CN109886497A - Surface air temperature interpolation method based on the improved inverse distance weight of latitude - Google Patents
Surface air temperature interpolation method based on the improved inverse distance weight of latitude Download PDFInfo
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- CN109886497A CN109886497A CN201910149169.6A CN201910149169A CN109886497A CN 109886497 A CN109886497 A CN 109886497A CN 201910149169 A CN201910149169 A CN 201910149169A CN 109886497 A CN109886497 A CN 109886497A
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- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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Abstract
The invention discloses a kind of surface air temperature interpolation methods based on the improved inverse distance weight of latitude, surface air temperature interpolation method based on the improved inverse distance weight of latitude passes through addition Influence of Latitude, inverse distance weight is set up to the temperature interpolation of targeted sites, influence of the latitude to temperature can not be accurately reflected by solving current inverse distance weight, improve the quality of automatic weather station observation temperature.
Description
Technical field
The present invention relates to meteorology temperature predictions, are related to a kind of surface air temperature based on the improved inverse distance weight of latitude
Interpolation method.
Background technique
Since ancient times, meteorological data has great importance for agricultural production, urban planning, Global Ecological variation, closely
The number at surface weather observation station is more and more over year, and the meteorological data generated therewith is also more and more huger.If can be to meteorological number
It is most important to preventing the following Weather Risk, being played to future city planning, the development of the national economy according to variation more accurately perception
Meaning.
It is more accurate detailed to obtain although each state is all establishing the more accurate meteorological observation website of more high density
Meteorological data, but due to the limitation that reality is adjusted, such as the limitation of fund, personnel and landform, having no idea, it is huge to establish quantity
Meteorological site that is big and being evenly distributed, to will affect the quality of the meteorological data of whole region, and then influences whole distract
Surface observing data quality, therefore improve the precision of the interpolation algorithm of meteorological data, and then temperature number can be improved
According to quality.Common multistation interpolation method has space recurrence and inverse distance weight.Existing inverse distance weight is utilized
Be Euclidean distance, therefore exist and a known sites be consistent to the result of same distance interpolation, but in reality
In the case of, the higher temperature of latitude is less than the temperature of low latitudes.
Summary of the invention
Goal of the invention: the object of the present invention is to provide a kind of surface air temperatures based on the improved inverse distance weight of latitude to insert
Value method, it is contemplated that the temperature difference as caused by latitude, so that the temperature interpolated data with same distance is more in line with latitude
Caused by difference.
Technical solution: the surface air temperature interpolation method of the present invention based on the improved inverse distance weight of latitude, step
Rapid 1, acquisition meteorological observation temperature observe data, and to data carry out quality control, by data from high latitude to low latitude into
Row arrangement obtains temperature and observes data set Ti, wherein i is observation station sequence number；
Step 2, it carries out calculating the latitude factor, formula using the data of earliest time point in data set are as follows:
Wherein, TjFor website temperature, LjFor the latitude of the website, L0For the latitude of unknown website, M is station number,For
1 year latitude air Temperature Difference, N are to choose year number, and γ is mean latitude air Temperature Difference,It is to be calculated by the latitude temperature temperature difference
The temperature record of unknown website；
Step 3, consider that the inverse distance weight on Geostatistical, formula are
Wherein, diFor the distance between unknown point and each known sites, ωiFor the weight of each website, z (x0) it is benefit
The predicted value obtained with inverse distance-weighting；
Step 4, latitude temperature interpolation method and inverse distance weighted interpolation method are overlapped, specific formula isWherein Y0For final temperature interpolation, α is the weight coefficient of latitude interpolation, β is inverse distance weighted interpolation
Weight coefficient.
By using above-mentioned technical proposal, temperature in a certain range of neighboring station certain time of Target Station observe data into
Row acquisition, then some basic quality controls are carried out, by each neighbouring observation station of inverse distance weight solving equations to target
The weight of observation station considers influence of the latitude to temperature later, calculates latitude air Temperature Difference, constructs latitude temperature interpolation equation, benefit
Target Station temperature record is predicted with inverse distance-weighting and latitude data, by Temperature prediction value, common inverse distance weight
It is compared with actual observed value, therefore this method compensates for current interpolation method to neighbouring observation station spatial distribution, temperature sky
Between latitude correlation consider less disadvantage, improve the performance of interpolation method.
