High spectral resolution ozone absorption cross-sections – Part 2: Temperature dependence

Serdyuchenko, Anna; Gorshelev, Victor; Weber, Mark; Chehade, W.; Burrows, J. P.

doi:10.5194/amt-7-625-2014

Cited by 367 publications

(289 citation statements)

References 25 publications

Supporting

Mentioning

282

Contrasting

Unclassified

Order By: Relevance

“…In 2016, the IO 3 C 10 recommended replacing the Bass and Paur (1985) ozone cross-sections with those of Gorshelev et al (2014) and Serdyuchenko et al (2014), partly because use of the latter improved total ozone agreement between Dobson and Brewer instruments (Redondas et al, 2014). Koukouli et al (2016) showed, in addition, the importance of correcting effective temperature errors in the Dobson spectrophotometers.…”

Section: Limiting the Range Of Uncertainties In Ozone Absorption Crosmentioning

confidence: 99%

The Network for the Detection of Atmospheric Composition Change (NDACC): History, status and perspectives

Mazière¹,

Thompson²,

Kurylo³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract. The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 80 stations making high quality measurements of atmospheric composition that began official operations in 1991 after five years of planning. Originally named the Network for the Detection of Stratospheric Change (NDSC), the goal of NDACC is to observe changes in the chemical and physical state of the stratosphere and upper troposphere and to 25 assess the impact of such changes on the lower troposphere and climate. NDACC's origins, station locations, organizational structure and data archiving are described. NDACC is structured around categories of ground-based observational techniques, timely cross-cutting themes (ozone, water vapour, measurement strategies and emphases), satellite measurement systems, and theory and analyses. To widen its scope, NDACC has established formal collaborative agreements with eight other Cooperating Networks. A brief history is provided, major accomplishments of NDACC during its first 25 years of 30 operation are reviewed, and a forward-looking perspective is presented.1 Atmos. Chem. Phys. Discuss., https://doi

show abstract

Section: Limiting the Range Of Uncertainties In Ozone Absorption Crosmentioning

confidence: 99%

The Network for the Detection of Atmospheric Composition Change (NDACC): History, status and perspectives

Mazière¹,

Thompson²,

Kurylo³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…(10) has to be modified to account for (a) the partitioning between NO and NO 2 and (b) chemical loss. (Seinfeld and Pandis, 2006). Therefore, the factor c L = 1.32 is introduced, which scales the measured NO 2 to NO x .…”

Section: Comparison To Mobile Car-doas Measurementsmentioning

confidence: 99%

High-resolution airborne imaging DOAS measurements of NO<sub>2</sub> above Bucharest during AROMAT

Meier¹,

Schönhardt²,

Bösch³

et al. 2017

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

Abstract. In this study we report on airborne imaging DOAS measurements of NO 2 from two flights performed in Bucharest during the AROMAT campaign (Airborne ROmanian Measurements of Aerosols and Trace gases) in September 2014. These measurements were performed with the Airborne imaging Differential Optical Absorption Spectroscopy (DOAS) instrument for Measurements of Atmospheric Pollution (AirMAP) and provide nearly gapless maps of column densities of NO 2 below the aircraft with a high spatial resolution of better than 100 m. The air mass factors, which are needed to convert the measured differential slant column densities (dSCDs) to vertical column densities (VCDs), have a strong dependence on the surface reflectance, which has to be accounted for in the retrieval. This is especially important for measurements above urban areas, where the surface properties vary strongly. As the instrument is not radiometrically calibrated, we have developed a method to derive the surface reflectance from intensities measured by AirMAP. This method is based on radiative transfer calculation with SCIATRAN and a reference area for which the surface reflectance is known. While surface properties are clearly apparent in the NO 2 dSCD results, this effect is successfully corrected for in the VCD results. Furthermore, we investigate the influence of aerosols on the retrieval for a variety of aerosol profiles that were measured in the context of the AROMAT campaigns. The results of two research flights are presented, which reveal distinct horizontal distribution patterns and strong spatial gradients of NO 2 across the city. Pollution levels range from background values in the outskirts located upwind of the city to about 4 × 10 16 molec cm −2 in the polluted city center. Validation against two co-located mobile car-DOAS measurements yields good agreement between the datasets, with correlation coefficients of R = 0.94 and R = 0.85, respectively. Estimations on the NO x emission rate of Bucharest for the two flights yield emission rates of 15.1 ± 9.4 and 13.6 ± 8.4 mol s −1 , respectively.

show abstract

“…Both OMTO3 and OMDOAO3 are retrieved for individual UV-2 pixels. The KOE data used in this study were processed with v 1.1.0 before 2 January 2006 and with v 1.1.1 since then (van Oss et al, 2001;Kroon et al, 2011). The KOE product is retrieved for one out of five UV-1 pixels along-track (i.e., retrieves for one UV1 pixel, then skips four pixels) (http://disc.sci.gsfc.nasa.gov/Aura/data-holings/ OMI/documents/v003/OMO3PRO_README.html).…”

Section: Ozone Monitoring Instrument (Omi) and Omi Ozone Algorithmsmentioning

confidence: 99%

“…These were both developed at the Royal Netherlands Meteorological Institute (KNMI). One is based on differential optical absorption spectroscopy (DOAS) (Veefkind et al, 2006) and the other on the optimal estimation (OE) inversion technique (KNMI OE, KOE) (van Oss et al, 2001;Kroon et al, 2011). The variety of OMI operational ozone data products offers a good opportunity to compare the total ozone retrieval performance among the different algorithms and to identify their strengths and shortcomings.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

View full text Add to dashboard Cite

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.

show abstract

High spectral resolution ozone absorption cross-sections – Part 2: Temperature dependence

Cited by 367 publications

References 25 publications

The Network for the Detection of Atmospheric Composition Change (NDACC): History, status and perspectives

The Network for the Detection of Atmospheric Composition Change (NDACC): History, status and perspectives

High-resolution airborne imaging DOAS measurements of NO<sub>2</sub> above Bucharest during AROMAT

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

Contact Info

Product

Resources

About

High spectral resolution ozone absorption cross-sections – Part 2: Temperature dependence

Cited by 367 publications

References 25 publications

The Network for the Detection of Atmospheric Composition Change (NDACC): History, status and perspectives

The Network for the Detection of Atmospheric Composition Change (NDACC): History, status and perspectives

High-resolution airborne imaging DOAS measurements of NO&lt;sub&gt;2&lt;/sub&gt; above Bucharest during AROMAT

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

Contact Info

Product

Resources

About

High-resolution airborne imaging DOAS measurements of NO<sub>2</sub> above Bucharest during AROMAT