TROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI
<p><span id="page7406"/>Ozone in the troposphere affects humans and ecosystems as a pollutant and as a greenhouse gas. Observing, understanding and modelling this dual role, as well as monitoring effects of international regulations on air quality and climate change, however,...
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Environmental engineering TA170-171 Earthwork. Foundations TA715-787 |
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Environmental engineering TA170-171 Earthwork. Foundations TA715-787 D. Hubert K.-P. Heue K.-P. Heue J.-C. Lambert T. Verhoelst M. Allaart S. Compernolle P. D. Cullis A. Dehn C. Félix B. J. Johnson A. Keppens D. E. Kollonige D. E. Kollonige C. Lerot D. Loyola M. Maata S. Mitro M. Mohamad A. Piters F. Romahn H. B. Selkirk H. B. Selkirk F. R. da Silva R. M. Stauffer R. M. Stauffer A. M. Thompson J. P. Veefkind H. Vömel J. C. Witte C. Zehner TROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI |
| description |
<p><span id="page7406"/>Ozone in the troposphere affects humans and ecosystems as a pollutant and as a
greenhouse gas. Observing, understanding and modelling this dual role, as
well as monitoring effects of international regulations on air quality and
climate change, however, challenge measurement systems to operate at opposite
ends of the spatio-temporal scale ladder. Aboard the ESA/EU Copernicus
Sentinel-5 Precursor (S5P) satellite launched in October 2017, the TROPOspheric
Monitoring Instrument (TROPOMI) aspires to take the next leap forward by
measuring ozone and its precursors at unprecedented horizontal resolution
until at least the mid-2020s. In this work, we assess the quality of
TROPOMI's first release (V01.01.05–08) of tropical tropospheric ozone column (TrOC) data. Derived with the convective cloud differential (CCD) method,
TROPOMI daily TrOC data represent the 3 d moving mean ozone column
between the surface and 270 <span class="inline-formula">hPa</span> under clear-sky conditions gridded at
0.5<span class="inline-formula"><sup>∘</sup></span> latitude by 1<span class="inline-formula"><sup>∘</sup></span> longitude resolution. Comparisons to
almost 2 years of co-located SHADOZ ozonesonde and satellite data (Aura OMI
and MetOp-B GOME-2) conclude to TROPOMI biases between <span class="inline-formula">−</span>0.1 and
<span class="inline-formula">+</span>2.3 <span class="inline-formula">DU</span> (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo><</mo><mo>+</mo><mn mathvariant="normal">13</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="32pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="2e43f0d3e714896b2ec25616412a0c6c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-14-7405-2021-ie00001.svg" width="32pt" height="10pt" src="amt-14-7405-2021-ie00001.png"/></svg:svg></span></span> <span class="inline-formula">%</span>) when averaged over the tropical belt.
The field of the bias is essentially uniform in space (deviations <span class="inline-formula"><1</span> <span class="inline-formula">DU</span>) and stable in time at the 1.5–2.5 <span class="inline-formula">DU</span> level. However,
the record is still fairly short, and continued monitoring will be key to
clarify whether observed patterns and stability persist, alter behaviour or
disappear. Biases are partially due to TROPOMI and the reference data records
themselves, but they can also be linked to systematic effects of the non-perfect co-locations. Random uncertainty due to co-location mismatch
contributes considerably to the 2.6–4.6 <span class="inline-formula">DU</span> (<span class="inline-formula">∼14</span> <span class="inline-formula">%</span>–23 <span class="inline-formula">%</span>)
statistical dispersion observed in the difference time series. We circumvent
part of this problem by employing the triple co-location analysis technique
and infer that TROPOMI single-measurement precision is better than
1.5–2.5 <span class="inline-formula">DU</span> (<span class="inline-formula">∼8</span> <span class="inline-formula">%</span>–13 <span class="inline-formula">%</span>), in line with uncertainty
estimates reported in the data files. Hence, the TROPOMI precision is judged
to be 20 <span class="inline-formula">%</span>–25 <span class="inline-formula">%</span> better than for its predecessors OMI and
GOME-2B, while sampling at 4 times better spatial resolution and almost
2 times better temporal resolution. Using TROPOMI tropospheric ozone columns at
maximal resolution nevertheless requires consideration of correlated errors at
small scales of up to 5 <span class="inline-formula">DU</span> due to the inevitable interplay of
satellite orbit and cloud coverage. Two particular types of sampling error
are investigated, and we suggest how these can be identified or remedied. Our
study confirms that major known geophysical patterns and signals of the
tropical tropospheric ozone field are imprinted in TROPOMI's 2-year data
record. These include the permanent zonal wave-one pattern, the pervasive
annual and semiannual cycles, the high levels of ozone due to biomass burning
around the Atlantic basin, and enhanced convective activity cycles associated
with the Madden–Julian Oscillation over the Indo-Pacific warm pool.
