CN106485371B - Method and system for predicting short-term climate of average temperature in summer in south China - Google Patents

Method and system for predicting short-term climate of average temperature in summer in south China Download PDF

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CN106485371B
CN106485371B CN201611018984.1A CN201611018984A CN106485371B CN 106485371 B CN106485371 B CN 106485371B CN 201611018984 A CN201611018984 A CN 201611018984A CN 106485371 B CN106485371 B CN 106485371B
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刘舸
彭京备
王慧美
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Chinese Academy of Meteorological Sciences CAMS
Institute of Atmospheric Physics of CAS
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Institute of Atmospheric Physics of CAS
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Abstract

The invention relates to a short-term climate qualitative prediction method for average temperature in summer in south of the Yangtze river of China, which comprises the steps of calculating average temperature data in summer in south of the Yangtze river of China, calculating weather parameter data of the tropical Indian ocean area in the early winter corresponding to the average temperature data in summer, particularly using TWNIO index representing abnormal thermal conditions in the tropical northwest Indian ocean area as the weather parameter, constructing a prediction model by using the average temperature data in summer and the weather parameter data, and predicting the average temperature in summer in the south of the Yangtze river of China in winter through the prediction model. The method can accurately and efficiently predict the temperature through early-stage meteorological parameters, has strong prediction capability particularly for high temperature in summer in the south of the Yangtze river of China, and can provide effective early warning information for national climate disasters.

Description

Method and system for predicting short-term climate of average temperature in summer in south China
Technical Field
The invention relates to the technical field of meteorological prediction.
Background
Generally speaking, for qualitative prediction of summer climate abnormality (such as drought, waterlogging, cold and warm) in east China, people often pay attention to early winter Pacific region Erleno (El)
Figure BDA0001150457530000011
) Or Ranina (La)
Figure BDA0001150457530000012
) The influence of the type sea temperature anomaly is taken as an important prophase factor for short-term climate prediction. However, the accuracy of prediction by using the factors is low at present, particularly, the prediction capability of the factors for strong high temperature is poor, and the strong high temperature often causes larger climate disasters, so that a prediction method capable of accurately predicting the climate in the east of China in summer, particularly, the prediction capability of the factors for strong high temperature is urgently needed at present, and therefore, effective help is provided for preventing the national climate disasters.
Disclosure of Invention
In view of the above problems in climate prediction, the present invention aims to provide a method for predicting temperature accurately and efficiently through early-stage meteorological parameters, especially temperature in summer in the south of the Yangtze river of China.
In order to achieve the above object, the present invention provides a method for qualitatively predicting short-term climate in south China and in summer average temperature, comprising,
step 1: calculating summer average temperature data of each of N consecutive years in the south of the Yangtze river of China;
step 2: selecting data of continuous M years in the continuous N years as a modeling period, acquiring corresponding summer average temperature data, and standardizing the data to obtain standardized summer average temperature data, wherein M is less than or equal to N;
and step 3: calculating weather parameter data of the former winter tropical northwest indian ocean area corresponding to the summer average temperature data of the modeling period, and normalizing the weather parameter data to obtain normalized weather parameters;
and 4, step 4: constructing a unitary regression model:
T＝a×I+b，
wherein T is the standardized average temperature in summer in the south of the Yangtze river of China, I is the standardized meteorological parameter in the northwest Indian ocean area of the tropical winter in the earlier period, and a and b are parameters; substituting data in a modeling period, obtaining values of corresponding parameters a and b through fitting, and substituting the obtained parameters a and b into the unitary regression model to form a prediction model;
and 5: the average temperature in summer in the south of the Yangtze river of China is predicted through the prediction model.
