Improvement of OMI ozone profile retrievals in the upper troposphere and lower stratosphere by the use of a tropopause-based ozone profile climatology

Bak, Juseon; Liu, Xueliang; Wei, Jennifer; Pan, Laura L.; Chance, K.; Kim, J. H.

doi:10.5194/amt-6-2239-2013

Cited by 42 publications

(51 citation statements)

References 37 publications

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“…In subsequent validation studies, good agreement was found between OMI SOE ozone profiles and high resolution ozone profiles made by satellite and ozonesonde (Liu et al, 2010b;Wang et al, 2011). The SOE algorithm was shown to capture very well the ozone variability in the extratropical tropopause region through comparison with aircraft and ozonesonde measurements (Pittman et al, 2009;Bak et al, 2013).…”

Section: Introductionmentioning

confidence: 85%

“…TOMS mean biases range from −2.1 % to −1.3 % and SOE mean biases are less than ± 0.4 % over all the given bins except at the lowest total ozone value where the mean bias is ∼ 1 %. Use of the improved tropopause-based ozone profile climatology presented by Bak et al (2013) in the SOE algorithm further reduces the total ozone dependence slightly in both mean biases at low ozone amounts and standard deviations at high ozone amounts (see the red dashed line in Fig. 5e).…”

Section: Total Ozone Column Dependencementioning

confidence: 99%

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Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements

et al. 2015

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Abstract. The accuracy of total ozone computed from the Smithsonian Astrophysical Observatory (SAO) optimal estimation (OE) ozone profile algorithm (SOE) applied to the Ozone Monitoring Instrument (OMI) is assessed through comparisons with ground-based Brewer spectrometer measurements from 2005 to 2008. We also compare the three OMI operational ozone products, derived from the NASA Total Ozone Mapping Spectrometer (TOMS) algorithm, the KNMI (Royal Netherlands Meteorological Institute) differential optical absorption spectroscopy (DOAS) algorithm, and KNMI's Optimal Estimation (KOE) algorithm. The best agreement is observed between SAO and Brewer, with a mean difference of within 1 % at most individual stations. The KNMI OE algorithm systematically overestimates Brewer total ozone by 2 % at low and mid-latitudes and 5 % at high latitudes while the TOMS and DOAS algorithms underestimate it by ∼ 1.65 % on average. Standard deviations of ∼ 1.8 % are calculated for both SOE and TOMS, but DOAS and KOE have higher values of 2.2 % and 2.6 %, respectively. The stability of the SOE algorithm is found to have insignificant dependence on viewing geometry, cloud parameters, or total ozone column. In comparison, the KOEBrewer differences are significantly correlated with solar and viewing zenith angles and show significant deviations depending on cloud parameters and total ozone amount. The TOMS algorithm exhibits similar stability to SOE with respect to viewing geometry and total column ozone, but has stronger cloud parameter dependence. The dependence of DOAS on observational geometry and geophysical conditions is marginal compared to KOE, but is distinct compared to the SOE and TOMS algorithms. Comparisons of all four OMI products with Brewer show no apparent long-term drift, but seasonal features are evident, especially for KOE and TOMS. The substantial differences in the KOE vs. SOE algorithm performance cannot be sufficiently explained by the use of soft calibration (in SOE) and the use of different a priori error covariance matrices; however, other algorithm details cause fitting residuals larger by a factor of 2-3 for KOE.

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Section: Introductionmentioning

confidence: 85%

Section: Total Ozone Column Dependencementioning

confidence: 99%

Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements

et al. 2015

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“…Vertically, the SD typically maximizes in the upper troposphere and lower stratosphere (UTLS) in all latitude bands due to significant ozone variability and a priori uncertainty. Bak et al (2013b) showed that the use of tropopause-based (TB) ozone profile climatology with NCEP Global Forecast System (GFS) daily tropopause pressure can significantly improve the a priori and eventually reduce the retrieval uncertainty. Consequently, the SDs of OMI-sonde differences in the UTLS at middle and high latitudes can be reduced through reducing the retrieval uncertainties in a future version of the algorithm that uses the TB climatology.…”

Section: Comparison Methodologymentioning

confidence: 99%

Validation of 10-year SAO OMI Ozone Profile (PROFOZ) product using ozonesonde observations

