CN107957598A - A kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index - Google Patents
A kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index Download PDFInfo
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Abstract
The present invention provides a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index, the air pollution forecasting model is to utilize history large scale weather system index and air quality index data, statistical analysis air quality index is with the relationship equation formula obtained after the correlation of other large scale weather system indexes, the relationship equation formula：Y (i)=13.174 × X1(i)+12.614×X2(i)+X3, wherein, y is air quality index, X1For pole whirlpool index, X2For Siberia index, X3For fixed value, i is date value.For the present invention using the medium-term and long-term weather prognostics conclusion of current mature, using the pole whirlpool and Siberian high pressure quantification characterizing method for being easily achieved computerization, forecasting process is easy to implement, and forecast result is cheer and bright, is easy to promote to masses.
Description
Technical field
The present invention relates to air pollution forecasting technical field, and in particular in a kind of combination large scale weather system index
Long term air pollution prediction model.
Background technology
The air pollution in China grows in intensity in recent years, serious threat people's health.In the operational forecast carried out
In, only for the air pollution forecasting within 7 days.Meanwhile using atmospheric dispersion model, pollution concentration is studied according to pollution sources
The method of change is relatively more, but such computational methods are intimately tied to discharge of pollutant sources inventory, for deficient to air pollution monitoring
Weary area, air pollution forecasting are relatively difficult.
Air pollution life disappear it is closely related with weather situation, based on weather situation come carry out air pollution Medium-long Term Prediction can
To make up the problem of polluting data deficiencies to a certain degree, Medium-long Term Prediction is carried out simultaneously for air pollution.Selection is for cold
Air event mostly important large scale weather system-Siberian high pressure, blocking anticyclone and pole whirlpool etc., the present invention utilizes can
Objective quantitative characterizes the index of above-mentioned circulation system, while mixing height index and AQI (air quality index) index, establishes
The Medium-long Term Prediction equation of one winter air pollution, the equation so obtained, can utilize in current weather forecast very well
Highly developed medium-term and long-term weather prognostics conclusion, can be that air pollution Medium-long Term Prediction can provide important references.
The content of the invention
In order to solve the problems in the existing technology, the present invention provides a kind of combination large scale weather system index
Medium-term and long-term air pollution forecasting model.
A kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index of the present invention is to utilize history
Large scale weather system index and air quality index data, statistical analysis air quality index and other large scale weather systems
The relationship equation formula obtained after the correlation of index, the relationship equation formula are：
Y (i)=13.174 × X1(i)+12.614×X2(i)+X3,
Wherein, y is air quality index, X1For pole whirlpool index, X2For Siberia index, X3For fixed value, i is the date
Value.
The history large scale weather system index and air quality index that the present invention uses are to utilize conventional meteorological data, U.S.
The meteorological data that National Meteorological Center of state and U.S.'s Center for Atmospheric Research provide obtains after being calculated by index of correlation calculation formula
Data, the medium-term and long-term air pollution forecasting model is the relationship equation formula obtained using SPSS statistical analyses.
In order to improve the accuracy of air pollution forecasting, the relationship equation formula by the air quality index in later stage not
It is disconnected to correct, the fixed value X3Value be 69, the degree of fitting of the relationship equation formula is 0.315.
In the relationship equation formula, X1And X2Lagging influence maximum to y is 15 days, X1And X2To the lagging influence of y
Coefficient is 0.328, and the relationship equation formula passes through 0.01 significance test.
The present invention has selected the weather system having a great influence for winter pollutant generating and vanishing process, including pole whirlpool, resistance first
High pressure and Siberian high pressure etc. are filled in, by newest achievement in research in the world, selection can realize the weather system of computerization
System quantitatively characterizing index, the medium-term and long-term weather forecast of winter air pollution is carried out further combined with contamination data, first, overcoming
The shortcomings that Local anesthesia source material is deficient, in addition, having taken into full account winter cold air process and mixed layer to air pollutants collection
Poly- and diffusion influence, the pollution prediction model of foundation, can with quantification forecast the state of air pollution 7-15 days following,
This is the useful supplement to current medium-term and long-term air pollution forecasting blank.
The present invention using current mature medium-term and long-term weather prognostics conclusion, using being easily achieved computerization
Pole whirlpool and Siberian high pressure quantification characterizing method, forecasting process is easy to implement, and forecast result is cheer and bright, is easy to masses
Promote.Forecasting model can not only provide definite AQI pollution prediction indexes, but also can provide the air quality trend of 1-15 days,
The blank of chronic contamination forecast can be filled up to a certain extent.
