# The Statistical Analysis of Anomalous Anticyclone in Ocean Atmosphere and Economy Based on Target Tracking Algorithm

Authors: Min Wan; Mishal Sohail
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Based on the importance of climate change in the environmental and economic fields, this study analyzes the impact of climate change on the economy based on the current situation, including the impact on sensitive industries and the impact on each district, in order to study the impact of abnormal atmospheric anticyclones on the economy. Furthermore, this study enhances the image-tracking algorithm, fuses the registered radar picture using the wavelet multiresolution analysis approach, and decomposes the registered radar image using the Mallat algorithm to retrieve the low- and high-frequency components of the image. Because the information included in the frequency components after wavelet decomposition differs, this study uses appropriate fusion algorithms for the decomposed low-frequency and high-frequency components, respectively, to produce panoramic photographs with acceptable visual effects. Through experimental research, it can be seen that the statistical analysis model of anomalous anticyclone in ocean atmosphere and economy based on the target tracking algorithm proposed in this study has a good simulation operation effect, and it also verifies that there is a certain correlation between the atmospheric anomalous anticyclone and the economy.


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## Body

## 1. Introduction

In recent decades, the global temperature has generally risen, the greenhouse effect has become an indisputable fact in the world, and the issue of climate change has also become a hot issue of concern to the world. As people continue to deepen their understanding of the relationship between human production activities and global climate change, the international community is also constantly strengthening research on climate-change issues. In March 2014, the IPCC issued the fifth assessment report on climate change. In September of the same year, the United Nations Climate Change Summit was held. The United Nations Climate Change Conference concluded a historic “Paris Agreement” in December 2015. Climate change has become a significant international concern impacting global development, affecting many elements of human social and economic growth. Global climate change has already had and will continue to have its impacts and adjustments. We will confront some degree of climate change in the future, even if the most hopeful strategy for decreasing greenhouse gas emissions is implemented. Furthermore, the effects of climate change are already being felt in Europe and across the world, and these effects and changes will outstrip society’s existing reaction capability [1]. Furthermore, governments in various countries have increasingly recognized that climate-change factors must be considered in order to maintain the economy’s and society’s stability and rapid development, and coping with climate change has become a basic consideration in policy planning in economic development [2]. Therefore, addressing climate change has become an urgent task facing the world, and mitigation and adaptation are the two major aspects of the response. Among them, mitigation is long-term and arduous, while adaptation is more realistic and urgent. In the short term, adaptation can reduce the risk of climate change and enhance the ability to adapt. In the long term, adaptation can promote the sustainable development of the economy and society. As climate-change mitigation actions cannot be effective for a while, under such circumstances, we need to adopt more targeted adaptation strategies and countermeasures, which have become a more urgent and important choice for all countries.Climate change is not only a typical global environmental issue but also a development issue. For example, climate change has brought major impacts and severe challenges to China’s climate, environment, and development. Therefore, adapting to climate change is a long-term task, and adaptation countermeasures and actions need to comprehensively consider a variety of factors, such as socioeconomic conditions, development plans, and local environment. Therefore, it is necessary to study economic development issues in the context of climate change based on the actual situation in the region, and to study climate change-related countermeasures from the standpoint and perspective of adaptation, promote the development and improvement of adaptation countermeasures, and improve adaptability. This can not only provide solutions to the climate-change constraints of economic development but also provide a certain strategic development direction for the long-term sustainable development of our country’s economy in the long run, which has important academic and practical significance [3].More and more countries, organizations, and the public are beginning to pay attention to climate change and respond to climate change. The most notable one is the concern about global warming and carbon dioxide emissions, which has become a key issue for scholars in various fields.This study combines the image target tracking algorithm to analyze the correlation between abnormal anticyclones in the ocean and the atmosphere and economic development. This method can not only effectively improve the efficiency of economic development, but also increase the risk of coping with disasters and improve social stability.


