CN106202939A - A kind of ENSO icing in period key element Variations method - Google Patents

Abstract

The invention discloses a kind of ENSO icing in period key element Variations method, the method is following step: (1), data acquisition；(2.1), ENSO exponent data divides；(2.2), icing key element data divide；(3), mean value calculation；(4.1), minimum temperature anomaly value calculates；(4.2), accumulative precipitation anomaly value calculates；(5), icing key element Variations.The invention has the beneficial effects as follows: in terms of the variation characteristic computational analysis of powerline ice-covering key element, 1, provide a kind of relatively simple method, can the most comprehensively recognize the powerline ice-covering key element response condition to ENSO event；2, workable；3, improve the specific aim of powerline ice-covering preventing and controlling.

Description

A kind of ENSO icing in period key element Variations method

Technical field

The invention belongs to electrical engineering technical field, particularly relate to a kind of ENSO icing in period key element variation characteristic and divide Analysis method.

Background technology

Winter, icing disaster caused huge threat to the safe and stable operation of transmission line of electricity.Base more than 1300 is caused to fall tower The southern ice damages in 2008 that accident, large-area power-cuts 7 days as long as, the wide electric railway in capital are stopped transport, have allowed the whole society recognize The seriousness of icing disaster.Winter in 2015, there is again serious ice damage event in the ground such as China Liaoning, North China, non-easily for tradition The northern area safe operation of electric network of icing has beaten alarm bell.Therefore, electrical network icing feature analysis in winter is carried out for rationally adjusting Join resource, reduce icing casualty loss necessity self-evident.Owing to 2015 is strong El Nino Years, it is considered to ENSO event (including EI Nino event and Ramsey numbers) just has prominent reality meaning to the impact of electrical network icing and icing key element Justice.Precipitation, temperature are the key elements that icing occurs, and also there are certain distant correlation properties with ENSO event simultaneously.Current Electrical network icing degree Forecasting Methodology carries out indifference statistics, when the most not taking into full account ENSO for the icing data of a period of time The particularity of phase, ignores the icing key element such as precipitation, temperature the most to a certain extent and responds the differentiation of ENSO.For deeply Enter to analyze response mechanism and the impact of icing key element of ENSO icing in period, set up a kind of simple ENSO period Icing key element Variations method is imperative, to carrying out the most ice-covering-proof work, reduces electrical network disaster The loss that may bring, safeguards power network safety operation.

Summary of the invention

Lack the present situation analyzed for ENSO powerline ice-covering in period key element variation characteristic, the present invention provides a kind of ENSO icing in period key element Variations method, the method thinking novelty, clear process, simple to operate.

For achieving the above object, technical scheme is as follows:

A kind of ENSO icing in period key element Variations method, comprises the steps:

(1), data acquisition, obtain to be analyzed area the past period in, the icing with time correspondence it is critical to Prime number evidence, ENSO exponent data and average temperature of the whole year data, icing key element data include that the area icing phase to be analyzed is every The minimum temperature of individual month and accumulative precipitation；

(2.1), ENSO exponent data divides, according to the character of ENSO event, by ENSO exponent data according to EI Nino Period data, La Nina's period data, non-ENSO period data are divided into three classes, and the data of each apoplexy due to endogenous wind are suitable still according to the time Sequence arranges；

(2.2), icing key element data divide, when compareing EI Nino period, the La Nina that ENSO index is characterized Phase, non-ENSO period, the icing key element data being analysed to area are respectively divided into EI Nino period data, La Nina Period data, non-ENSO period data three class, the data of each apoplexy due to endogenous wind arrange still according to time sequencing；

(3), mean value calculation, selecting step (2.2) obtains to be analyzed area EI Nino icing in period it is critical to Prime number evidence, according to formula (1), is calculated the meansigma methods of its EI Nino minimum temperature in periodWith putting down of accumulative precipitation AverageChoose area to be analyzed La Nina's icing in period data again, according to formula (1), be calculated its La Nina period The meansigma methods of low temperatureMeansigma methods with accumulative precipitationChoose area to be analyzed non-ENSO icing in period data, according to Formula (1), is calculated the meansigma methods of its non-ENSO minimum temperature in periodMeansigma methods with accumulative precipitation

