Improvement of forecasting system with optimal interpolation focusing on korea

Abstract:

A system for forecasting future air quality can play an important role as part of an air quality management system working in concert with more traditional emissions-based approaches. However, there are still a lot of uncertainties in modeling atmospheric. Data assimilation makes use of observation in order to reduce the uncertainties. This paper presents experiments of PM10(particulate matter <10㎛ in diameter) data assimilation with the optimal interpolation method. In order to improve the performance of chemical transport models (CTM) models in predicting pollutant concentrations for PM10, data assimilation techniques can be used. Model (CMAQ : Community Multiscale Air Quality Model) to simulate and assimilate PM10 concentration over Korea peninsula. The observations are provided by AAQMS (Ambient Air Quality Monitoring Stations in Korea).Data assimilation techniques combine measurements of the pollutant concentrations with model results to obtain better estimates of the true concentration levels(unknown). The method is then applied in operational-forecast conditions. It is found that the assimilation of PM10 observations significantly improves the one-day forecast of PM10, whereas the improvement is non significant for the tow-day forecast. We focus on the horizontal and temporal impacts of the data assimilation. The strategy followed in this paper with the optimal interpolation could be useful for operational forecasts.