An improved TROPOMI tropospheric NO<sub>2</sub> research product over Europe
<p>Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km <span class="inline-formula">×</span> 3....
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Copernicus Publications
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/25c54614be3d4a66bf7603d6aac8a46e |
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| Sumario: | <p>Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km <span class="inline-formula">×</span> 3.5 km (7 km <span class="inline-formula">×</span> 3.5 km before 6 August 2019). The DLR nitrogen dioxide (<span class="inline-formula">NO<sub>2</sub></span>) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved DLR tropospheric <span class="inline-formula">NO<sub>2</sub></span> retrieval algorithm from TROPOMI measurements over Europe is presented.
The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric <span class="inline-formula">NO<sub>2</sub></span> on the viewing geometry by up to <span class="inline-formula">2×10<sup>14</sup></span> molec./cm<span class="inline-formula"><sup>2</sup></span>. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is <span class="inline-formula">3.5×10<sup>14</sup></span> molec./cm<span class="inline-formula"><sup>2</sup></span> in the case of significant tropospheric sources. Applied to actual measurements, the smooth variation of stratospheric <span class="inline-formula">NO<sub>2</sub></span> at low latitudes is conserved, and stronger stratospheric variation at higher latitudes is captured.</p>
<p><span id="page7298"/>For AMF calculation, the climatological surface albedo data are replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori <span class="inline-formula">NO<sub>2</sub></span> profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a Clouds-As-Layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the Clouds-As-Reflecting-Boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors.</p>
<p>For the error analysis, the tropospheric AMF uncertainty, which is the largest source of <span class="inline-formula">NO<sub>2</sub></span> uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric <span class="inline-formula">NO<sub>2</sub></span> column in the 30 %–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the new DLR tropospheric <span class="inline-formula">NO<sub>2</sub></span> data show good correlations for nine European urban/suburban stations, with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from <span class="inline-formula">−</span>55.3 % to <span class="inline-formula">−</span>34.7 % on average in comparison with the DLR reference retrieval. When the satellite averaging kernels are used to remove the contribution of a priori profile shape, the relative difference decreases further to <span class="inline-formula">∼</span> <span class="inline-formula">−</span>20 %.</p> |
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