The utility model has the advantages that the surface air temperature interpolation method based on the improved inverse distance weight of latitude by addition latitude because
Element, it is established that the temperature interpolation of targeted sites, solving current inverse distance weight can not accurately reflect inverse distance weight
Influence of the latitude to temperature improves the quality of automatic weather station observation temperature.
Detailed description of the invention
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is Gaochun station LIDW, IDW, true value comparison diagram；
Fig. 3 is the evaluation index table of each website.
Specific embodiment
As shown in Figure 1, a kind of surface air temperature interpolation method based on the improved inverse distance weight of latitude, as follows:
One, acquisition meteorological observation temperature observe data, and basic quality is carried out to data and is controlled, by data from height
Latitude is arranged to low latitude, and if there is the website on Same Latitude, temperature record is replaced with its average value, obtains temperature
Observe data set Ti, wherein i is observation station sequence number.These data all into the control of three-level quality is crossed, eliminate gross error.Its
In to data carry out quality control the inspection of mode scope, extreme value inspection and internal consistency inspection.
Two, formula is utilized For 1 year latitude air Temperature Difference, M was station
Point number,For 1 year latitude air Temperature Difference.
Three, formula is utilizedThe mean latitude air Temperature Difference in several years is calculated, N is to choose year
Number, γ are annual latitude air Temperature Difference.Choose 1961-1975 temperature record to do mean latitude air Temperature Difference.
Four, formula is utilizedCalculate the latitude of unknown website
Temperature interpolation is spent,It is the temperature record of the unknown website calculated by the latitude temperature temperature difference, M is station number, LjFor this
The latitude of website, L0For the latitude of unknown website, γ is annual latitude air Temperature Difference.
For example, to the surface air temperature data ground timing of the 1961-1975 in 6 areas in Nanjing (02:00,08:00,14:
00,20:00) temperature and relative humidity observational data as research object, the website in 6 areas be respectively six directions station (station number:
58235), Pukou station (station number: 58237), Nanjing station (station number: 58238), Jiangning station (station number: 58333), Gaochun station (station number:
58339), Lishui station (station number: 58340).M=6, N=15 in example.
Five, the temperature interpolation on unknown website longitude is calculated using inverse distance-weighting formula, formula is as follows:
Wherein, diFor the distance between unknown point and each known sites, in the present embodiment using on longitude
Distance, ωiIt is the function of inverse distance, z (x for the weight of each website: weight0) be exactly obtained using inverse distance-weighting it is pre-
Measured value.
Six, latitude temperature interpolation method and inverse distance weighted interpolation method are overlapped, specific formula isWherein Y0For final temperature interpolation, α, β are latitude interpolation and the respective weight of inverse distance weighted interpolation
Coefficient.
Wherein n is the year of parameter training, Yi0, Zi(X0),The true value of the targeted sites for i is respectively corresponded,
Inverse distance-weighting predicted value, latitude air Temperature Difference predicted value.
| Website | Longitude | Latitude |
| 58235 six directions | 118.81 | 32.32 |
| 58237 Pukou | 118.72 | 32.10 |
| 58238 Nanjing | 118.79 | 32.06 |
| 58333 Jiangning | 118.84 | 31.95 |
| 58340 Lishuis | 119.02 | 31.65 |
| 58339 Gaochuns | 118.87 | 31.30 |
Table 1
According to table 1 and Fig. 2, α in the present embodiment is calculated, β is 1/2.
As shown in figure 3, the application's is improved anti-based on latitude so that Gaochun station is in 1961-2007 Temperature records as an example
The surface air temperature interpolation method of distance weighting method, abbreviation LIDW, compared to what is be calculated with common inverse distance weight (IDW)
Temperature prediction, the calculating of the application improve computational accuracy closer to true temperature.
Seven, predicted value, common inverse distance-weighting among the above is compared with actual observed value, is missed by average absolute
Poor MAE, root-mean-square error RMSE evaluation model.
