Evaluation On The Products Of CMA Land Data Assimilation System

As the Earth’s important component, land surface exchanges the material and energy with atmosphere and plays an important role in atmosphere and climate change. Compared to atmospheric and oceanic data assimilation, land data assimilation started late. CMA Land Data Assimilation System (CLDAS) is the only real-time operating system in the field of land data assimilation system in China, which can provide high-resolution, high accuracy atmospheric forcing data and land surface model data by integrating a large number of actual observations. In this paper, CLDAS were assessed. First, Applicability of surface Meteorological elements (surface air temperature, relative humidity, the wind speed, atmospheric pressure) from CLDAS, ECMWF (European Centre for Medium-Range Weather Forecasts), JMA (Japan Meteorological Agency) and GFS (Global Forecast System) is evaluated in China by comparison with the automatic station observations (2421stations) obtained from Chinese Meteorological Administration for the periods from July1,2010to June30,2013. Second, sensible and latent heat flux of CLDAS were evaluated by observations from Heihe Watershed Allied Telemetry Experimental Research. And the soil temperature of CLDAS were assessed by the automatic station observations (2421stations) obtained from CMA. The results show that:(1) The atmospheric forcing data of CLDAS integrated with observations is superior than that of numerical model data. ECMWF is the best among numerical models. And the quality of numerical models is better in analysis field than in prediction field, three numerical models (ECMWF, JMA and GFS) could display spatiotemporal consistence with observation basically. And the performance of numerical models in east area of China is more reliable than in west area.(2) Sensible heat flux of CLDAS simulated by CLM3.5are more consistent with the actual observations than that of GLDAS simulated by Noah, indicating that the quality improvement of atmospheric forcing data is good to CLM3.5simulating sensible heat flux. For latent heat flux, CLDAS is slightly closer to the actual observation than GLDAS. However, there are also differences between the actual observation and CLDAS, probably because the land surface feature of study area located in Zhang Ye City of Gansu Province is complex, and model didn’t describe such surface accurately.(3) For the soil temperature of CLDAS simulated by CLM3.5, seasonal variation trend is the alignment with observations. For the first and second layer soil temperature, there are largest differences for CLDAS with observation at about2.0K in summer and winter, and for the third layer about3.0K in winter, due to the uncertainty of the model parameters. This little difference owe in to the precision atmospheric forcing data of CLDAS with high spatial and temporal resolution. The third layer soil temperature seasonal variation amplitude is smaller than the above two. In addittion, with the deepening of soil depth, diurnal variation characteristic of soil temperature get small, indicating the deep soil temperature are less affected by the land-atmosphere interaction than surface soil temperature.