A RESEARCH OF APPLYING GNSS BASED METEOROLOGICAL DATA ON OPERATIONAL WEATHER FORECASTING

摘要

The vertical profile of refractivity, temperature, pressure and water vapor without limitation of time and space can be retrieved from Radio Occultation (RO) events between GNSS (Global Navigation and Satellite System) and Low Earth Orbiters (LEO). Ground-based GNSS network can provide Integrated Water Vapor (IWV) with the high temporal resolution for all weather conditions. These GNSS data have many possibilities of applications to the operational weather forecasting. GNSS based meteorological data can be assimilated into the Numerical Weather Prediction (NWP) model so that it may improve the short term predictability of severe weather such as heavy rainfall over Korea caused by the large scale background fields. At first, we checked the quality of IWV, refractivity, pressure and temperature data retrieved from GNSS by comparing with other observation data. Through the analysis of correlation between the GNSS data and actual meteorological events, the usefulness of the GNSS data was verified. Next, we assimilated the refractivity from ROreflecting the background of the large scale in the mother domain and IWV from ground-based GNSS networks in the fine domain into the Weather Research and Forecasting Model using 3-Dimensional VARiational Data Assimilation (WRF-3DVAR). Quality control reveals that RO data have a lot of affect on the initial conditions and had negative bias at lower atmosphere due to the characteristics of GNSS signal. It was still notified that GNSS IWV provided the possibility as a predictor in NWP. The sensitivity experiments conducted to evaluate GNSS RO and IWV data assimilation effects the initial time of NWP model. It showed that the predictability of precipitation mainly depended on the quantity and quality of GNSS based meteorological data. Because the impact of the GNSS concentrated on initial integrated time of NWP and the influence gradually went down, the continuous assimilating technique was the important factor to improve the predictability of NWP. On the basis of the experiments, though GNSS RO technique has a weak point to retrieve data at lower atmosphere, it can be mitigated with an assimilating GNSS IWV and optimization techniques of assimilation in NWP model. We conclude that the GNSS data assimilation can improve the short-term predictability of heavy rainfall.