CN110188944B - Surge monitoring and early warning method - Google Patents
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Abstract
The invention belongs to the technical field of wave monitoring, and relates to a surge monitoring and early warning method. The method comprises the following steps: firstly, collecting sea surface wind field data and topographic data. And secondly, calculating sea surface wind field data and topographic data by adopting a sea wave mode WW3 and SWAN to obtain sea wave big data. And thirdly, calculating by using the water depth data and the surge data in the sea wave big data in the second step to obtain the surge energy flow density. And fourthly, defining the swell index. And fifthly, solving a surge propagation path. And sixthly, solving a surge source. And seventhly, calculating the time required by the surge propagation. And eighthly, calculating the attenuation rate in the surge propagation process. And ninthly, calculating the seasonal period of the surge energy flow density of the concerned region and the source by utilizing wavelet analysis and the surge energy flow density data. The method can accurately calculate the propagation path, speed, source and destination of the surge, and limit the source of the surge to a small sea area, thereby being more beneficial to realizing accurate monitoring of the surge.
Description
Technical Field
The invention belongs to the technical field of sea wave monitoring, and relates to a surge monitoring and early warning method.
Background
In all marine disasters, casualties of people and property caused by sea waves are in the front, particularly, swell has the characteristics of huge energy and strong destructiveness, can form the phenomena of sagging, mid-arch and the like, and easily causes serious damage and even damage to ships. Anything is twosided. Because the energy of the surge is huge, the stability is good, and the surge often dominates in the mixed waves, the surge power generation is more and more emphasized internationally in recent years. Deep research on surge characteristics has practical value for surge monitoring and early warning, wave power generation, sea water desalination and other wave energy projects, wave and wave energy forecasting and the like.
By the contribution of predecessors, the method well grasps the aspects of the wind wave and surge separation technology, the surge indexes, the surge pool, the wave spectrum and the like, but the research on surge propagation is still rare. Research shows that after the surge is generated, the surge can be spread for thousands of kilometers until the surge is spread to a coast and broken, and the energy loss is small in the spreading process. The surge source can be monitored, and the surge monitoring and early warning in the concerned area can be realized by combining the propagation path, the speed and the attenuation condition. Therefore, a set of methods is needed to quantitatively display the surge propagation characteristics. The traditional method for analyzing the surge propagation characteristics has the following problems:
1) The existing method is generally used for qualitatively showing the surge propagation by drawing the surge wave height and the wave direction, can show the general propagation direction of the surge, but cannot accurately show the surge propagation path. Furthermore, the wave direction is not suitable for demonstrating the propagation path of the swell, such as the left half circle of the advancing direction of the typhoon, which is nearly opposite.
2) At present, no suitable method is available for tracing the source of surge, which is a key link for monitoring and early warning of surge.
3) At present, researches on the propagation speed of the surge are extremely rare, and the researches directly determine the advance of the surge monitoring and early warning.
4) The study on the attenuation situation during the propagation of the swell is almost blank, and the swell monitoring is also concerned closely.
At present, although the qualitative analysis of the surge propagation characteristics is concerned, no method can realize the tracing of the surge source and quantitatively display the propagation path and speed of the surge and the attenuation condition in the propagation process, which is closely concerned in the practical application process of the surge monitoring and early warning.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a surge monitoring and early warning method, which provides scientific basis for the prevention of surge threat, wave energy development, sea wave prediction and the like.
A surge monitoring and early warning method comprises the following steps:
firstly, collecting sea surface wind field data and topographic data.
And secondly, calculating the sea surface wind field data and the topographic data by adopting a sea wave mode WW3 and SWAN to obtain sea wave big data with a long time sequence, high space-time resolution and separated storms and swell.
And thirdly, calculating according to the energy flow density calculation method by using the water depth data and the swell data in the sea wave big data in the second step to obtain the swell energy flow density (WPD) with a long time sequence and high time resolution.
The calculation method of the fluence comprises the following steps:
in shallow water (d/lambda < 1/20):
under the condition of deep water (d/lambda is more than or equal to 1/2):
in the case of medium water depth (1/20. Ltoreq. D/lambda < 1/2):
wherein WPD is energy flow density (unit: kW/m), H s Is the effective wave height (unit: m), T e Is the wave period (unit: s), d is the water depth (unit: m), tanh and sinh are hyperbolic tangent and hyperbolic sine functions respectively, k is the wave number, lambda is the wavelength (unit: m), rho is the sea water density,
and step four, defining the Swell Index (SI).