## 2. Related Work

The warming impact of greenhouse gases was conceptually acknowledged in the literature [4]. The particular influence of greenhouse gases on temperature variations was explained in the literature [5]. According to the research [6] when carbon dioxide concentrations double, the global average temperature increases. Many scientists progressively concluded that man-made greenhouse gas emissions had an impact on climate change, which paved the way for human climate research and gave rise to the field of “climate change economy.” The “Pigouvian tax” hypothesis of internalizing external costs was presented in the literature [7], establishing the groundwork for environmental economic theories and initiatives. The literature [8] issued an economic study on climate change from the standpoint of economic losses and expenses, urging the world to adopt effective legislative actions to prevent climate change as soon as feasible. The scientific foundation, effect, adaptability, susceptibility, and danger of climate change, and technology and policy approaches for mitigating and adapting to climate change, were all examined in detail in the literature [9]. It gives other areas, disciplines, specialists, and researchers greater information and expertise to investigate climate-change adaptation and policy.The Paris Agreement reached in 2015 not only includes decisions such as nationally determined contributions and strengthened actions before 2020, but also includes goals, mitigation, adaptation, funding, inventory mechanisms, and technologies, which also points out the direction for future cooperation among countries around the world to address climate change, and marks the starting point of a new global climate order [10]. In addition, there are more experts and scholars studying climate-change issues, the impact of climate change on various industries, and how to deal with climate change from the perspective of economics. The literature [11] studied the impact of climate change on tourism in the Caribbean through empirical analysis and pioneered that climate change has had an adverse impact on coastal tourism by causing sea level rise. The literature [12] analyzed the impact of meteorological factors on the output value of various industries in the United States, and explained the relationship between meteorological factors and the US economy. The literature [13] used the Ricardian model to study the effects of regional temperature and precipitation on crop yields, and concluded that crop yields are more sensitive to temperature changes, the temperature has a positive impact on farmers’ income, and precipitation has a negative impact. The literature [14] used the production function economic model to also assess the impact of climate change on crop yields. The literature [15] analyzed the progress of developed countries in promoting adaptation to climate change. The literature [16] illustrated the need for economic policies to solve the funding problem for climate-change adaptation. The literature [17] analyzed European countries’ climate policy decisions from multiple fields. The literature [18] combined socioeconomic scenarios and adaptation plans to quantitatively analyze the current and future flood risks of coastal cities, and infer the economic losses that may be suffered due to floods.The literature [19] clearly pointed out that one of the essences of carbon emission reduction problems caused by climate change is economic problems, because emission reduction must pay economic costs. The literature [20] used mathematical models and related analytical methods to quantitatively assess the vulnerability of agricultural floods in southern Hunan. The literature [21] divided the vulnerable areas of the water resource system by establishing vulnerability evaluation indicators, and summarizes the vulnerability of water resources and the response to climate change. The literature [22] constructed a new economic-climate model to evaluate the impact of climate change on food production. The conclusions obtained are basically in line with the research results of agricultural meteorology and the actual situation of the agricultural economy.


