Abstract
Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM2.5 forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM2.5 concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM2.5 forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts.
Highlights
We evaluated the Community Multiscale Air Quality (CMAQ) air quality model of the nature run (NR) module by comparing the modelled and the observed PM2.5 concentrations at the surface monitoring networks located in China and Korea for October
We presented a recently developed Observing System Simulation Experiments (OSSE) framework and the results of a case study conducted over Northeast Asia
We focused on the potential benefits of new PM observations from the Gobi Desert and North Korea (NEWPM) and new aerosol optical depths (AOD)
Summary
Air quality deterioration owing to the abundance of PM2.5 (particulate matter with an aerodynamic diameter below 2.5 μm) is a widespread and severe environmental problem threatening human and ecosystem health [1]. Air quality prediction or the forecasting of PM2.5 air quality is a useful tool for the governmental agencies in terms of planning and for the public with regard to health protection and air quality management. There are significant differences between the predicted and observed concentrations, due to the high uncertainties embedded in the numerical simulation of atmospheric PM. Enhancing the accuracy of PM2.5 concentration prediction by adding more and reliable information to the modelling system is important. Data assimilation (DA), where the Remote Sens.
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