Brief description of the drawings
Fig. 1 is technical scheme schematic diagram.
Embodiment
Specific examples below, the medium-term and long-term air pollution to a kind of combination large scale weather system index of the present invention are pre-
Report model is described in further detail.
Embodiment 1：
The medium-term and long-term air pollution forecasting model of a kind of combination large scale weather system index of the present invention, first with sight
The number that the conventional meteorological data and NCEP (National Weather center) that measure, NCAR (U.S.'s Center for Atmospheric Research) are provided
According to passing through Fortran program calculations pole whirlpool index and Siberian high index according to the algorithm of correlation formula.
Secondly, by SPSS (statistical correlation software can seek the dependency relation between data) software, stepwise regression method is used
Air quality index (AirQualityIndex) AQI is established, pole whirlpool index, Siberia with the same time period that calculates
The correlation of index, data length are the 2000-2013 data of totally 14 years.Found using the advanced, same period and Time-delayed correlation analysis,
Inside the large scale weather system for influencing air pollution, Siberian high pressure and pole vortex pair influence the most in air pollution processes
Significantly.Especially found in Time-delayed correlation analysis, Siberian high index and the pollution of pole whirlpool exponent pair in weather system index
There is obvious lagging influence, wherein reached maximum at 15 days, related coefficient 0.328.Based on this, air pollution forecasting is established
Equation：Y (i)=13.174 × X1(i)+12.614×X2(i)+113.582, wherein y (i) are AQI indexes, X1For pole whirlpool index,
X2For Siberia index, the degree of fitting of the equation reaches 0.315, is examined by the conspicuousness of 0.01 (Pearson two-tailed tests)
Test.
Finally obtained dependent equation is examined as actual value by the use of the AQI observations data of 2014/1/1-2015/4/30
Test correction.That is the match value with equation and actual value comparative analysis, because weather situation mainly influences the diffusion and elimination of pollutant
Process, the source emission inventory of no pollutant can not consider the situation of pollution sources, and equation corrects result and is：Y (i)=13.174 ×
X1(i)+12.614×X2(i)+69。
The present invention using current mature medium-term and long-term weather prognostics conclusion, using being easily achieved computerization
Pole whirlpool and Siberian high pressure quantification characterizing method, forecasting process is easy to implement, and forecast result is cheer and bright, is easy to masses
Promote.Prognostic equation can not only provide definite AQI pollution prediction indexes, but also can provide the air quality trend of 1-15 days,
The blank of chronic contamination forecast can be filled up to a certain extent.Pass through inspection, effect for the value of forecasting of Lanzhou
Preferably, it is shown that larger further potentiality to be exploited, can promote and apply to other areas and city.
Embodiment 2：
- 17 days on the 3rd pole December in 2014 whirlpool is obtained using Geopotential Height Fields data, sea level pressure field material computation to refer to
Number, Siberian high index, bring prognostic equation into：
Y (i)=13.174 × X1(i)+12.614×X2(i)+69
The AQI values of the period are obtained, with observing obtained actual value contrast such as following table：
The AQI and actual comparison for the forecast that table 1. is obtained using equation
| Calculated value | 81 | 85 | 105 | 92 | 100 | 91 | 85 | 91 | 95 | 97 | 100 | 91 | 90 | 104 | 97 |
| Actual value | 70 | 105 | 158 | 92 | 113 | 120 | 67 | 106 | 95 | 95 | 125 | 138 | 94 | 94 | 89 |
As shown in table 1, the error range of calculated value and actual value is 20 or so, divided rank rate of accuracy reached 73%.Remove
Individual data calculating is incorrect, and other data are generally all hovered at criteria for classifying edge, and actual division grade accuracy rate is higher than
73%.
Air quality grade divides：
It it is 1 grade when air pollution index (i.e. AQI) is up to 0-50；It is 2 grades when 51-100；It is 3 grades when 101-200；
It is 4 grades when 201-300；It is 5 grades when more than 300.Wherein 3 grades belong to slight pollution, and 4 grades belong to intermediate pollution, and 5 grades then belong to
Serious pollution.
Grade is evaluated：Predict that obtained AQI values fall in a certain section, air quality is corresponding grade.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement made within refreshing and principle etc., should all be included in the protection scope of the present invention.
