Infusing satellite data into aerosol forecast for near real-time episode detection and diagnosis in East Asia

Sheng Po Chen, Cheng Hsuan (Sarah) Lu, James E. Davies, Chang Feng Ou-Yang, Neng Huei Lin, Amy K. Huff, Bradley R. Pierce, Shobha Kondragunta, Jia Lin Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A near-real-time (NRT) aerosol forecast and diagnostic approach is developed based on the system of Infusing satellite Data into Environmental Applications for East Asia, herein denoted as IDEA-EA. The design incorporates a 0.5-degree Global Forecast System (GFS) and Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol and cloud retrievals for meteorological and remote sensing inputs. The primary output of IDEA-EA includes aerosol forward and backward air mass trajectory forecasts, migration visualization, and data synthesis purposed for NRT aerosol detection, monitoring, and source tracing in East Asia. Two aerosol episodes of Southeast Asia (SEA) biomass burning and Chinese haze infusion with Gobi dust are illustrated by IDEA-EA to demonstrate its forecast and source tracing capabilities. In the case of SEA biomass burning (late March 2021), forward trajectories of IDEA-EA forecasted air masses with high aerosol optical depth (AOD) from SEA affecting Taiwan. The IDEA-EA forecasts were verified by increased AOD and surface PM2.5 observations at a mountain site. In the case of the Chinese haze (October 30, 2019), backward trajectories from the northern tip of Taiwan traced air masses back to the east coast of mainland China and possibly further to the Gobi Desert. Compared with conventional numerical model simulations, the combination of the state-of-the-art aerosol remote sensing and trajectory modeling in IDEA-EA provides a cost-effective alternative for air quality management.

Original languageEnglish
Article number158797
JournalScience of the Total Environment
Volume856
DOIs
StatePublished - 15 Jan 2023

Keywords

  • Aerosol retrievals
  • Remote sensing
  • Trajectory modeling

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