CN102749664B - Method for predicting icing degree of power gird - Google Patents

Method for predicting icing degree of power gird Download PDF

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CN102749664B
CN102749664B CN201210248539.XA CN201210248539A CN102749664B CN 102749664 B CN102749664 B CN 102749664B CN 201210248539 A CN201210248539 A CN 201210248539A CN 102749664 B CN102749664 B CN 102749664B
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temperature
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陆佳政
徐勋建
李波
张红先
方针
杨莉
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
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Hu'nan Huicui Electric Power Technology Co Ltd
Electric Power Research Institute of State Grid Hunan Electric Power Co Ltd
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Abstract

The invention discloses a method for predicting the icing degree of a power gird, comprising the following steps of: finding out seventeen meteorological impact factors with high relativity by using historical icing data, the geographic information meteorological data, computing the weight coefficient of each meteorological impact factor by an analytic hierarchy process, and synthesizing the seventeen meteorological impact factors according to the weight to predict the icing degree of the power gird. The method can predict the icing degree of the power gird one moth in advance, so that a corresponding emergency disposal plan can be made according to a predicting conclusion, a most economical and feasible power gird icing solution can be selected, the power gird deicing equipment, material assets and team can be reserved, the safe and stable running when the power gird is iced can be guaranteed, the early response to the icing of the power gird can be realized, and the loss caused by the icing of the power gird can be reduced.

Description

电网覆冰程度预测方法Prediction Method of Icing Degree of Power Grid

技术领域technical field

本发明属于输配电技术领域，具体涉及一种电网覆冰程度预测方法。The invention belongs to the technical field of power transmission and distribution, and in particular relates to a method for predicting the icing degree of a power grid.

背景技术Background technique

覆冰严重威胁电网的安全稳定运行，2008年初的南方冰灾，国家电网220kV及以上输电线路倒塔1300多基，财产损失120多亿元，工厂、医院和居民区停电，京广电气化铁路停运，对社会稳定和人民生产与生活构成了严重威胁。提前进行电网覆冰长期预测，可以根据电网覆冰预测结果提前做好应对措施，进行提前预警，减少电网覆冰所造成的损失。Icing seriously threatens the safe and stable operation of the power grid. In the southern ice disaster in early 2008, more than 1,300 towers of 220kV and above transmission lines of the State Grid collapsed, property losses exceeded 12 billion yuan, power outages occurred in factories, hospitals and residential areas, and the Beijing-Guangzhou electrified railway was shut down. It poses a serious threat to social stability and people's production and life. The long-term prediction of grid icing in advance can make countermeasures in advance according to the forecast results of grid icing, and carry out early warning to reduce the losses caused by grid icing.

目前国内外的输电线路覆冰预测研究主要考虑输电线路在特定气象条件下覆冰增长的微观过程，如具有代表性的Imai模型、Lenhard模型、Goodwin模型、Chaine模型和Makkonen模型，属于输电线路覆冰增长速率的短期预测，并且预测模型非常复杂，只适合在假定气象条件下的短期覆冰预测，没有探索输电线路覆冰气候的形成规律。At present, domestic and foreign studies on transmission line icing prediction mainly consider the microscopic process of transmission line icing growth under specific meteorological conditions, such as the representative Imai model, Lenhard model, Goodwin model, Chaine model and Makkonen model, which belong to the transmission line icing growth model. The short-term prediction of ice growth rate, and the prediction model is very complex, which is only suitable for short-term icing prediction under assumed meteorological conditions, and does not explore the formation law of transmission line icing climate.

发明内容Contents of the invention

本发明的目的是提供一种能进行长期覆冰预测的电网覆冰程度预测方法。The purpose of the present invention is to provide a method for predicting the degree of icing of a power grid capable of performing long-term icing prediction.

