CN110929776A - Remote sensing wind field data quality evaluation method and device based on sea surface wind field stability statistic zoning - Google Patents
Remote sensing wind field data quality evaluation method and device based on sea surface wind field stability statistic zoning Download PDFInfo
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Abstract
The invention discloses a wind field remote sensing data quality evaluation method and device based on sea surface wind field stability statistics division, comprising the following steps: establishing a wind speed interval division model and a wind direction stable grade classification model; performing space-time matching on the acquired actually measured wind field data and the wind field remote sensing data; calculating a wind direction stability coefficient of the actually measured wind field data; carrying out wind speed interval division on the actually measured wind field data by using a wind speed interval division model; carrying out wind direction stability grade division on the actually measured wind field data by using a wind direction stability grade classification model; and for the measured wind field data and the corresponding wind field remote sensing data contained in each wind direction stability grade, performing quality evaluation on the wind field remote sensing data according to the correlation between the measured wind field data and the wind field remote sensing data. The method and the device accurately evaluate the quality of the wind field remote sensing data.
Description
Technical Field
The invention belongs to the technical field of ocean information, and particularly relates to a remote sensing wind field data quality evaluation method and device based on sea surface wind field stability statistics division.
Background
The sea surface wind field information obtained by the microwave remote sensing sensor and the actual data observed by the equivalent points of the actual buoy have obvious time and space scale difference, and are very unfavorable for quantitative application of remote sensing inversion information. The main sources of errors and uncertainties of remote sensing products mainly include data source errors, model errors, errors caused by variation (space-time variation) in time windows and space scales in the process of verifying data matching, and the like. The process of evaluating the quality of the data product output by the system by an independent method, i.e. the authenticity check, is mainly worked by determining the errors of the geophysical data product by using on-site monitoring data and extrapolation modes. The monitoring contents comprise atmospheric conditions, optical characteristics of a water-gas interface and a secondary water surface, sea surface meteorological power and geophysical quantities to be detected.
The authenticity check can effectively evaluate the product quality of the remote sensing data, thereby improving the reliability of the satellite remote sensing data. The authenticity verification work of marine remote sensing satellites is always paid attention internationally, a calibration and authenticity verification working Group is established by the international earth observation satellite committee as early as 1984 to coordinate relevant work of authenticity verification of the remote sensing satellites in various countries, and a continuous authenticity verification work is carried out by an NASA (Ocean Biology Processing Group, OBPG) marine biological Processing Group in the life cycle of the satellites by utilizing data in the global range, and a plurality of beneficial achievements are obtained in the aspects of precision evaluation of marine remote sensing products, long-term stability evaluation of satellite measurement, on-orbit calibration precision verification of the satellites and the like.
The satellite product and the field measured data have different space-time sampling characteristics, a reasonable space-time matching window of the measured-remote sensing data is determined according to the spatial resolution of the satellite product and the space-time variation and uniformity of the water body, and the determination principle of the international universal space-time window is as follows: space window 3 x 3 or 5 x 5 pixel, time window + -3 h, however, the space-time matching principle is often difficult to be strictly executed due to the limitation of remote sensing effective pixel data and sea conditions. Even if the principle requirement of the space-time matching is met, due to the seasonal and regional variation characteristics of the ocean, particularly the characteristic of high dynamic variation of a wind field, the performance of the precision verification result in different seasons and regions is different, and the reliability and representativeness of the precision verification result need to be further evaluated.
Therefore, how to scientifically and effectively select the time and space matching rules of the actual measurement-remote sensing data, analyze the representative characteristics of the precision evaluation data set and improve the reliability of the precision evaluation result is one of the key scientific problems to be solved.
Disclosure of Invention
The invention aims to provide a wind field remote sensing data quality evaluation method and device based on sea surface wind field stability statistics division.
In order to realize the purpose of the invention, the following technical scheme is provided:
a wind field remote sensing data quality evaluation method based on sea surface wind field stability statistic zoning comprises the following steps:
establishing a wind speed interval division model according to the wind speed;
determining a wind direction stability coefficient based on historical wind field data, and establishing a wind direction stability grade classification model according to the wind direction stability coefficient;
acquiring actually measured wind field data and wind field remote sensing data, and performing space-time matching on the acquired actually measured wind field data and the acquired wind field remote sensing data to acquire a corresponding relation between the actually measured wind field data and the wind field remote sensing data;
calculating a wind direction stability coefficient of the actually measured wind field data;
carrying out wind speed interval division on the actually measured wind field data by using a wind speed interval division model to determine a wind speed interval to which the remote sensing wind field data belongs;
aiming at the actually measured wind field data and the wind direction stability coefficient thereof in each wind speed interval, carrying out wind direction stability grade division on the actually measured wind field data by using a wind direction stability grade classification model so as to determine the wind direction stability grade to which the remote sensing wind field data belongs;
and for the measured wind field data and the corresponding wind field remote sensing data contained in each wind direction stability grade, performing quality evaluation on the wind field remote sensing data according to the error indexes and the correlation of the measured wind field data and the wind field remote sensing data.
