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    Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem

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    Author
    Raman, Aishwarya
    Arellano, Avelino F.
    Delle Monache, Luca
    Alessandrini, Stefano
    Kumar, Rajesh
    Affiliation
    Department of Hydrology and Atmospheric Sciences, University of Arizona
    Issue Date
    2021-02
    Keywords
    Aerosol optical depth
    Analog ensemble
    Kalman filter
    WRF-Chem
    
    Metadata
    Show full item record
    Publisher
    Elsevier BV
    Citation
    Raman, A., Arellano, A. F., Delle Monache, L., Alessandrini, S., & Kumar, R. (2021). Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem. Atmospheric Environment, 246, 118134.
    Journal
    Atmospheric Environment
    Rights
    © 2020 Published by Elsevier Ltd.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    We implement and test an analog-based post-processing method to improve short range forecasts of aerosol optical depth (AOD) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model postprocessing of AOD is performed using historical analog forecasts and a Kalman Filter (KF). Analog forecasts are selected from WRF-Chem simulations based on a set of environmental predictors (AOD, wind speed, precipitable water, and particulate matter) that exhibit past values similar to the current forecasts. Space-borne AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra and Aqua satellites corresponding to the analogs are used to build the analog ensemble. This study focuses on a spatial domain covering the AERONET sites in contiguous United States. We use the analog ensemble weighted mean (AN) and Kalman filter analog (KFAN) algorithms, which are both trained using WRF-Chem AOD forecasts for the months of June to August during 2008–2011 and tested during the same months for 2012. Overall, the AOD forecast are more skillful when the forecast errors are corrected using a combination of analogs and Kalman filter in KFAN. This is especially true for the western US where the correlation of AOD with PM2.5, PM10, and surface horizontal wind speed are higher than those for other predictors. In fact, the overall biases in AOD are significantly reduced close to zero, with KFAN AOD being statistically indistinguishable to MODIS. However, both methods show mixed results (albeit still showing overall improvements) in eastern and central U.S., where AOD and its variability are highest. We find that, during the summer, PM is not the only predominant factor driving AOD in these regions, unlike western United States (U.S.) (except New Mexico and Arizona). We note, however, that the quality of the analogs depends on the model's capability to accurately simulate total precipitable water, which in turn influences aerosol sources and sinks.
    Note
    24 month embargo; available online 10 December 2020
    ISSN
    1352-2310
    DOI
    10.1016/j.atmosenv.2020.118134
    Version
    Final accepted manuscript
    Sponsors
    National Aeronautics and Space Administration
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.atmosenv.2020.118134
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    UA Faculty Publications

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