Assimilation of AERONET and MODIS AOT observations using variational and ensemble data assimilation methods and its impact on aerosol forecasting skill. Issue 9 (11th May 2017)

• Record Type: Journal Article
• Authors: Rubin, Juli I.; Reid, Jeffrey S.; Hansen, James A.; Anderson, Jeffrey L.; Holben, Brent N.; Xian, Peng; Westphal, Douglas L.; Zhang, Jianglong
• Is Part Of: Journal of geophysical research. Volume 122:Issue 9(2017)
• Page Start: 4967
• Page End: 4992
• Publication Date: 2017-05-11
• Subjects: aerosol -- data assimilation -- forecasting -- ensemble -- variational; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2016JD026067
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Data assimilation of Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness (AOT) for aerosol forecasting was tested within the Navy Aerosol Analysis Prediction System (NAAPS) framework, using variational and ensemble data assimilation methods. Navy aerosol forecasting currently makes use of a deterministic NAAPS simulation coupled to Navy Variational Data Assimilation System for aerosol optical depth, a two‐dimensional variational data assimilation system, for MODIS AOT assimilation. An ensemble version of NAAPS (ENAAPS) coupled to an ensemble adjustment Kalman filter (EAKF) from the Data Assimilation Research Testbed was recently developed, allowing for a range of data assimilation and forecasting experiments to be run with deterministic NAAPS and ENAAPS. The main findings are that the EAKF, with its flow‐dependent error covariances, makes better use of sparse observations such as AERONET AOT. Assimilating individual AERONET observations in the two‐dimensional variational system can increase the analysis errors when observations are located in high AOT gradient regions. By including AERONET with MODIS AOT assimilation, the magnitudes of peak aerosol events (AOT > 1) were better captured with improved temporal variability, especially in India and Asia where aerosol prediction is a challenge. Assimilating AERONET AOT with MODIS had little impact on the 24 h forecast skill compared to MODIS assimilation only, but differences were found downwind of AERONET sites. The 24 h forecast skill was approximately the same for forecasts initialized with analyses from AERONET AOT assimilation alone compared to MODIS assimilation, particularly in regions where the AERONET network is dense; including the United States and Europe, indicating that AERONET could serve as a backup observation network for over‐land synoptic‐scale aerosol events. Key Points: Ensemble data assimilation is better suited for assimilating sparse aerosol‐related observations like AERONET than a 2‐D variational system Assimilating AERONET with MODIS AOT is beneficial in capturing peak AOT events and temporal variability Results indicate that AERONET could serve as a back‐up observation network for forecasting of synoptic‐scale aerosol events over land