$\stackrel{\‾}{x}=\frac{\underset{i=1}{\overset{n}{\Σ}}{x}_i}{n}---\left(1\right)$

In formula,For minimum temperature or accumulative fall in interim a certain period when non-ENSO period, EI Nino period or La Nina The meansigma methods of the water yield, x_iFor minimum temperature in this or the data of accumulative precipitation, n is this period data total amount in period；

(4.1), minimum temperature anomaly value calculates, by minimum for period for the area to be analyzed EI Nino obtained in step (3) Temperature averagesENSO period minimum temperature meansigma methods non-with area to be analyzedSubtracting each other, the result obtained is as to be analyzed Area EI Nino minimum temperature in period anomaly valueWithSubtract each other with area to be analyzed average temperature of the whole year, The result arrived is as area to be analyzed minimum temperature correction value M_a, then use A_a1Divided by M_a, when the result that obtains is as EI Nino Phase minimum temperature anomaly percentage ratio

(4.2), accumulative precipitation anomaly value calculates, by tired for period for the area to be analyzed EI Nino obtained in step (3) Meter precipitation meansigma methodsENSO non-with area to be analyzed adds up precipitation meansigma methods periodSubtract each other, the result conduct obtained Area to be analyzed EI Nino adds up precipitation anomaly value periodUse A again_b1Divided byThe result obtained is made Precipitation anomaly percentage ratio is added up period for EI Nino

(4.3), respectively with area to be analyzed La Nina's minimum temperature in period meansigma methodsWith accumulative precipitation meansigma methods ReplaceWithRepeat step (4.1)～(4.2), obtain area to be analyzed La Nina's minimum temperature in period anomaly value A_a2With tired Meter precipitation anomaly value A_b2, and La Nina minimum temperature in period anomaly percentage ratio a₂With accumulative precipitation anomaly percentage ratio b₂；

(5.1), icing key element Variations, during by step (4.1)～(4.2) calculated EI Nino Phase anomaly value is analyzed, if area to be analyzed EI Nino minimum temperature in period anomaly value A_a1Or accumulative precipitation anomaly value A_b1For just, then it is assumed that EI Nino easily causes the increase that minimum temperature rises or adds up precipitation；Otherwise it is assumed that EI Nino Easily cause the minimizing that minimum temperature declines or adds up precipitation；

The absolute value of step (4.1)～two anomaly percentage ratios in (4.2) calculated EI Nino period is carried out Contrast, if a₁Absolute value is more than b₁Absolute value, then it is assumed that the variation characteristic of EI Nino minimum temperature in period is than accumulative precipitation Variation characteristic is the most prominent, if a₁Absolute value is less than b₁Absolute value, then it is assumed that EI Nino adds up the variation characteristic of precipitation period It is more prominent than the variation characteristic of minimum temperature, otherwise it is assumed that the two situation of change is close；Again by above-mentioned a₁And b₁Substitute into formula (2) EI Nino icing in period key element response coefficient, it is calculated:

E=5 × (| a |+| b |) (2)

In formula, E is ENSO icing in period key element response coefficient, and it obtains ell after substituting into EI Nino period data Nino response coefficient in period, obtains La Nina's response coefficient in period after substituting into La Nina's period data, when a is corresponding EI Nino Phase or La Nina's minimum temperature in period anomaly percentage ratio, b is to add up precipitation anomaly percentage ratio this period, and 5 is amplification coefficient；

