Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method,...Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.展开更多
Lee Suk-Ho和Seo Jin Keun提出的基于高斯曲率的去噪方法在处理低梯度区域时,虽然对于保留图像的细节特征非常有效,但是步长选择稍大时,会产生黑白点,过小又会增加迭代次数。针对此问题,提出了一种用Tukey s biweight函数来控制曲率扩...Lee Suk-Ho和Seo Jin Keun提出的基于高斯曲率的去噪方法在处理低梯度区域时,虽然对于保留图像的细节特征非常有效,但是步长选择稍大时,会产生黑白点,过小又会增加迭代次数。针对此问题,提出了一种用Tukey s biweight函数来控制曲率扩散的修正模型,该模型可以在较大时间步长的情况下避免黑白点的出现。进一步,为了利用高阶去噪方法对高梯度区域进行快速去噪,提出了一种将高斯曲率去噪方程和四阶偏微分方程相融合的去噪模型,以便可以根据具体的图像合理地分配两部分的权重。数值实验证明,该模型不仅可以处理曲面拟合方法所不能消除的椒盐噪声,而且可以实现两种方法的优点互补,既能保持边界,又较好地保留了细节特征。展开更多
本文通过对2013年6月20日至7月20日GRAPES(Global and Regional Assimilation and Prediction System)_RAFS(Rapid Analysis and Forecast System)系统每天8个时次每3 h的2 m温度预报进行分析,发现各时次的预报均能较好地表征2 m温度日...本文通过对2013年6月20日至7月20日GRAPES(Global and Regional Assimilation and Prediction System)_RAFS(Rapid Analysis and Forecast System)系统每天8个时次每3 h的2 m温度预报进行分析,发现各时次的预报均能较好地表征2 m温度日变化特征,但预报与实况存在一定的偏差,其中西藏东部川西高原、云贵高原、江南武夷山脉偏低于实况可达3℃,而华北地区偏高于实况3℃以上。为了减小GRAPES_RAFS系统偏差对2 m温度预报的影响,本文采用平均法、双权重平均法、滑动平均法和滑动双权重平均法分别对GRAPES_RAFS系统2 m温度预报产品进行偏差订正,并对订正前后的结果进行检验分析和对比。结果表明:2 m温度订正后的平均误差大部地区减小到(-1~1℃),而均方根误差大部地区降低到2.5℃内。对于偏差较大地区,订正效果更为明显,如西藏东部川西高原,经过订正,平均误差绝对值由订正前3℃以上降低到1℃内,而RMSE由订正前4℃以上控制到3℃内。对比四种订正方法,双权重订正方法与平均法订正整体效果接近,但对个别站点,双权重订正法要优于平均法,经过滑动的订正方法比无滑动的订正方法订正效果更好,订正效果最好的是滑动双权重平均法,全国平均误差大部分在(-0.5~0.5℃)内,不超过(-1~1℃)的范围。展开更多
利用双权重算法对观测增量(卫星观测亮度温度与用辐射传输模式模拟的背景场亮度温度之差)进行质量控制,模式背景场采用的是NCEP再分析资料,观测算子使用的是RTTOV(9.3v)(the fast radiative transfer for(A)TOVS model)。质量控制分两...利用双权重算法对观测增量(卫星观测亮度温度与用辐射传输模式模拟的背景场亮度温度之差)进行质量控制,模式背景场采用的是NCEP再分析资料,观测算子使用的是RTTOV(9.3v)(the fast radiative transfer for(A)TOVS model)。质量控制分两步进行:粗检验和离群值检验,目的是剔除受地表发射率或云影响的离群资料。结果表明:质量控制后观测增量标准差显著减小,偏差接近无偏正态分布,FY-3卫星微波温度探测器辐射率数据的质量得到很大的改善,为FY-3微波温度计观测亮温资料在数值预报资料同化系统中的应用奠定基础。展开更多
This study investigates multi-model ensemble forecasts of track and intensity of tropical cyclones over the western Pacific, based on forecast outputs from the China Meteorological Administration, European Centre for ...This study investigates multi-model ensemble forecasts of track and intensity of tropical cyclones over the western Pacific, based on forecast outputs from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts, Japan Meteorological Agency and National Centers for Environmental Prediction in the THORPEX Interactive Grand Global Ensemble(TIGGE) datasets. The multi-model ensemble schemes, namely the bias-removed ensemble mean(BREM) and superensemble(SUP), are compared with the ensemble mean(EMN) and single-model forecasts. Moreover, a new model bias estimation scheme is investigated and applied to the BREM and SUP schemes. The results showed that, compared with single-model forecasts and EMN, the multi-model ensembles of the BREM and SUP schemes can have smaller errors in most cases. However, there were also circumstances where BREM was less skillful than EMN, indicating that using a time-averaged error as model bias is not optimal. A new model bias estimation scheme of the biweight mean is introduced. Through minimizing the negative influence of singular errors, this scheme can obtain a more accurate model bias estimation and improve the BREM forecast skill. The application of the biweight mean in the bias calculation of SUP also resulted in improved skill. The results indicate that the modification of multi-model ensemble schemes through this bias estimation method is feasible.展开更多