et al. 2017

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Abstract. We validate the Ozone Monitoring Instrument (OMI) Ozone Profile (PROFOZ) product from October 2004 through December 2014 retrieved by the Smithsonian Astrophysical Observatory (SAO) algorithm against ozonesonde observations. We also evaluate the effects of OMI row anomaly (RA) on the retrieval by dividing the dataset into before and after the occurrence of serious OMI RA, i.e., pre-RA (2004) and post-RA (2009-2014. The retrieval shows good agreement with ozonesondes in the tropics and midlatitudes and for pressure <∼ 50 hPa in the high latitudes. It demonstrates clear improvement over the a priori down to the lower troposphere in the tropics and down to an average of ∼ 550 (300) hPa at middle (high) latitudes. In the tropics and midlatitudes, the profile mean biases (MBs) are less than 6 %, and the standard deviations (SDs) range from 5 to 10 % for pressure <∼ 50 hPa to less than 18 % (27 %) in the tropics (midlatitudes) for pressure >∼ 50 hPa after applying OMI averaging kernels to ozonesonde data. The MBs of the stratospheric ozone column (SOC, the ozone column from the tropopause pressure to the ozonesonde burst pressure) are within 2 % with SDs of < 5 % and the MBs of the tropospheric ozone column (TOC) are within 6 % with SDs of 15 %. In the high latitudes, the profile MBs are within 10 % with SDs of 5-15 % for pressure <∼ 50 hPa but increase to 30 % with SDs as great as 40 % for pressure >∼ 50 hPa. The SOC MBs increase up to 3 % with SDs as great as 6 % and the TOC SDs increase up to 30 %. The comparison generally degrades at larger solar zenith angles (SZA) due to weaker signals and additional sources of error, leading to worse performance at high latitudes and during the midlatitude winter. Agreement also degrades with increasing cloudiness for pressure >∼ 100 hPa and varies with cross-track position, especially with large MBs and SDs at extreme off-nadir positions. In the tropics and midlatitudes, the post-RA comparison is considerably worse with larger SDs reaching 2 % in the stratosphere and 8 % in the troposphere and up to 6 % in TOC. There are systematic differences that vary with latitude compared to the pre-RA comparison. The retrieval comparison demonstrates good long-term stability during the pre-RA period but exhibits a statistically significant trend of 0.14-0.7 % year −1 for pressure <∼ 80 hPa, 0.7 DU year −1 in SOC, and −0.33 DU year −1 in TOC during the post-RA period. The spatiotemporal variation of retrieval performance suggests the need to improve OMI's radiometric calibration especially during the post-RA period to maintain the long-term stability and reduce the latitude/season/SZA and cross-track dependency of retrieval quality.

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“…The a priori data for ozone are one of the key optimal estimation inputs, because the retrieval so-lution comes mainly from a priori information rather than measurement information where the instrument sensitivity to the true ozone profile is insufficient. The a priori value (X a ) and a priori error covariance (S a ) of ozone is taken from the tropopause-based ozone profile climatology that is optimized to represent the dynamical ozone variability in the upper troposphere and lower stratosphere (Bak et al, 2013b). The measurement vector Y is defined as the logarithm of the earthshine radiances normalized to the daily solar irradiance.…”

Section: Omps Ozone Profile Retrievalsmentioning

confidence: 99%

“…Its successors continued the role of GOME for atmospheric ozone monitoring with SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY; Bovensmann et al, 1999) aboard the Environmental Satellite (ENVISAT), GOME-2s (EUMETSAT, 2006) aboard the MetOp-A and MetOp-B and Ozone Monitoring Instrument (OMI; Levelt et al, 2006) flown on the EOS Aura spacecraft. The high performance of OMI ozone profile retrievals in both stratosphere and troposphere has been demonstrated through extensive validation efforts using ozonesondes, aircraft, satellite data and ground-based total ozone data (Pittman et al, 2009;Liu et al, 2010;Bak et al, 2013bBak et al, , 2015Huang et al, 2017a, b). However, a portion of OMI radiance measurements has been affected by the partial blockage of the instrument's entrance slit, a problem termed the row anomaly, which started in 2007 and grew serious in January 2009 (Schenkeveld, 2017).…”

Section: Introductionmentioning

confidence: 99%

Characterization and correction of OMPS nadir mapper measurements for ozone profile retrievals

et al. 2017

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Abstract. This paper verifies and corrects the Ozone Mapping and Profiler Suite (OMPS) nadir mapper (NM) level 1B v2.0 measurements with the aim of producing accurate ozone profile retrievals using an optimal-estimation-based inversion method to fit measurements in the spectral range 302.5-340 nm. The evaluation of available slit functions demonstrates that preflight-measured slit functions represent OMPS measurements well compared to derived Gaussian slit functions. Our initial OMPS fitting residuals contain significant wavelength and cross-track-dependent biases, resulting in serious cross-track striping errors in the tropospheric ozone retrievals. To eliminate the systematic component of the fitting residuals, we apply "soft calibration" to OMPS radiances. With the soft calibration the amplitude of fitting residuals decreases from ∼ 1 to 0.2 % over low and middle latitudes, and thereby the consistency of tropospheric ozone retrievals between OMPS and the Ozone Monitoring Instrument (OMI) is substantially improved. A common mode correction is also implemented for additional radiometric calibration; it improves retrievals especially at high latitudes where the amplitude of fitting residuals decreases by a factor of ∼ 2. We estimate the noise floor error of OMPS measurements from standard deviations of the fitting residuals. The derived error in the Huggins band (∼ 0.1 %) is twice the OMPS L1B measurement error. OMPS noise floor errors constrain our retrievals better, leading to improving information content of ozone and reducing fitting residuals. The final precision of the fitting residuals is less than 0.1 % in the low and middle latitudes, with ∼ 1 degrees of freedom for signal for the tropospheric ozone, meeting the general requirements for successful tropospheric ozone retrievals.

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Improvement of OMI ozone profile retrievals in the upper troposphere and lower stratosphere by the use of a tropopause-based ozone profile climatology

Cited by 42 publications

References 37 publications

Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements

Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements

Validation of 10-year SAO OMI Ozone Profile (PROFOZ) product using ozonesonde observations

Characterization and correction of OMPS nadir mapper measurements for ozone profile retrievals

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