Direct retrieval of NO2 vertical columns from UV-Vis
(390-495 nm) spectral radiances using a neural network
Li, C., Xu, X., Liu, X., Wang, J., Sun, K.,
van Geffen, J., Zhu, Q., Ma, J., Jin, J., Qin, K., He, Q., Xie, P.,
Ren, B. and Cohen, R. C.: 2022,
J. Remote Sensing 2022, article ID 9817134, 17~pp.
Abstract
Satellite retrievals of columnar nitrogen dioxide (NO2) are essential for
the characterization of nitrogen oxides (NOx) processes and impacts. The
requirements of modeled a priori profiles present an outstanding bottleneck
in operational satellite NO2 retrievals. In this work, we instead use a
neural network (NN) trained from over 360,000 radiative transfer (RT)
simulations to translate TROPOMI radiances across 390-495 nm to total NO2
vertical column (NO2C). Despite the wide variability of the many input
parameters in the RT simulations, only a small number of key variables were
found essential to the accurate prediction of NO2C, including observing
angles, surface reflectivity and altitude, and several key principal
component scores of the radiances. In addition to the NO2C, the NN training
and cross validation experiments show the wider retrieval window allows some
information about the vertical distribution to be retrieved.
Applying the trained NN model to four months of TROPOMI data, the retrieved
NO2C shows strong ability to reproduce the ground-based NO2C of the Pandonia
Global Network. The R^2 (0.75) and normalized mean bias (NMB, -33%) are
competitive with the level 2 operational TROPOMI product (R^2=0.81,
NMB=-36%) over clear (geometric cloud fraction (geometric cloud fraction
< 0.2) and polluted (NO2C ≥ 7.5 x 10^15 molecules/cm^2) regions.
The NN retrieval approach is ~12 times faster than predictions using high
spatial resolution (~3 km) a priori profiles, which is especially attractive
to the handling of large-volume geostationary satellite data.
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created: 11 January 2021
last modified: 9 May 2022