The south area of china referred to in the present invention is an area that meets the relevant regulations of the national standard of the people's republic of china about the meteorological geographic divisions in china, that is, the south area of china is the areas of north of lake, south of lake, west of river, zhejiang (north), anhui, jiangsu, shanghai, and north of fujian (extending from south ridge to east), etc., which are included between the Yangtze river and the south ridge, and the data of the specific embodiment of the present invention is from the south area of the river having specific coordinates of (110 ° to 122 ° E, 26 ° to 32 ° N). The short-term climate forecast refers to a forecast of average climate conditions in the month, season or year, and herein specifically refers to a forecast of average climate temperature in summer. The modeling period in the invention refers to a continuous period of time for constructing a regression model; the verification period refers to a continuous period of time during which the predicted and observed results are compared and verified. The normalization of the data, as referred to in the present invention, is a value calculated by a conventional normalization algorithm used by those skilled in the art, i.e., normalized value (original value-year average)/standard deviation. The average air temperature referred to in the present invention is an average value of air temperature data at a corresponding period. The definition of the invention for summer and winter is respectively: the summer is 6-8 months in the current year, and the early winter is 12-2 months in the previous year. The tropical northwest indian ocean area described in the present invention is a part of the tropical region in a division range of the indian ocean area in meteorology. The most common indian ocean indices are three: the first is an Indian ocean basin modulus index, called IOBM index for short, which represents the sea temperature change of the whole Indian ocean basin scale; the second is Indian ocean dipole index, called IOB index for short, which represents the reverse change of Indian ocean east and west ocean temperature; the third is the tropical indian ocean temperature anomaly index, abbreviated as TIO index, which indicates the ocean temperature variation across the indian ocean tropics. Wherein the tropical northwest indian ocean (TNWIO) index described in this application is different from any of the conventional indices mentioned above, but is calculated to meet specific prediction requirements by: firstly, removing linear trend of the surface air temperature field of the whole Indian ocean winter (average from 12 months to 2 months in the same year in the last year of the forecast year), then selecting key areas (55-75 DEG E, 5-15 DEG N, 40-55 DEG E and 15-0 DEG S), and calculating the average surface air temperature of the areas in the areas, wherein the obtained result is the TNWIO index. The linear trend is removed by subtracting a linear fitting quantity which increases or decreases with time from the original quantity.
Another objective of the present invention is to provide a method for predicting the temperature in summer in the south of the china, which can screen out specific meteorological parameters capable of effectively predicting the temperature in a specific area from a plurality of existing meteorological parameters, and establish a prediction model according to the selected meteorological parameters, thereby predicting the temperature more accurately and efficiently, and particularly requires that the method for predicting the temperature in summer in the south of the china has strong high-temperature prediction capability, and can provide effective early warning information for national climate disasters.
In order to achieve the above-mentioned another object, the present invention provides a method for qualitatively predicting short-term climate in south China of the summer average air temperature, comprising,
step 1: calculating summer average temperature data of each of N consecutive years in the south of the Yangtze river of China;
step 2: selecting continuous M data years in the continuous N years as a modeling period, acquiring corresponding summer average temperature data, and standardizing the data to obtain standardized summer average temperature data;
and step 3: calculating a plurality of different meteorological parameter data of the early winter tropical indian ocean area corresponding to the summer average air temperature data of the modeling period, and normalizing the data of each meteorological parameter to obtain a plurality of different normalized meteorological parameter data;
and 4, step 4: constructing a unitary regression model:
T＝a×I+b，
wherein T is the standardized summer average temperature in the south China's south China, I is the standardized weather parameter in the tropical Indian ocean in the early winter, and a and b are parameters; substituting the data in the modeling period, and obtaining the corresponding values of the parameters a and b through fitting;
and 5: selecting a plurality of years except the modeling period in the N years as verification periods, and acquiring corresponding summer average temperature data;
step 6: verifying the models determined by the parameters a and b corresponding to the meteorological parameters of a plurality of different early-stage winter tropical indian and ocean regions by the summer average temperature data in the verification period, and calculating whether the prediction results of the models can reach the expected statistical confidence; if the statistical confidence coefficient exceeds a preset threshold value, the meteorological parameters used at the moment are used as meteorological parameters of early winter tropical Indian ocean areas used by the prediction model, and the parameters a and b corresponding to the meteorological parameters are determined as parameters of the prediction model;
and 7: and substituting the obtained parameters a and b into the unitary regression model to obtain a prediction model, and predicting the summer average temperature in the south of the Yangtze river of China through the prediction model.
