1. Discrete-wavelength DOAS NO2 slant column retrievals from OMI and TROPOMI
- Author
-
Roland Leigh, Cristina Ruiz Villena, Joshua Vande Hey, Claire E. Parfitt, Jasdeep Anand, and Paul S. Monks
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,Differential optical absorption spectroscopy ,Hyperspectral imaging ,010501 environmental sciences ,01 natural sciences ,13. Climate action ,Diurnal cycle ,Temporal resolution ,Environmental science ,Satellite ,Optical filter ,Image resolution ,Spatial analysis ,0105 earth and related environmental sciences ,Remote sensing - Abstract
The use of satellite NO2 data for air quality studies is increasingly revealing the need for observations with higher spatial and temporal resolution. The study of the NO2 diurnal cycle, global sub-urban-scale observations, and identification of emission point sources are some examples of important applications not possible at the resolution provided by current instruments. One way to achieve increased spatial resolution is to reduce the spectral information needed for the retrieval, allowing both dimensions of conventional 2-D detectors to be used to record spatial information. In this work we investigate the use of 10 discrete wavelengths with the well-established differential optical absorption spectroscopy (DOAS) technique for NO2 slant column density (SCD) retrievals. To test the concept we use a selection of individual OMI and TROPOMI Level 1B swaths from various regions around the world, which contain a mixture of clean and heavily polluted areas. To discretise the data we simulate a set of Gaussian optical filters centred at various key wavelengths of the NO2 absorption cross section. We perform SCD retrievals of the discrete data using a simple implementation of the DOAS algorithm and compare the results with the corresponding Level 2 SCD products, namely QA4ECV for OMI and the operational TROPOMI product. For OMI the overall results from our discrete-wavelength retrieval are in very good agreement with the Level 2 data (mean difference
- Published
- 2020
- Full Text
- View/download PDF