Improvement of NCEP Numerical Weather Prediction with Use of Satellite Land Measurements

Abstract:

Over the past two decades, satellite measurements are being increasingly used in weather and climate prediction systems and have made a considerable progress in accurate numerical weather and climate predictions. However, it is noticed that the utilization of satellite measurements over land is far less than over ocean, because of the high land surface inhomogeneity and the high emissivity variabilities in time and space of surface characteristics. In this presentation, we will discuss the application efforts of satellite land observations in the National Centers for Environmental Prediction (NCEP) operational Global Forecast System (GFS) in order to improve the global numerical weather prediction (NWP). Our study focuses on use of satellite data sets such as vegetation type and green vegetation fraction, assimilation of satellite products such as soil moisture retrieval, and direct radiance assimilation. Global soil moisture data products could be used for initialization of soil moisture state variables in numerical weather, climate and hydrological forecast models. A global Soil Moisture Operational Product System (SMOPS) has been developed at NOAA-NESDIS to continuously provide global soil moisture data products to meet NOAA-NCEP's soil moisture data needs. The impact of the soil moisture data products on numerical weather forecast is assessed using the NCEP GFS in which the Ensemble Kalman Filter (EnKF) data assimilation algorithm has been implemented. In terms of radiance assimilation, satellite radiance measurements in various spectral channels are assimilated through the JCSDA Community Radiative Transfer Model (CRTM) on the NCEP Gridpoint Statistical Interpolation (GSI) system, which requires the CRTM to calculate model brightness temperature (Tb) with input of model atmosphere profiles and surface parameters. Particularly, for surface sensitive channels (window channels), Tb largely depends on surface parameters such as land surface skin temperature, soil moisture, and emissivity. Therefore, in order to improve radiance data assimilation, we have to improve not only surface emissivity calculation but also model surface parameters simulation. Some improvements of satellite radiance assimilation over land will be presented.