Yue Tianxiang

Solar Radiation Climatology Calculation in China

Abstract The Angstrom-Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Fitting the coefficients is carried out using linear regression and in recent years it has been found that these coefficients have obvious spatial variability. A common solution is to divide the study area into several subregions and fit the coefficients one by one. Here, we use ground observation data for sunshine hours and solar radiation from 1961 to 2010. Adopting extraterrestrial radiation as the initial value, Angstrom-Prescott coefficients are obtained by Geographically Weighted Regression at a national scale. The surfaces of solar radiation are obtained on the basis of the surfaces of sunshine hours interpolated by high accuracy surface modeling and astronomical radiation; results from spatially nonstationary and error comparison tests show that Angstrom-Prescott coefficients have significant spatial nonstationarity. Compared to existing research methods, the method presented here achieves a better simulation effect.

A review of responses of typical terrestrial ecosystems to climate change

Analyses of ecosystem change trends since the 1920s have indicated that forest, farmland and grassland ecosystems have had strong responses to climate change. Many ecosystems have obviously changed in composition, structure and distribution. Ecosystem productivity has a decreasing trend because of plant disease and insect pests, frequent occurrence of extreme weather, and increasing mortality of plant species. Scenarios of responses of typical ecosystems to climate change show that structure, distribution, species and productivity would greatly change in areas at high altitude and latitude. However, these responses are very complex, because of complex interactions among biotic communities. Understanding of ecosystem change is still very elementary. There is no definite conclusion, especially regarding impacts of climate change on plant species, extreme climate consequences, and plant diseases and insect pests. Comprehensive assessment of climate change effects on typical ecosystems is difficult to accomplish with current knowledge. We need to construct a platform for simulating ecosystem dynamics by integrating remote sensing and ground-truth data, based on studying the mechanisms of ecosystem response to climate change.

An improved HASM method for dealing with large spatial data sets

Surface modeling with very large data sets is challenging. An efficient method for modeling massive data sets using the high accuracy surface modeling method (HASM) is proposed, and HASM_Big is developed to handle very large data sets. A large data set is defined here as a large spatial domain with high resolution leading to a linear equation with matrix dimensions of hundreds of thousands. An augmented system approach is employed to solve the equality-constrained least squares problem (LSE) produced in HASM_Big, and a block row action method is applied to solve the corresponding very large matrix equations. A matrix partitioning method is used to avoid information redundancy among each block and thereby accelerate the model. Experiments including numerical tests and real-world applications are used to compare the performances of HASM_Big with its previous version, HASM. Results show that the memory storage and computing speed of HASM_Big are better than those of HASM. It is found that the computational cost of HASM_Big is linearly scalable, even with massive data sets. In conclusion, HASM_Big provides a powerful tool for surface modeling, especially when there are millions or more computing grid cells.