Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S.

Abstract

Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, including the Moderate-resolution Imaging Spectroradiometers (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product collections C6.0 and C6.1, and the NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) AOD from the NOAA Enterprise Processing System (EPS) algorithm. Compared with the Aerosol Robotic Network (AERONET) data, all three products show strong linear correlations with MAIAC C6.1 and VIIRS presenting overall low bias (<0.06). The accuracy of MAIAC C6.1 is found to be substantially improved with respect to MAIAC C6.0 that drastically underestimated AOD over thick smoke, which validates the effectiveness of updates made in MAIAC C6.1 in terms of an improved representation of smoke aerosol optical properties. VIIRS AOD exhibits comparable uncertainty with MAIAC C6.1 with a slight tendency of increased positive bias over the AERONET AOD range of 0.5–3.0. Averaging coincident retrievals from MAIAC C6.1 and VIIRS provides a lower root mean square error and higher correlation than for the individual products, motivating the benefit of blending these datasets. MAIAC C6.1 and VIIRS are further compared to provide insights on their retrieval strategy. When gridded at 0.1° resolution, MAIAC C6.1 and VIIRS provide similar monthly AOD distribution patterns and the latter exhibits a slightly higher domain average. On daily scale, over thick plumes near fire sources, MAIAC C6.1 reports more valid retrievals where VIIRS tends to have retrievals designated as low or medium quality, which tends to be due to internal quality checks. Over transported smoke near scattered clouds, VIIRS provides better retrieval coverage than MAIAC C6.1 owing to its higher spatial resolution, pixel-level processing, and less strict cloud masking. These results can be used as a guide for applications of satellite AOD retrievals during wildfire events and provide insights on future improvement of retrieval algorithms under heavy smoke conditions.

Estimating Fire Radiative Power Using Weather Radar Products for Wildfires

“…Finding this model has improved skill compared to simulations using satellite FRP as emission driver will support future use of this approach for extreme fire events. This work would be facilitated by the fact that multiple metrics for model evaluation of smoke predictions have been established using ground‐based, airborne, and satellite observations (Ye et al., 2021), and because satellite smoke retrievals have been found to be accurate during periods of extreme fires as the ones studied here (Ye et al., 2022).…”

Sensitivity of Burned Area and Fire Radiative Power Predictions to Containment Efforts, Fuel Density, and Fuel Moisture Using WRF‐Fire

“…HRRR-Smoke (Ahmadov et al, 2017) and NCAR WRF-CHEM (Kumar et al, 2021) are smoke forecasting models which have fixed diurnal cycles that we compare WRF-Fire diurnal activity to. The diurnal cycles are normalized by the magnitude of the sum of FRP values (Ye et al, 2019(Ye et al, , 2021.…”

Ephemeral prediction of aerosol optical depth associated with precipitation: A data‐driven approach

“…This is a relatively recent work in this direction. VIIRS has less study in the arena of wildfire, AOD (Andrew M Sayer et al, 2011; Ye et al, 2022). Work with VIIRS and the implementation of machine learning for future predictions is sparse.…”