التفاصيل البيبلوغرافية
| العنوان: |
The potential of drone observations to improve air quality predictions by 4D-var. |
| المؤلفون: |
Erraji, Hassnae, Franke, Philipp, Lampert, Astrid, Schuldt, Tobias, Tillmann, Ralf, Wahner, Andreas, Lange, Anne Caroline |
| المصدر: |
EGUsphere; 3/19/2024, p1-31, 31p |
| مصطلحات موضوعية: |
AIR quality, AIR pollutants, AIR pollution measurement, ATMOSPHERIC boundary layer, OZONE layer, AIR pollution, DRONE aircraft |
| مستخلص: |
Vertical profiles of atmospheric pollutants, acquired by unmanned aerial vehicles (UAVs, known as drones), represent a new type of observation that can help to fill the existing observation gap in the planetary boundary layer. In this article, the first study of assimilating air pollutant observations from drones is presented to evaluate the impact on local air quality analysis. The study uses the high-resolution air quality model EURAD-IM (EURopean Air pollution Dispersion – Inverse Model), including the four-dimensional variational data assimilation system (4D-var), to perform the assimilation of ozone (O3) and nitrogen oxide (NO) vertical profiles. 4D-var takes advantage of the inverse technique and allows for simultaneous adjustments of initial values and emissions rates. The drone data was collected during the MesSBAR (Automatisierte luftgestützte Mes sung der S chadstoff B elastung in der erdnahen A tmosphäre in urbanen R äumen / Automated airborne measurement of air pollution levels in the near earth atmosphere in urban areas) field campaign, which was conducted on 22–23 September 2021 in Wesseling, Germany. The two-day analyses reveal that the 4D-var assimilation of high-resolution drone measurements has a beneficial impact on the representation of regional air quality in the model. On both days, a significant improvement in the vertical distribution of O3 and NO is noticed in the analysis compared to the reference simulation without data assimilation. Moreover, the validation against independent observations shows an overall improvement in the bias, root-mean-square error, and correlation for O3, NO, and NO2 (nitrogen dioxide) ground concentrations at the measurement site as well as in the surrounding region. Furthermore, the assimilation allows for the deduction of emission correction factors in the grid cells surrounding the measurement site, which significantly contribute to the observed improvement in the analysis. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
