基于欧洲中期天气预报中心(European Center for Medium-range Weather Forecasts,ECMWF)集合预报资料及浙江全省自动站降水观测资料,采用贝叶斯模型平均(Bayesian Model Average,BMA)方法对2020年浙江超长梅汛期开展降水概率预报订正...基于欧洲中期天气预报中心(European Center for Medium-range Weather Forecasts,ECMWF)集合预报资料及浙江全省自动站降水观测资料,采用贝叶斯模型平均(Bayesian Model Average,BMA)方法对2020年浙江超长梅汛期开展降水概率预报订正试验。采用平均绝对误差、连续等级概率评分、布莱尔评分B_(S)、Talagrand、概率积分变换(Probability Integral Transform,PIT)直方图及属性图检验方法对本次过程BMA订正前后的概率预报进行对比分析,结果表明:(1)50 d为适用于浙江梅汛期ECMWF集合预报订正的BMA最优训练期,经最优训练期的BMA订正后,预报离散度有所增加,预报误差有所下降;(2)BMA对0.1 mm、10.0 mm和25.0 mm阈值降水的订正效果显著,经BMA订正后3个阈值的降水预报B_(S)下降率分别为25.92%、19.29%、4.76%,但对超过50.0 mm的降水订正效果不明显,且随着降水阈值增加,BMA的订正效果减弱;(3)在强降水个例中,BMA能有效减少各阈值降水预报概率大值落区偏差,使订正后的降水预报概率大值区与观测落区更一致。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
文摘基于欧洲中期天气预报中心(European Center for Medium-range Weather Forecasts,ECMWF)集合预报资料及浙江全省自动站降水观测资料,采用贝叶斯模型平均(Bayesian Model Average,BMA)方法对2020年浙江超长梅汛期开展降水概率预报订正试验。采用平均绝对误差、连续等级概率评分、布莱尔评分B_(S)、Talagrand、概率积分变换(Probability Integral Transform,PIT)直方图及属性图检验方法对本次过程BMA订正前后的概率预报进行对比分析,结果表明:(1)50 d为适用于浙江梅汛期ECMWF集合预报订正的BMA最优训练期,经最优训练期的BMA订正后,预报离散度有所增加,预报误差有所下降;(2)BMA对0.1 mm、10.0 mm和25.0 mm阈值降水的订正效果显著,经BMA订正后3个阈值的降水预报B_(S)下降率分别为25.92%、19.29%、4.76%,但对超过50.0 mm的降水订正效果不明显,且随着降水阈值增加,BMA的订正效果减弱;(3)在强降水个例中,BMA能有效减少各阈值降水预报概率大值落区偏差,使订正后的降水预报概率大值区与观测落区更一致。
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.
