validation of sciamachy total ozone: esa/dlr v5(w) and iup wfdoas v2(w) m. weber, s. dikty, j....
Post on 13-Dec-2015
213 Views
Preview:
TRANSCRIPT
Validation of SCIAMACHY total ozone: ESA/DLR V5(W) and IUP WFDOAS V2(W)
M. Weber, S. Dikty, J. P.Burrows, M. Coldewey-Egbers(1),
V. E. Fioletov(2), S. M. Frith(3), and D. Loyola(1)
Contact: weber@uni-bremen.de(1) DLR Oberpfaffenhofen(2) Environment Canada(3) NASA GSFC
SQWG Meeting, Bremen, Germany, 13-14 June 2013
The datasets
• ESA/DLR V5(W)• WFDOAS V2m(W)
– with V7 L1 m-factor• WFDOAS V2(W)
– without V7 L1 m-factor
Correlative datasets
• WOUDC database (brewer/dobson/filter)– monthly mean zonal mean
data (Fioletov et al. 2002)– Daily station averages
(collocated data)
• SBUV merged data V8.6– Monthly mean zonal mean
data (Frith et al., 2012)
Bias and drifts of SCIA WFDOAS wrt GOME
Drift (%/decade) Bias (% in 2002)
w/o m-factors
with m-factors
Bias and drifts of SCIA WFDOAS wrt GOME
• GOME stable over a 16 year period
• m-factors (Bramstedt et al., 2009 mainly reduces the drifts at low latitudes, little changes above 50°
• however, the drift and bias pattern looks a bit more complicated (e.g. some seasonal effects)
Drift (%/decade) Bias (% in 2002)
with m-factors
Zonal mean comparisons with WOUDC
• ESA/DLR higher than WFDOAS (~1.5%), but both in very good agreement with WOUDC (within ~1-2%, ~3-6 DU)
• Small (negative) drift evident in ESA/DLR and WFDOAS with m-factor wrt to WOUDC
• no systematic drifts between ESA and WFDm
Zonal mean comparisons with SBUV V8.6
• Very good agreement with SBUV merged for both WFD V2m and ESA V5 (within 2%)
• at polar latitudes (high SZA) negative biases in ESA/DLR
• gradient in the bias between SCIA and SBUV from tropics to high latitudes (bias decreases)
• weak positive drift with time in the tropics
Collocation with ground data
• Collocation criteria: – 300 km– distance weighted SCIA averages (within
collocation radius)
• Separate comparison with dobsons and brewers
– Seasonal cycle in differences to Dobson generally larger than to brewers
– constant T in ground retrievals– temperature sensitivity lower in brewers
Example: comparison with Brewer at Hohenpeissenberg, Germany (47°N)
ESA/DLR
WFD(m)
WFD
Dependence by SZA
• x
WFDm-brewer WFDm-dobson
ESA-brewer ESA-dobson
• Little SZA dependence• SZA dependence in Dobson comparison related to seasonal variations (T issues)
Combined ozone and SZA dependency: ESA V5
• Low illumination conditions: high ozone and/or high SZA: – Bias to ground increases (straylight issues with both ground and satellite
data)• Special conditions: ozone hole conditions (very low ozone):
– Ground data tend to underestimate by up to 4% (Bernhard et al., 2005)
Combined ozone and SZA dependency: WFDOAS V2
• Low illumination conditions: high ozone and high SZA• Specual conditions: ozone hole conditions (very low ozone
Summary & Conclusion
• Very good agreement between SCIAMACHY (ESA & IUP) and WOUDC & SBUV merged (mostly within 1%)
• Some issues with ESA/DLR at polar latitudes (low bias)
• Small differences in bias and seasonal patterns (ESA/DLR, WFDOAS) in differences to SBUV and WOUDC are the result of slightly differing settings (different scalings of Bogumil cross-sections, choice of ozone profile climatology, different algorithm approach, and so on)
• The m-factor approach for L1 V7 successfully removes the drift in SCIAMACHY total ozone data (still some issues in the first year of the data record)
• WFDOAS V2 with m-factor agrees better than ESA V5, with the new GTO merged dataset (based upon GODFIT, Lerot et al. 2014, Chiou et al., 2013)
• RECOMMENDATION: GODFIT as the future ESA V6 will be an improvement over SGP 5 (see also Lerot et al. 2014)
DOAS total ozone retrieval and ozone temperature
• DOAS satellite retrievals (OMI, GOMEs, SCIAMACHY)
– 325-335 nm (WFDOAS: 326.6-334.5 nm)
• U Bremen retrieval: Weighting function DOAS (Coldewey-Egbers et al., 2005, Weber et al., 2005, Lee et al., 2008)
– scalar temperature shift in the a-priori temperature profile
– effective ozone temperature TO3
• Both total ozone and temperature depend on ozone cross-section choice
Radiation transfer modelColdewey-Egbers et al., 2005
Weighting function DOAS
retrieved total ozone
retrievedozone temperature
Ozone and temperature terms in WFDOAS equation
– Anti-correlation between ozone and ozone temperature term
– Depending on fitting window size and position correlation ranges between r = -0.4 and -0.6
Coldewey-Egbers et al., 2005
GOME
WFDOAS total ozone data sets & cross-section used
• WFDOAS applied to GOME (1995-2011), SCIAMACHY (2002-2012), and GOME-2 (since 2006)– GOME1/ERS : Burrows et al. 1999 (GOME FM), shift: +0.017 nm– SCIAMACHY/ENVISAT: Bogumil et al., 2003 (SCIA FM), scaled 5.3%, shift: +0.008 nm– GOME2/METOP A: Burrows et al., 1999, convolved, shift: +0.017nm
• agreement to within 1% with WOUDC brewer and dobsons• Nevertheless: use of a single cross-section data for all instruments are needed to better understand calibration differences between instruments
merged WFDOAS data record (Weber et al. 2011, 2012 )
top related