The invention also aims to provide a system which can accurately and efficiently predict the temperature, in particular the temperature in summer in the south of the Yangtze river of China, through early-stage meteorological parameters, has strong high-temperature prediction capability, and can provide effective early warning information for national climate disasters.
In order to achieve the above object, the present invention provides a short-term qualitative forecast system for climate, comprising,
a temperature data acquisition module that calculates summer average temperature data for each of N years of the area a, and normalizes the data to obtain normalized summer average temperature data;
a weather parameter acquiring module for calculating a plurality of types of early-stage winter weather parameters for each of N years corresponding to the region B, and normalizing data of each weather parameter to acquire a plurality of different types of normalized weather parameter data;
a modeling module, which selects continuous M years in the continuous N years as a modeling period and acquires standardized summer average temperature data of corresponding time and a plurality of different standardized meteorological parameter data; constructing a unitary regression model:
T＝a×I+b，
wherein T is the normalized mean temperature in summer of region A, I is the normalized weather parameters in early winter of region B, and a and B are parameters; substituting the data in the modeling period, and obtaining the corresponding values of the parameters a and b through fitting;
the verification and meteorological parameter selection module: selecting a plurality of years except the modeling period in the N years as verification periods, and acquiring corresponding standardized summer average temperature data; verifying the models determined by the parameters a and B corresponding to the early-stage winter meteorological parameters of the regions B of different types obtained by fitting through the standardized summer average temperature data in the verification period, and calculating whether the prediction results of the models can reach the expected statistical confidence; if the statistical confidence exceeds a preset threshold, determining the meteorological parameters used by the model at the moment as the early-stage winter meteorological parameters of the region B used by the final prediction model, and determining the parameters a and B corresponding to the meteorological parameters as the parameters of the final prediction model;
a prediction module: and substituting the finally determined parameters a and b into the unitary regression model to obtain a final prediction model, and predicting the summer average temperature of the area A through the final prediction model.
Particularly, the area A is a south China area, the area B is a tropical Indian ocean area, the meteorological parameter I is a TNWIO index which indicates that the thermal condition of the tropical northwest Indian ocean area is abnormal, two rectangular areas with the coordinate ranges of (55-75 degrees E, 5-15 degrees N) and (40-55 degrees E, 15 degrees S-0 ℃) are selected after the linear trend of the whole Indian ocean winter surface air temperature field is removed, and the average surface air temperature of the areas is calculated to obtain the TNWIO index.
In conclusion, the short-term climate qualitative prediction method for the summer average temperature in the south of the Yangtze river of China provided by the invention can accurately and efficiently predict the summer average temperature in the south of the Yangtze river of China, can also screen the existing meteorological parameters, selects the parameter with higher statistical confidence coefficient as the prediction parameter, determines the prediction model, and can dynamically adjust the prediction of different periods and different specific regions, thereby having strong high-temperature prediction capability and providing effective early warning information for the national climate disasters.
Drawings
FIG. 1, (a) a graph relating summer air temperature in the south of the Yangtze region of China to the early winter Indian ocean surface temperature (SST); (b) similar to (a), but related to the Surface Air Temperature (SAT). The gray shaded regions represent more than 95% statistical confidence; the black boxes in the figure indicate key regions in the tropical northwest indian ocean (TNWIO).
FIG. 2, (a) the relative distribution of the early winter TNWIO index and the temperature in the east summer of China in 1980-2015; the light and dark grey shaded regions represent over 95% and 99% statistical confidence, respectively; black boxes indicate regions in the south of the river; (b) comparison of the previous winter TNWIO index (dashed circle line) with the average summer air temperature (solid black dot line) sequence in the south of the china.
