A geostatistical approach for producing daily Level-3 MODIS aerosol optical depth analyses

• Published in: Atmospheric environment (1994) Vol. 79; pp. 395 - 405
• Main Authors: Ruiz-Arias, J.A., Dudhia, J., Lara-Fanego, V., Pozo-Vázquez, D.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-11-2013; Elsevier
• Subjects: Aerosol optical depth; Earth, ocean, space; Exact sciences and technology; External geophysics; Level-3 MODIS; Meteorology; Particles and aerosols; Surface solar irradiance; United States; North America; Level-3 MODIS; Aerosol optical depth; Surface solar irradiance; geostatistics; Spectroradiometry; aerosols; Moderate resolution imaging spectroradiometer; Space remote sensing; Satellite observation; solar radiation; North America; irradiance; Error correction; Optical thickness; statistical analysis
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2013.07.002

The daily Level-3 MODIS (dL3M) aerosol optical depth product is a global daily spatial aggregation of the Level-2 MODIS aerosol optical depth (10-km spatial resolution) into a regular grid with a resolution of 1° × 1°. Aerosol optical depth is a seminal parameter for surface solar radiation assessment, in particular, for those applications involving direct irradiance. However, the dL3M AOD is prone to data gaps originated mostly by the unfeasibility of retrieving reliable estimates under cloudy conditions. In addition, its usability is also constrained by regional biases owing to some other reasons. In this work we propose a methodology for bias reduction and data-gaps removal of the dL3M AOD dataset. The result is a database of daily regularly-gridded AOD suitable for use in surface solar radiation applications and large-scale and long-term studies involving AOD without requiring a previous costly data assimilation process involving numerical weather prediction models. The method consists of an empirical approach to bias reduction, data-gaps removal by kriging interpolation and, finally, where reliable ground observations are available, an optimal interpolation procedure. The method was tested in the North American region, where it was able to reduce the initial mean error from 0.067 to 0.001, the root mean square error from 0.130 to 0.057, and increase the squared correlation coefficient from 23% to 58%, as compared against ground measurements.
•Method for data gaps removal and bias correction of daily Level-3 MODIS AOD data.•Data assimilation without costly NWP models, easy to apply and computationally cheap.•For surface solar radiation applications or to build up AOD climatologies.•We evaluate the method in the Continental United States during 2009 and 2010.•We evaluate the potential improvement for assessment of surface direct normal irradiance.