NitroNet – a machine learning model for the prediction of tropospheric NO<sub>2</sub> profiles from TROPOMI observations
<p>We introduce NitroNet, a deep learning model for the prediction of tropospheric <span class="inline-formula">NO<sub>2</sub></span> profiles from satellite column measurements. NitroNet is a neural network trained on synthetic <span class="inline-for...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2024-11-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/17/6485/2024/amt-17-6485-2024.pdf |
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| Summary: | <p>We introduce NitroNet, a deep learning model for the prediction of tropospheric <span class="inline-formula">NO<sub>2</sub></span> profiles from satellite column measurements. NitroNet is a neural network trained on synthetic <span class="inline-formula">NO<sub>2</sub></span> profiles from the regional chemistry and transport model WRF-Chem, which was operated on a European domain for the month of May 2019. This WRF-Chem simulation was constrained by in situ and satellite measurements, which were used to optimize important simulation parameters (e.g. the boundary layer scheme). The NitroNet model receives <span class="inline-formula">NO<sub>2</sub></span> vertical column densities (VCDs) from the TROPOspheric Monitoring Instrument (TROPOMI) and ancillary variables (meteorology, emissions, etc.) as input, from which it reproduces <span class="inline-formula">NO<sub>2</sub></span> concentration profiles. Training of the neural network is conducted on a filtered dataset, meaning that <span class="inline-formula">NO<sub>2</sub></span> profiles showing strong disagreement (<span class="inline-formula">>20</span> %) with colocated TROPOMI column measurements are discarded.</p>
<p>We present a first evaluation of NitroNet over a variety of geographical and temporal domains (Europe, the US West Coast, India, and China) and different seasons. For this purpose, we validate the <span class="inline-formula">NO<sub>2</sub></span> profiles predicted by NitroNet against satellite, in situ, and MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) measurements. The training data were previously validated against the same datasets. During summertime, NitroNet shows small biases and strong correlations with all three datasets: a bias of <span class="inline-formula">+6.7 <i>%</i></span> and <span class="inline-formula"><i>R</i>=0.95</span> for TROPOMI <span class="inline-formula">NO<sub>2</sub></span> VCDs, a bias of <span class="inline-formula">−10.5 <i>%</i></span> and <span class="inline-formula"><i>R</i>=0.75</span> for AirBase surface concentrations, and a bias of <span class="inline-formula">−34.3 <i>%</i></span> to <span class="inline-formula">+99.6 <i>%</i></span> with <span class="inline-formula"><i>R</i>=0.83</span>–<span class="inline-formula">0.99</span> for MAX-DOAS measurements. In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. During wintertime considerable low biases arise because the summertime/late-spring training data are not fully representative of all atmospheric wintertime characteristics (e.g. longer <span class="inline-formula">NO<sub>2</sub></span> lifetimes). Nonetheless, the wintertime performance of NitroNet is surprisingly good and comparable to that of classic regional chemistry and transport models. NitroNet can demonstrably be used outside the geographic and temporal domain of the training data with only slight performance reductions. What makes NitroNet unique when compared to similar existing deep learning models is the inclusion of synthetic model data, which offers important benefits: due to the lack of <span class="inline-formula">NO<sub>2</sub></span> profile measurements, models trained on empirical datasets are limited to the prediction of surface concentrations learned from in situ measurements. NitroNet, however, can predict full tropospheric <span class="inline-formula">NO<sub>2</sub></span> profiles. Furthermore, in situ measurements of <span class="inline-formula">NO<sub>2</sub></span> are known to suffer from biases, often larger than <span class="inline-formula">+20</span> %, due to cross-sensitivities to photooxidants, which other models trained on empirical data inevitably reproduce.</p> |
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| ISSN: | 1867-1381 1867-8548 |