Assimilating aerosol observations with a “hybrid” variational‐ensemble data assimilation system

Abstract

Total 550 nm aerosol optical depth, surface fine particulate matter (PM 2.5 ), and meteorological observations were assimilated with continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and hybrid variational-ensemble data assimilation systems. The hybrid system's background error covariances (BECs) were a blend of those in 3DVAR and produced by the cycling EnSRF system, and the 3DVAR, EnSRF, and hybrid systems differed almost exclusively by their BECs. New analyses were produced every 6 h between 0000 UTC 1 June and 1800 UTC 14 July 2010 over a domain encompassing the contiguous United States (CONUS) and adjacent areas. Additionally, a control experiment that only assimilated meteorological observations was performed. Each 1800 UTC analysis initialized a 48 h Weather Research and Forecasting with Chemistry model forecast. These forecasts were evaluated with a focus on air quality prediction. The ensemble aerosol spread was generally insufficient, particularly over the western CONUS. However, despite the suboptimal ensemble spread, the hybrid system performed quite well and usually produced the best aerosol forecasts. Additionally, both the 3DVAR-and EnSRF-initialized forecasts typically outperformed the control. These results are encouraging and suggest the resiliency of the hybrid method. Improved aerosol ensembles should translate into even better future hybrid forecasts.

SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm

“…The retrieved AOD can also be used for data assimilation with other satellite data (MODIS, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol product, etc. ), ground-based measurements (AERONET, particulate matter (PM) observations, and meteorological observations) and model data (GOCART aerosol data) to further improve the air quality analyses and forecasts [56,57].…”

Improving PM<sub>2. 5</sub> forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

“…The EnSRF algorithm was introduced by Whitaker and Hamill (2002) and its expansion to analysing aerosol ICs was described by Schwartz et al (2014). The traditional EnKF with perturbed observations (Evensen, 1994) introduces sampling errors by perturbing the observations.…”

“…Peng et al: Improving PM 2.5 forecast over China et al, 2010a, b;Pagowski and Grell, 2012;Dai et al, 2014;Rubin et al, 2016;Ying et al, 2016;Yumimoto et al, 2016) and the hybrid variational-ensemble DA approach (Schwartz et al, 2014) have also been applied to aerosol predictions. All these studies have shown that DA is one of the most effective ways of improving aerosol forecasting through assimilating aerosol observations from multiple sources (e.g.…”

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

“…The use of 6 h re-analyses for initialization led to notable improvements when both satellite and surface data were assimilated. More recently, Schwartz et al (2014) assimilated the same AOD and PM 2.5 surface concentration data using two additional methods: the EnSRF and a hybrid ensemble 3D-Var method. All three methods led to mostly improved forecasts, with the hybrid method showing the best performance and 3D-Var generally showing better performance than the EnSRF.…”