TROPOMI's combination of higher precision and higher resolution reveals details
of these patterns and the processes involved, at considerably smaller spatial
and temporal scales and with more complete coverage than contemporary
satellite sounders. If the accuracy of future TROPOMI data proves to remain
stable with time, these hold great potential to be included in Climate Data
Records, as well as serve as a travelling standard to interconnect the
upcoming constellation of air quality satellites in geostationary and low
Earth orbits.</p> |
| format |
article |
| author |
D. Hubert K.-P. Heue K.-P. Heue J.-C. Lambert T. Verhoelst M. Allaart S. Compernolle P. D. Cullis A. Dehn C. Félix B. J. Johnson A. Keppens D. E. Kollonige D. E. Kollonige C. Lerot D. Loyola M. Maata S. Mitro M. Mohamad A. Piters F. Romahn H. B. Selkirk H. B. Selkirk F. R. da Silva R. M. Stauffer R. M. Stauffer A. M. Thompson J. P. Veefkind H. Vömel J. C. Witte C. Zehner |
| author_facet |
D. Hubert K.-P. Heue K.-P. Heue J.-C. Lambert T. Verhoelst M. Allaart S. Compernolle P. D. Cullis A. Dehn C. Félix B. J. Johnson A. Keppens D. E. Kollonige D. E. Kollonige C. Lerot D. Loyola M. Maata S. Mitro M. Mohamad A. Piters F. Romahn H. B. Selkirk H. B. Selkirk F. R. da Silva R. M. Stauffer R. M. Stauffer A. M. Thompson J. P. Veefkind H. Vömel J. C. Witte C. Zehner |
| author_sort |
D. Hubert |
| title |
TROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI |
| title_short |
TROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI |
| title_full |
TROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI |
| title_fullStr |
TROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI |
| title_full_unstemmed |
TROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI |
| title_sort |
tropomi tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, gome-2b and omi |
| publisher |
Copernicus Publications |
| publishDate |
2021 |
| url |
https://doaj.org/article/8b36420f14684dbbb7cb4fe2cac50185 |
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oai:doaj.org-article:8b36420f14684dbbb7cb4fe2cac501852021-11-30T06:46:28ZTROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI10.5194/amt-14-7405-20211867-13811867-8548https://doaj.org/article/8b36420f14684dbbb7cb4fe2cac501852021-11-01T00:00:00Zhttps://amt.copernicus.org/articles/14/7405/2021/amt-14-7405-2021.pdfhttps://doaj.org/toc/1867-1381https://doaj.org/toc/1867-8548<p><span id="page7406"/>Ozone in the troposphere affects humans and ecosystems as a pollutant and as a greenhouse gas. Observing, understanding and modelling this dual role, as well as monitoring effects of international regulations on air quality and climate change, however, challenge measurement systems to operate at opposite ends of the spatio-temporal scale ladder. Aboard the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aspires to take the next leap forward by measuring ozone and its precursors at unprecedented horizontal resolution until at least the mid-2020s. In this work, we assess the quality of TROPOMI's first release (V01.01.05–08) of tropical tropospheric ozone column (TrOC) data. Derived with the convective cloud differential (CCD) method, TROPOMI daily TrOC data represent the 3 d moving mean ozone column between the surface and 270 <span class="inline-formula">hPa</span> under clear-sky conditions gridded at 0.5<span class="inline-formula"><sup>∘</sup></span> latitude by 1<span class="inline-formula"><sup>∘</sup></span> longitude resolution. Comparisons to almost 2 years of co-located SHADOZ ozonesonde and satellite data (Aura OMI and MetOp-B GOME-2) conclude to TROPOMI biases between <span class="inline-formula">−</span>0.1 and <span class="inline-formula">+</span>2.3 <span class="inline-formula">DU</span> (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M8" display="inline" overflow="scroll" dspmath="mathml"><mrow><mo><</mo><mo>+</mo><mn