文摘Changepoint detection faces challenges when outlier data are present. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method. Our method is double robust which is suitable for detecting mean changepoints in multivariate normal data with high correlations between variables that include outliers. Simulation results demonstrate that our method provides strong guarantees on both the number and location of changepoints in the presence of outliers. Finally, our method is well applied in an ACGH dataset.
文摘Lee Suk-Ho和Seo Jin Keun提出的基于高斯曲率的去噪方法在处理低梯度区域时,虽然对于保留图像的细节特征非常有效,但是步长选择稍大时,会产生黑白点,过小又会增加迭代次数。针对此问题,提出了一种用Tukey s biweight函数来控制曲率扩散的修正模型,该模型可以在较大时间步长的情况下避免黑白点的出现。进一步,为了利用高阶去噪方法对高梯度区域进行快速去噪,提出了一种将高斯曲率去噪方程和四阶偏微分方程相融合的去噪模型,以便可以根据具体的图像合理地分配两部分的权重。数值实验证明,该模型不仅可以处理曲面拟合方法所不能消除的椒盐噪声,而且可以实现两种方法的优点互补,既能保持边界,又较好地保留了细节特征。
文摘利用双权重算法对观测增量(卫星观测亮度温度与用辐射传输模式模拟的背景场亮度温度之差)进行质量控制,模式背景场采用的是NCEP再分析资料,观测算子使用的是RTTOV(9.3v)(the fast radiative transfer for(A)TOVS model)。质量控制分两步进行:粗检验和离群值检验,目的是剔除受地表发射率或云影响的离群资料。结果表明:质量控制后观测增量标准差显著减小,偏差接近无偏正态分布,FY-3卫星微波温度探测器辐射率数据的质量得到很大的改善,为FY-3微波温度计观测亮温资料在数值预报资料同化系统中的应用奠定基础。
基金Special Research Program for Public Welfare(Meteorology)of China(GYHY200906009,GYHY201006015,GYHY200906007)National Natural Science Foundation of China(4107503541475044)
文摘This study investigates multi-model ensemble forecasts of track and intensity of tropical cyclones over the western Pacific, based on forecast outputs from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts, Japan Meteorological Agency and National Centers for Environmental Prediction in the THORPEX Interactive Grand Global Ensemble(TIGGE) datasets. The multi-model ensemble schemes, namely the bias-removed ensemble mean(BREM) and superensemble(SUP), are compared with the ensemble mean(EMN) and single-model forecasts. Moreover, a new model bias estimation scheme is investigated and applied to the BREM and SUP schemes. The results showed that, compared with single-model forecasts and EMN, the multi-model ensembles of the BREM and SUP schemes can have smaller errors in most cases. However, there were also circumstances where BREM was less skillful than EMN, indicating that using a time-averaged error as model bias is not optimal. A new model bias estimation scheme of the biweight mean is introduced. Through minimizing the negative influence of singular errors, this scheme can obtain a more accurate model bias estimation and improve the BREM forecast skill. The application of the biweight mean in the bias calculation of SUP also resulted in improved skill. The results indicate that the modification of multi-model ensemble schemes through this bias estimation method is feasible.