EI Nino icing in period based on accumulative precipitation anomaly percentage ratio and minimum temperature anomaly percentage ratio it is critical to Element responsiveness judgment mode is: setting up a coordinate system, abscissa is that ENSO minimum temperature in period anomaly percentage ratio absolute value is taken advantage of With 5, minimum temperature anomaly percentage ratio absolute value substitute into obtain after EI Nino period data EI Nino minimum temperature in period away from Flat percentage ratio absolute value, obtains La Nina's minimum temperature in period anomaly percentage ratio absolute value after substituting into La Nina's period data, vertical Coordinate adds up precipitation anomaly percentage ratio absolute value period be multiplied by 5 for corresponding EI Nino period or La Nina, and icing it is critical to Element response coefficient E has 4 and settles in an area, and respectively radius is quadrant region S1, inner circle radius 2.5 exradius 5 of 2.5 Sector region S2, the sector region S3 of inner circle radius 5 exradius 10 and other regions of first quartile S4, such as Fig. 2；Really Determine settling in an area of E, if settling in an area in S1 district, then it represents that icing key element exists weak response to ENSO；If settling in an area in S2 district, then it represents that There is moderate response；If settling in an area in S3 district, then it represents that there is strong response；If settling in an area in S4 district, then it represents that exist extremely strong Response；

(5.2), respectively step (4.3) calculated La Nina anomaly in period value and anomaly percentage ratio are replaced strategic point respectively That Nino anomaly in period value and anomaly percentage ratio, repeat step (5.1), carry out La Nina's icing in period key element variation characteristic Analyze.

The invention has the beneficial effects as follows:

1, in terms of the variation characteristic computational analysis of powerline ice-covering key element, a kind of relatively simple side is provided Method, can the most comprehensively recognize the powerline ice-covering key element response condition to ENSO event；

2, workable；

3, improve the specific aim of powerline ice-covering preventing and controlling.According to analysis result, for icing key element, special It not the precipitation area obvious to ENSO event response, carry out in the winter in ENSO period and tackle work in advance, reduce electricity Network loss is lost.

Accompanying drawing explanation

Fig. 1 is the flow chart that the embodiment of the present invention analyzes method.

Fig. 2 is icing key element responsiveness schematic diagram in the embodiment of the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawings and example, the present invention will be further described.

The thinking of the embodiment of the present invention is: a kind of ENSO icing in period key element Variations method, is consideration Area to be analyzed powerline ice-covering key element, at the general variation characteristic in ENSO period, is set up and is added up period based on ENSO The response characteristic of precipitation anomaly and minimum temperature anomaly analyzes method.

As it is shown in figure 1, the method comprises the steps:

(1), data acquisition.Obtaining in area the past period to be analyzed, the icing with time correspondence it is critical to Prime number evidence, ENSO exponent data and average temperature of the whole year data.Obtain the area to be analyzed icing of 20 years in the past and it is critical to prime number According to, including minimum temperature and the accumulative precipitation of regional every month icing phase to be analyzed.Obtain area to be analyzed average temperature of the whole year Data.Obtain the ENSO exponent data of every month 20 years icing phases in the past；

(2.1), ENSO exponent data divides.According to ENSO event character (ENSO event include EI Nino event and Ramsey numbers), by ENSO exponent data according to EI Nino period data, La Nina's period data, non-ENSO period data Being divided into three classes, the data of each apoplexy due to endogenous wind arrange still according to time sequencing；

(2.2), icing key element data divide.When compareing EI Nino period, the La Nina that ENSO index is characterized Phase, non-ENSO period, the icing key element data being analysed to area are respectively divided into EI Nino period data, La Nina Period data, non-ENSO period data three class, the data of each apoplexy due to endogenous wind arrange still according to time sequencing；

(3), mean value calculation.The area to be analyzed EI Nino icing in period obtained in selecting step (2.2) it is critical to Prime number evidence, according to formula (1), is calculated the meansigma methods of its EI Nino minimum temperature in periodAverage with accumulative precipitation ValueChoose area to be analyzed La Nina's icing in period data again, according to formula (1), be calculated its La Nina minimum for period The meansigma methods of temperatureMeansigma methods with accumulative precipitationChoose area to be analyzed non-ENSO icing in period data, according to Formula (1), is calculated the meansigma methods of its non-ENSO minimum temperature in periodMeansigma methods with accumulative precipitation

$\stackrel{\‾}{x}=\frac{\underset{i=1}{\overset{n}{\Σ}}{x}_i}{n}---\left(1\right)$

