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http://dx.doi.org/10.7780/kjrs.2014.30.4.1

Validation of OMI HCHO with EOF and SVD over Tropical Africa  

Kim, J.H. (Department of Atmospheric Science, Pusan National University)
Baek, K.H. (Department of Atmospheric Science, Pusan National University)
Kim, S.M. (Global Environment System Research Lab., National Institute of Meteorological Research)
Publication Information
Korean Journal of Remote Sensing / v.30, no.4, 2014 , pp. 417-430 More about this Journal
Abstract
We have found an error in the operational OMI HCHO columns, and corrected it by applying a background parameterization derived on a 4th order polynomial fit to the time series of monthly average OMI HCHO data. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. A new scientific approach, statistical analyses with EOF and SVD, was adapted to reanalyze the consistency of the corrected OMI HCHO with other satellite measurements of HCHO, CO, $NO_2$, and fire counts over Africa. The EOF and SVD analyses with MOPITT CO, OMI $NO_2$, SCIAMAHCY, and OMI HCHO show the overall spatial and temporal pattern consistent with those of biomass burning over these regions. However, some discrepancies were observed from OMI HCHO over northern equatorial Africa during the northern biomass burning seasons: The maximum HCHO was found further downwind from where maximum fire counts occur and the minimum was found in January when biomass burning is strongest. The statistical analysis revealed that the influence of biogenic activity on HCHO wasn't strong enough to cause the discrepancies, but it is caused by the error in OMI HCHO from using the wrong Air Mass Factor (AMF) associated with biomass burning aerosol. If the error is properly taken into consideration, the biomass burning is the strongest source of HCHO seasonality over the regions. This study suggested that the statistical tools are a very efficient method for evaluating satellite data.
Keywords
EOF; SVD; HCHO; biomass burning;
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