## 3. Harris Feature Point Matching

After the Harris feature points are extracted, they need to be matched to eliminate the matching points that do not meet the requirements. The matching process of feature points contains two key strategies: similarity measurement and search strategy.The similarity measurement is used to describe the correlation between the reference image and the feature point set of the image to be spliced, and a good measurement criterion is effective for image registration. Image matching algorithms based on feature points usually use various distance functions as measurement criteria for matching. This section intends to coarsely match the Harris feature points of the radar image through the improved Euclidean distance.The search strategy refers to the method of finding the best match between the images to be spliced in the feature space through the selected similarity criterion. This is of great significance for reducing the calculation time of the matching process. A suitable search strategy is chosen to optimize the matching algorithm based on the features of the pictures to be spliced and the performance metrics necessary for matching. In this study, the Harris feature points for matching are derived from the contour of the radar picture. In comparison to the original picture, the number of feature points has been drastically decreased, and the image is much more accurate. As a consequence, the feature point matching employs a reasonably simple exhaustive search technique, the computation amount is not greatly increased, and the matching result is satisfactory.We assume that the feature points in the reference image and the image to be stitched area and b, and the descriptors of a and b are Da and Db, respectively. The Euclidean distance is defined as follows:(1)dist=∑i=1HDai−Dbi2.Compared with the traditional Euclidean distance matching algorithm, this study adopts an improved Euclidean distance judgment criterion. In this study, the ratio of the Euclidean distance between the closest feature point and the second adjacent feature point of the feature point extracted from the reference image and the feature point extracted from the image to be stitched is used as the matching decision criterion. If the ratio is greater than the threshold set in advance, the two feature points are considered to meet the requirements and are output as matching feature points. In this study, the threshold value is selected as 0.8, and the matching process is shown in Figure1.Figure 1
Harris feature point matching process.The result of Harris feature point matching based on improved Euclidean distance matching is shown in Figure2. It can be seen from the figure that most of the matching results are good, and there are only a few mismatched points.Figure 2
Harris feature point matching results.In the process of matching picture feature points, there are inevitable mismatches owing to image quality issues or the limits of similarity measuring methodologies. Mismatching will result in erroneous coordinate transformation connections between spliced pictures, perhaps causing the whole process to fail. This issue is well solved by the random sampling consensus method.Although the extraction of the respective feature point sets is utilized to remove the erroneous matching points after extracting the contour feature points and Harris feature points of the stitched picture, the experimental findings show that mismatching still persists to some extent. Because of its robust approach and excellent accuracy, the RANSAC algorithm is commonly employed in picture stitching to minimize mismatches. The feature points that can be correctly matched are called “inner points,” and the feature points that deviate far from the normal range or the feature points that introduce noise are called “outer points.” The main task of the RANSAC algorithm is to identify the many feature points, find a straight line so that this straight line can fit all the feature points well, that is, the feature points on and near the straight line are the most, and use the feature points on the straight line to estimate the parameter transformation model between the two images to be spliced.The RANSAC algorithm model fitting is shown in Figure3. The left of the figure is the original feature point set, and the straight line shown on the right is the best model obtained by the RANSAC fitting of the original feature point set. The blue marked point on the fitted line is the “inner point,” and the red marked point outside the line is the “outer point.”Figure 3