A wind field remote sensing data quality evaluation device based on sea surface wind field stability statistic zoning comprises a computer memory, a computer processor and a computer program which is stored in the computer memory and can be executed on the computer processor, wherein a wind speed interval division model and a wind direction stability grade classification model are also stored in the computer memory, and the computer processor executes the computer program to realize the following steps:
acquiring actually measured wind field data and wind field remote sensing data, and performing space-time matching on the acquired actually measured wind field data and the acquired wind field remote sensing data to acquire a corresponding relation between the actually measured wind field data and the wind field remote sensing data;
calculating a wind direction stability coefficient of the actually measured wind field data;
calling the wind speed interval division model to carry out wind speed interval division on the actually measured wind field data so as to determine a wind speed interval to which the remote sensing wind field data belongs;
aiming at the actually measured wind field data and the wind direction stability coefficient thereof in each wind speed interval, calling a wind direction stability grade classification model to perform wind direction stability grade division on the actually measured wind field data so as to determine the wind direction stability grade to which the remote sensing wind field data belongs;
and for the measured wind field data and the corresponding wind field remote sensing data contained in each wind direction stability grade, performing quality evaluation on the wind field remote sensing data according to the correlation between the measured wind field data and the wind field remote sensing data.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method and a device for comprehensively evaluating wind field remote sensing data by a comprehensive wind speed and wind direction stability grade division from the practical problem of influence of an inversion mechanism and space-time variation of the wind field remote sensing data on verification errors. The method and the device can accurately evaluate the quality of the remote sensing data of the wind field, and a user can scientifically screen and apply the remote sensing data of the wind field according to an evaluation result. The method has the advantages of strong feasibility in the implementation process, convenience in program integration in the calculation process, reliable calculation result precision, high industrial popularization value and application prospect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a wind field remote sensing data quality evaluation method based on a sea surface wind field stability statistic zoning according to an embodiment;
FIG. 2 is a schematic diagram of calculation of the stability of the time wind field provided by the embodiment;
FIG. 3 is a schematic view of calculation of stability of a spatial wind field according to an embodiment;
fig. 4 is a schematic diagram of a result of a wind direction stability space zoning according to an embodiment;
FIG. 5 is a wind farm data rating evaluation scatter plot provided by an embodiment.
FIG. 6 is a comparison graph of the evaluation results for different wind speed intervals provided by the embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is a flowchart of a wind field remote sensing data quality evaluation method based on a sea surface wind field stability statistic zoning according to an embodiment. As shown in fig. 1, the wind field remote sensing data quality evaluation method based on the sea surface wind field stability statistic zoning comprises the following steps:
s101, establishing a wind speed interval division model according to the wind speed.
The different sea surface roughness caused by different wind speeds and the different interference degrees on satellite receiving signals are different, and the remote sensing wind field data quality evaluation in different wind speed intervals needs to be processed differently, so that the wind speed interval division model provided by the embodiment is mainly used for determining the wind speed interval to which the wind speed belongs according to the size of the wind speed. Specifically, the wind speed interval division model includes a mapping relationship between the wind speed and the wind speed interval, as follows:
| wind speed | Interval of wind speed |
| 0～3m/s | A first wind speed interval noted |
| 3～15m/s | A second wind speed interval, denoted as |
| 15m/s | The third wind speed interval, noted as Lsp3 |
S102, determining a wind direction stability coefficient based on historical wind field data, and establishing a wind direction stability grade classification model according to the wind direction stability coefficient.
The wind field data includes wind speed data and wind direction data. When the wind direction stability grade classification model is constructed, the historical wind field data is used as a data source, namely, the wind direction stability coefficient of the historical wind field data is calculated. In the embodiment, a Wind Sat Wind field historical data set is selected, and a multi-year spatiotemporal Wind direction stability coefficient is calculated.
Specifically, the method for determining the wind direction stability coefficient includes:
calculating a first wind direction stability coefficient of the wind field data on a time sequence;
calculating a second wind direction stability coefficient of the wind field data on the space grid;
and fusing the first wind direction stability coefficient and the second wind direction stability coefficient to determine the wind direction stability coefficient.