本发明提供的电网覆冰程度预测方法，包括以下步骤：The grid icing degree prediction method provided by the present invention comprises the following steps:

（1）收集、整理、统计分析省级区域内60年的历史气象资料和电网覆冰(1) Collect, collate, and statistically analyze 60 years of historical meteorological data and power grid icing in provincial areas

资料，统计出平均覆冰日数，划分电网覆冰程度级别；Data, count the average number of days covered with ice, and classify the level of ice covered in the power grid;

（2）按照覆冰相关性计算公式计算出历史上的气象参数与平均覆冰日数的相关系数，覆冰相关性计算公式为：(2) Calculate the correlation coefficient between the historical meteorological parameters and the average number of icing days according to the calculation formula of icing correlation. The calculation formula of icing correlation is:

RR Xx == nno ΣΣ ii == 11 nno xx ii dd ii -- ΣΣ ii == 11 nno xx ii ·· ΣΣ ii == 11 nno dd ii nno ΣΣ ii == 11 nno xx ii 22 -- (( ΣΣ ii == 11 nno xx ii )) 22 ·· nno ΣΣ ii == 11 nno dd ii 22 -- (( ΣΣ ii == 11 nno dd ii )) 22

式中RX为气象参数与平均覆冰日数的相关系数，xi为气象参数的值，i为年份序号，n为历史年份数量，di为某一年份的平均覆冰日数,平均覆冰日数为各个覆冰监测站点的覆冰日数相累加除以监测站数;In the formula, R X is the correlation coefficient between meteorological parameters and the average number of ice-covered days, x i is the value of meteorological parameters, i is the serial number of the year, n is the number of historical years, d i is the average number of ice-covered days in a certain year, and the average number of ice-covered days The number of days is the sum of the number of icing days at each icing monitoring station divided by the number of monitoring stations;

（3）从步骤（2）的计算结果中筛选出相关性最强的17个气象影响因子；(3) Screen out the 17 meteorological impact factors with the strongest correlation from the calculation results of step (2);

（4）利用统计分析，找出步骤（3）所述的17个气象影响因子影响电网覆冰的特征规律；(4) Use statistical analysis to find out the characteristic law of the 17 meteorological influence factors mentioned in step (3) affecting the icing of the power grid;

（5）利用数理统计，求出17个气象影响因子的基于特征规律的各种覆冰程度的统计概率，建立各因子特征规律的覆冰程度发生频率统计表；(5) Using mathematical statistics, the statistical probabilities of various icing degrees based on the characteristic laws of the 17 meteorological influencing factors were obtained, and a statistical table of the occurrence frequency of icing degrees based on the characteristic laws of each factor was established;

（6）收集整理需预测电网覆冰程度年份的气象数据，输入对应的17因子预测年份的数据；(6) Collect and sort out the meteorological data of the year that needs to predict the icing degree of the power grid, and input the data of the corresponding 17-factor forecast year;

（7）利用预测年份的数据，综合特征规律，并查询覆冰程度发生频率统计表，得出预测年份的轻度覆冰频率为si，中度覆冰频率为mi，严重覆冰频率为li，特别严重频率为ni，i=1,2,...,17；(7) Using the data of the forecast year, comprehensive characteristics and rules, and querying the frequency statistics table of the degree of icing, it is obtained that the frequency of light icing in the forecast year is s i , the frequency of moderate icing is m i , and the frequency of severe icing is is l i , especially serious frequency is n i , i=1,2,...,17;

（8）根据预报经验，给出17因子对电网覆冰日数影响的权重系数，分别为ɑ1，ɑ2,...ɑ17；(8) According to the forecasting experience, the weight coefficients of the 17 factors affecting the number of ice-covered days of the power grid are given, which are ɑ 1 , ɑ 2 ,...ɑ 17 ;

（9）利用权重系统，通过加权平均，计算出预测年份在17因子影响下的各种覆冰程度的总预测概率，计算公式为：(9) Using the weight system, calculate the total forecast probability of various icing degrees under the influence of 17 factors in the forecast year through weighted average. The calculation formula is:

