Aerosol optical thicknesses over North Africa: 1. Development of a product for model validation using Ozone Monitoring Instrument, Multiangle Imaging Spectroradiometer, and Aerosol Robotic Network

摘要

Daily aerosol optical thickness (AOT) at 0.55 μ m over the desert regions is needed as a source of validation for numerical models such as the United Kingdom's Numerical Weather Prediction Unified Model. We examined the relationship between monthly mean ultraviolet (UV) absorbing aerosol index (AI) from the Ozone Monitoring Instrument (OMI) that is available on a daily basis with the Multiangle Imaging Spectroradiometer (MISR) AOT that is available once every nine days over North Africa. We then developed spatiotemporal AI‐AOT relationships on a monthly mean basis that can be used to convert the daily AI to AOT during months when dust concentrations are high (June–August) to compare against months when a mixture of dust and biomass burning aerosols are present (January–March). We further examined the AOT data from the ground to validate our methods and results. While previous studies have examined the Total Ozone Mapping Spectrometer AI with limited ground‐based Sun photometer data, our study extends this to the OMI over 2 years (2005–2006) and for the entire north African region (20°W–40°E and 0–30°N). Our results confirm that the MISR is an excellent sensor for retrieving AOT over desert regions. Comparisons between MISR and Aerosol Robotic Network (AERONET) data over multiple locations indicate that the linear correlation coefficient is 0.89. The AI‐AOT relationship is region specific and is robust over locations where AI and AOT are high during June–August especially when the predominant aerosol is dust. This relationship breaks down closer to the equator when aerosol loading is small especially when biomass‐burning aerosols are prevalent during January–March. Our analysis indicates that the estimated AOT (EAOT) from the AI‐AOT relationship is within 28% of the MISR AOT for optical depths between 0.2 and 2.0 with large uncertainties (75%) for smaller optical depths (<0.2). The EAOT for January–March 2006 is well correlated with the AERONET AOT with a linear correlation coefficient of 0.83 with a relative mean error of 23%. The methods and products developed here can be used as a first proxy for validating model‐derived AOT that is shown by Greed et al. (2008).