Harmonized cloud datasets for the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) using the O 2 –O 2 477 nm absorption band

Abstract

We present a new cloud retrieval algorithm using the O 2 –O 2 absorption band at 477 nm, designed to provide harmonized cloud datasets from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI). The goal of these derived cloud data is to mitigate the influence of clouds on the retrieval of tropospheric trace gases from UV–Visible nadir satellite spectrometers. The retrieval process consists of two main steps. First, spectral fitting is performed using the differential optical absorption spectroscopy (DOAS) method to determine the O 2 –O 2 slant column and calculate the reflectance at the center of the fitting window. Second, these parameters are used to derive cloud fraction and cloud pressure. This retrieval algorithm builds on the OMI O 2 –O 2 operational cloud algorithm (OMCLDO2) with several improvements. The fitting procedure uses a broader fitting window, incorporating the O 2 –O 2 absorption bands at 446 and 477 nm, to more accurately derive O 2 –O 2 slant column densities (SCDs). A de-striping correction is applied to address across-track variability, and an offset correction of −0.08 × 10 43 molec. 2 cm −5 , motivated by radiative transfer simulations, is applied in the TROPOMI retrieval to improve the consistency with OMI. Additionally, a temperature correction factor is included to account for the temperature dependence of both the O 2 –O 2 SCD and the O 2 –O 2 absorption cross-section. Consistent auxiliary data, such as meteorological information and a surface albedo database, are used for both sensors. Due to the inadequate signal-to-noise ratios in the daily solar irradiance measurements by OMI, a fixed annual-averaged irradiance for 2005 is used as a reference for the reflectance spectra in the spectral fittings. To evaluate the performance of our retrieval approach, we compare it with the OMCLDO2 algorithm for both OMI and TROPOMI. The cloud fraction retrievals demonstrate good agreement, whereas the cloud pressure retrievals show a systematic bias, particularly in nearly cloud-free scenes. Our cloud pressure estimates tend to be higher than OMCLDO2 for OMI and lower for TROPOMI. Notably, our approach demonstrates improved consistency in cloud parameters, especially cloud pressure, between the two sensors compared to OMCLDO2. However, a consistent bias of approximately 0.05 in cloud fraction retrievals is observed, primarily attributed to differences in L1b data that show systematic biases between the OMI and TROPOMI reflectances. Applying these cloud corrections to NO 2 retrievals reveals that the average impact of cloud corrections ranges from −6 % to 11 % in polluted regions. Differences in NO 2 air mass factor (AMF) resulting from varying cloud correction methods can exceed 10 %. Importantly, the new correction approach achieves better consistency in NO 2 retrievals between OMI and TROPOMI.