Precipitable water vapor (PWV) is important for various climate and weather studies, but difficult to monitor under various weather conditions. This study shows that surface-based spectral irradiance combined with deep neural network models can accurately estimate PWV under various atmospheric conditions. Models using global, direct, and diffuse irradiances performed best, while even global-only data gave reliable results.

We developed an advanced algorithm for global retrieval of TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO₂ vertical column densities with much improved consistency. Sensitivity tests demonstrate the complexity and nonlinear interactions of auxiliary parameters in the air mass factor calculation. An improved agreement is found with measurements from a global ground-based instrument network. The scientific retrieval provides a useful source of information for studies combining HCHO and NO₂.

In this study, we evaluated the GEMS glyoxal products by comparing them with TROPOMI and MAX-DOAS measurements. GEMS and TROPOMI VCDs present similar spatial distributions. Monthly variations in GEMS VCDs and TROPOMI and MAX-DOAS VCDs differ in northeastern Asia, which we attributed to a polluted reference spectrum and high NO₂ concentrations. GEMS glyoxal products with unparalleled temporal resolution would enrich our understanding of VOC emissions and diurnal variation.

Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.

Fine particles suspended in the atmosphere are a major form of air pollution and an important public health burden. However, measurements of particulate matter are sparse in space and in places like East Asia monitors are established after regulatory policies to improve pollution have changed. In this paper, we use machine learning to fill in the gaps. We train an algorithm to predict pollution at the surface from the atmosphere’s opacity, then produce high resolution maps of data without gaps.