RANSAC algorithm model fitting.The RANSAC algorithm is a hypothesis validation estimation method, so the first key problem it faces is the estimation of the number of random samplingk. We suppose that the probability of randomly selecting a sample of size n, that is, all “inside points” from N feature points, is P. Then, P(I) is as follows:(2)pI=InNn=∏j=0n−1I−jN−j≤εn.In formula (2), ε is the ratio of “inner points” in the feature point concentration, and I is the number of “inner points”. If it is assumed that the probability of extracting at least one n pairs of matching points without “outside points” is PT, then the probability of extracting a sample that does not meet the condition, that is, η=1−pT. Then, the random sampling number k is calculated as follows:(3)η=1−pIkk.Ifη is less than or equal to the threshold η0, then(4)η=1−pIkk≤η0,kη0≤lnη0ln1−pIk≤lnη0ln1−εn.Figure4 depicts the RANSAC algorithm stages in detail. This study employs perspective transformation, necessitating the employment of four pairs of matching points. First, the algorithm picks four pairs of matching points as “inner points” from all feature points and calculates the distance between other points and the straight line formed by these four pairs of matching points. The threshold t is established by the algorithm, and the value used in this study is 0.001. The algorithm adds feature points less than the threshold t to the set of interior points, repeats k times, and selects the set with the largest number of interior points as the best purification result.Figure 4
RANSAC algorithm flow.There are different degrees of mismatch between the feature point set obtained by the closed contour matching of the radar image and the Harris feature point set obtained by the Euclidean distance rough matching. We merge the above two sets into a comprehensive feature point set for precise purification of the RANSAC algorithm. The experimental results are shown in Figure5.Figure 5
Matching results of comprehensive feature point set.It can be seen from Figures5 and 6 that mismatching is effectively suppressed, image matching has achieved good results, the number of matching points is significantly reduced, and the operating efficiency is greatly improved.Figure 6
RANSAC exact matching result.The normalized projection transformation matrix H obtained by the above two radar images to be spliced through the RANSAC algorithm is shown below as follows:(5)H=1.01570.0032−118.1537−0.00950.99550.2673−0.00020.00001.0000.The experiment sets the scale factor of the two radar images to be spliceds = 1 and the angle rotation factor θ=0; then, the theoretical value of the image projection transformation matrix is as follows:(6)Htrue=h1=s×cosθ=1h2=s×sinθ=0h3h4=s×cosθ=0h5=s×cosθ=1h6h7h81.The scale factor iss = 1.015, the error is 0.015, the angle rotation factor is θ=0.181, and the angle error is 0.181 degrees, as determined by comparing the abovementioned projection transformation matrix H with the theoretical value He. It can be shown that the matching precision approaches subpixel levels and fulfills the radar image stitching criteria.The lens of the camera and the portion or all of the scene to be studied help explain the concept of multiresolution analysis. When the scale factor an is increased, it is analogous to zooming in on the camera’s focal length, allowing us to capture the full scene. When the scale factor an is lowered, the focal length of the camera is shortened, and we can catch the scene’s delicate local details. The core of the multiresolution analysis is this sort of step-by-step objective from rough to fine and from far to close.The basic idea of the Mallat pyramid algorithm can be summarized as follows.The object of this study is the Doppler radar image, and the image is a two-dimensional matrix, so we need to expand the wavelet to two-dimensional space when we study radar image. Similarly, multiresolution analysis also needs to be extended from one-dimensional to two-dimensional spaceL2R2. The two-dimensional Mallat algorithm is briefly described as follows.IfVj is a series of closed subspaces satisfying multiresolution analysis in L2(R), then Vj2 is the generalization of two-dimensional space (RS). If it is assumed that the two-dimensional signal analyzed is fx,y∈Vj2, then the two-dimensional Mallat decomposition algorithm can be obtained as follows:(7)fx,y=Aj+1f+Dj+11f+Dj=12f+Dj+13f.Among them, the first-level two-dimensional Doppler radar image is composed of four parts after wavelet transformation as follows:(8)Aj+1f=∑m1,m2∈ZCj+1;m1,m2ϕj+1;m1,m2,Dj+1if=∑m1,m2∈ZDj+1;m1,m2ψj+1;m1,m2ii=1,2,3.(9)Cj+1f=∑k1,k2∈Zhk1−2m1hk2−2m2Cj:k1,k2,Dj+11f=∑k1,k2∈Zhk1−2m1gk2−2m2Cj:k1,k2,Dj+12f=∑k1,k2∈Zhk1−2m1gk2−2m2Cj:k1,k2,Dj+11f=∑k1,k2∈Zhk1−2m1gk2−2m2Cj:k1,k2.