The improvement of the seasonal forecast
for the global coupled air-sea model using Argo assimilation data
1. National Climate Center, Beijing 100081
2. Chinese Academy of Science, Beijing 100081
Abstract: Short-term weather forecast is an important research content at Climate arability and Predictability (CLIVAR) international weather research program in the flowing 10-15 years. It has a tightness rely on the ocean observation data (especial the profiling data on the global oceanic temperature, salinity and current). Argo program is in order to collect the upper ocean data (temperature, salinity and current) quickly, accurately and in a large-scale. It will greatly improve the initial field of the ocean model by assimilating the Argo data, and than improve the accurate of the short-term weather forecast.
This research using an experiment to compare the National Climate Center’s (NCC, CMA) global air-sea coupled model with the GODAS, analysis the impact of the dynamic seasonal forecast by the Argo assimilation data. At first, we get two assimilation data in GODAS using or not using Argo assimilation data; then using these two different assimilation data as the initial field, using global air-sea coupled model to obtain the 11 years (1993-2003) feedback experiment result. The result shows that there is an improvement that the initial field with Argo data than no Argo data in the experiment of using global coupled air-sea model to feedback 1993-2003 China summer precipitation and temperature. There are also some problems in using Argo assimilation data into operation forecast test run, but we can see the blight respect of it.