In formula,For a certain period (non-ENSO period, EI Nino period or La Nina period) minimum temperature or accumulative fall The meansigma methods of the water yield, x_iFor minimum temperature in this or the data of accumulative precipitation, n is this period data total amount in period；

(4.1), minimum temperature anomaly value calculates.By step (3) obtains to be analyzed area EI Nino minimum for period Temperature averagesENSO period minimum temperature meansigma methods non-with area to be analyzedSubtracting each other, the result obtained is as to be analyzed Area EI Nino minimum temperature in period anomaly valueWithSubtract each other with area to be analyzed average temperature of the whole year, The result arrived is as area to be analyzed minimum temperature correction value M_a, then use A_a1Divided by M_a, when the result that obtains is as EI Nino Phase minimum temperature anomaly percentage ratio

(4.2), accumulative precipitation anomaly value calculates.The area to be analyzed EI Nino obtained in step (3) is tired out period Meter precipitation meansigma methodsENSO non-with area to be analyzed adds up precipitation meansigma methods periodSubtract each other, the result conduct obtained Area to be analyzed EI Nino adds up precipitation anomaly value periodUse A again_b1Divided byThe result obtained is made Precipitation anomaly percentage ratio is added up period for EI Nino

(4.3), respectively with area to be analyzed La Nina's minimum temperature in period meansigma methodsWith accumulative precipitation meansigma methods ReplaceWithRepeat step (4.1)～(4.2), obtain area to be analyzed La Nina's minimum temperature in period anomaly value A_a2With Accumulative precipitation anomaly value A_b2, and La Nina minimum temperature in period anomaly percentage ratio a₂With accumulative precipitation anomaly percentage ratio b₂；

(5.1), icing key element Variations.During by step (4.1)～(4.2) calculated EI Nino Phase anomaly value is analyzed, if area to be analyzed EI Nino minimum temperature in period anomaly value A_a1Or accumulative precipitation anomaly value A_b1For just, then it is assumed that EI Nino easily causes the increase that minimum temperature rises or adds up precipitation.Otherwise it is assumed that EI Nino Easily cause the minimizing that minimum temperature declines or adds up precipitation.

The absolute value of step (4.1)～two anomaly percentage ratios in (4.2) calculated EI Nino period is carried out Contrast, if a₁Absolute value is more than b₁Absolute value, then it is assumed that the variation characteristic of EI Nino minimum temperature in period is than accumulative precipitation Variation characteristic is the most prominent, if a₁Absolute value is less than b₁Absolute value, then it is assumed that EI Nino adds up the variation characteristic of precipitation period It is more prominent than the variation characteristic of minimum temperature, otherwise it is assumed that the two situation of change is close.Again by above-mentioned a₁And b₁Substitute into formula (2) EI Nino icing in period key element response coefficient, it is calculated:

E=5 × (| a |+| b |) (2)

In formula, E is that ENSO icing in period key element response coefficient (obtains ell Buddhist nun after substituting into EI Nino period data Promise response coefficient in period, obtains La Nina's response coefficient in period after substituting into La Nina's period data), a is correspondence (ell Buddhist nun in period Promise period or La Nina period) minimum temperature anomaly percentage ratio, b is to add up precipitation anomaly percentage ratio this period, and 5 for amplifying Coefficient.

EI Nino icing in period based on accumulative precipitation anomaly percentage ratio and minimum temperature anomaly percentage ratio it is critical to Element responsiveness judgment mode is: setting up a coordinate system, abscissa is that ENSO minimum temperature in period anomaly percentage ratio absolute value is taken advantage of With 5, minimum temperature anomaly percentage ratio absolute value substitute into obtain after EI Nino period data EI Nino minimum temperature in period away from Flat percentage ratio absolute value, obtains La Nina's minimum temperature in period anomaly percentage ratio absolute value after substituting into La Nina's period data, vertical Coordinate adds up precipitation anomaly percentage ratio absolute value period be multiplied by 5 for corresponding EI Nino period or La Nina, and icing it is critical to Element response coefficient E has 4 and settles in an area, and respectively radius is quadrant region S1, inner circle radius 2.5 exradius 5 of 2.5 Sector region S2, the sector region S3 of inner circle radius 5 exradius 10 and other regions of first quartile S4, such as Fig. 2；Really Determine settling in an area of E, if settling in an area in S1 district, then it represents that icing key element exists weak response to ENSO；If settling in an area in S2 district, then it represents that There is moderate response；If settling in an area in S3 district, then it represents that there is strong response；If settling in an area in S4 district, then it represents that exist extremely strong Response；