轻度覆冰发生概率： Probability of light icing occurrence:

中度覆冰发生概率： M = Σ i = 1 17 a i × m i Probability of moderate icing occurrence: m = Σ i = 1 17 a i × m i

严重覆冰发生概率： Severe icing occurrence probability:

特别严重覆冰发生概率： Occurrence probability of particularly severe icing:

本发明从覆冰形成的气候规律开展宏观方面的研究，利用历史覆冰、地理信息气象资料，找出相关性强的17个气象影响因子，利用层次分析法计算出各气象影响因子的权重系数，根据权重综合17个气象影响因子，形成电网覆冰程度预测方法，在每年冬季覆冰来临之前，进行电网输电线路覆冰程度预测。对省级电网区域，提前一个月对冬季电网覆冰程度进行预测，解决了电网覆冰长期预测的难题。根据预测结论，做好相应的应急处置预案，选择最为经济可行的电网覆冰应对措施，储备电网除冰设备、物资和队伍，确保电网覆冰时的安全稳定运行，实现电网覆冰的提前应对，减少电网覆冰所造成的损失。The present invention conducts macro research on the climate law formed by ice covering, uses historical ice covering and geographical information meteorological data, finds out 17 meteorological influence factors with strong correlation, and calculates the weight coefficient of each meteorological influence factor by using the analytic hierarchy process According to the weights of 17 meteorological influence factors, a prediction method for grid icing degree is formed, and the grid transmission line icing degree is predicted before the winter icing comes every year. For the provincial power grid area, the winter power grid icing degree is predicted one month in advance, which solves the problem of long-term prediction of power grid icing. According to the forecast conclusion, make corresponding emergency response plans, choose the most economical and feasible grid icing countermeasures, reserve grid deicing equipment, materials and teams, ensure the safe and stable operation of the grid during icing, and realize the early response to grid icing , to reduce the losses caused by grid icing.

附图说明Description of drawings

图1为本发明所述的电网覆冰程度预测方法的流程框图。Fig. 1 is a flow chart of the method for predicting the icing degree of the power grid according to the present invention.

具体实施方式Detailed ways

以下是用本发明方法对湖南省电网覆冰程度预测的一个实施例：从2008-2009年开始，每年11月进行了湖南电网覆冰长期预报，截至目前为止，长期预报均准确。其具体方法是：The following is an example of using the method of the present invention to predict the degree of icing of the power grid in Hunan Province: From 2008-2009, a long-term forecast of icing in Hunan power grid was carried out in November every year. So far, the long-term forecasts have been accurate. The specific method is:

（一）、首先收集、整理、统计分析湖南省省级级区域内60年的历史气象资料和电网覆冰资料，统计出平均覆冰日数，划分电网覆冰程度级别。划分电网覆冰程度级别为：(1) First, collect, collate, and statistically analyze 60 years of historical meteorological data and power grid icing data in the provincial-level area of Hunan Province, calculate the average number of icing days, and classify the level of power grid icing. The level of grid icing degree is divided into:

轻度覆冰：0天＜平均覆冰日数≤3天；Slight icing: 0 days < average icing days ≤ 3 days;

中度覆冰：3天＜平均覆冰日数≤5天；Moderate icing: 3 days < average icing days ≤ 5 days;

严重覆冰：5天＜平均覆冰日数＜11天；Severe icing: 5 days < average icing days < 11 days;

特别严重覆冰：平均覆冰日数≥11天。Particularly severe icing: average icing days ≥ 11 days.

平均覆冰日数为：各个覆冰监测站点的覆冰日数相累加除以监测站数。The average number of ice-covered days is: the sum of the number of ice-covered days at each ice-covered monitoring station divided by the number of monitoring stations.

