Publications by authors named "Chul Han Song"
Article Synopsis
- A new data assimilation (DA) system using the ensemble square root filter (EnSRF) was developed to enhance the accuracy of predicting atmospheric particulate matter concentrations in the Community Multiscale Air Quality (CMAQ) model.
- This study compared EnSRF with other DA methods, like the ensemble Kalman filter (EnKF) and 3DVAR, using the same experimental setup and surface fine particulate matter data over East Asia from May to June 2016.
- The results showed that EnSRF provided superior performance in both reanalysis and prediction outputs, demonstrating significant improvements in normalized mean biases compared to traditional methods.
Weather is affected by a complex interplay of factors, including topography, location, and time. For the prediction of temperature in Korea, it is necessary to use data from multiple regions. To this end, we investigate the use of deep neural-network-based temperature prediction model time-series weather data obtained from an automatic weather station and image data from a regional data assimilation and prediction system (RDAPS).
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