针对油浸式电力变压器瞬态温升计算效率过低的问题,该文提出本征正交分解-αATS(proper orthogonal decomposition-adaptive time stepping based onαfactor,POD-αATS)降阶自适应变步长瞬态计算方法。首先,推导变压器绕组瞬态温升计...针对油浸式电力变压器瞬态温升计算效率过低的问题,该文提出本征正交分解-αATS(proper orthogonal decomposition-adaptive time stepping based onαfactor,POD-αATS)降阶自适应变步长瞬态计算方法。首先,推导变压器绕组瞬态温升计算的有限元离散方程;其次,采用POD降阶算法改善传统瞬态计算中存在的条件数过大及方程阶数过高的问题;同时对于瞬态计算中的时间步长选择问题,提出适用于非线性问题的αATS变步长策略;然后,为验证方法的有效性,基于110 kV油浸式电力变压器绕组的基本结构建立二维八分区数值计算模型,同时将计算结果与基于110 kV绕组的温升实验结果进行对比。数值计算及实验结果表明,所提算法与全阶定步长算法在流场和温度场中的精度几乎相同,且流场计算效率提升约45倍,温度场计算效率提升约38倍,计算速度得到显著提高。这一点在温升实验中同样得到验证,说明该文所提算法的准确性、高效性及一定的工程实用性。展开更多
Precipitation prediction is essential for disaster prevention,yet it still remains a challenging issue in weather and climate studies.This paper proposes an effective prediction method for summer precipitation over ea...Precipitation prediction is essential for disaster prevention,yet it still remains a challenging issue in weather and climate studies.This paper proposes an effective prediction method for summer precipitation over eastern China(PEC) by combining empirical orthogonal function(EOF) analysis with the interannual increment approach.Three statistical prediction models are individually developed for respective predictions of the three principal components(PCs) corresponding to the three leading EOF modes for the interannual increment of PEC(hereafter DY;EC).Each model is run for the month of March with two previous predictors derived from sea-ice concentration/soil moisture/sea surface temperature/snow depth/sea level pressure over specific regions.The predicted PCs are projected to the EOF modes derived from observations of DY;EC to produce a new DY;EC.This new DY;EC is then added to the observed PEC of the previous year to obtain the final predicted PEC.The spatial features of the predicted PEC are highly consistent with observations,with the anomaly correlation coefficient skill ranging from 0.32 to 0.64 during 2012-2020.The method is applied for real-time prediction of PEC in 2021.And the results indicate two rain belts located over northeastern China and the Yangtze-Huaihe River valley,respectively,although the chance for the occurrence of a "super" mei-yu with a similar intensity to that in 2020 would be rare in 2021.展开更多
In this paper, the notion of orthogonal vector-valued wavelet packets of space L2 （R^s, C^n） is introduced. A procedure for constructing the orthogonal vector-valued wavelet packets is presented. Their properties ar...In this paper, the notion of orthogonal vector-valued wavelet packets of space L2 （R^s, C^n） is introduced. A procedure for constructing the orthogonal vector-valued wavelet packets is presented. Their properties are characterized by virtue of time-frequency analysis method, matrix theory and finite group theory, and three orthogonality formulas are obtained. Finally, new orthonormal bases of space L2（R^s,C^n） are extracted from these wavelet packets.展开更多
Air temperature and relative humidity have been the main parameters of meteorology study. In the past data could be obtained from in-situ observations, but the observations are local and sparse, especially over ocean....Air temperature and relative humidity have been the main parameters of meteorology study. In the past data could be obtained from in-situ observations, but the observations are local and sparse, especially over ocean. Now we can get them from satellites, yet it is hard to estimate them from sat- ellites directly so far. This paper presents a new method to retrieve monthly averaged sea air temper- ature （SAT） and relative humidity （RH） near sea surface from satellite data with artificial neural networks （ANN）. Compared with the observations in Pacific and Atlantic, the root mean square （RMS） and the correlation between the estimated SAT and the observations are about 0.91 ~C and 0.99, respectively. The RMS and the correlation of RH are about 3.73% and 0.65, respectively. Compared with the multiple regression method, the ANN methodology is more powerful in building nonlinear relations in this research. Thus the global monthly average SAT and RH are retrieved from the fixed ANN network from July 1987 to May 2004. In general the annual average SAT shows the increasing trend in recent 18 years. The abnormality of SAT is decomposed with the empirical or- thogonal function （EOF）. The leading three EOFs could explain 84% of the total variation. EOF1 （76.1%） presents the seasonal change of the SAT abnormality. EOF2 （4.6%） is mainly related with ENSO. EOF3 （3.3%） shows some new interesting phenomena appearing in the three main currents in Pacific, Atlantic and Indian Ocean.展开更多
