CN109190963B - A kind of determining meteorological data fusion rules refer to calibration method - Google Patents
A kind of determining meteorological data fusion rules refer to calibration method Download PDFInfo
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- CN109190963B CN109190963B CN201810975585.7A CN201810975585A CN109190963B CN 109190963 B CN109190963 B CN 109190963B CN 201810975585 A CN201810975585 A CN 201810975585A CN 109190963 B CN109190963 B CN 109190963B
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- 238000002790 cross-validation Methods 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims description 11
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- 238000012545 processing Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 1
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Abstract
The embodiment of the present invention proposes a kind of determining meteorological data fusion rules and refers to calibration method, comprising: obtains the interpolation result and the corresponding actual observed value u of interpolation scheme of at least two meteorological data interpolation schemes0(xi,yi)；Respectively to each interpolation scheme, its root-mean-square error RMSE and cross validation CV are calculated；According to the root-mean-square error RMSE and cross validation CV of each difference result, the optimal interpolation scheme of meteorological data fusion is determined.Above scheme proposes a kind of determining meteorological data fusion rules and refers to calibration method, can assess the result of different interpolation schemes, with determining with the immediate interpolation scheme of actual observed value.
Description
Technical field
The present invention relates to technical field of data processing, determine meteorological data fusion rules more particularly, to a kind of China's Mainland
Refer to calibration method.
Background technique
With the development of society, more and more fields all begin to use data analysis and data processing technique.With society
Development, more and more fields all begin to use data analyze and data processing technique.Using mass data many fields,
It, all may be in face of the risk of partial data loss in particular for the field for extracting data from magnanimity detection device.
Meteorological data is a kind of extremely typical mass data collection, and meteorological data will integrate the magnanimity number of multiple data sources
According to the data that wherein station detects are extremely important a part.But the station is due to weather or equipment fault reason,
May sporadic data lose the case where；And some areas lead to the not set station due to various reasons.All due to meteorological data
With spatial continuity and time continuity, therefore the data of missing can be estimated by the way of interpolation.In the prior art
There are many schemes of interpolation, but are directed to same data set, and different results may be obtained using different interpolation schemes.But
It is that can not determine which kind of interpolation scheme is to be most suitable for data set in the prior art.
Summary of the invention
The result difference obtained for current different interpolation schemes leads to not determine which kind of interpolation scheme is most appropriate
Problem, the embodiment of the present invention propose a kind of determining meteorological data fusion rules and refer to calibration method, can be to different interpolation schemes
Obtained result is assessed with the interpolation scheme of the most appropriate model of determination.
To achieve the goals above, the embodiment of the invention provides a kind of sides of determining meteorological data fusion rules index
Method, comprising:
Step 1, the interpolation result for obtaining at least two meteorological data interpolation schemes and the corresponding practical sight of interpolation scheme
Measured value u0(xi,yi)；
Step 2, respectively to each interpolation scheme, calculate its root-mean-square error RMSE and cross validation CV；
Wherein N is the corresponding station quantity summation of meteorological data,It is in (xi,yi) estimated value on position；u0
(xi,yi) it is observation；
WhereinIt is in position (xi,yi) on estimated value；Subscript-i indicates position (xi,yi) at estimated value
Do not use observation u0(xi,yi)；
Step 3, root-mean-square error RMSE and cross validation CV according to each difference result determine meteorological data fusion
Optimal interpolation scheme.
Further, the method also includes:
Step 4, to analyze again data and determine optimal interpolation scheme compare to assess.
Further, the step 4 specifically includes:
Using following formula to analyze again data and determine optimal interpolation scheme compare:
Wherein m is the lattice comprising one of observation analysis of data again, and M is the sum of this lattice in region,It is lattice
The mean value observed in m, urIt (m) is to analyze data again on lattice m,It is interpolation scheme all 5 kilometers of resolution ratio in lattice m
Sublattice point be averaged, and do not include observe data in lattice m.
Technical solution of the present invention has the advantage that above scheme proposes a kind of determining meteorological data fusion rules and refers to
Calibration method can assess the result of different interpolation schemes, with the determining and immediate interpolation scheme of actual observed value.
Detailed description of the invention
By with reference to the accompanying drawing to a preferred embodiment of the present invention carry out description, technical solution of the present invention and
Its technical effect will become clearer, and more easily understand.Wherein:
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
A preferred embodiment of the present invention is described below with reference to appended attached drawing.
The technology of the embodiment of the present invention can be applied to various basins, follow in the embodiment of the present invention using Yalong river valley water
Ring moulds intend Integration ofTechnology, to be illustrated to the technology of the embodiment of the present invention.
The determination meteorological data fusion rules proposed in the embodiment of the present invention refer to calibration method, and core ideas is will to observe
Value is used as reference value, and the interpolation result for evaluating which of a variety of interpolation schemes is closer from observing.
As shown in Figure 1, the determination meteorological data fusion rules of the embodiment of the present invention refer to calibration method, comprising:
Step 1, the interpolation result for obtaining at least two meteorological data interpolation schemes and the corresponding practical sight of interpolation scheme
Measured value u0(xi,yi)；
Step 2, respectively to each interpolation scheme, calculate its root-mean-square error RMSE and cross validation CV；
Wherein N is the corresponding station quantity summation of meteorological data,It is in (xi,yi) estimated value on position；u0
(xi,yi) it is observation；
WhereinIt is in position (xi,yi) on estimated value；Subscript-i indicates position (xi,yi) at estimated value
Do not use observation u0(xi,yi)；
Step 3, root-mean-square error RMSE and cross validation CV according to each difference result determine meteorological data fusion
Optimal interpolation scheme；
Step 4, to analyze again data and determine optimal interpolation scheme compare to assess.