(5.2), respectively step (4.3) calculated La Nina anomaly in period value and anomaly percentage ratio are replaced strategic point respectively That Nino anomaly in period value and anomaly percentage ratio, repeat step (5.1), carry out La Nina's icing in period key element variation characteristic Analyze.

Relative to prior art, beneficial effects of the present invention has:

1, in terms of the variation characteristic calculating of powerline ice-covering key element, a kind of relatively simple method is provided, can With the most comprehensively understanding powerline ice-covering key element response condition to ENSO event；

2, workable；

3, improve the specific aim of powerline ice-covering preventing and controlling.According to analysis result, for icing key element, special It not the precipitation area obvious to ENSO event response, carry out in the winter in ENSO period and tackle work in advance, reduce electricity Network loss is lost.

As a example by Hunan Province, the inventive method is illustrated below.The method comprises the steps:

(1), data acquisition.Icing key element data that the acquisition time is corresponding and ENSO exponent data, obtain Hunan mistake Go the icing key element data of 20 years, including minimum temperature and the accumulative precipitation of every month icing phase.Obtain and put down in Hunan year All temperature records.Obtain ONI (ocean NINO index, a kind of ENSO index) data of every month 20 years icing phases in the past；

(2.1), ENSO exponent data divides.By ONI data according to EI Nino period data, La Nina's period data, Non-ENSO period data is divided into three classes, and the data of each apoplexy due to endogenous wind arrange still according to time sequencing；

(2.2), icing key element data divide.EI Nino period that comparison ONI is characterized, La Nina period, non- ENSO period, the icing key element data in Hunan Province are respectively divided into EI Nino period data, La Nina's period data, Non-ENSO period data three class, the data of each apoplexy due to endogenous wind arrange still according to time sequencing；

(3), mean value calculation.EI Nino icing in the period key element data obtained in selecting step (2.2), according to Formula (1), is calculated the meansigma methods 5.1 DEG C of its EI Nino minimum temperature in period and the meansigma methods of accumulative precipitation 73.5mm.Choose La Nina's icing in period data again, according to formula (1), be calculated the flat of its La Nina minimum temperature in period Average 4.7 DEG C and meansigma methods 84.2mm of accumulative precipitation.Choose non-ENSO icing in period data, according to formula (1), calculate Meansigma methods 5.6 DEG C and meansigma methods 69.8mm of accumulative precipitation to its non-ENSO minimum temperature in period；

(4.1), minimum temperature anomaly value calculates.EI Nino minimum temperature in the period meansigma methods that will obtain in step (3) Subtracting each other with non-ENSO minimum temperature in period meansigma methods, the result obtained is as EI Nino minimum temperature in period anomaly value-0.5 ℃；Choose Hunan average temperature of the whole year value 17.3 DEG C, then be calculated EI Nino minimum temperature in period anomaly percentage ratio and be 4.3%；

(4.2), accumulative precipitation anomaly value calculates.The EI Nino obtained in step (3) is added up period precipitation put down Average and non-ENSO add up precipitation meansigma methods and subtract each other periods, and the result obtained adds up precipitation anomaly as EI Nino period Value 3.7mm；It is calculated EI Nino again and adds up precipitation anomaly percentage ratio 5.3% period；

(4.3), it is calculated La Nina's minimum temperature in period anomaly value-0.9 DEG C and accumulative precipitation anomaly value 14.4mm, And La Nina's minimum temperature in period anomaly percentage ratio 7.7% and accumulative precipitation anomaly percentage ratio 20.6%；

