EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Extending and Improving JEDI-based Global Aerosol Data Assimilation System for UFS-Aerosols

Bo Huang1, Mariusz Pagowski1, Cory Martin2, Andrew Tangborn3, Maryam Abdi-Oskouei4, Jérôme E. Barré4, Shobha Kondragunta5, Georg Grell6, and Gregory Frost7
Bo Huang et al.
  • 1University of Colorado Boulder and NOAA/OAR/GSL

A global aerosol data assimilation (DA) system based on the ensemble-variational (EnVar) application in the Joint Efforts for Data assimilation Integration (JEDI) was recently developed for the Global Ensemble Forecast System - Aerosols (GEFS-Aerosols) in operations at NOAA/NWS/NCEP. The aerosol optical depth (AOD) retrievals at 550 nm are assimilated to improve the GEFS-Aerosols initial conditions and its subsequent forecasts. To account for aerosol emission uncertainty in the ensemble forecasts and thus enhance AOD assimilation, a stochastically-perturbed emission (SPE) approach was implemented in the Common Community Physics Package (CCPP)-based GEFS-Aerosols. The performance of this JEDI-based EnVar aerosol DA system has been evaluated using the CCPP-based GEFS-Aerosols in the near-real time (NRT) experiments at NOAA/OAR/GSL and the global aerosol reanalysis products that  assimilate 550 nm AOD retrievals from the the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, respectively. The NRT experiment results are displayed on the GSL website ( Both the NRT experiment results and global aerosol reanalyses demonstrate that compared to the six-hour forecasts without AOD assimilation, the analyses and subsequent six-hour forecasts resulting from AOD assimilation show significantly improved agreement with AOD retrievals from VIIRS, MODIS and the Aerosol Robotic NETwork (AERONET), and AOD analyses/reanalyses from NASA and ECMWF. Although AOD retrievals, due to their column-integral nature, provide limited information regarding aerosol compositions and vertical profiles, AOD assimilation in our experiments generally contributes to improved aerosol analyses and forecasts verified against those from NASA and ECMWF.  

One of the ongoing Unified Forecast System (UFS)-Research to Operations (R2O) efforts aims to integrate and improve aerosol prediction within UFS (hereafter referred to as UFS-Aerosols). UFS-Aerosols will eventually replace the standalone GEFS-Aerosols for operations at NOAA/NWS/NCEP. Compared to GEFS-Aerosols, UFS-Aerosols is coupled with NASA's second-generation Goddard Chemistry Aerosol Radiation and Transport (GOCART) model including additional nitrate aerosol species, adopts improved biomass burning and dust emissions, and allows for aerosol-radiation interactions. Motivated by the promising results of assimilating AOD for GEFS-Aerosols and to advance aerosol assimilation and prediction in UFS, we are extending and improving this JEDI-based EnVar aerosol DA system for UFS-Aerosols. It requires further development of AOD forward operator, its tangent linear and adjoint models in JEDI’s Unified Forward Operator (UFO) to accommodate additional nitrate aerosol species in UFS-Aerosols. Enhancements to this DA system for UFS-Aerosols include implementing SPE within UFS-Aerosols to improve background ensemble and implementing assimilation of log-transformed AOD within JEDI to better satisfy the Gaussian assumptions in the DA update. To evaluate these new developments for UFS-Aerosols, cycled DA experiments will be performed to assimilate 550 nm AOD retrievals from VIIRS instruments on board NOAA’s satellites, and verified against various aerosol observations and reanalyses. Results will be presented.

How to cite: Huang, B., Pagowski, M., Martin, C., Tangborn, A., Abdi-Oskouei, M., E. Barré, J., Kondragunta, S., Grell, G., and Frost, G.: Extending and Improving JEDI-based Global Aerosol Data Assimilation System for UFS-Aerosols, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17426,, 2023.