Assimilation Of GPS Data In LAPS And Its Application In Mesoscale Analysis And Precipitation Forecasts

Severe convective weather, such as strong wind, hailstone, short-time strong rainfallis a type of fine spatial scale weather, it changes rapidily. Blaming to its low spatial and temporal resolution, conventional meteorological data cannot meet the requirement of accuracy for severe convective weather analysis and forecasting. However, thanks for its low cost and high spatial-temporal resolution, GPS/PWV data is a remedy for conventional meteorological data. With a rich diversity of meteorological data and a low level of data utilization, it is badly in need of a technology platform which can process and assimilate meteorological data with different spatial-temporal resolution and from different observation platforms. As a local analysis and prediction system, LAPS can assimilate and analyse multi-source meteorological data. The output fields of LAPS which are quite similar to the observed fields have high spatial and temporal resolution, as a result application of LAPS in research of mesoscale analysis is of great significance. Based on the LAPS localization, taking two heavy rainfall processes as experimental research objects, we analyse the application of LAPS in Severe convective weather analysis and forecasting, and evaluate the improvement of the GPS/PWV data assimilation to the initial humidity field and precipitation forecast field of numerical model. The main conclusions are as follows:(1) By comparing the LAPS output fields with NCEP reanalysis fields and Doppler radar reflectivity field, it shows that the large scale circulation situation of both data is basically the same. However, LAPS output fields are in higher spatial-temporal resolution and more credible for meso-micro scale weather analysis. (2) By analysing the changes in the humidity field, tempreture field and dynamic field of LAPS, we find that the output fields of LAPS can reveal the deep characteristics of severe convective weather. By comparing the total precipitable water analyzed by LAPS in different schemes, it shows that the assimilation of GPS/PWV is effective in improving LAPS humidity field in all levels. The impacts of GPS/PWV are greater than radar data by comparing the total precipitable water, but its impact on height and wind fields is not so notable.(3) We use the LAPS and WRF model for heavy rain forecasting (applying the output fields of LAPS as the initial moisture field in WRF) and compare the outputs with the observed ones. The result shows that the initial moisture field in WRF is significantly improved by using GPS/PWV data, and the improved moisture field in initial condition lead to positive effect on the forecast of rainfall.