Claims (10)
- A kind of 1. medium-term and long-term air pollution forecasting model of combination large scale weather system index, it is characterised in that the middle length Phase air pollution forecasting model is to utilize history large scale weather system index and air quality index data, statistical analysis air Performance figure and the relationship equation formula obtained after the correlation of other large scale weather system indexes.
- 2. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 1, its It is characterized in that, the relationship equation formula is：Y (i)=13.174 × X1(i)+12.614×X2(i)+X3, wherein, y is air Performance figure, X1For pole whirlpool index, X2For Siberia index, X3For fixed value, i is date value.
- 3. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 1, its It is characterized in that, the history large scale weather system index and air quality index are to utilize conventional meteorological data, American National The data that the meteorological data that Meteorological Center and U.S.'s Center for Atmospheric Research provide obtains after being calculated by index of correlation calculation formula.
- 4. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 1, its It is characterized in that, the medium-term and long-term air pollution forecasting model is the relationship equation formula obtained using SPSS statistical analyses.
- 5. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 1, its It is characterized in that, the relationship equation formula is constantly corrected by the air quality index in later stage.
- 6. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 2, its It is characterized in that, the fixed value X3Value be 69.
- 7. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 2, its It is characterized in that, the degree of fitting of the relationship equation formula is 0.315.
- 8. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 7, its It is characterized in that, in the relationship equation formula, X1And X2Lagging influence maximum to y is 15 days.
- 9. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 8, its It is characterized in that, in the relationship equation formula, X1And X2Lagging influence coefficient to y is 0.328.
- 10. a kind of medium-term and long-term air pollution forecasting model of combination large scale weather system index as claimed in claim 9, its It is characterized in that, the relationship equation formula passes through 0.01 significance test.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110489836A (en) * | 2019-08-07 | 2019-11-22 | 成都市环境保护科学研究院 | Long-term prediction of air quality system and method in a kind of predrive |
| CN111339092A (en) * | 2020-02-24 | 2020-06-26 | 江苏省南通环境监测中心 | Deep learning-based multi-scale air quality forecasting method |
| CN117631090A (en) * | 2024-01-25 | 2024-03-01 | 南京信息工程大学 | Cold tide identification method and device |
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| CN105243444A (en) * | 2015-10-09 | 2016-01-13 | 杭州尚青科技有限公司 | City monitoring station air quality prediction method based on online multi-core regression |
| CN106940359A (en) * | 2017-03-13 | 2017-07-11 | 山东佳星环保科技有限公司 | A kind of warning of air pollution device |
| CN107229834A (en) * | 2017-06-27 | 2017-10-03 | 国网江苏省电力公司电力科学研究院 | A kind of complicated landform emergency response air pollution DIFFUSION PREDICTION method |
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- 2017-12-16 CN CN201711355048.4A patent/CN107957598A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105243444A (en) * | 2015-10-09 | 2016-01-13 | 杭州尚青科技有限公司 | City monitoring station air quality prediction method based on online multi-core regression |
| CN106940359A (en) * | 2017-03-13 | 2017-07-11 | 山东佳星环保科技有限公司 | A kind of warning of air pollution device |
| CN107229834A (en) * | 2017-06-27 | 2017-10-03 | 国网江苏省电力公司电力科学研究院 | A kind of complicated landform emergency response air pollution DIFFUSION PREDICTION method |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110489836A (en) * | 2019-08-07 | 2019-11-22 | 成都市环境保护科学研究院 | Long-term prediction of air quality system and method in a kind of predrive |
| CN110489836B (en) * | 2019-08-07 | 2023-01-24 | 成都市环境保护科学研究院 | Pre-driven medium-and-long-term air quality forecasting system and method |
| CN111339092A (en) * | 2020-02-24 | 2020-06-26 | 江苏省南通环境监测中心 | Deep learning-based multi-scale air quality forecasting method |
| CN111339092B (en) * | 2020-02-24 | 2023-09-08 | 江苏省南通环境监测中心 | Multi-scale air quality forecasting method based on deep learning |
| CN117631090A (en) * | 2024-01-25 | 2024-03-01 | 南京信息工程大学 | Cold tide identification method and device |
| CN117631090B (en) * | 2024-01-25 | 2024-05-14 | 南京信息工程大学 | Cold tide identification method and device |
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