Fig. 3 is a flow chart of physical links for influencing summer air temperature in south of the china due to abnormal thermal conditions in key areas of tropical indian ocean in early winter.
FIG. 4 shows the 20-year sliding correlation coefficient of the previous winter TNWIO index and the average summer air temperature sequence in the south of the Yangtze river of China. The 95% statistical confidence level is represented in the graph as a straight line.
FIG. 5, a standardization sequence of actual summer temperatures (black solid line) in the south of Yangtze river and summer temperatures (circular dotted line and triangular dotted line) in the south of Yangtze river obtained according to a prediction model in 1980-2015; wherein, the dotted circle line is the fitting result of the modeling period, and the dotted triangle line is the prediction result of the prediction period. In the figure RI0.56, representing the correlation coefficient between the fitting and the actual temperature in the south of the Yangtze river in the modeling period; rII0.52, representing the correlation coefficient between the prediction and the actual temperature in the south of the Yangtze river in the inspection period; both exceed 95% statistical confidence.
Detailed Description
Research shows that in recent decades (1980-2015), the temperature in summer (6-8 months) in south China (110-122 degrees E, 26-32 degrees N; see the range shown by the black frame in figure 2 a) is closely related to the thermal conditions (including sea surface temperature and surface temperature) in the key regions in the northwest India ocean in the tropical north of the early winter (12 months to 2 months in the year) in the previous year (see the range shown by the black frame in figure 1, specifically 55-75 degrees E, 5-15 degrees N and 40-55 degrees E, 15 degrees S-0 degrees). Since the south-river air temperature and the Indian ocean temperature both show rising trends in the global warming background, and more attention is paid to the annual changes in climate prediction, all meteorological elements are subjected to a linear trend treatment.
The average summer surface air temperature of the key area of the northwest Indian ocean of the tropical winter is used for representing the thermal condition of the area, and the average summer surface air temperature is defined as an abnormal index of the thermal condition of the Indian ocean of the tropical winter, which is called as TNWIO index for short. Referring to fig. 2a, the correlation of the early winter TNWIO index with the eastern summer air temperature field in china shows that a significant positive correlation occurs in the south of the river. Meanwhile, the early-stage winter TWNIO index sequence (a circular dotted line in FIG. 2 b) and the south-of-the-river region average summer air temperature sequence (a black-dot solid line in FIG. 2 b) also show good consistency, and the correlation coefficient of the two sequences is as high as 0.53 and exceeds 99% of statistical confidence coefficient during 1980-2015. This further indicates that the early winter TNWIO index can well indicate the temperature in summer in the south of the river in china.
Abnormal heat conditions in key areas of tropical indian and ocean in early winter can affect the summer air temperature in south China. The physical mechanism of this effect is as follows, see fig. 3: due to the large heat capacity of the ocean, the continuance of the abnormal signals is good, so that the warmer abnormal signals of the key areas of the tropical indian ocean in the early winter can be continued from winter to summer, and cause abnormal much rainfall in summer in the areas of the Arabic sea and the northwest India. In summer rainfall in the region, the high-pressure abnormality of the ancillary tropical zone of the western Pacific ocean can be forced in the lower layer of the troposphere through the heating of condensation latent heat, so that the ancillary tropical zone is stronger and extends southwest to control the region in the south of the Yangtze river; meanwhile, the atmospheric pressure of the middle-high layer of the troposphere of the downstream area is abnormal by exciting Rossby waves in summer rainfall of the area, so that the south Asia high pressure is enhanced to extend east. Under the common control of high pressure in the subsidiary tropical zone of the western pacific and high pressure abnormality in south Asia, deep high pressure abnormality occurs in the south of the Yangtze river, and further the temperature in summer is higher. On the contrary, when the key area of the tropical Indian ocean in the winter of the current period is cold, the temperature in the south of the Yangtze river is low in summer.