(10)ck,mjdk,mj,1dk,mj,2dk,mj,3.As shown in equation (10), the wavelet coefficients obtained from the radar image after wavelet transformation correspond to LL, HL, LH, and HH, respectively. LL stands for low-frequency components, and it keeps the majority of the information from the original picture, which is an approximate representation. High-frequency components, such as edges and area contours, are represented by the letters HL, LH, and HH. A low-pass filter and a high-pass filter are combined to create each component’s information. The low-frequency component LL may be decomposed at the following level if the first-level breakdown does not fulfill the predefined parameters. The Mallat algorithm, on the other hand, does not decompose the impact; the more layers, the better. It must be examined in light of unique issues. The quantity of computation will grow dramatically if there are too many decomposed layers, and the picture processing result will not be greatly enhanced.According to the idea of wavelet multiresolution analysis and the Mallat algorithm, for two-dimensional images, wavelet multiresolution analysis has good local characteristics and multiscale characteristics in both the space domain and the frequency domain. After the wavelet decomposition of the Doppler radar image, we can intuitively understand the information of each frequency band component after the radar image is decomposed, and provide a specific theoretical basis for the radar image fusion method and rule below.The wavelet decomposition of the radar image is shown in Figure7.Figure 7
First-level wavelet decomposition of the original image.In this study, a method based on wavelet multiresolution analysis is used to fuse the registered radar images. The wavelet multiresolution analysis method has been described in the previous section. The registered radar image is decomposed by the Mallat algorithm to obtain the low-frequency and high-frequency components of the image. Since the information contained in the frequency components after wavelet decomposition is different, if the frequency components are not distinguished, a single fusion rule is adopted. The final fusion effect will be greatly reduced, so this study adopts respective suitable fusion strategies for the decomposed low-frequency components and high-frequency components, and a panoramic image with good visual effects can be obtained.Wavelet multiresolution analysis may be used to fuse radar pictures and provide decent results; however, the wavelet decomposition level must be modest. In general, the transition region of image fusion will be smoother and the visual impact will be greater when there are more levels of decomposition, but when the number of layers of picture decomposition is too high, the image information will be lost and the fusion effect will not be proportionate. The improvement, and the quantity of computation required, is too big and time-consuming. Therefore, when choosing the number of decomposition layers, comprehensive consideration should be made according to the type of image to be processed. In this study, the number of wavelet decomposition layers is selected as three layers, which can already ideally fulfill the predetermined requirements.After the registered radar image is decomposed by Mallat, it is necessary to select an appropriate fusion strategy for each frequency component. The selection of the fusion strategy is a key step for the final fusion of the radar image. The radar image fusion process is shown in Figure8.Figure 8
Radar image fusion based on wavelet transform.The fusion rule of the low-frequency domain in this study adopts the weighted average fusion method. The weighted average fusion method is similar to the average value method. However, in the overlapping area, the low-frequency coefficients are not simply superimposed, but are weighted and then averaged. The weighted average method is simple and intuitive, has a small amount of calculation, and maximizes the use of the pixel value information of the image. IffNAx,y and fNBRx,y are the low-frequency coefficients of the two images, respectively, then there are the following:(11)fNFx,y=fNAx,yx,y∈I1,w1x,yfNAx,y+w2x,yfNBx,yx,y∈I1∩I2,fNBx,yx,y∈I2.w1 and w2 represent corresponding weights and satisfy w1+w2=1,0<w1<1,0<w2<1. The selection of weight coefficients w1 and w2 is very important for the weighted average fusion algorithm. If the horizontal boundary coordinates of the overlapping parts of the two radar images to be stitched are xmax and xmin, and the vertical boundary coordinates are ymax and ymin, the weight coefficients are defined as follows:(12)w1=xmax−xxmax−xminw2=1−w1.The fusion rule of the high-frequency domain in this study adopts a window-based method to take the absolute value of the coefficient. First, the absolute value of the high-frequency coefficient of the two radar images to be spliced is compared, and the larger one is selected as the final result. IfgiAx,y and giBx,y are the high-frequency coefficients of the two figures, respectively, then there are the following:(13)giFx,y=giAx,ygiAx,y>giBx,y,giBx,ygiAx,y<giBx,y.The final result calculated by the above formula will be verified for window consistency. If it is assumed that the high-frequency coefficients in the center of the window are fused from the reference imagegx,y and the coefficients in the window neighborhood (3 × 3 or 5 × 5 windows) are taken from the image giBx,y to be spliced, then the fusion coefficient is changed to giBx,y and vice versa. The abovementioned image high-frequency coefficient fusion method increases the correlation between a single pixel and the surrounding neighborhood, the fusion effect is more comprehensive, and it has a wide range of applicability.


