Ensemble filter based estimation of spatially distributed parameters in a mesoscale dust model: experiments with simulated and real data

摘要

The ensemble adjustment Kalman filter (EAKF) is used to estimate theerodibility fraction parameter field in a coupled meteorology and dustaerosol model (Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS))over the Sahara desert. Erodibility is often employed as the keyparameter to map dust source. It is used along with surface winds (or surfacewind stress) to calculate dust emissions. Using the Saharan desert as a testbed, a perfect model Observation System Simulation Experiments (OSSEs) with40 ensemble members, and observations of aerosol optical depth (AOD), theEAKF is shown to recover correct values of erodibility at about 80% of thepoints in the domain. It is found that dust advected from upstream gridpoints acts as noise and complicates erodibility estimation. It is also foundthat the rate of convergence is significantly impacted by the structure ofthe initial distribution of erodibility estimates; isotropic initialdistributions exhibit slow convergence, while initial distributions withgeographically localized structure converge more quickly. Experiments usingobservations of Deep Blue AOD retrievals from the MODIS satellite sensorresult in erodibility estimates that are considerably lower than the valuesused operationally. Verification shows that the use of the tuned erodibilityfield results in better predictions of AOD over the west Saharaand the Arabian Peninsula.