And (3) carrying out regional averaging on the surge energy flow density (WPD) of the concerned region at a certain moment to obtain a Surge Index (SI) of the concerned region at the moment, and obtaining the Surge Index (SI) of a long-time sequence and high time resolution by the same method.
And fifthly, solving a surge propagation path.
5.1 selecting concerned seasons, calculating the synchronous correlation between the Surge Index (SI) and the surge energy flux density (WPD) on each grid point (the grid point number is determined by the spatial resolution of the sea wave big data) of the sea area, obtaining a correlation coefficient field distribution diagram, and only displaying the area of which the correlation coefficient passes the significance test.
5.2 calculating the contemporaneous correlation between the Surge Index (SI) lagging by 24 hours and the surge energy flow density (WPD) on each grid point of the sea area, obtaining a correlation coefficient field distribution diagram, and only displaying the area of which the correlation coefficient passes the significance test.
5.3, respectively calculating lags 48, 72, 96, 120, 144, 168 \8230, 8230, n hours and the contemporaneous correlation of surge energy flow density (WPD) on each grid point of the sea area by using the same method, correspondingly obtaining a correlation coefficient field distribution diagram, and only displaying the area of which the correlation coefficient passes significance test.
And 5.4, highlighting the large-value center of each correlation coefficient field obtained by 5.1-5.3, connecting all highlighted areas by adopting a solid line, pointing the highlighted areas to the attention area, and representing the surge propagation direction by using arrows to obtain the surge propagation path. The same method can be adopted to obtain the propagation path of the surge in each season.
And sixthly, solving a surge source.
Based on the second step, when the Surge Index (SI) lags behind by n hours and is not significantly correlated with the surge energy flow density (WPD) at each grid point of the sea area (i.e. the correlation coefficient cannot pass the significance test), the area where the correlation coefficient passes the significance test at the previous moment is the source of the surge in the area of interest.
And seventhly, calculating the time required by the surge propagation.
After the surge source is obtained, calculating the synchronous correlation, the advance correlation and the delay correlation of the Surge Index (SI) and the source surge energy flux density (WPD), wherein when the SI lags for i hours, the correlation coefficients of the SI and the source surge energy flux density can reach the peak value, and the SI and the source surge energy flux density can be transmitted to a concerned area from the source for i hours through significance test.
And eighthly, calculating the attenuation rate of the surge propagation process.
The method for calculating the attenuation rate of the surge energy is as follows:
in the formula: d is a radical of t WPD is the attenuation rate (unit:%) of surge energy at a certain time t t The WPD is the surge energy flow density (unit: kW/m) of a certain time tsource (t-i) The value standard of i is that the surge energy flow density (unit: kW/m) of an attention area lags behind a source for i hours is as follows: when the area of interest lags behind the source by i hours, the correlation coefficient of the surge energy flow density of the area of interest and the source reaches a peak value.
And ninthly, calculating the seasonal periods of the surge energy flow density of the concerned area and the source by utilizing wavelet analysis and the surge energy flow density data of long-time sequence and high time resolution, and proving that the seasonal oscillation characteristics of the two areas have commonality.
And step ten, calculating the phase difference of the surge energy flux density of the concerned area and the source on a common period by using the cross wavelet, the long-time sequence and the surge energy flux density data with high time resolution, and proving the correctness of the propagation time calculated by using the lead-lag correlation.
And eleventh, repeating the third step to the tenth step to realize surge propagation characteristic analysis in each season, wherein the surge propagation characteristic analysis comprises a surge source, a propagation path, propagation time and attenuation conditions in the surge propagation process of the concerned area.
The surge source, the surge propagation path, the required time and the attenuation condition of the attention area in each season are obtained, the factors are comprehensively utilized, and the surge monitoring and early warning of the attention area can be realized.
The invention has the beneficial effects that:
(1) The traditional method for analyzing the surge propagation can approximately show the source of the surge, but the source range is large, so that the method is not beneficial to focusing a small-range sea area to realize the surge monitoring. The method can accurately limit the surge source to a certain small-range sea area, and is more favorable for realizing accurate surge monitoring.