It can be seen that the close relation between the abnormal heat conditions of the key areas of the tropical Indian ocean in the early winter and the summer air temperature in the south of the Yangtze river of China is not an accidental phenomenon, and the related physical link process is clear and reasonable. Therefore, the relationship between them is stable. Referring to fig. 4, the 20-year sliding correlation coefficient of the previous winter TNWIO index and the summer air temperature sequence in the south of the Yangtze river always exceeds the statistical confidence of 95%, which further proves that the close relationship between the two is stable and reliable. Therefore, a short-term climate qualitative prediction model aiming at the summer temperature in the south China is constructed by utilizing the close and stable relation between the two, and the summer temperature in the south China is predicted.
Based on the close and stable relation between the tropical Indian ocean thermal condition (expressed by TNWIO index) in the early winter and the summer air temperature in the south of the Yangtze river of China, 1980-1996 are selected as a modeling period, and a unitary regression model (I for short) is constructedTNWIOPrediction model):
TJN＝0.695×ITNWIO-0.004
wherein T isJNIs the standardized summer temperature in the south of the Yangtze river, ITNWIOIs the normalized early winter TNWIO index. The F-test value for this model was 7.02, with more than 95% statistical confidence. The prediction ability of the model can be tested by taking 1997-2015 as a prediction period. The F value of the prediction model can measure the fitting effect of the regression model. The calculation formula is as follows:
Figure BDA0001150457530000081
wherein r is a correlation coefficient, and n is the number of samples. The regression equation is established by using data of 17 years in 1980-1996, so that the sample number n is 17. In this period, the correlation coefficient r is 0.565, and F is calculated to be 7.02. Looking up the F distribution table, the numerator degree of freedom is 1, the denominator degree of freedom is 15 (i.e., n-2), and F at the 95% confidence level is 4.54. And we here F7.02, higher than 4.54, so the regression model was considered significant, with more than 95% statistical confidence.
FIG. 5 compares the actual temperature in summer in the south of the Yangtze river from 1980 to 2015 with the temperature according to the above ITNWIOAnd predicting a summer air temperature sequence in the south of the Yangtze river obtained by the model. As can be seen, the predicted result and the actual result show more consistent changes. The correlation coefficient of the two in the modeling period (1980-1996, dashed circle line) is 0.56, and the correlation coefficient of the two in the prediction period (1997-2015, dashed triangle line) is 0.52, both of which exceed 95% of statistical confidence. Fig. 5 reflects the better fitting and predicting effects of the model more intuitively.
Table 1 below gives ITNWIOAnd (4) qualitatively predicting the result of the prediction model in each year. When the predicted temperature in the south of the Yangtze river is the same as the actual temperature (namely the predicted temperature is warmer, the actual result is warmer, or the predicted temperature is colder, the actual result is colder), the qualitative prediction is correct. The result shows that the prediction accuracy reaches 68% in 13 years and the prediction error in 6 years in total within the prediction period of 19 years. In addition, since strong high temperature in the south of the Yangtze river often causes larger climate disasters in summer, we also pay attention to the prediction capability of the model for the strong high temperature. The years of the predicted strong high temperature (positive abnormality exceeds 0.3 standard deviation) are 1998, 2003, 2007, 2009, 2010 and 2013 for 6 years, wherein only 2010 prediction is wrong, and the true strong high temperature occurs in the other 5 years. Therefore, the model can effectively predict the basic characteristics of cold and warm in summer in the south of the river and is also suitable for predicting strong high temperature.
