Retrieval of Total Ozone Column Using Differential Optical Absorption Spectroscopy (DOAS) Algorithm from Ultraviolet Solar Radiation Data

Author(s):  
Wan Li ◽  
Yonggang Qian ◽  
Ning Wang ◽  
Kun Li ◽  
Lingling Ma ◽  
...  
2021 ◽  
Vol 13 (11) ◽  
pp. 2098
Author(s):  
Yuanyuan Qian ◽  
Yuhan Luo ◽  
Fuqi Si ◽  
Haijin Zhou ◽  
Taiping Yang ◽  
...  

Global measurements of total ozone are necessary to evaluate ozone hole recovery above Antarctica. The Environmental Trace Gases Monitoring Instrument (EMI) onboard GaoFen 5, launched in May 2018, was developed to measure and monitor the global total ozone column (TOC) and distributions of other trace gases. In this study, some of the first global TOC results of the EMI using the differential optical absorption spectroscopy (DOAS) method and validation with ground-based TOC measurements and data derived from Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) observations are presented. Results show that monthly average EMI TOC data had a similar spatial distribution and a high correlation coefficient (R ≥ 0.99) with both OMI and TROPOMI TOC. Comparisons with ground-based measurements from the World Ozone and Ultraviolet Radiation Data Centre also revealed strong correlations (R > 0.9). Continuous zenith sky measurements from zenith scattered light differential optical absorption spectroscopy instruments in Antarctica were also used for validation (R = 0.9). The EMI-derived observations were able to account for the rapid change in TOC associated with the sudden stratospheric warming event in October 2019; monthly average TOC in October 2019 was 45% higher compared to October 2018. These results indicate that EMI TOC derived using the DOAS method is reliable and has the potential to be used for global TOC monitoring.


2020 ◽  
pp. 13
Author(s):  
P. F. Orte ◽  
E. Luccini ◽  
E. Wolfram ◽  
F. Nollas ◽  
J. Pallotta ◽  
...  

<p>Total ozone column (TOC) measurements through the Ozone Monitoring Instrument (OMI/NASA EOSAura) are compared with ground-based observations made using Dobson and SAOZ instruments for the period 2004–2019 and 2008–02/2020, respectively. The OMI data were inverted using the Differential Optical Absorption Spectroscopy algorithm (overpass OMI-DOAS). The four ground-based sites used for the analysis are located in subpolar and subtropical latitudes spanning from 34°S to 54°S in the Southern Hemisphere, in the Argentine cities of Buenos Aires (34.58°S, 58.36°W; 25 m a.s.l.), Comodoro Rivadavia (45.86°S, 67.50°W; 46 m a.s.l.), Río Gallegos (51.60°S, 69.30°W; 72 m a.s.l.) and Ushuaia (54.80°S, 68.30°W; 14 m a.s.l.). The linear regression analyzes showed correlation values greater than 0.90 for all sites. The OMI measurements revealed an overestimation of less than 4 % with respect to the Dobson instruments, while the comparison with the SAOZ instrument presented a very low underestimation of less than 1 %.</p>


2007 ◽  
Vol 7 (1) ◽  
pp. 69-79 ◽  
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
T. Deutschmann ◽  
M. Grzegorski ◽  
U. Platt

Abstract. A new method for the satellite remote sensing of different types of vegetation and ocean colour is presented. In contrast to existing algorithms relying on the strong change of the reflectivity in the red and near infrared spectral region, our method analyses weak narrow-band (few nm) reflectance structures (i.e. "fingerprint" structures) of vegetation in the red spectral range. It is based on differential optical absorption spectroscopy (DOAS), which is usually applied for the analysis of atmospheric trace gas absorptions. Since the spectra of atmospheric absorption and vegetation reflectance are simultaneously included in the analysis, the effects of atmospheric absorptions are automatically corrected (in contrast to other algorithms). The inclusion of the vegetation spectra also significantly improves the results of the trace gas retrieval. The global maps of the results illustrate the seasonal cycles of different vegetation types. In addition to the vegetation distribution on land, they also show patterns of biological activity in the oceans. Our results indicate that improved sets of vegetation spectra might lead to more accurate and more specific identification of vegetation type in the future.


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