Abstract
Abstract. Using the Vector LInearized Discrete Ordinate Radiative Transfer (VLIDORT) code as the main driver for forward model simulations, a first-of-its-kind data assimilation scheme has been developed for assimilating Ozone Monitoring Instrument (OMI) aerosol index (AI) measurements into the Naval Aerosol Analysis and Predictive System (NAAPS). This study suggests that both root mean square error (RMSE) and absolute errors can be significantly reduced in NAAPS analyses with the use of OMI AI data assimilation when compared to values from NAAPS natural runs. Improvements in model simulations demonstrate the utility of OMI AI data assimilation for aerosol model analysis over cloudy regions and bright surfaces. However, the OMI AI data assimilation alone does not outperform aerosol data assimilation that uses passive-based aerosol optical depth (AOD) products over cloud-free skies and dark surfaces. Further, as AI assimilation requires the deployment of a fully multiple-scatter-aware radiative transfer model in the forward simulations, computational burden is an issue. Nevertheless, the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, and our study shows the potential of direct radiance assimilation at both UV and visible spectrums, possibly coupled with AOD assimilation, for aerosol applications in the future. Additional data streams can be added, including data from the TROPOspheric Monitoring Instrument (TROPOMI), the Ozone Mapping and Profiler Suite (OMPS), and eventually the Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission.
Highlights
Operational chemical transport modeling (CTM) of atmospheric aerosol particles, including simulation of sources and sinks and long-range transport of aerosol events such as biomass burning aerosols from fires and dust outbreaks, is commonplace at global meteorology centers for air quality and visibility forecasts (e.g., Sessions et al, 2015; Lynch et al, 2016)
To complement existing aerosol optical depth (AOD) assimilating systems, we have developed an aerosol index (AI) data assimilation (AI-DA) system that is capable of assimilating Ozone Monitoring Instrument (OMI) AI over bright surfaces and cloudy regions for aerosol analyses and forecasts
The study region was chosen to examine the performance of OMI AI data assimilation over bright surfaces such as the deserts of northern Africa and to study aerosol advection over clouds, in this case smoke off the west coast of southern Africa
Summary
Operational chemical transport modeling (CTM) of atmospheric aerosol particles, including simulation of sources and sinks and long-range transport of aerosol events such as biomass burning aerosols from fires and dust outbreaks, is commonplace at global meteorology centers for air quality and visibility forecasts (e.g., Sessions et al, 2015; Lynch et al, 2016). MODIS and VIIRS provide near-global daily daytime coverage (e.g., Levy et al, 2013; Hsu et al, 2019), and GOES and Himawari are capable of retrieving AOD over North American and East Asian regions at sub-hourly temporal resolution (e.g., Bessho et al, 2016). To date, these traditional passive-based satellite AOD retrievals have been limited to darker surfaces and relatively cloud-free conditions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
