Diurnal variation of solar radiation at surface is of importance to data assimilation, weather and climate model
assessment. However, the shortage of solar radiation data has limited full use of other meteorological data. Solar
radiation at surface can not be simply calculated by interpolation in any time interval because it is heavily influenced by
solar hour angle, cloud, water vapor and aerosols etc., which brings great troubles to model applications. This paper
presents a method to compute mean solar radiation at surface in any time interval and develops a data set of hourly mean
solar radiation that can be used to assess models by use of NCEP 6-hourly mean of downward solar radiation flux at
surface. Also, while comparing to measured hourly mean of solar radiation, results show that the calculated hourly mean
solar radiation agrees closely with observation in numerical value and variation trend, which illuminates that the method
is efficient. The calculated hourly-mean solar radiation reflects the diurnal variation all over the world and it can be used
as land model forcing, It is helpful to simulation, validation and assessment of the weather and climate model and can
make up the shortage of measured solar radiation data.
In the first part of this paper, a 3DVar Land Data Assimilation Scheme (LDAS) is presented. With virtue of this land data assimilation system, this part of the paper demonstrates the results and error analysis of assimilating air temperature data observed at various meteorological stations in China into the output of ECMWF ERA-40. The air temperature distribution of sparse observation zones is obtained, which shows the validity of the assimilation procedure. The 3DVar LDAS can greatly improve the ECMWF background estimates with the high quality observations of air temperature from the Chinese meteorological stations. By comparing the assimilated air temperature field and the ECMWF background field to the observations, the assimilation outputs have better agreement with the air temperature variation trend than the ECMWF background. Another advantage of the assimilated result is that it can describe the extreme air temperature more accurately.
Land surface states have significant control to the water and energy exchanges between land surface and the atmosphere.
Thus land surface information is crucial to the global and regional weather and climate predictions. China has built
abundant meteorological stations that collect land surface data with good quality for many years. But applications of these data in their numerical weather and climate prediction models are quite low efficient. To take the advantages of land surface data in numerical weather and climate models, we have developed a three dimension variational (3DVar) Land Data Assimilation Scheme (LDAS). In Part 1 of this paper, we present the mathematical design of the 3DVar LDAS. By assimilating a single point observational datum into a background setup, the LDAS is tested to demonstrate its capability and usage. In the other part of this paper, we will demonstrate the results and error analysis of assimilating China's air temperature observational data of the meteorological stations into ECMWF's model background using the 3DVar Land Data Assimilation Scheme.