(2) The method can accurately show the propagation path and speed of the surge, and can know from what source the surge starts, propagates along what path and influences the concerned area when the surge is traced back to the concerned source, thereby providing accurate surge monitoring and early warning for the concerned area, preventing the threat caused by the surge and having practical value for the safety guarantee of ocean energy development, offshore construction and ocean platform.
(3) The method has the advantages of huge surge energy, good stability, dominance in mixed waves, mastering the propagation characteristics of the surge, being beneficial to improving the collection and conversion efficiency of wave energy, and providing scientific basis for the business operation of wave power generation, seawater desalination and other work.
(4) The propagation path of the surge and the attenuation condition in the propagation process are mastered, scientific basis can be provided for route planning and risk avoidance of ocean navigation, and the threats of sagging, arching, propeller idling and the like caused by the surge are prevented; and a theoretical basis can be provided for wave energy forecasting, so that the wave energy collection efficiency is improved.
(5) Because the propagation path, speed, source, attenuation condition and the like of the surge cannot be accurately mastered, the surge propagation outside the region cannot be well considered when the region sea wave is simulated, and the simulation effect is influenced. In order to improve the simulation accuracy, a region nesting method is generally adopted, the simulation range is appropriately expanded on the basis of the region of interest, and boundary conditions are provided for the region of interest by expanding the simulation result of the region. As for how much the area is expanded, there is no certain standard and basis, and the ocean swell is often not considered sufficiently. The method can accurately show the propagation characteristics of the swell, and has scientific basis for expanding the selection range of the area when the areas simulated by the sea waves are nested.
Drawings
Fig. 1 shows a method for acquiring big data of sea waves according to the present invention.
Fig. 2 is a method of the surge propagation process of the present invention.
Fig. 3 shows the surge energy flow density at 8, 16 and 2002.
FIG. 4 is a field distribution diagram of correlation coefficients related to the same period of months 6-8 from 2001.
FIG. 5 is a plot of the correlation coefficient field for contemporaneous correlation with a 24 hour lag in the Swell Index (SI).
Fig. 6 (a), 6 (b), 6 (c), 6 (d), 6 (e), 6 (f) are contemporaneously correlated correlation coefficient field profiles of Swell Index (SI) lag 48, 72, 96, 120, 144, 168 hours, respectively.
Fig. 7 is a schematic diagram of the propagation path of a surge.
Fig. 8 is a diagram illustrating the peak value of the correlation coefficient.
Fig. 9 is a diagram illustrating the attenuation rate of the surge propagation process.
FIG. 10 is an intra-seasonal chart of the area of interest, source swell energy flow density.
Fig. 11 is a phase difference diagram of the source surge energy flow density in the attention area on the common period.
Detailed Description
In order that the manner in which the present invention is attained and can be understood in detail, a more particular description of the invention briefly summarized above may be had by reference to the embodiments thereof which are illustrated in the appended drawings.
Firstly, installing and debugging WW3 and SWAN ocean wave modes; the method comprises the steps of collecting ERA-Interim sea surface wind field and Etopo1 water depth data, and processing the data into a sea wave mode identification format.
And secondly, driving a WW3 and SWAN sea wave mode by ERA-Interim sea surface wind field and Etopo1 water depth data (WW 3 is adopted in the ocean, SWAN is adopted in the near shore), and obtaining sea wave big data with spatial resolution of 0.5 degrees multiplied by 0.5 degrees, and wind waves and surge separation, wherein 12 months in 2001 to 2018, 3 hours (one data in every 3 hours) by 3 hours.
And thirdly, calculating the 6-hour per-year surging energy flow density (WPD) in 2001-2018 according to the energy flow density calculation method by using the water depth data and the surging data in the sea wave big data in the second step, and referring to fig. 3.
And step four, defining the Swell Index (SI). Here, the srilanca sea area is taken as an area of interest, and the example is shown. The swell fluence (WPD) of the area of interest (srilanka sea area, 0 ° -10 ° S,75 ° E-85 ° E) at 1 month 1 day 00 of 2001 was area-averaged to obtain a value, which was defined as the SI of the southwest indian ocean at that time, and the SI of the southwest indian ocean was obtained 6 hours by 6 hours in 2001-2018 in the same manner.
And fifthly, solving a surge propagation path.
5.1 select concerned season (6-8 months 2001), calculate the contemporaneous correlation of the Surge Index (SI) and the surge energy flow density (WPD) on each grid point (the grid point number is determined by the space resolution of the big data of the sea waves) of the Indian ocean, obtain a correlation coefficient field distribution diagram, and only display the area where the correlation coefficient passes the significance test, as shown in figure 4.