（二）、利用覆冰相关性计算公式(2) Using the icing correlation calculation formula

RR Xx == nno &Sigma;&Sigma; ii == 11 nno xx ii dd ii -- &Sigma;&Sigma; ii == 11 nno xx ii &CenterDot;&CenterDot; &Sigma;&Sigma; ii == 11 nno dd ii nno &Sigma;&Sigma; ii == 11 nno xx ii 22 -- (( &Sigma;&Sigma; ii == 11 nno xx ii )) 22 &CenterDot;&CenterDot; nno &Sigma;&Sigma; ii == 11 nno dd ii 22 -- (( &Sigma;&Sigma; ii == 11 nno dd ii )) 22

式中，RX为气象参数X与平均覆冰日数的相关系数，xi为气象参数的值，i为年份序号，n为历史年份数量，di为某一年份的平均覆冰日数。In the formula, R X is the correlation coefficient between the meteorological parameter X and the average number of ice-covered days, x i is the value of the meteorological parameter, i is the serial number of the year, n is the number of historical years, and d i is the average number of ice-covered days in a certain year.

计算出历史上的气象影响因子与平均覆冰日数的相关系数，从计算结果中筛选出相关系数最大的17个气象影响因子，这17个气象影响因子分别为：太阳黑子、海温异常、大气环流、副热带高压、副热带高压强度季节变换、副热带高压面积与脊线、副热带高压与相似年比较、东亚环流、旱涝、二月最高气温、九月中旬平均气温、九月下半月高温天数、十月最高气温、四五月气温距平、九月降水量、北极涛动、严重和特别严重覆冰年后。按照这个方法收集预测年份的相关性最强的17个气象影响因子资料。利用统计分析，找出17个气象影响因子影响电网覆冰的特征规律，在本试验中17个气象影响因子影响电网覆冰的特征规律是：Calculate the correlation coefficient between the historical meteorological influencing factors and the average number of ice-covered days, and select 17 meteorological influencing factors with the largest correlation coefficients from the calculation results. These 17 meteorological influencing factors are: sunspots, sea temperature anomalies, atmospheric Circulation, subtropical high, seasonal change of subtropical high intensity, area and ridge of subtropical high, comparison between subtropical high and similar years, East Asian circulation, drought and flood, maximum temperature in February, average temperature in mid-September, number of high-temperature days in the second half of September, ten Monthly maximum temperature, April and May temperature anomalies, September precipitation, Arctic Oscillation, severe and particularly severe ice years. According to this method, the data of the 17 meteorological impact factors with the strongest correlation in the forecast year are collected. Using statistical analysis, find out the characteristic law of 17 meteorological influence factors affecting power grid icing. In this experiment, the characteristic law of 17 meteorological influence factors affecting power grid icing is:

太阳黑子：当太阳黑子数处于谷年份，容易发生电网特别严重覆冰；Sunspots: When the number of sunspots is in a valley year, the power grid is likely to be particularly seriously iced;

海温异常：出现厄尔尼诺或拉尼娜现象容易引起冬季电网严重及特别严重覆冰；Abnormal sea temperature: the appearance of El Niño or La Niña is likely to cause severe and particularly severe ice covering of the power grid in winter;

大气环流：当8月北半球极涡面积指数偏小，冬季冷空气偏弱，不易发生覆冰，当8月北半球极涡面积指数偏大，冬季冷空气偏强，容易发生寒潮，易发生覆冰；Atmospheric circulation: When the polar vortex area index in the northern hemisphere is relatively small in August, the cold air in winter is weak, and icing is not likely to occur; when the polar vortex area index in the northern hemisphere is relatively large in August, the cold air in winter is relatively strong, and cold waves and icing are prone to occur ;

副热带高压：七月副热带高压脊点位置越西，冬季冷空气越强，越容易出现特别严重覆冰；Subtropical high pressure: The wester the subtropical high pressure ridge point in July, the stronger the cold air in winter, and the more likely to have particularly severe ice cover;

副热带高压强度季节变换：春夏和夏秋副热带高压强度变换越大，当年冬季越容易出现覆冰；Seasonal changes in the intensity of the subtropical high: the greater the change in the intensity of the subtropical high in spring-summer and summer-autumn, the more likely ice will appear in that winter;