(5.1), icing key element Variations.During by step (4.1)～(4.2) calculated EI Nino Phase anomaly value is analyzed, and owing to EI Nino minimum temperature in period anomaly value in Hunan Province's is negative, accumulative precipitation anomaly value is Just, it is taken as that EI Nino easily causes the increase that minimum temperature declines and adds up precipitation；

The absolute value of step (4.1)～two anomaly percentage ratios in (4.2) calculated EI Nino period is carried out Contrast, accumulative precipitation anomaly percentage ratio absolute value is more than minimum temperature anomaly percentage ratio absolute value, it is believed that EI Nino period The variation characteristic of accumulative precipitation is more prominent than the variation characteristic of minimum temperature.It is calculated EI Nino by formula (2) Period, icing key element response coefficient was 0.48, fell in Tu2 S1 district, it is taken as that EI Nino is had by icing key element Weak response；

(5.2), respectively with La Nina's anomaly in period value and anomaly percentage ratio replace respectively EI Nino anomaly in period value and Anomaly percentage ratio, repeats step (5), carries out La Nina's icing in period key element Variations, it is again seen that La Nina Easily causing the decline of Hunan Province's winter minimum temperature and the increase of precipitation, the variation characteristic of accumulative precipitation compares minimum temperature Variation characteristic the most prominent, response coefficient 1.415, fall in Tu2 S1 district, icing key element has weak sound to Ramsey numbers Should.

Claims (1)

1. ENSO icing in a period key element Variations method, it is characterised in that comprise the steps:

(1), data acquisition, obtain to be analyzed area the past period in, the icing with time correspondence it is critical to prime number According to, ENSO exponent data and average temperature of the whole year data, icing key element data include area to be analyzed every month icing phase Minimum temperature and accumulative precipitation；

(2.1), ENSO exponent data divides, according to the character of ENSO event, by ENSO exponent data according to EI Nino period Data, La Nina's period data, non-ENSO period data are divided into three classes, and the data of each apoplexy due to endogenous wind are arranged still according to time sequencing Row；

(2.2), icing key element data divide, EI Nino period that comparison ENSO index is characterized, La Nina period, non- ENSO period, it is analysed to the icing key element data in area and is respectively divided into EI Nino period data, La Nina period Data, non-ENSO period data three class, the data of each apoplexy due to endogenous wind arrange still according to time sequencing；

(3), mean value calculation, selecting step (2.2) obtains to be analyzed area EI Nino icing in period it is critical to prime number According to, according to formula (1), it is calculated the meansigma methods of its EI Nino minimum temperature in periodMeansigma methods with accumulative precipitationChoose area to be analyzed La Nina's icing in period data again, according to formula (1), be calculated its La Nina lowest temperature in period The meansigma methods of degreeMeansigma methods with accumulative precipitationChoose area to be analyzed non-ENSO icing in period data, according to formula (1) meansigma methods of its non-ENSO minimum temperature in period, it is calculatedMeansigma methods with accumulative precipitation

$\stackrel{\‾}{x}=\frac{\underset{i=1}{\overset{n}{\Σ}}{x}_i}{n}---\left(1\right)$

In formula,For minimum temperature or accumulative precipitation in interim a certain period when non-ENSO period, EI Nino period or La Nina Meansigma methods, x_iFor minimum temperature in this or the data of accumulative precipitation, n is this period data total amount in period；

(4.1), minimum temperature anomaly value calculate, will step (3) obtain to be analyzed area EI Nino minimum temperature in period Meansigma methodsENSO period minimum temperature meansigma methods non-with area to be analyzedSubtracting each other, the result obtained is as area to be analyzed EI Nino minimum temperature in period anomaly valueWithSubtract each other with area to be analyzed average temperature of the whole year, obtain Result is as area to be analyzed minimum temperature correction value M_a, then use A_a1Divided by M_a, the result that obtains is as EI Nino period Low temperature anomaly percentage ratio