Further, the step 4 specifically includes:
Using following formula to analyze again data and determine optimal interpolation scheme compare:
Wherein m is the lattice comprising one of observation analysis of data again, and M is the sum of this lattice in region,It is lattice
The mean value observed in m, urIt (m) is to analyze data again on lattice m,It is interpolation scheme all 5 kilometers of resolution ratio in lattice m
Sublattice point be averaged, and do not include observe data in lattice m.
It is evaluated in the embodiment of the present invention using two indices, first evaluation index is root-mean-square error (root-
Mean-square error, RMSE)；Second index is cross validation (Cross Validation, CV)；
Specifically, root-mean-square error RMSE is calculated using the following equation:
Wherein N is the station quantity summation at all moment of survey region,It is in (xi,yi) estimation on position
Value, it can be the trend surface of estimation, be also possible to the revised interpolation result of trend surface, be also possible to again analysis of data in platform
Interpolation result on standing；u0(xi,yi) it is observation.
Specifically, cross validation CV uses following formula:
WhereinIt is in position (xi,yi) on estimated value, it can be the trend surface of estimation, is also possible to become
The revised interpolation result in gesture face is also possible to again interpolation result of the analysis of data in the station；Subscript-i indicates position (xi,
yi) at estimated value do not use observation u0(xi,yi)。
RMSE and CV can represent the goodness of fitting.RMSE is in estimation point (xi,yi) value when used the sight of the point
Measured value u0(xi,yi), but do not used but when calculating CV.Therefore in a sense for, RMSE is represented in observatory
The error of fitting in denser region, however CV can be regarded as the verifying for assessing interpolation method on independent data.
Analysis of data again on thick lattice point, which is interpolated into observation point, will cause very big interpolation error.Station data are used above
Directly the superiority and inferiority of analysis of data and interpolation data of the embodiment of the present invention is less fair to analysis of data again again for verifying.The present invention is implemented
Analysis of data and interpolation of embodiment of the present invention data are excellent on the thick lattice point of analysis of data again again using following index evaluation for example
It is bad,
Wherein m is the lattice of an analysis of data again comprising observation, and M is the sum of this lattice in region,It is lattice m
In the mean value observed, urIt (m) is to analyze data again on lattice m,It is the interpolation method using the embodiment of the present invention in lattice m
In the sublattice points of all 5 kilometers of resolution ratio be averaged, but observe data in lattice m and estimatingWhen be removed.
For person of ordinary skill in the field, with the development of technology, present inventive concept can be in different ways
It realizes.Embodiments of the present invention are not limited in embodiments described above, and can carry out within the scope of the claims
Variation.
Claims (1)
1. a kind of determining meteorological data fusion rules refer to calibration method characterized by comprising
Step 1, the interpolation result and the corresponding actual observed value of interpolation scheme for obtaining at least two meteorological data interpolation schemes
u0(xi,yi)；
Step 2, respectively to each interpolation scheme, calculate its root-mean-square error RMSE and cross validation CV；
Wherein N is the corresponding station quantity summation of meteorological data,It is in (xi,yi) estimated value on position；u0(xi,
yi) it is observation；
WhereinIt is in position (xi,yi) on estimated value；Subscript-i indicates position (xi,yi) at estimated value do not use
To observation u0(xi,yi)；
Step 3, root-mean-square error RMSE and cross validation CV according to each difference result determine the best of meteorological data fusion
Interpolation scheme；
Step 4, to analyze again data and determine optimal interpolation scheme compare to assess；
The step 4 specifically includes: using following formula to analyze again data and determine optimal interpolation scheme compare:
Wherein m is the lattice comprising one of observation analysis of data again, and M is the sum of this lattice in region,It is in lattice m
The mean value of observation, urIt (m) is to analyze data again on lattice m,It is the son of interpolation scheme all 5 kilometers of resolution ratio in lattice m
Lattice point is averaged, and does not include observing data in lattice m.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106340018A (en) * | 2016-08-31 | 2017-01-18 | 中国水利水电科学研究院 | Method for determining optimal hydrometeorological element spatial interpolation resolution |
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- 2018-08-24 CN CN201810975585.7A patent/CN109190963B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106340018A (en) * | 2016-08-31 | 2017-01-18 | 中国水利水电科学研究院 | Method for determining optimal hydrometeorological element spatial interpolation resolution |
Non-Patent Citations (3)
| Title |
|---|
| 《不同气象插值方法在新疆草地NPP估算中的可靠性评价》;任璇 等;《草业科学》;20170331;第34卷(第3期);全文 |
| 基于GIS的气象要素空间插值方法研究;马轩龙 等;《草业科学》;20081130;第25卷(第11期);全文 |
| 新疆地区平均气温空间插值方法研究;王智 等;《高原气象》;20120229;第31卷(第1期);3.1、3.2、4.3 |
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Address after: 100875, 19, Xinjie street, Haidian District, Beijing Co-patentee after: Shandong Institute of ecological environment planning Patentee after: BEIJING NORMAL University Address before: 100875, 19, Xinjie street, Haidian District, Beijing Co-patentee before: SHANDONG ACADEMY FOR ENVIRONMENTAL PLANNING Patentee before: BEIJING NORMAL University |
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