副热带高压脊线：年初（2月）脊线偏南和年中（6月）脊线偏北时，冬季容易发生覆冰；Subtropical high pressure ridge: When the ridge is southward at the beginning of the year (February) and northward in the middle of the year (June), icing is likely to occur in winter;

副热带高压与相似年比较：当副热带高压的强度变化相似，则覆冰情况也相似；Comparing the subtropical high with similar years: when the intensity of the subtropical high changes similarly, the icing situation is also similar;

东亚环流：4月纬向环流和8月经向环流偏小时，当年冬季容易发生覆冰；Circulation in East Asia: The zonal circulation in April and the meridional circulation in August are relatively small, and icing is prone to occur in the winter of that year;

旱涝：旱年的冬季容易覆冰，涝年的冬季不容易覆冰；Drought and Flood: It is easy to be covered with ice in the winter of dry years, but not easy to be covered with ice in the winter of flooded years;

二月最高气温：当二月最高气温高于平均值时，当年冬季容易发生覆冰；Maximum temperature in February: When the maximum temperature in February is higher than the average, ice is prone to occur in the winter of that year;

九月中旬平均气温：九月中旬平均气温高于历史同期平均气温时，冬季容易发生严重覆冰；Average temperature in mid-September: When the average temperature in mid-September is higher than the average temperature in the same period of history, it is prone to severe icing in winter;

九月下半月高温天数：九月下半月高温天数多的年份，冬季温度较高，不易发生严重覆冰。Number of high-temperature days in the second half of September: In years with a large number of high-temperature days in the second half of September, the temperature in winter is relatively high, and severe icing is unlikely to occur.

十月最高气温：十月最高气温高于29℃时，冬季容易发生覆冰；Maximum temperature in October: When the maximum temperature in October is higher than 29°C, ice is prone to occur in winter;

四五月气温距平：长沙四五月气温距平值均较大时，容易发生覆冰；Temperature anomalies in April and May: When the temperature anomalies in April and May in Changsha are relatively large, icing is likely to occur;

九月降水量：长沙九月降水量小于平均值，冬季容易发生覆冰；Precipitation in September: The precipitation in September in Changsha is less than the average, and ice is prone to occur in winter;

北极涛动：北极涛动处于正相位时，容易发生覆冰；Arctic Oscillation: When the Arctic Oscillation is in positive phase, icing is prone to occur;

严重和特别严重覆冰年后：严重和特别严重覆冰年后不容易发生严重及以上程度覆冰。（三）、利用数理统计，统计出17个影响因子的基于特征规律的各种覆冰程度的发生次数（发生频率），建立各因子特征规律的覆冰程度发生频率统计表，如表1所示；收集预测年份的17预测因子的特征规律，如表2所示；利用预测年份的数据，综合特征规律，并查询覆冰程度发生频率统计表，得出预测年份的轻度覆冰频率为si，中等覆冰频率为mi，严重覆冰频率为li，特别严重频率为ni，i=1,2,...,17（如太阳黑子2008年处于谷值年附近，通过查询表1可知（蓝色标注），谷值年的四种程度覆冰的发生频率就是2008年的四种程度覆冰的发生频率s1=40%，m1=40%，l1=20%，n1=0）；根据多年预报经验，将17个因子中副热带高压面积与脊点、二月最高气温、四五月气温距平、严重和特别严重覆冰年后四个因子的权重设为2，其余的设为1，则通过归一化，可得出各因子加权平均系统ɑ1，ɑ2,...ɑ17，如表2中所示；Severe and particularly severe ice-covered years: Severe and above-level ice-covered years are not likely to occur in severe and particularly severe ice-covered years. (3) Using mathematical statistics, the occurrence times (occurrence frequency) of various icing degrees based on the characteristic law of the 17 influencing factors were counted, and a statistical table of the occurrence frequency of icing degree based on the characteristic law of each factor was established, as shown in Table 1. The characteristic law of the 17 predictors in the forecast year is collected, as shown in Table 2; using the data of the forecast year, the comprehensive feature law, and querying the statistical table of the occurrence frequency of ice cover, the light ice cover frequency in the forecast year is obtained as s i , the medium icing frequency is mi , the severe icing frequency is l i , and the particularly severe icing frequency is n i , i=1,2,...,17 (for example, the sunspot is near the valley value year in 2008, through It can be seen from Table 1 (marked in blue) that the occurrence frequency of the four levels of icing in the valley year is the frequency of occurrence of the four levels of icing in 2008 s 1 =40%, m 1 =40%, l 1 =20 %, n 1 =0); based on the multi-year forecast experience, the weights of the subtropical high area and the ridge point, the maximum temperature in February, the temperature anomaly in April and May, and the severe and particularly severe ice-covered years among the 17 factors Set it to 2, and set the rest to 1, then through normalization, the weighted average system of each factor ɑ 1 , ɑ 2 ,...ɑ 17 can be obtained, as shown in Table 2;

