Abstract
Solar radiation is an important input for various land-surface energy balance models. Global solar radiation data retrieved from the Japanese Geostationary Meteorological Satellite 5 (GMS-5)/Visible and Infrared Spin Scan Radiometer (VISSR) has been widely used in recent years. However, due to the impact of clouds, aerosols, solar elevation angle and bidirectional reflection, spatial or temporal deficiencies often exist in solar radiation datasets that are derived from satellite remote sensing, which can seriously affect the accuracy of application models of land-surface energy balance. The goal of reconstructing radiation data is to simulate the seasonal variation patterns of solar radiation, using various statistical and numerical analysis methods to interpolate the missing observations and optimize the whole time-series dataset. In the current study, a reconstruction method based on data assimilation is proposed. Using a Kalman filter as the assimilation algorithm, the retrieved radiation values are corrected through the continuous introduction of local in-situ global solar radiation (GSR) provided by the China Meteorological Data Sharing Service System (Daily radiation dataset_Version 3) which were collected from 122 radiation data collection stations over China. A complete and optimal set of time-series data is ultimately obtained. This method is applied and verified in China’s northern agricultural areas (humid regions, semi-humid regions and semi-arid regions in a warm temperate zone). The results show that the mean value and standard deviation of the reconstructed solar radiation data series are significantly improved, with greater consistency with ground-based observations than the series before reconstruction. The method implemented in this study provides a new solution for the time-series reconstruction of surface energy parameters, which can provide more reliable data for scientific research and regional renewable-energy planning.
Highlights
The parameters of surface energy balances are important inputs for research on global climate change, crop-yield assessment and ecological environment evaluation
The spatial variation of the solar radiation data was relatively stable, and the temporal variation was significant. Such a variation pattern of the time series is obviously seasonal, which can be used as the basis for precision analyses and the reconstruction of time series of radiation data products retrieved from remote sensing data
In this study, based on daily solar radiation retrieved from remote-sensing data, a complete set of time-series reconstruction methods for solar radiation values was proposed
Summary
The parameters of surface energy balances are important inputs for research on global climate change, crop-yield assessment and ecological environment evaluation. The hourly parameters retrieved from geostationary meteorological satellite data are combined into daily average data, and 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) radiation products are combined from daily data Despite these interpolation methods, the problems of missing data or unstable data quality are still very serious at the regional scale [5], and this limitation will eventually affect the accuracy of land surface-energy balance analyses and simulations [6]. Assimilation results based on the data at the testing points showed that an ensemble Kalman filter algorithm improved the calculations of energy and moisture variables using the model. Using climate-zoning information, the method could be applied at a large regional scale to produce a refined solar radiation time-series dataset
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