What temporal resolution is required for remote sensing of regional aerosol concentrations using the Himawari-8 geostationary satellite. (1st November 2019)

• Record Type: Journal Article
• Authors: Sowden, M.; Mueller, U.; Blake, D.
• Is Part Of: Atmospheric environment. Volume 216(2019)
• Publication Date: 2019-11-01
• Subjects: Temporal resolution -- Geostationary -- Particulate matter -- Brightness temperature difference -- Himawari-8; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2019.116914
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Few studies have directly addressed the question of what temporal resolution is required for air quality studies using geostationary remote sensing data. If timescales are too large, there is a risk that events affecting air quality may be missed; and if too small, there is a possibility that large data files may be processed frequently, at significant computing cost and potentially without concomitant improvements in the monitoring of air quality. The problem is particularly significant in sparsely populated regional areas such as the Pilbara in Western Australia, where air quality issues arising from a range of events, dispersed over a vast area, increase the risk of environmental health and ecosystems impacts and where the use of conventional monitoring is impractical. This study aimed to establish an optimum temporal sampling interval for air quality studies using geostationary data and determine the impact of different timescales on ground level concentrations. The study was based on an analysis of Himawari-8 satellite data relating to a dust storm within a roll cloud which occurred near Onslow in Western Australia on 8 March 2017. Data from the Himawari-8 satellite were obtained from the Australian Bureau of Meteorology relating to the event and were used to: (1) assess the probability of a satellite overpass coinciding with the event; (2) determine scale factors of different time periods during the event; and (3) undertake an analysis of event duration using remote sensing data. The analysis identified numerous sub-phases of the dust event, each lasting between 30 and 50 min. Data analysis considered all thermal infrared bands and Taylor plot analysis reduced the ten wavelength bands to six independent bands. Principal component analysis of brightness temperature difference between these six bands identified the rate of aerosol compositional change and established that an optimal geostationary sampling frequency of five to 10 min would be required to quantify these temporal changes effectively.