Department of Industry, Innovation, Science and Research (DIISR) ISL Competitive Grant Program, 2008-2011.

DIISR recently announced the outcomes of its International Science Linkage (ISL) Competitive Grants (Round 13) and The Centre of Satellite Positioning and Navigation (SPAN) has been awarded the following ISL grant (CG130127) in collocation with Joint Center of Satellite Data Assimilation (JCSDA) of the United States of America, The Australia Bureau of Meteorology and The Center for Space and Remote Sensing Research, The National Central University of Taiwan.

[Title] Assimilation of GPS Radio Occultation Data with Numerical Weather Prediction System for Climate Monitoring

[Summary] Continuous improvements in the reliability of weather forecast are crucial for the prevention of damage caused by severe weather phenomena such as tropical cyclones and thunderstorms. Monitoring of the climate changes is of great importance considering that the evolution of the Earth¡¯s climate system is increasingly influenced by human activities. The improvements of forecast accuracy of operational numerical weather prediction (NWP) systems and the reliable analysis of climate changes rely on the availability of new technologies, new measurements, and optimal fusion of new sources of data in the data assimilation process. Due to unprecedented high vertical resolution, high accuracy, global coverage and long-term stability, the GP S radio occultation (RO) technique has a great potential to complement other meteorological observation systems and improve NWP forecasts and global weather analyses. The aim of this project is to investigate new methods for assimilating GPS RO data in NWP models for weather forecast and climate monitoring. New procedures for improving the precision of temperature and moisture profiles in the troposphere using a combination of retrievals from GPS RO data and observations from high-resolution instruments such as the hyperspectral Atmospheric Infrared Sounder (AIRS) will be studied. The impacts of different types of observations and their integration on global NWP forecasts will be investigated both within a theoretical one-dimensional variational analysis (1DVar) framework and also in a three- and four-dimensional variational analysis (3DVar and 4DVar) framework. The final aim of this project is to determine atmospheric state to very high precision using GPS RO data in an optimal combination with remote sensing data from around 36 operational satellite instruments (e.g. HIRS, AMSU, AIRS, QuikSCAT, SSMI) for vital NWP and climate applications.