(4.2), the calculating of precipitation anomaly value is added up, the area to be analyzed EI Nino accumulative fall in period that will obtain in step (3) Water yield meansigma methodsENSO non-with area to be analyzed adds up precipitation meansigma methods periodSubtracting each other, the result obtained is as treating point Analysis area EI Nino adds up precipitation anomaly value periodUse A again_b1Divided byThe result obtained is as strategic point You add up precipitation anomaly percentage ratio by Nino period

(4.3), respectively with area to be analyzed La Nina's minimum temperature in period meansigma methodsWith accumulative precipitation meansigma methodsReplaceWithRepeat step (4.1)～(4.2), obtain area to be analyzed La Nina's minimum temperature in period anomaly value A_a2With accumulative fall Water yield anomaly value A_b2, and La Nina minimum temperature in period anomaly percentage ratio a₂With accumulative precipitation anomaly percentage ratio b₂；

(5.1), icing key element Variations, by step (4.1)～(4.2) calculated EI Nino period away from Level values is analyzed, if area to be analyzed EI Nino minimum temperature in period anomaly value A_a1Or accumulative precipitation anomaly value A_b1For Just, then it is assumed that EI Nino easily causes the increase that minimum temperature rises or adds up precipitation；Otherwise it is assumed that EI Nino is easy Cause minimum temperature to decline or add up the minimizing of precipitation；

The absolute value of step (4.1)～two anomaly percentage ratios in (4.2) calculated EI Nino period is contrasted, If a₁Absolute value is more than b₁Absolute value, then it is assumed that the variation characteristic of EI Nino minimum temperature in period is than the change of accumulative precipitation Feature is the most prominent, if a₁Absolute value is less than b₁Absolute value, then it is assumed that EI Nino adds up the variation characteristic of precipitation period to be compared The variation characteristic of low temperature is the most prominent, otherwise it is assumed that the two situation of change is close；Again by above-mentioned a₁And b₁Substitute into formula (2), meter Calculation obtains EI Nino icing in period key element response coefficient:

E=5 × (| a |+| b |) (2)

In formula, E is ENSO icing in period key element response coefficient, and it obtains EI Nino after substituting into EI Nino period data Response coefficient, substituted into after La Nina's period data and obtained La Nina's response coefficient in period period, a be correspondence EI Nino period or La Nina's minimum temperature in period anomaly percentage ratio, b is to add up precipitation anomaly percentage ratio this period, and 5 is amplification coefficient；

EI Nino icing in period key element based on accumulative precipitation anomaly percentage ratio and minimum temperature anomaly percentage ratio rings The degree judgment mode of answering is: set up a coordinate system, and abscissa is that ENSO minimum temperature in period anomaly percentage ratio absolute value is multiplied by 5, Minimum temperature anomaly percentage ratio absolute value obtains EI Nino minimum temperature in period anomaly hundred after substituting into EI Nino period data Proportion by subtraction absolute value, obtains La Nina's minimum temperature in period anomaly percentage ratio absolute value, vertical coordinate after substituting into La Nina's period data Being multiplied by 5 for corresponding EI Nino period or La Nina adding up precipitation anomaly percentage ratio absolute value period, icing key element rings Answering coefficient E to have 4 to settle in an area, respectively radius is quadrant region S1, the fan of inner circle radius 2.5 exradius 5 of 2.5 Shape region S2, the sector region S3 of inner circle radius 5 exradius 10 and other regions of first quartile S4, such as Fig. 2；Determine E's Settle in an area, if settling in an area in S1 district, then it represents that icing key element exists weak response to ENSO；If settling in an area in S2 district, then it represents that exist Moderate response；If settling in an area in S3 district, then it represents that there is strong response；If settling in an area in S4 district, then it represents that there is extremely strong response；

(5.2), respectively step (4.3) calculated La Nina anomaly in period value and anomaly percentage ratio are replaced ell Buddhist nun respectively Promise anomaly in period value and anomaly percentage ratio, repeat step (5.1), carry out La Nina's icing in period key element variation characteristic and divide Analysis.