Evaluating the impact of multisensor data assimilation on a global aerosol particle transport model

By evaluating quality-assured Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target (DT), MODIS Deep Blue (DB), Multiangle Imaging Spectroradiometer (MISR), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol products assimilated into the U. S. Navy Aerosol Analysis and Prediction System (NAAPS), the impact of single-sensor and multisensor data assimilation on aerosol optical depth (AOD) analysis and forecast skill is characterized using ground-based Level 2 Aerosol Robotic Network (AERONET) data sets during the 2007 boreal summer (June-August 2007). The single-sensor assimilation experiment suggests that all products tested can improve NAAPS performance on a regional or a global scale. The multisensor assimilation experiment suggests that model improvement is greatest with the combined use of Terra and Aqua MODIS DT products, largely due to data density. Incremental improvements are identified, as a function of data density, over regions such as the Saharan desert when adding MISR and MODIS DB products. The inclusion of CALIOP data is mass-neutral by definition and has an insignificant impact on the NAAPS 00 h analysis. CALIOP assimilation does improve the 48 h forecast from NAAPS due to more accurate 00 h vertical distribution and hence forecasted advection. Root-mean-square errors exceeding 0.1 are found over East Asia and North Africa for both the NAAPS analysis and satellite AOD data, indicating that satellite aerosol products in these two regions need improvement. Similarly, low correlation is found between NAAPS and AERONET over Australia, even with the use of all available satellite aerosol products, suggesting that more detailed examination of some critical regions is necessary.