利用权重系统，通过加权平均，计算出预测年份在17因子影响下的各种覆冰程度的总预测概率，计算公式为：Using the weight system and weighted average, the total forecast probability of various icing degrees in the forecast year under the influence of 17 factors is calculated. The calculation formula is:

轻度覆冰发生概率： Probability of light icing occurrence:

中度覆冰发生概率： M = &Sigma; i = 1 17 a i &times; m i Probability of moderate icing occurrence: m = &Sigma; i = 1 17 a i &times; m i

严重覆冰发生概率： Severe icing occurrence probability:

特别严重覆冰发生概率： Occurrence probability of particularly severe icing:

根据层次分析的加权系数，可得出加权平均后总覆冰概率，如表2所示。According to the weighting coefficient of AHP, the total icing probability after weighted average can be obtained, as shown in Table 2.

表1 覆冰程度发生频率统计表（太阳黑子）Table 1 Statistics table of occurrence frequency of icing degree (sunspots)

表2 多因子耦合2008-2009年预报结果Table 2 Multi-factor coupling forecast results for 2008-2009

2008-2009年冬季覆冰预测结论为轻度覆冰，具体发生概率为：轻度60%、中等29%、严重8%、特别严重3%，实际覆冰天数1.7天，属轻度覆冰，预测准确。The forecast conclusion of winter icing in 2008-2009 is mild icing, and the specific probability of occurrence is: 60% for mild, 29% for moderate, 8% for serious, 3% for extreme seriousness, and the actual number of icing days is 1.7 days, which belongs to light icing , the prediction is accurate.

Claims (2)