Earlier used early winter Pacific region early Elnino (El)
Figure BDA0001150457530000091
) Or Ranina (La)
Figure BDA0001150457530000092
) The type sea temperature anomaly is used as a prediction factor for short-term climate prediction. In practice, however, since 1980, the early winter Nino3.4 index (abbreviated as I)Nino3.4(ii) a The index can be used to indicate El
Figure BDA0001150457530000093
Abnormal sea temperature) and the temperature in the south of the Yangtze river in summer are weak, so I is usedNino3.4The fitting and prediction capabilities of the constructed prediction model are also relatively low: the correlation coefficient of the two is 0.03 in the modeling period (1980-1996), and is-0.25 in the prediction period (1997-2015), which is not significant. In Table 1INino3.4The prediction result of the prediction model shows that the prediction error is 12 years in total within the prediction period of 19 years, the prediction is correct in only 7 years, and the prediction accuracy is only 37%. In addition, the years of the predicted strong high temperature (positive abnormality exceeds 0.3 standard deviation) are 8 years in total in 1997, 1999, 2000, 2001, 2006, 2008, 2009 and 2010, wherein the true strong high temperature is generated only in 2006 and 2009, and the prediction is wrong in other years, and the accuracy is only 25%. As can be seen, it is currently mainly based on Elrano (El)
Figure BDA0001150457530000103
) Or Ranina (La)
Figure BDA0001150457530000104
) The prediction of the abnormal sea temperature is not suitable for the temperature in summer in the south of the Yangtze river and cannot effectively predict the high-temperature year.
TABLE 1 prediction period (1997-2015 years), ITNWIOPrediction model and INino3.4The prediction results of the prediction model in each year (● indicates the prediction is correct, and
Figure BDA0001150457530000101
indicating a prediction error); the last row in the table gives the prediction accuracy; the letter Y indicates correct prediction for strong hyperthermia, and the letter N indicates incorrect prediction for strong hyperthermia
Figure BDA0001150457530000102
In summary, the prediction method based on the early winter tropical northwest indian ocean thermal condition and the prediction model constructed according to the method can remarkably improve the short-term climate qualitative prediction level of the summer temperature in the south of the Yangtze river of China, particularly remarkably improve the strong high temperature prediction capability, and have important practical values.

Claims (22)

1. A method for establishing a short-term climate qualitative prediction model of average temperature in summer in the south of the Yangtze river of China comprises,
step 1: calculating summer average temperature data of each of N consecutive years in the south of the Yangtze river of China;
step 2: selecting continuous M years in the continuous N years as a modeling period, acquiring corresponding summer average temperature data, and standardizing the data to obtain standardized summer average temperature data, wherein M is less than or equal to N;
and step 3: calculating weather parameter data of the early winter tropical indian ocean area corresponding to the summer average air temperature data of the modeling period in time, and normalizing the weather parameter data to obtain a normalized weather parameter;
and 4, step 4: constructing a unitary regression model:
T = a × I + b，
wherein T is the standardized summer average temperature in the south China's south China, I is the standardized weather parameter in the tropical Indian ocean in the early winter, and a and b are parameters; and substituting the data in the modeling period, obtaining the values of the corresponding parameters a and b through fitting, and substituting the obtained parameters a and b into the unitary regression model to form a prediction model.
2. The method of claim 1, wherein after the step 4, further comprising the steps of:
step 4.1: selecting a plurality of years except the modeling period in the N years as verification periods, and acquiring summer average temperature data of the corresponding years;
step 4.2: verifying whether the prediction result of the prediction model determined by the parameters a and b obtained by fitting can reach a preset threshold value or not through the summer average temperature data in the verification period; if the prediction result exceeds a preset threshold value, the prediction is determined to be performed by using the prediction model determined by the parameters a and b.
3. The process of claim 1 or 2, wherein M is in the range of M ≧ 10 in M consecutive years.
4. The method of claim 2, wherein the data for the verification period is data for more than 5 years.
5. The method as claimed in claim 1 or 2, wherein the former winter tropical indian ocean area meteorological parameter I used by the prediction model is TNWIO index indicating abnormal thermal conditions in tropical northwest indian ocean area, and the TNWIO index is obtained by selecting two rectangular areas having coordinate ranges of (55 ° -75 ° E, 5 ° -15 ° N) and (40 ° -55 ° E, 15 ° S-0 °) after removing linear trend of the whole indian ocean winter surface air temperature field, and calculating average surface air temperatures of the areas.
