Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem
نویسندگان
چکیده
We implement and test an analog-based post-processing method to improve short range forecasts of aerosol optical depth (AOD) using the Weather Research Forecasting model coupled with Chemistry (WRF-Chem). Model postprocessing AOD is performed historical analog a Kalman Filter (KF). Analog are selected from WRF-Chem simulations based on set environmental predictors (AOD, wind speed, precipitable water, particulate matter) that exhibit past values similar current forecasts. Space-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra Aqua satellites corresponding analogs used build ensemble. This study focuses spatial domain covering AERONET sites in contiguous United States. use ensemble weighted mean (AN) filter (KFAN) algorithms, which both trained for months June August during 2008–2011 tested same 2012. Overall, forecast more skillful when errors corrected combination KFAN. especially true western US where correlation PM2.5, PM10, surface horizontal speed higher than those other predictors. In fact, overall biases significantly reduced close zero, KFAN being statistically indistinguishable MODIS. However, methods show mixed results (albeit still showing improvements) eastern central U.S., its variability highest. find that, summer, PM not only predominant factor driving these regions, unlike States (U.S.) (except New Mexico Arizona). note, however, quality depends model's capability accurately simulate total turn influences sources sinks.
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ژورنال
عنوان ژورنال: Atmospheric Environment
سال: 2021
ISSN: ['1352-2310', '1873-2844']
DOI: https://doi.org/10.1016/j.atmosenv.2020.118134