Exploring aerosol data from DSCOVR EPIC Level 2 Aerosol Version 3

[ASDC - igarcias]

We investigate the time lag between wildfire smoke detected by satellite scans and the corresponding readings from ground-based sensors. This analysis is crucial for understanding the behavior and transport of wildfire smoke plumes as they descend from the atmosphere to ground level, impacting air quality. We utilize satellite-derived Aerosol Index (AI) data, which indicates the presence of aerosols in the atmosphere, and ground-based Particulate Matter (PM) measurements, particularly PM2.5, to quantify smoke concentrations at the surface. To analyze the time lag, we first normalize the AI units from satellite data and PM2.5 units from the ground sensors to create comparable time series. By shifting the ground sensor data over a range of time intervals, we aim to identify the optimal lag where the ground-based PM2.5 readings align with peaks in the satellite-derived AI values. This approach allows us to estimate the delay between smoke detection in the upper atmosphere and its eventual impact on air quality at the surface. Various statistical methods, such as cross-correlation, are employed to quantify this time lag and improve the predictive understanding of smoke dispersion patterns.

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