Ponente
Descripción
A near-real-time (NRT) global aerosol data assimilation (DA) and forecast system has been developed to produce global aerosol analyses and forecasts at NOAA/OAR/GSL. The aerosol DA is performed using the Joint Effort for Data assimilation Integration (JEDI) led by the Joint Center for Satellite Data Assimilation (JCSDA). The GSL’s Common Community Physics Package (CCPP) version of Global Ensemble Forecast System - Aerosols (GEFS-Aerosols) is used to produce global aerosol forecasts. This aerosol forecast model relies on the Finite-Volume Cubed-Sphere (FV3) dynamical core and the Global Forecast System (GFS) physics. The aerosol parameterization is based on the Goddard Chemistry Aerosol Radiance and Transport (GOCART) model. Daily Blended Global Biomass Burning Emissions Product (GBBEPx) and Fire Radiative Product (FRP) are used for wildfire emissions. In the DA that uses JEDI-based applications, the 3D ensemble-variational (3DEnVar) approach is used to update our deterministic analysis and the local ensemble kalman filter (LETKF) is used to update the ensemble members. It assimilates aerosol optical depth (AOD) retrievals at 550 nm derived from the Visible/Infrared Imager Radiometer Suite (VIIRS) instrument. AOD forward operator is calculated using NASA aerosol scattering lookup tables. To reduce modeled AOD bias and increase aerosol ensemble spread, a parameterization of scaling and stochastically perturbing emissions was developed and implemented in the GSL’s CCPP version of GEFS-Aerosols model. Our recent cycled NRT experiments show that assimilation of VIIRS AOD retrievals at 550 nm efficiently reduces bias and error of simulated AOD compared to those from the Aerosol Robotic Network (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. Furthermore, our aerosol analyses demonstrate comparable skills to NASA and ECMWF analyses, although our current system is run at a much lower resolution compared to the aerosol DA systems at these two meteorological centers. System development and our NRT experiment results will be presented.