文摘针对油浸式电力变压器瞬态温升计算效率过低的问题,该文提出本征正交分解-αATS(proper orthogonal decomposition-adaptive time stepping based onαfactor,POD-αATS)降阶自适应变步长瞬态计算方法。首先,推导变压器绕组瞬态温升计算的有限元离散方程;其次,采用POD降阶算法改善传统瞬态计算中存在的条件数过大及方程阶数过高的问题;同时对于瞬态计算中的时间步长选择问题,提出适用于非线性问题的αATS变步长策略;然后,为验证方法的有效性,基于110 kV油浸式电力变压器绕组的基本结构建立二维八分区数值计算模型,同时将计算结果与基于110 kV绕组的温升实验结果进行对比。数值计算及实验结果表明,所提算法与全阶定步长算法在流场和温度场中的精度几乎相同,且流场计算效率提升约45倍,温度场计算效率提升约38倍,计算速度得到显著提高。这一点在温升实验中同样得到验证,说明该文所提算法的准确性、高效性及一定的工程实用性。
基金sponsored by the National Natural Science Foundation of China [grant numbers 420881014199128342025502]。
文摘Precipitation prediction is essential for disaster prevention,yet it still remains a challenging issue in weather and climate studies.This paper proposes an effective prediction method for summer precipitation over eastern China(PEC) by combining empirical orthogonal function(EOF) analysis with the interannual increment approach.Three statistical prediction models are individually developed for respective predictions of the three principal components(PCs) corresponding to the three leading EOF modes for the interannual increment of PEC(hereafter DY;EC).Each model is run for the month of March with two previous predictors derived from sea-ice concentration/soil moisture/sea surface temperature/snow depth/sea level pressure over specific regions.The predicted PCs are projected to the EOF modes derived from observations of DY;EC to produce a new DY;EC.This new DY;EC is then added to the observed PEC of the previous year to obtain the final predicted PEC.The spatial features of the predicted PEC are highly consistent with observations,with the anomaly correlation coefficient skill ranging from 0.32 to 0.64 during 2012-2020.The method is applied for real-time prediction of PEC in 2021.And the results indicate two rain belts located over northeastern China and the Yangtze-Huaihe River valley,respectively,although the chance for the occurrence of a "super" mei-yu with a similar intensity to that in 2020 would be rare in 2021.
基金Foundation item： Supported by the Natural Science Foundation of China（10571113）
文摘In this paper, the notion of orthogonal vector-valued wavelet packets of space L2 （R^s, C^n） is introduced. A procedure for constructing the orthogonal vector-valued wavelet packets is presented. Their properties are characterized by virtue of time-frequency analysis method, matrix theory and finite group theory, and three orthogonality formulas are obtained. Finally, new orthonormal bases of space L2（R^s,C^n） are extracted from these wavelet packets.
基金Supported by The National Key Technology R&D Program(No.2013BAD13B01)the National High Technology Research and Development Program of China(No.2001AA633060)
文摘Air temperature and relative humidity have been the main parameters of meteorology study. In the past data could be obtained from in-situ observations, but the observations are local and sparse, especially over ocean. Now we can get them from satellites, yet it is hard to estimate them from sat- ellites directly so far. This paper presents a new method to retrieve monthly averaged sea air temper- ature （SAT） and relative humidity （RH） near sea surface from satellite data with artificial neural networks （ANN）. Compared with the observations in Pacific and Atlantic, the root mean square （RMS） and the correlation between the estimated SAT and the observations are about 0.91 ~C and 0.99, respectively. The RMS and the correlation of RH are about 3.73% and 0.65, respectively. Compared with the multiple regression method, the ANN methodology is more powerful in building nonlinear relations in this research. Thus the global monthly average SAT and RH are retrieved from the fixed ANN network from July 1987 to May 2004. In general the annual average SAT shows the increasing trend in recent 18 years. The abnormality of SAT is decomposed with the empirical or- thogonal function （EOF）. The leading three EOFs could explain 84% of the total variation. EOF1 （76.1%） presents the seasonal change of the SAT abnormality. EOF2 （4.6%） is mainly related with ENSO. EOF3 （3.3%） shows some new interesting phenomena appearing in the three main currents in Pacific, Atlantic and Indian Ocean.
