Machine Learning for Efficient Prediction of High Spatial Resolution NO2 a Priori Profiles
Abstract
Nitrogen dioxide (NO2) is a toxic air pollutant involved in the formation of surface-level ozone and particulate matter. Tropospheric NO2 can be measured from space using satellite-based UV/VIS spectrometers (e.g., OMI, TROPOMI, TEMPO). However, because the sensitivity of space-based spectroscopic instruments varies with altitude, accurate high-resolution NO2 retrievals require high-resolution a priori vertical profiles. A priori profiles simulated using chemical transport models (CTM, e.g., WRF-Chem), which includes both a full chemistry mechanism and meteorological fields, are computationally expensive and time consuming, preventing routine development of profiles at the space and time resolution of the current generation of satellite instruments. We propose an efficient alternative that would use as inputs a meteorological forecast or analysis and a high resolution emission inventory to produce profiles at the native resolution of the measurements. Our method uses a random forest learning model trained on a long record of 12km profiles calculated with WRF- CHEM to predict a priori NO2 vertical profiles. We describe the accuracy of the predicted profiles and associated air mass factors and compare the computational expense of the two approaches.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMA027...05Z
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0345 Pollution: urban and regional;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0365 Troposphere: composition and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES