A multilevel downscaling model for enhancing nocturnal aerosol optical depth reanalysis from CAMS over the Beijing-Tianjin-Hebei region, China

Nocturnal aerosol optical depth (AOD) serves a critical indicator for investigating the diurnal
aerosol’s climatic and environmental effects. However, the nocturnal AOD product is lacking due
to that current daytime AOD retrieval algorithms are inapplicable to nighttime. Despite important
contribution of spatiotemporal continuous global reanalysis datasets to producing atmospheric
composition forecasts and analyses, its feasibility for the characterization of nocturnal aerosol
variation over small scales is still a major challenge due to its coarse resolution. In this study, a
multilevel two-stage downscaled (TSD) model by integrating a linear mixed effect (LME) and a
geographic weight regression (GWR) model is proposed to improve the spatial resolution of
Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) nocturnal AOD. The multilevel
downscaled model is referred to as the M_TSD model. The M_TSD model is employed over the
Beijing-Tianjin-Hebei (BTH) region in China for the years from 2018 to 2022. Cross-validation of
the retrieval results versus original CAMSRA data shows good performance of the M_TSD model
with a determination coefficient (R2) of daily nocturnal AOD of 0.9569, root mean squared
prediction error (RMSE) and the relative prediction error (RPE) of 0.0939 and 15.1 %, respec-
tively. The inter-annual average nocturnal AOD indicate significant spatial variation with high
value in southeastern plain and low value in the northwestern mountainous and plateau areas of
BTH. Meanwhile, the nocturnal AOD is subject to seasonal variability. The M_TSD model may
serve as a valuable reference to provide nocturnal AOD data with high spatial resolution for small
scale.