6. The method of claim 5, wherein the predictive model is:
T = 0.695 × I – 0.004，
wherein T is the normalized average temperature in summer in the south of the Yangtze river of China, and I is the normalized TNWIO index indicating abnormal thermal conditions in the northwest Indian ocean of the tropical zone in the early winter.
7. The method according to claim 1 or 2, wherein the meteorological parameters I of the early winter tropical indian ocean area used by the predictive model are meteorological parameters such that the statistical confidence of the prediction results obtained by the predictive model exceeds 95%.
8. The method of claim 2, wherein the predetermined threshold is an F-test value of 7.02 for the determined predictive model, exceeding a 95% statistical confidence.
9. A method for establishing a short-term climate qualitative prediction model of the summer average temperature in the south China comprises,
step 1: calculating summer average temperature data of each of N consecutive years in the south of the Yangtze river of China;
step 2: selecting continuous M years in the continuous N years as a modeling period, acquiring corresponding summer average temperature data, and standardizing the data to obtain standardized summer average temperature data;
and step 3: calculating a plurality of different kinds of weather parameter data of the early winter tropical indian ocean area corresponding to the summer average air temperature data of the modeling period in time, and normalizing the data of each weather parameter to obtain a plurality of different kinds of normalized weather parameter data;
and 4, step 4: constructing a unitary regression model:
T = a × I + b，
wherein T is the standardized summer average temperature in the south China's south China, I is the standardized weather parameter in the tropical Indian ocean in the early winter, and a and b are parameters; substituting the data in the modeling period, and obtaining the corresponding values of the parameters a and b through fitting;
and 5: selecting a plurality of years except the modeling period in the N years as verification periods, and acquiring corresponding standardized summer average temperature data;
step 6: verifying the models determined by the parameters a and b corresponding to the meteorological parameters of a plurality of different types of early-stage winter tropical indian and ocean regions obtained by fitting through the standardized summer average temperature data in the verification period, and calculating whether the prediction results of the models can reach a preset threshold value; if the prediction result exceeds a preset threshold value, the meteorological parameters used by the model at the moment are used as the meteorological parameters of the early winter tropical Indian ocean area used by the final prediction model, and the parameters a and b corresponding to the meteorological parameters are determined as the parameters of the final prediction model;
and 7: and substituting the finally determined parameters a and b into the unary regression model to obtain a final prediction model.
10. The method of claim 9, wherein M is a value in the range of M ≧ 10 in M consecutive years.
11. The method according to claim 9 or 10, wherein the data of the verification period is data of 5 years or more.
12. The method as claimed in claim 9 or 10, wherein the previous winter tropical indian ocean area weather parameter I used by the final prediction model is TNWIO index indicating abnormal thermal conditions in the tropical northwest indian ocean area, and the TNWIO index is obtained by selecting two rectangular areas having coordinate ranges of (55 ° -75 ° E, 5 ° -15 ° N) and (40 ° -55 ° E, 15 ° S-0 °) after removing linear trend of the entire indian ocean winter surface air temperature field, and calculating the average surface air temperature of the areas.
13. The method of claim 12, wherein the final predictive model is:
T = 0.695 × I – 0.004，
wherein T is the normalized average temperature in summer in the south of the Yangtze river of China, and I is the normalized TNWIO index indicating abnormal thermal conditions in the northwest Indian ocean of the tropical zone in the early winter.
14. The method according to claim 9 or 10, wherein the meteorological parameters I of the previous winter tropical indian ocean area used by the final predictive model are meteorological parameters such that the statistical confidence of the prediction results obtained by the predictive model exceeds 95%.
15. The method of claim 9 or 10, wherein the predetermined threshold is a determined F-test value of the predictive model of 7.02 with a statistical confidence of over 95%.
