura data (HIRDLS-MLS)Valery Yudin, NCAR/ACD


Acknowledgements to

Aura Instrument Science Teams and, GFS/NOAA, GEOS/GMAO, and GMI/GSFC-UMBC, SHADOZgroups for data and simulations

Motivation by orbital plots hirdls vs geos5 in the extra tropical utls
Motivation by orbital plots: impact of Aura data (HIRDLS-MLS)HIRDLS vs GEOS5 in the extra-tropical UTLS

HIRDLS: Jan 23 2006

GEOS5: Jan 23, 2006






How often and why current ozone analyses fail to reproduce ozone thin layers characterized by negative vertical gradiebts ?

GMAO/GEOS-5.1.0 impact of Aura data (HIRDLS-MLS)540x360x72

dz ~250m vs 1km

NOAA/GFS-SMOBA 360x180x36

dz ~ 500 m vs 1km

GMI-CTM-GEOS4 144x91x45

CAM-Chem-GFS 144x96x28

dz ~ 1 km vs 1km

WACCM3-SST/QBO 72x45x76 dz ~ 2 km vs 2km

WACCM3-CTM-GEOS5 180x91x72


dz ~ 1 km vs 1km


Column-based O3 data:OMI-TOMS


Vertical O3 profiles: HIRDLS, MLS, ACE with dx/dz ~ N/f can optimize dynamics;

Type-3:Smoothed profiles

Nadir-viewing ozone sensors => Layer-averaged or sub-column data (AIRS, TES, METOP…):

dX/dZ~ dX/Hp ~[1-5]


in-situ VP data (no horizontal sampling) Correlative sondes (SHADOZ and WOUDC) to evaluate ozone UTLS streamers, spectra of oscillations.

Analyses, models, data and spatial resolutions of observing systems (Fox-Rabinovitz & Lindzen) desirable dz ~N/fdh, for extra-tropical UTLS

Analyses and Models

Four types of O3 data

Comparing o 3 colums du ctm waccm climate geos5 2006 omi 2005 08
Comparing O impact of Aura data (HIRDLS-MLS)3-colums (DU): CTM, WACCM-climate, GEOS5 (2006),OMI (2005-08)









O3-columns are succesfully reproduced


OMI: 2005-2008

Characterization of O impact of Aura data (HIRDLS-MLS)3 profiles by Resolution (or Averaging) Kernels:Rows and Images (Sharpness, Values and Properties)

MLS: 0-30 km range

HIRDLS (2/3 km) , MLS (2.5 km) , PV (~1km), TES (~2 km) => orbital patterns

Data Analysis schemes (DA) is easy to implement when….

a) resolutions of data and models are comparable;

b) consistent dynamics of “errors” provide chances to insert the realistic data-driven vertical transport

c) Characterization of data by resolution kernels is critical for optimal observing systems. It helps to understand what can be constrained from data

Extra-tropical UTLS: Resolution-consistent sampling (dx/dz=N/f) of HIRDLS and MLS matches PV-distributions of GEOS5

23 01 2006 142 o w hirdls o 3 and ctm o 3 forecast blending comparable scales due to d x d z n f
23-01-2006, 142 orbital patternsoW: HIRDLS O3 and CTM O3 Forecast, blending comparable scales (due to dx/dz ~N/f)

Thickness of ozone streamers ~2-3 km


23 01 06 12ut o 3 ctms cam gfs gmi geos4 and analyses gmao and noaa
23/01/06: 12UT, O orbital patterns3: CTMs (CAM-GFS & GMI-GEOS4) and Analyses (GMAO and NOAA)

Zonal Mean O3

Lon-Lat O3 at 200 hPa (~11.5 km)





142 o w 23 01 2006 12 ut pv o 3 analyses and ctm
142 orbital patternsoW, 23/01/2006, 12 UT: PV & O3 Analyses and CTM

Spectra of O3-vertical oscillations, UTLS from sondes (sampl.~ 100m)


1-3 km


March 2006 lamination frequency reproduced by ctm analyses and hirdls retrievals
March 2006: Lamination frequency reproduced by CTM, Analyses and HIRDLS retrievals

O3 Profiles


GEOS5 (x2.5)


Lamina rate production (P-PV, R-tracer) and HIRDLS retrievals/ Appenzeller & Holton, 1996/

D(Rz)/Dt ~ f/R{R, T} + v.terms

D(Pz)/Dt ~ f/R{P, T} + v.terms

How often these lamina exist ?

Analysis of column-based data =>

“Assimilation” Vertical Diffusion.

~2-3 km in UTLS can add ~ 10-20 DU, and may affect regional TOR estimations

UTLS: Ozone streamers and lamina with sharp vertical gradients, how often MLS and HIRDLS instruments can observe them ?

Apr 2006 and 2007, Frequency of lamination events


30% sonde-HIRDLScoincidences =>UTLS lamina


Utls lamination frequencies march 2006
UTLS Lamination frequencies: March 2006 and HIRDLS retrievals

Lamina-range counts

Frequency of o 3 laminations seen by hirdls simulated by ctm and produced o 3 analyses
Frequency of O and HIRDLS retrievals3-laminations seen by HIRDLS, simulated by CTM and produced O3-analyses





x 2


x 2


x 2

Examples assimilating ls o 3 intrusions in ut with vertical resolution of hirdls
Examples: Assimilating LS O and HIRDLS retrievals3 intrusions in UT with vertical resolution of HIRDLS

142oE: 01/23/2006

154o W: Hilo, Hawaii


7 layers of GEOS-5



2-SBUV vs 7-GCM

High PV->O3


Independent DATA

Vertical resolutions: and HIRDLS retrievalsdata and analysis grids,resolved (visible) and invisible (forbidden-Nyquist) scales

