Application of cycling 3D-VAR to weather forecasting for operational ocean forecasting system

Abstract

Due to the availability of high-resolution regional weather models and computational facilities, today, it is possible to better resolve mesoscale weather events and hence to give reasonably good forecasts in the short range. The limitations in such forecasts lie in the availability of initial conditions at model resolutions. To overcome the limitations, a number of operational forecasting systems are now routinely running the regional weather model with the optimum application of data assimilation (DA). Since 2015, 3-hourly cycling three-dimensional variational DA (3D-VAR) method based on the Weather Research and Forecasting Model (WRF) has been test-operated to improve accuracy of predicted sea surface wind and air pressure around Korea. Observations around East Asia assimilated in the cycling 3D-VAR include hourly observations in synoptic stations and 12-hourly radiosonde observations. In addition, we have used observation data from buoys and AWS stations around Korea. The in situ surface observations contain hourly 2-m temperature and humidity, 10-m wind, and pressure. The radiosonde profiles include 12-hourly temperature, humidity, and wind at vertical levels extending from the surface up to about 10 hPa. In this study, we have highlighted the impact of the cycling 3D-VAR on forecasting sea surface wind and air pressure.s in such forecasts lie in the availability of initial conditions at model resolutions. To overcome the limitations, a number of operational forecasting systems are now routinely running the regional weather model with the optimum application of data assimilation (DA). Since 2015, 3-hourly cycling three-dimensional variational DA (3D-VAR) method based on the Weather Research and Forecasting Model (WRF) has been test-operated to improve accuracy of predicted sea surface wind and air pressure around Korea. Observations around East Asia assimilated in the cycling 3D-VAR include hourly observations in synoptic stations and 12-hourly radiosonde observations. In addition, we have used observation data from buoys and AWS stations around Korea. The in situ surface observations contain hourly 2-m temperature and humidity, 10-m wind, and pressure. The radiosonde profiles include 12-hourly temperature, humidity, and wind at vertical levels extending from the surface up to about 10 hPa. In this study, we have highlighted the impact of the cycling 3D-VAR on forecasting sea surface wind and air pressure.