Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem. (1st February 2021)

• Record Type: Journal Article
• Authors: Raman, Aishwarya; Arellano, Avelino F.; Delle Monache, Luca; Alessandrini, Stefano; Kumar, Rajesh
• Is Part Of: Atmospheric environment. Volume 246(2021)
• Publication Date: 2021-02-01
• Subjects: Aerosol optical depth -- WRF-Chem -- Analog ensemble -- Kalman filter; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2020.118134
• Languages: English
• ISSNs: 1352-2310

Abstract

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.