mathvariant="normal">13</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="32pt" height="10pt" class="svg-formula" dspmath="mathimg" md5hash="2e43f0d3e714896b2ec25616412a0c6c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-14-7405-2021-ie00001.svg" width="32pt" height="10pt" src="amt-14-7405-2021-ie00001.png"/></svg:svg></span></span> <span class="inline-formula">%</span>) when averaged over the tropical belt. The field of the bias is essentially uniform in space (deviations <span class="inline-formula"><1</span> <span class="inline-formula">DU</span>) and stable in time at the 1.5–2.5 <span class="inline-formula">DU</span> level. However, the record is still fairly short, and continued monitoring will be key to clarify whether observed patterns and stability persist, alter behaviour or disappear. Biases are partially due to TROPOMI and the reference data records themselves, but they can also be linked to systematic effects of the non-perfect co-locations. Random uncertainty due to co-location mismatch contributes considerably to the 2.6–4.6 <span class="inline-formula">DU</span> (<span class="inline-formula">∼14</span> <span class="inline-formula">%</span>–23 <span class="inline-formula">%</span>) statistical dispersion observed in the difference time series. We circumvent part of this problem by employing the triple co-location analysis technique and infer that TROPOMI single-measurement precision is better than 1.5–2.5 <span class="inline-formula">DU</span> (<span class="inline-formula">∼8</span> <span class="inline-formula">%</span>–13 <span class="inline-formula">%</span>), in line with uncertainty estimates reported in the data files. Hence, the TROPOMI precision is judged to be 20 <span class="inline-formula">%</span>–25 <span class="inline-formula">%</span> better than for its predecessors OMI and GOME-2B, while sampling at 4 times better spatial resolution and almost 2 times better temporal resolution. Using TROPOMI tropospheric ozone columns at maximal resolution nevertheless requires consideration of correlated errors at small scales of up to 5 <span class="inline-formula">DU</span> due to the inevitable interplay of satellite orbit and cloud coverage. Two particular types of sampling error are investigated, and we suggest how these can be identified or remedied. Our study confirms that major known geophysical patterns and signals of the tropical tropospheric ozone field are imprinted in TROPOMI's 2-year data record. These include the permanent zonal wave-one pattern, the pervasive annual and semiannual cycles, the high levels of ozone due to biomass burning around the Atlantic basin, and enhanced convective activity cycles associated with the Madden–Julian Oscillation over the Indo-Pacific warm pool. TROPOMI's combination of higher precision and higher resolution reveals details of these patterns and the processes involved, at considerably smaller spatial and temporal scales and with more complete coverage than contemporary satellite sounders. If the accuracy of future TROPOMI data proves to remain stable with time, these hold great potential to be included in Climate Data Records, as well as serve as a travelling standard to interconnect the upcoming constellation of air quality satellites in geostationary and low Earth orbits.</p>D. HubertK.-P. HeueK.-P. HeueJ.-C. LambertT. VerhoelstM. AllaartS. CompernolleP. D. CullisA. DehnC. FélixB. J. JohnsonA. KeppensD. E. KollonigeD. E. KollonigeC. LerotD. LoyolaM. MaataS. MitroM. MohamadA. PitersF. RomahnH. B. SelkirkH. B. SelkirkF. R. da SilvaR. M. StaufferR. M. StaufferA. M. ThompsonJ. P. VeefkindH. VömelJ. C. WitteC. ZehnerCopernicus PublicationsarticleEnvironmental engineeringTA170-171Earthwork. FoundationsTA715-787ENAtmospheric Measurement Techniques, Vol 14, Pp 7405-7433 (2021) |