As shown in fig. 2, a method for calculating a first wind direction stability coefficient of wind field data in a time series includes:
as shown in fig. 3, the method for calculating the second wind direction stability coefficient of the wind field data on the spatial grid includes:
the calculation method for fusing the first wind direction stability coefficient and the second wind direction stability coefficient comprises the following steps:
wherein S (t) is a first wind direction stability coefficient, ui,viRespectively a warp wind and a weft wind, i is an index of a wind field data sample, t is the total number of the wind field data samples, S (j) is a second wind direction stability coefficient, uj,vjThe method is characterized in that the wind direction is latitude wind and longitude wind in the grid window m × m, j is an index of the grid window, and S is a wind direction stability coefficient.
After the wind direction stability coefficient is obtained, a wind direction stability grade classification model can be constructed according to the wind direction stability coefficient. When the wind direction stability levels in different time periods are determined, the grid size of the historical data is required to be consistent with that of the verification data set, and if the grid sizes are inconsistent, a resampling method is required to be adopted to enable the sizes of the historical grid data verification data to be consistent. The wind direction stability grade classification model comprises a mapping relation between a wind direction stability coefficient and a wind direction stability grade, and comprises the following steps:
fig. 4 is a diagram illustrating the results of the wind direction stability space division.
S103, acquiring measured wind field data and wind field remote sensing data, performing space-time matching on the acquired measured wind field data and wind field remote sensing data, and acquiring a corresponding relation between the measured wind field data and the wind field remote sensing data.
And S104, calculating the wind direction stability coefficient of the actually measured wind field data.
The method for calculating the wind direction stability coefficient of the actually measured wind field data in S104 is the same as the method for calculating the wind direction stability coefficient in S103, and details are not repeated here.
And S105, carrying out wind speed interval division on the actually measured wind field data by using a wind speed interval division model to determine a wind speed interval to which the remotely sensed wind field data belongs.
And S106, aiming at the actually measured wind field data and the wind direction stability coefficient thereof in each wind speed interval, performing wind direction stability grade division on the actually measured wind field data by using a wind direction stability grade classification model so as to determine the wind direction stability grade to which the remotely sensed wind field data belongs.
And S107, for the actually measured wind field data and the corresponding wind field remote sensing data contained in each wind direction stability grade, performing quality evaluation on the wind field remote sensing data according to the correlation between the actually measured wind field data and the wind field remote sensing data.
In an embodiment, the quality evaluation of the wind field remote sensing data according to the correlation between the measured wind field data and the wind field remote sensing data comprises:
calculating the average absolute relative error, the root mean square error, the average relative deviation and the correlation coefficient of the actually measured wind field data and the wind field remote sensing data:
wherein RE is the average absolute relative error, RMSE is the root mean square error, BLAS is the average relative deviation, gamma is the correlation coefficient, the value range of gamma is between-1 and +1, M is the data number, r is the correlation coefficientiAs wind field remote sensing data, siIn order to measure the wind field data,
and
respectively the arithmetic mean values of the wind field remote sensing data and the actually measured wind field data;
and taking the average absolute relative error, the root mean square error, the average relative deviation and the correlation coefficient as four evaluation indexes, and integrating the four evaluation indexes to determine the quality of the wind field remote sensing data.
The embodiment also provides a wind field remote sensing data quality evaluation device based on sea surface wind field stability statistics zoning, which comprises a computer memory, a computer processor and a computer program stored in the computer memory and executable on the computer processor, wherein the computer memory further stores a wind speed interval division model and a wind direction stability grade classification model, and the computer processor implements the following steps when executing the computer program:
acquiring actually measured wind field data and wind field remote sensing data, and performing space-time matching on the acquired actually measured wind field data and the acquired wind field remote sensing data to acquire a corresponding relation between the actually measured wind field data and the wind field remote sensing data;
calculating a wind direction stability coefficient of the actually measured wind field data;
calling the wind speed interval division model to carry out wind speed interval division on the actually measured wind field data so as to determine a wind speed interval to which the remote sensing wind field data belongs;
aiming at the actually measured wind field data and the wind direction stability coefficient thereof in each wind speed interval, calling a wind direction stability grade classification model to perform wind direction stability grade division on the actually measured wind field data so as to determine the wind direction stability grade to which the remote sensing wind field data belongs;
and for the measured wind field data and the corresponding wind field remote sensing data contained in each wind direction stability grade, performing quality evaluation on the wind field remote sensing data according to the correlation between the measured wind field data and the wind field remote sensing data.
