Physical Sciences and Mathematics Commons^™

Development Of An Infrared Pollution Index To Identify Ground-Level Compositional, Particle Size, And Humidity Changes Using Himawari-8, M. Sowden, D. Blake, D. Cohen, A. Atanacio, Ute Mueller

Research outputs 2014 to 2021

Speciated air quality data informs health studies and quantitates impacts. However, monitoring is concentrated around populated regions whilst, large remote and rural regions remain unmonitored despite risks of dust-storms or wild-fires. Sub-hourly, infrared, geostationary data, such as the 10-min data from Himawari 8, could potentially be used to quantify regional air quality continually. Monitoring of Aerosol Optical Depth (AOD) is restricted to visible spectra (i.e. daytime only), while newer quantification methods using geostationary infrared (IR) data have focused on detecting the presence, or absence, of an event. Limited attention has been given to the determination of particle size and aerosol …

Which Dual-Band Infrared Indices Are Optimum For Identifying Aerosol Compositional Change Using Himawari-8 Data?, Miles Sowden, D Blake

Research outputs 2014 to 2021

Aerosol optical depth algorithms predominantly use the visible portion of the electromagnetic spectrum. However, quantifying sporadic dust events throughout the full 24-h period requires using continuous wavelengths such as infrared (IR). Identifying aerosols, using IR from geostationary data, has relied on subtraction indices rather than normalised differences. Limited attention has been given to determining which IR indices could be suitable for identifying aerosol compositional change. Suitable IR indices could potentially result in multi-spectral data from geostationary satellites, such as Himawari, being used to separate dust from other types of aerosols.

This study evaluated three index types: subtraction (brightness temperature difference …