We developed a correction algorithm for improving emission inventory by using MODIS satellite observations. The approach was applied to the customized FEER 1 km emission data. Our initial analysis indicates that biases in these data caused by three satellite fire detection limitations are from (a) the cloud cover; (b) the insensitivity of fire detection at off-nadir; and (c) the gaps in MODIS swaths at the ground in low latitude regions. These biases are corrected in our study to generate a new spatially continuous emission
inventory.
The old and new inventories were applied to WRF-Chem model to simulate smoke loading in the atmosphere. The case study was conducted over NSSA in the high fire month, January 2010. The comparison with MODIS AOD revealed that the new emission lead to an overall improvement in WRF-Chem simulated spatial and temporal
distribution of AOD in the study region. Both the daily and monthly mean simulation values are evaluated in our study. When we conducted the emission correction, the emission amount was increased due to filled emission values in regions with cloud cover, large satellite scan angle, and swath gaps. The effect of emission correction method for improving model performance was also confirmed by another model simulation using
“scale old” emission. The “scale old” emission has increased old emission amounts measured using the daily ratio of the new and old emissions. The daily and monthly comparison shows that just increasing emission amount is not enough to reduce satellite
fire detection biases. Though there is improvement in standard deviation when comparing
“scale old” results with MODIS, there is no enhancement of the correlation between model simulated results and satellite AOD retrievals. The model improvement from the emission correction is also apparent by comparing night time CALIOP-derived AOD at 532 nm with model simulated vertical aerosol loading along CALIPSO track when CALIPSO overpasses the same day’s Aqua large VZA or gap regions.
Reducing uncertainties of biomass burning emission is crucial to the reliability of model simulations of atmospheric aerosol physical properties (Zhang et al. 2014). The case study here only presents a simple approach in improving the emission that has inherent limitations from polar-orbiting satellite-based fire detection algorithm in characterizing fires in cloudy conditions and at the edge of or gap areas between satellite ground swath.
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Tables
Table 1. Comparisons of different smoke emission inventories based on bottom-up method using satellite data
Study Reid et al.
Table 2. Comparisons of different smoke emission inventories based on top-down
IGBP Rather than using
EF and conversion
Swath gap Yes (Previous day’s data)
Cloud cover Yes (Clear sky FRP density stands 𝛼∗ is a conversion factor that links FRP to dry matter combustion rate.
Table 3. Configuration Options Employed by WRF-Chem in This Study Atmospheric Processes Model Options
Shortwave radiation Goddard
Longwave radiation RRTM
Gas-phase mechanism RADM2
Aerosol model MADE/SORGAM
Land surface model Noah
Boundary layer scheme YSU
Microphysics Lin et al.
Cumulus New Grell
Table 4. Filters, thresholds, and data loss in QA for Terra DT (Aqua DT in parentheses)
QA flag SA Cloud fraction Buddy
Thresholds “Very Good” SA<170° 𝐹𝑐𝑙𝑑 <0% Isolated retrieval
Thresholds “Very Good” SA<170° 𝐹𝑐𝑙𝑑 <0% Isolated retrieval