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).
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 aerosol optical depth (AOD) products over cloud-free skies and dark surfaces.
Nevertheless, the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, and this 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.
Technical Description of Figure(s):
(a)True-color composite Aqua MODIS for July 28, 2007, showing dust plumes transported to the Atlantic Ocean and smoke plumes from central and southern Africa transported to the west coast of South Africa. b) As shown by the OMI AI data, dust plumes from northern Africa are mobilized to the north corner of the west coast of northern Africa. Smoke plumes are also visible in southern Africa and are transported to the west coast and over the Atlantic. Smoke plumes as seen by OMI, are also found over cloudy regions. c) NAAPS AOD natural run map of July 28, 2007. d) Resulting NAAPS AOD after OMI AI data assimilation. e) Simulated AI using data from NAAPS natural run (c). f) Simulated AI using data from (d).
Note the resemblance of the field in f) to the OMI retrieved field in b)
Scientific significance, societal relevance, and relationships to future missions:
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). 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 usefulness of OMI AI data assimilation for aerosol model analysis over cloudy regions and bright surfaces. Since the newly developed modeling system contains the necessary ingredients for assimilation of radiances in the ultraviolet (UV) spectrum, this 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. In addition to OMI, UV data assimilation can also be applied to observations from Ozone Mapping and Profiler Suite (OMPS), TROPOspheric Monitoring Instrument (TROPOMI), and the future Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) mission. These sensors include near UV channels that can detect UV-absorbing aerosol particles, such as black-carbon-laden smoke or iron-bearing dust, over bright surfaces, such as desert, snow- and ice-covered regions, and aerosol plumes above clouds.
Data Availability:
Three datasets are used in this study. These are (i) the OMI level 2 UV aerosol product (OMAERUV; Torres et al., 2007), (ii) the Aerosol Robotic Network (AERONET; Holben et al., 1998) AOD product, and (iii) reanalysis data from the Naval Aerosol Analysis and Prediction System (NAAPS; Lynch et al., 2016), which was the first operational global aerosol mass transport model available to the community. The assimilation system is based on spatial and temporal variations of aerosol particles from NAAPS (Zhang and Reid, 2006; Zhang et al., 2008), and the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT; Spurr, 2006) code is used to construct a forward model for the AI-DA system.
References:
Zhang, J., Spurr, R. J. D., Reid, J. S., Xian, P., Colarco, P. R., Campbell, J. R., Hyer, E. J., and Baker, N. L.: Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum, Geosci. Model Dev., 14, 27–42, https://doi.org/10.5194/gmd-14-27-2021, 2021.
12.2021