Resolution averaging kernels rows and images sharpness values and properties
Resolution/Averaging Kernels. and HIRDLS retrievalsRows and Images: (Sharpness, Values and Properties)

MLS: 0-30 km range

Resolution dependent analyses schemes

A) and HIRDLS retrievalsPreserving vertical O3 short-wavecomponents by resolution-dependent schemes

B) Treating “layer-averaged” data as a “point-wise” observations applying “backward” interpolation from the data space => analysis grid

Resolution-dependent analyses schemes

Aura mission equatorial o 3 anomaly 10 o s 10 o n
Aura mission: Equatorial O and HIRDLS retrievals3-anomaly (10oS-10oN)







2004 2005 2006 2007 2008

2004 2005 2006 2007 2008

H and HIRDLS retrievals2O-O3 stratospheric analyses GEOS5 vs MLS analysis problems ? (HIRDLS impact !!! What can be seen from ERA-40 and ECMWF)



H2O tape recorders

O3-stratospheric QBO “sticks”

Equatorial oscillations of O and HIRDLS retrievals3 and H2O: MLS and GCM:similar shapes of constituent variations(WACCM3-QBOSC runs performed by Katja Matthes, FUB)



2004 2005 2006 2007 2008

2004 2005 2006 2007 2008

Conclusions for resolution dependent analyses
Conclusions for Resolution-Dependent Analyses and HIRDLS retrievals

  • Characterization of MI data by resolution kernels are needed to advance and derive MI-O3 products (analyses, combined retrievals).

  • LV sensors deliver data => (dx/dz = N/f ~100) consistent with model dynamics and transport; MLS and HIRDLS even with 14-orbits help to characterize transport in thin layers of the extra-tropical UTLS.

  • NV sensors (dx/dz ~1-5) report smoothed profiles. They are still column-based data even with DFS ~3-4 (dz > Hp). Their treatment as the point-wise data may degrade dynamics of “leading” vertical scales in the UTLS analyses (O3-lamination frequencies).

  • Numerics of DAS is important, similar to numerics of transport problem for the MI-O3 analyses.

  • Main message => Don’t blend observational and simulated information that belong to incomparable vertical scales, constrain only scales visible to the instrument, preserving short-scale strictures of chemicals.

Mi o 3 data analysis and simulations
MI-O and HIRDLS retrievals3 data, analysis and simulations

Jan 2006

A priori ?

Ozone data analysis in the utls

Why UTLS ? and HIRDLS retrievals

UTLS: Largest variability and uncertainties of O3

Across the Tropopause: Largest and variable O3 gradients, tendency and fluxes; desires toseparate ozone sub-columns and ozone fluxes;

Goal of Multi-instrumental O3 data => reduce uncertainties and constrain the climate and day-to-day predictions

Several issues for MI-O3 analysis:

1) How to insert and combine O3 data with different spatial resolutions

2) How to design data QC & identify biases

3) Formulate and develop Resolution-dependent data analysis and retrieval schemes:

Ozone data analysis in the UTLS



3-5 km layer

GEOS5 O3-var, %:01/2005-01/2008

Variations of o 3 colums model ctm geos 5 2006 omi 2005 2008
Variations of O and HIRDLS retrievals3-colums (%):Model, CTM, GEOS-5 (2006), OMI (2005-2008)

2005 2008 january monthly zm o 3 in utls geos5 gmao and mls aura
2005-2008: January Monthly-ZM O and HIRDLS retrievals3 in UTLS: GEOS5/GMAO and MLS/Aura

a priori

Mid-troposphere data are needed to evaluate O3-forecasts/analyses (AIRS/TES), and chemical forecast system may need comprehensive troposopheric chemistry scheme (?)

Biases in da and inverse estimation studies example of wavy t biases during winter aug 2001

Attractive feature of sharp HIRDLS vertical sampling against AMSU-channels~5-10 km “smoothed” Jacobian widths;

Analyses schemes: can introduce similar “assimilation” diffusion shown by “ozone”/GEOS5 and GFS assimilation

Analysis of NV T-channels may dump short-wave components of T-forecast diffusing them and degrading vertical transport and dynamics predicted models

Biases in DA and inverse estimation studies /example of wavy T-biases during winter, Aug 2001/

Dee, 2005


STRAT: AMSU-A rad-es


Resolution of o 3 data research and operational
Resolution of O UTLS-2006: MLS & GMI-CTM3-data: research and operational

12-Umkehr Layers

SBUV-2 ozone sub-column layers and

GFS/NOAA-analysis grid

30 lev

Xr-Xa =A(Xt-Xa)

Cr =Ca - ACa

Layer-based or column-based data


Point-wise (or grid-box) data;

A = KW

2-layers in UTLS

> 10 lev

“-” impact

MI Aura O3 Data (OMI/TES/MLS/HIRDLS) – Do we need QC tool ? Property of Resolution Kernels –basis for QC (DFS, symmetry, limits)


Sonde UTLS-2006: MLS & GMI-CTM

No assim


Comparison simulations and assimilation of TES O3 with IONS-06 (Aug) sonde profilesfrom Mark Parrington et al. (2007/2008)



“-” impact



“+” impact




Importance of Tropospheric Chemistry UTLS-2006: MLS & GMI-CTMin Chemistry-Climate Models (WACCM3-example):Tropospheric and UTLS Representation of Ozone in WACCM3 simulations by two chemical mechanisms and comparison with monthly ozone-sonde data

40o N

20o N

Red = 115 Species sim.

Blue = 57 Species sim.

Black = Ozonesonde (Logan, 1999).

Temperature and Nitric Acid Cross-sections support Laminae: 1 April 2006, (Doug Kinnison, Artistic Colors, Apr 10/2006)