16. A short-term climate qualitative prediction model building system comprises,
a temperature data acquisition module which calculates summer average temperature data of each year in N years in the south of the Yangtze river of China, and standardizes the data to obtain standardized summer average temperature data;
a weather parameter acquisition module which calculates a plurality of types of early-season winter weather parameters for each year corresponding to N years in tropical Indian ocean areas, and normalizes data of each of the weather parameters to obtain a plurality of different types of normalized weather parameter data;
a modeling module, which selects continuous M years in the N years as a modeling period and acquires standardized summer average temperature data of corresponding time and a plurality of different standardized meteorological parameter data; constructing a unitary regression model:
T = a × I + b，
wherein T is the standardized average temperature in summer in the south of the Yangtze river of China, I is the standardized early-stage winter meteorological parameter in the Indian ocean of the tropics, and a and b are parameters; substituting the data in the modeling period, and obtaining the corresponding values of the parameters a and b through fitting;
the verification and meteorological parameter selection module selects a plurality of years except the modeling period in the N years as verification periods and acquires corresponding standardized summer average temperature data; verifying the models determined by the parameters a and b corresponding to the early-stage winter meteorological parameters of a plurality of different types of tropical indian ocean regions obtained by fitting through the standardized summer average temperature data in the verification period, and calculating whether the prediction results of the models can reach a preset threshold value; if the prediction result exceeds a preset threshold value, determining the meteorological parameters used by the model at the moment as the early winter meteorological parameters of the tropical Indian ocean area used by the final prediction model, and determining the parameters a and b corresponding to the meteorological parameters as the parameters of the final prediction model;
and the prediction module substitutes the finally determined parameters a and b into the unary regression model to obtain a final prediction model.
17. The system of claim 16, wherein M in M consecutive years has a value in the range of M ≧ 10.
18. The system according to claim 16 or 17, wherein the data of the verification period is data of more than 5 years.
19. The system according to claim 16 or 17, wherein the weather parameter I of the previous winter tropical indian ocean area used by the final prediction model is a TNWIO index indicating abnormal thermal conditions of the tropical northwest indian ocean area, and the TNWIO index is obtained by selecting two rectangular areas having coordinate ranges of (55 ° -75 ° E, 5 ° -15 ° N) and (40 ° -55 ° E, 15 ° S-0 °) after removing the linear trend of the entire indian ocean winter surface air temperature field, and calculating the average surface air temperature of the areas.
20. The system of claim 19, wherein the final predictive model is:
T = 0.695 × I – 0.004，
wherein T is the normalized average temperature in summer in the south of the Yangtze river of China, and I is the normalized TNWIO index indicating abnormal thermal conditions in the northwest Indian ocean of the tropical zone in the early winter.
21. The system of claim 16 or 17, wherein the early winter tropical indian ocean weather parameter I used by the final predictive model is a weather parameter that provides a statistical confidence of the prediction results obtained by the predictive model of more than 95%.
22. The system of claim 16 or 17, wherein the predetermined threshold is a F-test value of 7.02 for the determined predictive model, exceeding a 95% statistical confidence.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102006020935A1 (en) * 2006-05-05 2007-11-08 Heinz-Helmut Vollmer Blasting e.g. for tropical cyclones during their formation, is set up such as that from regions of Caribbean, Pacific, Philippines, Indian Ocean and Australia meteorological stations
CN105260603A (en) * 2015-10-14 2016-01-20 成都信息工程大学 Climatic event risk evaluation method and system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102006020935A1 (en) * 2006-05-05 2007-11-08 Heinz-Helmut Vollmer Blasting e.g. for tropical cyclones during their formation, is set up such as that from regions of Caribbean, Pacific, Philippines, Indian Ocean and Australia meteorological stations
CN105260603A (en) * 2015-10-14 2016-01-20 成都信息工程大学 Climatic event risk evaluation method and system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
2013 年盛夏中国持续性高温事件诊断分析;杨涵洧 等;《高原气象》;20160430;第35卷(第2期);第484-494页 *
The Impact of Indian Ocean Variability on High Temperature Extremes across the Southern Yangtze River Valley in Late Summer;HU Kaiming, et al.;《ADVANCES IN ATMOSPHERIC SCIENCES》;20121231;第29卷(第1期);第91-100页 *

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