In the wind field remote sensing data quality evaluation device, a wind speed interval division model is established according to the wind speed, and the wind speed interval division model comprises a mapping relation between the wind speed and the wind speed interval as follows:
| wind speed | Interval of wind speed |
| 0～3m/s | A first wind speed interval noted |
| 3～15m/s | A second wind speed interval, denoted as Lsp2 |
| Greater than 15m/s | The third wind speed interval, noted as Lsp3 |
In the wind field remote sensing data quality evaluation device, the construction method of the wind direction stability grade classification model comprises the following steps: and determining a wind direction stability coefficient based on historical wind field data, and establishing a wind direction stability grade classification model according to the wind direction stability coefficient.
In the wind field remote sensing data quality evaluation device, the construction method of the wind speed interval division model and the wind direction stability grade classification model is the same as that of the wind speed interval division model and the wind direction stability grade classification model in the wind field remote sensing data quality evaluation method based on the sea surface wind field stability statistical zoning, and the description is omitted here.
The method and the device can accurately evaluate the quality of the remote sensing data of the wind field, and a user can scientifically screen and apply the remote sensing data of the wind field according to the evaluation result.
Examples of the experiments
In the experimental example, the measured wind speed-wind direction data is the NDBC historical data (website information as shown in the following: https:// www.ndbc.noaa.gov) of the American national data buoy center. The remote sensing data is Wind Sat sea surface Wind field microwave remote sensing data provided by an earth microwave data center (the website information is as follows: http:// www.remss.com/missions/windows /). The Wind Sat sea surface Wind field microwave remote sensing data to be evaluated are daily Wind speed-Wind direction data in 2014, and the historical remote sensing data are Wind Sat sea surface Wind field microwave remote sensing data in 2004-2013. And 1328 pairs of verification data sets are obtained by performing space-time matching on the actually measured wind field data and the wind field remote sensing data.
The wind field data is divided 1328 by using a wind speed interval division model, and the division result is shown in table 1:
TABLE 1
| Interval of wind speed | 0～3m/ |
3～15m/s | >15m/s |
| Number of data pairs | 122 | 776 | 430 |
Then, wind direction stability grade division is performed according to actually measured wind field data and a wind direction stability coefficient of the actually measured wind field data in each wind speed interval, and fig. 5 is a verification data grade evaluation scatter diagram in an experimental example.
And finally, for the actually measured wind field data and the corresponding wind field remote sensing data contained in each wind direction stability grade, performing quality evaluation on the wind field remote sensing data according to the correlation between the actually measured wind field data and the wind field remote sensing data.
The statistical result of the wind direction is: RE gave a Level1 of 18.36% and a maximum of Level2, 20.54% and an overall RE of 19.59. RMSE with a Level1 of 27.71 and also a maximum Level2 gave a 33.09% overall RMSE of 31.14. RE gave a Level1 of 19.22% and a maximum Level2 of 23.70% with an overall RE of 19.88. The minimum value of RMSE was Level2, resulting in a value of 1.59, and the maximum was Level3, resulting in a value of 1.97, with an overall RMSE of 1.74.
FIG. 6 shows the matching analysis result of the measured-remote sensing data in different wind speed intervals, and it can be seen from the figure that the wind speed is 3-15 m/s, the data inspection accuracy result with the division Level of Level2 is the best, RE and BIAS are both small, and the correlation coefficient is the largest. Therefore, the verification data set is subjected to representative evaluation by using the mutation level zoning result, the precision result of the remote sensing product can be more effectively and comprehensively evaluated, the error source of the remote sensing product is revealed, and guidance is provided for optimizing the space-time strategy of the actual measurement sampling point (navigation and buoy).
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (9)
1. A wind field remote sensing data quality evaluation method based on sea surface wind field stability statistic zoning is characterized by comprising the following steps:
establishing a wind speed interval division model according to the wind speed;
determining a wind direction stability coefficient based on historical wind field data, and establishing a wind direction stability grade classification model according to the wind direction stability coefficient;
acquiring actually measured wind field data and wind field remote sensing data, and performing space-time matching on the acquired actually measured wind field data and the acquired wind field remote sensing data to acquire a corresponding relation between the actually measured wind field data and the wind field remote sensing data;
calculating a wind direction stability coefficient of the actually measured wind field data;
carrying out wind speed interval division on the actually measured wind field data by using a wind speed interval division model to determine a wind speed interval to which the remote sensing wind field data belongs;
aiming at the actually measured wind field data and the wind direction stability coefficient thereof in each wind speed interval, carrying out wind direction stability grade division on the actually measured wind field data by using a wind direction stability grade classification model so as to determine the wind direction stability grade to which the remote sensing wind field data belongs;
and for the measured wind field data and the corresponding wind field remote sensing data contained in each wind direction stability grade, performing quality evaluation on the wind field remote sensing data according to the error indexes and the correlation of the measured wind field data and the wind field remote sensing data.