Specifically, evaluation index formula is as follows:
Wherein n is sample points, Z*(xi) it is i-th of predicted value, Z (xi) it is i-th of observation.
| Website | Six directions station | Pukou station | Nanjing station | Jiangning station | Lishui station | Gaochun station | Average value |
| MAE(LIDW) | 0.21 | 0.06 | 0.09 | 0.22 | 0.17 | 0.18 | 0.155 |
| MAE(IDW) | 0.39 | 0.09 | 0.10 | 0.25 | 0.13 | 0.50 | 0.24 |
| RMSE(LIDW) | 0.07 | 0.01 | 0.02 | 0.07 | 0.04 | 0.04 | 0.04 |
| RMSE(IDW) | 0.19 | 0.01 | 0.02 | 0.08 | 0.02 | 0.27 | 0.10 |
Table 2
In conjunction with table 2, the surface air temperature interpolation method (LIDW) based on the improved inverse distance weight of latitude of the application and
It is with above-mentioned 6th area of Nanjing by evaluation index formula calculation error range compared to common inverse distance weight (IDW)
Example, the application obtain error of the predicting temperature values compared with true temperature and obtain result less than common inverse distance weight.Further
It is more accurate to demonstrate surface air temperature interpolation method prediction of the application based on the inverse distance weight in latitude difference.
Claims (5)
1. a kind of surface air temperature interpolation method based on the improved inverse distance weight of latitude, it is characterised in that including following step
It is rapid:
Step 1, acquisition meteorological observation temperature observe data, and to data carry out quality control, by data from high latitude to
Low latitude is arranged, and is obtained temperature and is observed data set Ti, wherein i is observation station sequence number；
Step 2, it carries out calculating the latitude factor, formula using the data of earliest time point in data set are as follows:
Wherein, TjFor website temperature, LjFor the latitude of the website, L0For the latitude of unknown website, M is station number,It is 1 year
Latitude air Temperature Difference, N are to choose year number, and γ is mean latitude air Temperature Difference,It is by the unknown of latitude temperature temperature difference calculating
The temperature record of website；
Step 3, consider that the inverse distance weight on Geostatistical, formula are
Wherein, diFor the distance between unknown point and each known sites, ωiFor the weight of each website, z (x0) it is using anti-
Distance weighted obtained predicted value；
Step 4, latitude temperature interpolation method and inverse distance weighted interpolation method are overlapped, specific formula isWherein Y0For final temperature interpolation, α is the weight coefficient of latitude interpolation, β is inverse distance weighted interpolation
Weight coefficient.
2. the surface air temperature interpolation method according to claim 1 based on the improved inverse distance weight of latitude, feature
It is in step 1, data are being carried out to appear in the website on Same Latitude, gas in alignment processes from high latitude to low latitude
Warm data are replaced with its average value.
3. the surface air temperature interpolation method according to claim 1 based on the improved inverse distance weight of latitude, feature
It is in step 4, α, the calculation formula of β isWherein n is ginseng
The year of number training, Yi0, Zi(X0),Respectively correspond the true value of the targeted sites for i, inverse distance-weighting predicted value,
Latitude air Temperature Difference predicted value.
4. the surface air temperature interpolation method according to claim 1 based on the improved inverse distance weight of latitude, feature
Be for predicted value, the common inverse distance-weighting in step 4 to be compared with actual observed value, by mean absolute error MAE,
Root-mean-square error RMSE evaluation model, evaluation index formula are as follows:
Wherein n is sample points, Z*(xi) it is i-th of predicted value, Z (xi) it is i-th of observation.
5. the surface air temperature interpolation method according to claim 1 based on the improved inverse distance weight of latitude, feature
It is that in step 1, the inspection of mode scope, extreme value inspection and internal consistency for carrying out quality control to data are examined
It looks into.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110909303A (en) * | 2019-11-19 | 2020-03-24 | 湖南大学 | Adaptive space-time heterogeneity inverse distance interpolation method |
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| CN105955929A (en) * | 2016-04-26 | 2016-09-21 | 上海大学 | Data scientific visualization-oriented inverse distance weighting mixed interpolation method |
| CN107180128A (en) * | 2017-05-04 | 2017-09-19 | 东南大学 | A kind of weighted mean computational methods for being applied to Chinese low latitudes |
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Patent Citations (2)
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|---|---|---|---|---|
| CN105955929A (en) * | 2016-04-26 | 2016-09-21 | 上海大学 | Data scientific visualization-oriented inverse distance weighting mixed interpolation method |
| CN107180128A (en) * | 2017-05-04 | 2017-09-19 | 东南大学 | A kind of weighted mean computational methods for being applied to Chinese low latitudes |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110909303A (en) * | 2019-11-19 | 2020-03-24 | 湖南大学 | Adaptive space-time heterogeneity inverse distance interpolation method |
| CN110909303B (en) * | 2019-11-19 | 2023-04-14 | 湖南大学 | Adaptive space-time heterogeneity inverse distance interpolation method |
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