A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the no...A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.展开更多
We describe the long-term stability and mean climatology of oceanic circulations simulated by version 2 of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS-s2）. Driven by pre-industrial forcing, the in...We describe the long-term stability and mean climatology of oceanic circulations simulated by version 2 of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS-s2）. Driven by pre-industrial forcing, the integration of FGOALS-s2 was found to have remained stable, with no obvious climate drift over 600 model years. The linear trends of sea SST and sea surface salinity （SSS） were -0.04℃ （100 yr）-1 and 0.01 psu （100 yr）-1, respectively. The simulations of oceanic temperatures, wind-driven circulation and thermohaline circulation in FGOALS-s2 were found to be comparable with observations, and have been substantially improved over previous FGOALS-s versions （1.0 and 1.1）. However, significant SST biases （exceeding 3℃） were found around strong western boundary currents, in the East China Sea, the Sea of Japan and the Barents Sea. Along the eastern coasts in the Pacific and Atlantic Ocean, a warm bias （〉3℃） was mainly due to overestimation of net surface shortwave radiation and weak oceanic upwelling. The difference of SST biases in the North Atlantic and Pacific was partly due to the errors of meridional heat transport. For SSS, biases exceeding 1.5 psu were located in the Arctic Ocean and around the Gulf Stream. In the tropics, freshwater biases dominated and were mainly caused by the excess of precipitation. Regarding the vertical dimension, the maximal biases of temperature and salinity were located north of 65°N at depths of greater than 600 m, and their values exceeded 4℃ and 2 psu, respectively.展开更多
The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed e...The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.展开更多
In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Esti...In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.展开更多
基金supported by a project of the National Natural Science Foundation of China (Grant No. 41305099)
文摘A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.
基金supported by the National Key Program for Developing Basic Sciences(GrantNos. 2010CB950502)the "Strategic Priority Research Program-Climate Change:Carbon Budget and Related Issues" of the Chinese Academy of Sciences (Grant No.XDA05110302)+1 种基金the National Natural Science Foundation of China(Grant Nos. 40906012 and 41023002)National High Technology Research and Development Program of China(Grant No. 2010AA012303)
文摘We describe the long-term stability and mean climatology of oceanic circulations simulated by version 2 of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS-s2）. Driven by pre-industrial forcing, the integration of FGOALS-s2 was found to have remained stable, with no obvious climate drift over 600 model years. The linear trends of sea SST and sea surface salinity （SSS） were -0.04℃ （100 yr）-1 and 0.01 psu （100 yr）-1, respectively. The simulations of oceanic temperatures, wind-driven circulation and thermohaline circulation in FGOALS-s2 were found to be comparable with observations, and have been substantially improved over previous FGOALS-s versions （1.0 and 1.1）. However, significant SST biases （exceeding 3℃） were found around strong western boundary currents, in the East China Sea, the Sea of Japan and the Barents Sea. Along the eastern coasts in the Pacific and Atlantic Ocean, a warm bias （〉3℃） was mainly due to overestimation of net surface shortwave radiation and weak oceanic upwelling. The difference of SST biases in the North Atlantic and Pacific was partly due to the errors of meridional heat transport. For SSS, biases exceeding 1.5 psu were located in the Arctic Ocean and around the Gulf Stream. In the tropics, freshwater biases dominated and were mainly caused by the excess of precipitation. Regarding the vertical dimension, the maximal biases of temperature and salinity were located north of 65°N at depths of greater than 600 m, and their values exceeded 4℃ and 2 psu, respectively.
文摘The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.
文摘In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.