2. The wind field remote sensing data quality assessment method based on sea surface wind field stability statistic zoning according to claim 1, wherein the wind speed interval division model comprises a mapping relation between wind speed and wind speed interval as follows:
。
3. The wind field remote sensing data quality assessment method based on sea surface wind field stability statistic zoning according to claim 1, wherein the determination method of the wind direction stability coefficient comprises the following steps:
calculating a first wind direction stability coefficient of the wind field data on a time sequence;
calculating a second wind direction stability coefficient of the wind field data on the space grid;
and fusing the first wind direction stability coefficient and the second wind direction stability coefficient to determine the wind direction stability coefficient.
4. The method for evaluating the quality of wind field remote sensing data based on the sea surface wind field stability statistic zoning according to claim 3, wherein the method for calculating the first wind direction stability coefficient of the wind field data in the time sequence comprises the following steps:
the method for calculating the second wind direction stability coefficient of the wind field data on the spatial grid comprises the following steps:
the calculation method for fusing the first wind direction stability coefficient and the second wind direction stability coefficient comprises the following steps:
wherein S (t) is a first wind direction stability coefficient, ui,viRespectively a warp wind and a weft wind, i is an index of a wind field data sample, t is the total number of the wind field data samples, S (j) is a second wind direction stability coefficient, uj,vjThe method is characterized in that the wind direction is latitude wind and longitude wind in the grid window m × m, j is an index of the grid window, and S is a wind direction stability coefficient.
5. The method for evaluating the quality of wind field remote sensing data based on sea surface wind field stability statistic zoning according to claim 3, wherein the wind direction stability grade classification model comprises a mapping relation between a wind direction stability coefficient and a wind direction stability grade as follows:
。
6. The wind field remote sensing data quality assessment method based on sea surface wind field stability statistic zoning according to claim 3, wherein the quality assessment of the wind field remote sensing data according to the correlation between the measured wind field data and the wind field remote sensing data comprises:
calculating the average absolute relative error, the root mean square error, the average relative deviation and the correlation coefficient of the actually measured wind field data and the wind field remote sensing data:
wherein RE is the average absolute relative error, RMSE is the root mean square error, BLAS is the average relative deviation, gamma is the correlation coefficient, the value range of gamma is between-1 and +1, M is the data number, r is the correlation coefficientiAs wind field remote sensing data, siIn order to measure the wind field data,
and
respectively the arithmetic mean values of the wind field remote sensing data and the actually measured wind field data;
and taking the average absolute relative error, the root mean square error, the average relative deviation and the correlation coefficient as four evaluation indexes, and integrating the four evaluation indexes to determine the quality of the wind field remote sensing data.
7. A wind field remote sensing data quality assessment device based on sea surface wind field stability statistic zoning comprises a computer memory, a computer processor and a computer program which is stored in the computer memory and can be executed on the computer processor, wherein a wind speed interval division model and a wind direction stability grade classification model are also stored in the computer memory, and the computer processor realizes the following steps when executing the computer program:
acquiring actually measured wind field data and wind field remote sensing data, and performing space-time matching on the acquired actually measured wind field data and the acquired wind field remote sensing data to acquire a corresponding relation between the actually measured wind field data and the wind field remote sensing data;
calculating a wind direction stability coefficient of the actually measured wind field data;
calling the wind speed interval division model to carry out wind speed interval division on the actually measured wind field data so as to determine a wind speed interval to which the remote sensing wind field data belongs;
aiming at the actually measured wind field data and the wind direction stability coefficient thereof in each wind speed interval, calling a wind direction stability grade classification model to perform wind direction stability grade division on the actually measured wind field data so as to determine the wind direction stability grade to which the remote sensing wind field data belongs;
and for the measured wind field data and the corresponding wind field remote sensing data contained in each wind direction stability grade, performing quality evaluation on the wind field remote sensing data according to the correlation between the measured wind field data and the wind field remote sensing data.
8. The device for evaluating the quality of wind field remote sensing data based on the sea surface wind field stability statistic zoning according to claim 7, wherein the wind speed interval division model is established according to the wind speed, and comprises a mapping relation between the wind speed and the wind speed interval as follows:
9. The wind field remote sensing data quality assessment device based on sea surface wind field stability statistic zoning according to claim 7, wherein the construction method of the wind direction stability grade classification model comprises the following steps:
and determining a wind direction stability coefficient based on historical wind field data, and establishing a wind direction stability grade classification model according to the wind direction stability coefficient.
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