1. an electrical network icing degree Forecasting Methodology, is characterized in that comprising the following steps:
(1) in collection, arrangement, statistical study provincial region, the Historical Meteorological Information of 60 years and electrical network icing data, count average icing number of days, divides electrical network icing degree rank;
(2) according to icing correlation calculations formula, calculate the related coefficient of historical meteorologic parameter and average icing number of days, icing correlation calculations formula is:
R in formula xfor the related coefficient of meteorologic parameter with average icing number of days, x ifor the value of meteorologic parameter, i is time sequence number, and n is historical time quantity, d ifor the average icing number of days in a certain time, the icing number of days that average icing number of days is each icing monitoring station is cumulative divided by monitoring station number mutually;
(3) from the result of calculation of step (2), filter out 17 meteorological effect factors that correlativity is the strongest;
(4) utilize statistical study, find out the characteristic rule of 17 described meteorological effect Effects of Factors electrical network icing of step (3);
(5) utilize mathematical statistics, obtain the statistical probability of the various icing degree based on characteristic rule of 17 meteorological effect factors, set up the icing degree occurrence frequency statistical form of each factor characteristic rule;
(6) compile the weather data that needs the prediction electrical network icing degree time, the data in 17 factor prediction times that input is corresponding;
(7) utilize the data in prediction time, according to characteristic rule, inquiry icing degree occurrence frequency statistical form, show that the slight icing frequency in prediction time is s i, moderate icing frequency is m i, serious icing frequency is l i, especially severe frequency is n i, i=1,2 ..., 17;
(8) experience according to weather report, provides the weight coefficient of 17 factor pair electrical network icing numbers of days impacts, is respectively ɑ 1, ɑ 2... ɑ 17;
(9) utilize weight system, by weighted mean, calculate total prediction probability of the various icing degree of prediction time under 17 Effects of Factors, computing formula is:
Slight icing probability of happening:
Moderate icing probability of happening:
Serious icing probability of happening:
Especially severe icing probability of happening:
Slight icing: the 0 day average icing of < number of days≤3 day;
Moderate icing: the 3 days average icing of < number of days≤5 day;
Serious icing: the average icing number of days of < < was 11 days in 5 days;
Especially severe icing: average icing number of days >=11 day;
Average icing number of days is: the icing number of days of each icing monitoring station is cumulative divided by monitoring station number mutually.
2. electrical network icing degree Forecasting Methodology according to claim 1, it is characterized in that: 17 meteorological effect factors that described correlativity is the strongest are respectively for Hunan Province: sunspot, sea temperature abnormality, general circulation, subtropical high, Strength of Subtropical High conversion in season, subtropical high area and crestal line, subtropical high and analog year comparison, East Asian Circulation, drought and waterlogging, February the highest temperature, mid-September temperature on average, the second half of the month in September high temperature number of days, October the highest temperature, four May temperature depature, September quantity of precipitation, Arctic Oscillation, after serious and especially severe icing year, the feature of described 17 meteorological effect Effects of Factors electrical network icing is respectively:
Sunspot: when sunspot number is in the paddy time, electrical network especially severe icing easily occurs;
Sea temperature abnormality: then or occur that EI Nino or La Nina Phenomenon easily cause the serious and especially severe icing of electrical network in winter the previous year;
General circulation: area index is less than normal when the Northern Hemisphere utmost point in August whirlpool, and winter, cold air was on the weak side, is difficult for icing occurs, and when the Northern Hemisphere utmost point in August whirlpool, area index is bigger than normal, and winter, cold air was partially strong, and cold wave easily occurs, and icing easily occurs;
Subtropical high: Yuexi, subtropical ridge point in July position, winter, cold air was stronger, more easily occurred especially severe icing;
Strength of Subtropical High conversion in season: spring and summer and the conversion of Xia Qiu Strength of Subtropical High are larger, more easily occur then icing winter;
Subtropical high crestal line: the beginning of the year crestal line by north and year in crestal line when by north, easily there is icing winter;
Subtropical high and analog year comparison: similar when the Strength Changes of subtropical high, icing situation is also similar;
East Asian Circulation: when April, zonal circulation and August, meridional circulation was less than normal, icing easily occurs then winter;
Drought and waterlogging: the easy icing in winter in the year of drought, is not easy icing the winter in waterlogging year;
February the highest temperature: when February, the highest temperature was higher than mean value, easily there is then icing winter;
Mid-September temperature on average: during temperature on average, easily there is serious icing higher than historical same period winter in temperature on average mid-September;
The second half of the month in September high temperature number of days: in the second half of the month in September in many times of high temperature number of days, winter temperature is higher, is difficult for occurring serious icing;
October the highest temperature: when October, the highest temperature was lower than 29 ℃, easily there is icing winter;
Four May temperature depature: when four Mays, temperature depature value was all large, easily there is icing;
September quantity of precipitation: September, quantity of precipitation was less than mean value, and icing easily occurs winter;
Arctic Oscillation: Arctic Oscillation, when positive phase, icing easily occurs;
After serious and especially severe icing year: seriously and after especially severe icing year be not easy to occur serious and above degree icing.
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