## 4. Statistical Analysis of Abnormal Anticyclones in Ocean Atmosphere and Economy Based on Target Tracking Algorithm

The target tracking approach is used in this research to do a statistical analysis of aberrant anticyclones in the ocean atmosphere and economy. The aberrant anticyclones of the ocean and atmosphere in past years are listed on this page. The data are taken from the Bureau of Meteorology’s website, and a correlational study is carried out between aberrant anticyclones in the ocean atmosphere and the agricultural economy with the greatest association with the weather. The statistical yearbook is where the agricultural research data originate from. Table1 shows the definitions of model variables and descriptive statistics.Table 1
Model variable definitions and descriptive statistics.
Variable nameVariable definitionsAverage valueStandard deviationMaximum valueMinimum valueY rice yieldTotal rice output (ten thousand tons)624.968707.1462581.9440.131A sown area of riceRice sown area (thousand hectares)930.5821008.8943361.6130.202M agricultural machineryTotal power of agricultural machinery used for rice production (10,000 kilowatts)431.316496.2782798.6580.173F fertilizer inputConverted amount of agricultural chemical fertilizer put into rice production (ten thousand tons)31.93734.180104.3550.010L labor forceNumber of agricultural laborers invested in rice production (10,000 people)84.87894.957323.9210.017I effective irrigation areaEffective irrigation area of rice (thousand hectares)319.323354.8091210.2050.148T temperatureMonthly average temperature of rice growing period (°C)20.7943.50728.12913.260P precipitationAverage monthly precipitation during rice growing period (mm)101.78157.836301.3218.504TE technology advancementReplace technological progress with time trends4.5342.3188.0001.000Area variable D1East China0.1440.3551.000≤0.001Area variable D2East China0.1810.3891.000≤0.001Area variable D3East China0.1090.3141.000≤0.001Area variable D4Southwest region0.1440.3551.000≤0.001Area variable D5Northwest region0.1400.3501.000≤0.001Area variable D6Northeast area0.1090.3141.000≤0.001This study uses panel data with cross section and time series to perform regression analysis. In order to further reduce the adverse effects of cross-sectional heteroscedasticity and time-series autocorrelation on regression, this study uses the panel generalized least squares estimation method to determine whether to choose a fixed-effects model or a mixed-effects model. In the end, this study draws the conclusion that the mixed-effects model is better than the fixed-effects model and uses period weighting to estimate on this basis. Table2 shows the regression results of the final mixed-effects model.Table 2
The regression analysis of the mixed-effects model.
VariableCoefficient std. error t-statistic prob.Sown area (LNA)1.8480.6123.0510.003Mechanical input (LNM)2.1090.4844.399≤0.001Fertilizer input (LNF)−1.4280.785−1.8370.072Labor force input (LNL)−1.6610.418−4.012≤0.001Effective irrigation area (LNI)0.6110.6171.0000.327Quadratic term of sown area ((LNA)2)−0.0270.283−0.0970.933Quadratic term of mechanical input ((LNM) 2)0.2450.1611.5400.130Quadratic term of fertilizer input ((LNF) 2)−0.5810.256−2.3000.024Quadratic term of labor input ((LNL)2)−0.3860.136−2.8450.005Quadratic term of effective irrigation area ((LNI) 2)−0.8830.238−3.746≤0.001Sown area and mechanical input (LNA × LNM)−0.9520.192−5.003≤0.001Sown area and fertilizer input (LNA × LNF)−0.3490.176−2.0040.049Sown area and labor input (LNA × LNL)0.7240.1674.398≤0.001Sown area and effective irrigation area (LNA × LNI)0.5150.2801.8590.068Machinery and fertilizer input (LNM × LNF)0.4470.1323.4130.001Machinery and labor force input (LNM × LNL)0.0700.1120.6240.543Machinery and effective irrigation area (LNM × LNI)0.1770.1201.4800.146Fertilizer and labor input (LNF × LNL)0.0660.1120.5890.567Fertilizer and effective irrigation area (LNF × LNI)0.5240.1403.773≤0.001Labor force and effective irrigation area (LNL × LNI)−0.4050.150−2.7130.008Temperature change (LNT)−4.2970.924−4.695≤0.001Quadratic term of temperature change ((LNT)2)1.1640.3483.3710.001Precipitation change (LNP)0.2780.1491.8810.065Quadratic term of precipitation change ((LNP)2))−0.0680.034−2.0170.047Technological progress (TE)2.6550.3138.557≤0.001Quadratic term of technological progress (TE2)−1.6630.303−5.552≤0.001East China (D1)0.1120.0522.2130.030North China (D2)−0.2060.068−3.0650.003Central China (D3)0.2130.0405.370≤0.001Southwest region (D4)0.1930.0802.4420.017Northwest region (D5)0.3190.0734.416≤0.001Northeast China (D6)0.0360.0760.4810.641R20.999Adjust R20.999From the above research, it can be seen that the statistical analysis model of anomalous anticyclone in ocean atmosphere and economy based on the target tracking algorithm proposed in this study has a good simulation operation effect, and it also verifies that there is a certain correlation between anomalous anticyclone in ocean atmosphere and economy.


## 5. Conclusions

This study analyzes the impact of climate change on the economy based on actual conditions, including the impact on sensitive industries and the impact on each district, and provides countermeasures and suggestions for adapting to climate change, based on the importance of climate change in the environmental and economic fields. Furthermore, the image target tracking technique is used in this work to investigate the relationship between the anomalous anticyclone in the ocean atmosphere and economic growth. This strategy can not only raise the risk of catastrophes and promote social stability, but it can also improve the efficiency of economic growth.In the context of climate change, based on panel data from various provinces across the country, this study first uses descriptive statistics to analyze the characteristics of climate change and China’s agricultural development. Second, this study uses the panel data model to simulate the data, analyze the impact of climate change on our country’s total agricultural output and its contribution rate, and simulate the impact of future climate change on the total agricultural output through various climate-change scenarios. The research shows that the statistical analysis model of anomalous anticyclone in ocean atmosphere and economy based on the target tracking algorithm proposed in this study has a good simulation operation effect, and also verifies that there is a certain correlation between atmospheric anomalous anticyclones and economy.



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