The important effects of snow cover to ground thermal decades. In the most of previous research, the effects were usually regime has received much attention of scholars during the past few evaluated through the numeri...The important effects of snow cover to ground thermal decades. In the most of previous research, the effects were usually regime has received much attention of scholars during the past few evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial （the ground profile） and time series analysis in the extremely snowy winter of 2012-2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011-2012 based on the measured data collected by 63 meteorological stations, Our results illustrate the positive （warmer） effect of snow cover on the ground temperature （GT） on the daily basis, the highest difference between GT and daily mean air temperature （DGAT） is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the resnonse denth of ground to the alteration of snow depth is far more than 40 cm.展开更多
An unequal time interval sequence or a sequence with blanks is usually completed with average generation in grey system theory. This paper discovers that there exists obvious errors when using average generation to ge...An unequal time interval sequence or a sequence with blanks is usually completed with average generation in grey system theory. This paper discovers that there exists obvious errors when using average generation to generate internal points of non-consecutive neighbours. The average generation and the preference generation of the sequence are discussed, the concave and convex properties show the status of local sequence and propose a new idea for using the status to build up the criteria of choosing generation coefficient. Compared with the general average method of the one-dimensional data sequence, the two-dimensional data sequence is defined and its average generation is discussed, and the coefficient decision method for the preference generation is presented.展开更多
Studies on the reconstruction of global mean temperature series are reviewed by introducing three series, Had- CRUT3, NCDC, and GISS in details. Satellite data have been used since 1982 in NCDC and GISS series. NCDC s...Studies on the reconstruction of global mean temperature series are reviewed by introducing three series, Had- CRUT3, NCDC, and GISS in details. Satellite data have been used since 1982 in NCDC and GISS series. NCDC series has the most complete spatial coverage among the three by using statistic interpolation technique. The weakened global warming in 2000-2009 as revealed in HadCRUT3 data is possibly caused by the lack of data coverage of this dataset over the Arctic. GISS and NCDC series showed much stronger warming trends during the last 10 years （-0.1 ℃ per 10 years）. Three series yielded almost the same warming trend for 1910-2009 （ 0.70-0.75 ℃ per 100 years）.展开更多
The need for accurate rainfall prediction is readily apparent when considering many benefits in which such information would provide for river control, reservoir operation, forestry interests, flood mitigation, etc.. ...The need for accurate rainfall prediction is readily apparent when considering many benefits in which such information would provide for river control, reservoir operation, forestry interests, flood mitigation, etc.. Due to importance of rainfall in many aspects, studies on rainfall forecast have been conducted since a few decades ago. Although many methods have been introduced, all the researches describe the study as complex because it involves numerous variables and still need to be improved. Nowadays, there are various traditional techniques and mathematical models available, yet, there are no result on which method provide the most reliable estimation. AR （auto-regressive）, ARMA （auto-regressive moving average）, ARIMA （auto-regressive integrated moving average） and ANNs （artificial neural networks） were introduced as a useful and efficient tool for modeling and forecasting. The conventional time series provide reasonable accuracy but suffer from the assumptions of stationary and linearity. The concept of neurons was introduced first which then developed to ANNs with back propagation training algorithm. Although certain ANNs） models are equivalent to time series model, but it is limited to short term forecasting. This Paper presents a mathematical approach for rainfall forecasting for Iran on monthly basic. The model is trained for monthly rainfall forecasting and tested to evaluate the performance of the model. The result Shows reasonably good accuracy for monthly rainfall forecasting.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41471289,41301368)Natural Science Foundation of Jilin Province(No.20140101158JC)Foundation of State Key Laboratory of Remote Sensing Science(No.OFSLRSS201517)
文摘The important effects of snow cover to ground thermal decades. In the most of previous research, the effects were usually regime has received much attention of scholars during the past few evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial （the ground profile） and time series analysis in the extremely snowy winter of 2012-2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011-2012 based on the measured data collected by 63 meteorological stations, Our results illustrate the positive （warmer） effect of snow cover on the ground temperature （GT） on the daily basis, the highest difference between GT and daily mean air temperature （DGAT） is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the resnonse denth of ground to the alteration of snow depth is far more than 40 cm.
文摘An unequal time interval sequence or a sequence with blanks is usually completed with average generation in grey system theory. This paper discovers that there exists obvious errors when using average generation to generate internal points of non-consecutive neighbours. The average generation and the preference generation of the sequence are discussed, the concave and convex properties show the status of local sequence and propose a new idea for using the status to build up the criteria of choosing generation coefficient. Compared with the general average method of the one-dimensional data sequence, the two-dimensional data sequence is defined and its average generation is discussed, and the coefficient decision method for the preference generation is presented.
基金supported by LASG Open Research Program and National Natural Science Foundation of China (No41005035/D0507)
文摘Studies on the reconstruction of global mean temperature series are reviewed by introducing three series, Had- CRUT3, NCDC, and GISS in details. Satellite data have been used since 1982 in NCDC and GISS series. NCDC series has the most complete spatial coverage among the three by using statistic interpolation technique. The weakened global warming in 2000-2009 as revealed in HadCRUT3 data is possibly caused by the lack of data coverage of this dataset over the Arctic. GISS and NCDC series showed much stronger warming trends during the last 10 years （-0.1 ℃ per 10 years）. Three series yielded almost the same warming trend for 1910-2009 （ 0.70-0.75 ℃ per 100 years）.
文摘The need for accurate rainfall prediction is readily apparent when considering many benefits in which such information would provide for river control, reservoir operation, forestry interests, flood mitigation, etc.. Due to importance of rainfall in many aspects, studies on rainfall forecast have been conducted since a few decades ago. Although many methods have been introduced, all the researches describe the study as complex because it involves numerous variables and still need to be improved. Nowadays, there are various traditional techniques and mathematical models available, yet, there are no result on which method provide the most reliable estimation. AR （auto-regressive）, ARMA （auto-regressive moving average）, ARIMA （auto-regressive integrated moving average） and ANNs （artificial neural networks） were introduced as a useful and efficient tool for modeling and forecasting. The conventional time series provide reasonable accuracy but suffer from the assumptions of stationary and linearity. The concept of neurons was introduced first which then developed to ANNs with back propagation training algorithm. Although certain ANNs） models are equivalent to time series model, but it is limited to short term forecasting. This Paper presents a mathematical approach for rainfall forecasting for Iran on monthly basic. The model is trained for monthly rainfall forecasting and tested to evaluate the performance of the model. The result Shows reasonably good accuracy for monthly rainfall forecasting.
