The female ofDiplectrus bistigmaeus Zhang, Ren et Ba, 2012 from Xizang was newly reported and supplementarily described. Two species of Oedemerinae were reported for the first time from China： Nacerdes （Xanthochroa...The female ofDiplectrus bistigmaeus Zhang, Ren et Ba, 2012 from Xizang was newly reported and supplementarily described. Two species of Oedemerinae were reported for the first time from China： Nacerdes （Xanthochroa） brendelli Svihla, 1987 and N. （Asiochroa） mimoncomeroides Svihla, 1998. The potential geographical distribution of these two species based on the known distribution was predicted by DIVA-GIS software.展开更多
Bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather models.However,successful implementation of BMA requires accurate estimates of the weig...Bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather models.However,successful implementation of BMA requires accurate estimates of the weights and variances of the individual competing models in the ensemble.Two methods,namely the Expectation-Maximization(EM) and the Markov Chain Monte Carlo(MCMC) algorithms,are widely used for BMA model training.Both methods have their own respective strengths and weaknesses.In this paper,we first modify the BMA log-likelihood function with the aim of removing the addi-tional limitation that requires that the BMA weights add to one,and then use a limited memory quasi-Newtonian algorithm for solving the nonlinear optimization problem,thereby formulating a new approach for BMA(referred to as BMA-BFGS).Several groups of multi-model soil moisture simulation experiments from three land surface models show that the performance of BMA-BFGS is similar to the MCMC method in terms of simulation accuracy,and that both are superior to the EM algo-rithm.On the other hand,the computational cost of the BMA-BFGS algorithm is substantially less than for MCMC and is al-most equivalent to that for EM.展开更多
基金supported by the National Natural Science Foundation of China (31093430)the Key Laboratory of Invertebrate Systematics and Application of Hebei,China.(2014010)the Science and Technology Programs for University by Hebei Educational Committee (QN 20131042)
文摘The female ofDiplectrus bistigmaeus Zhang, Ren et Ba, 2012 from Xizang was newly reported and supplementarily described. Two species of Oedemerinae were reported for the first time from China： Nacerdes （Xanthochroa） brendelli Svihla, 1987 and N. （Asiochroa） mimoncomeroides Svihla, 1998. The potential geographical distribution of these two species based on the known distribution was predicted by DIVA-GIS software.
基金supported by National Basic Research Program of China (Grant No. 2010CB428403)National Natural Science Foundation of China (Grant No.41075076)Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No.KZCX2-EW-QN207)
文摘Bayesian model averaging(BMA) is a recently proposed statistical method for calibrating forecast ensembles from numerical weather models.However,successful implementation of BMA requires accurate estimates of the weights and variances of the individual competing models in the ensemble.Two methods,namely the Expectation-Maximization(EM) and the Markov Chain Monte Carlo(MCMC) algorithms,are widely used for BMA model training.Both methods have their own respective strengths and weaknesses.In this paper,we first modify the BMA log-likelihood function with the aim of removing the addi-tional limitation that requires that the BMA weights add to one,and then use a limited memory quasi-Newtonian algorithm for solving the nonlinear optimization problem,thereby formulating a new approach for BMA(referred to as BMA-BFGS).Several groups of multi-model soil moisture simulation experiments from three land surface models show that the performance of BMA-BFGS is similar to the MCMC method in terms of simulation accuracy,and that both are superior to the EM algo-rithm.On the other hand,the computational cost of the BMA-BFGS algorithm is substantially less than for MCMC and is al-most equivalent to that for EM.