5.2 calculating the contemporaneous correlation of the Surge Index (SI) lag of 24 hours with the surge fluence (WPD) at each grid point in the Indian ocean, a correlation coefficient field profile was obtained showing only the regions where the correlation coefficient passed the significance test, see FIG. 5.
5.3, respectively calculating lag 48, 72, 96, 120, 144, 168 \8230, \8230, n hours and the contemporaneous correlation of surge energy flow density (WPD) on each grid point of the sea area by using the same method, correspondingly obtaining a correlation coefficient field distribution diagram, and only displaying the area of which the correlation coefficient passes the significance test, as shown in figure 6.
And 5.4, highlighting the large-value center of each correlation coefficient field obtained by 5.1-5.3, connecting all highlighted areas by adopting a solid line, pointing the highlighted areas to the attention area, and representing the surge propagation direction by using arrows to obtain the surge propagation path, which is shown in figure 7. The same method can be adopted to obtain the propagation path of the surge in each season.
And sixthly, solving a surge source.
Based on the second step, when the Surge Index (SI) lags behind by 168 hours and the surge energy flow density (WPD) at each grid point of the sea area is not significantly correlated (i.e. the correlation coefficient cannot pass the significance test), the area where the correlation coefficient passes the significance test at the previous moment is the source of the surge in the area of interest, as shown in fig. 7.
And seventhly, calculating the time required by the surge propagation.
After the surge source is obtained, the synchronous correlation, the advance correlation and the delay correlation of the Surge Index (SI) and the source surge energy flow density (WPD) are calculated, when the SI lags for 168 hours, the correlation coefficients of the SI and the source surge energy flow density can reach the peak value, and the significance test is carried out, namely the surge takes 168 hours to propagate from the source to the attention area, which is shown in figure 8.
And step eight, calculating the attenuation rate of the surge propagation process, as shown in figure 9.
And ninthly, calculating the seasonal periods of the surge energy flux densities of the concerned region and the source by utilizing wavelet analysis and the surge energy flux density data of long-time sequence and high time resolution, and proving that the seasonal oscillation characteristics of the two regions have commonality, as shown in figure 10.
And step ten, calculating the phase difference of the surge energy flux density of the concerned area and the source on a common period by using the cross wavelet, the long-time sequence and the surge energy flux density data with high time resolution, and proving the correctness of the propagation time calculated by using the lead-lag correlation, as shown in fig. 11.
And eleventh, repeating the third step to the tenth step to realize the surge propagation characteristic analysis in each season, including the surge source, the propagation path, the propagation time and the attenuation condition in the surge propagation process of the attention area.
The above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person skilled in the art can modify the technical solution of the present invention or substitute the same without departing from the spirit and scope of the present invention, and the scope of the present invention should be determined by the claims.
Claims (2)
1. A surge monitoring and early warning method is characterized by comprising the following steps:
firstly, collecting sea surface wind field data and topographic data;
secondly, calculating sea surface wind field data and topographic data by adopting a sea wave mode WW3 and SWAN to obtain sea wave big data;
thirdly, calculating surge energy flux density according to a calculation method of energy flux density by using water depth data and surge data in the sea wave big data in the second step;
the calculation method of the fluence comprises the following steps:
in shallow water, d/λ <1/20:
under the condition of deep water, d/lambda is more than or equal to 1/2:
under the condition of medium water depth, d/lambda is more than or equal to 1/20 and less than 1/2:
wherein WPD is energy flow density, unit is kW/m; h s Is the effective wave height, unit is m; t is e Is the wave period, unit is s; d is the water depth, unit is m; tan h and sinh are hyperbolic tangent and hyperbolic sine functions respectively; k is the wave number; λ is wavelength, unit is m; rho is the density of the seawater;
fourthly, defining a surge index;
carrying out regional average on the surge energy flow density of a concerned region at a certain moment to obtain a surge index of the concerned region at the moment, and obtaining the surge index by the same method;
fifthly, solving a surge propagation path;
5.1, selecting concerned seasons, calculating the synchronous correlation between the surge index and the surge energy flow density on each grid point of the sea area to obtain a correlation coefficient field distribution diagram, and only displaying the area of which the correlation coefficient passes the significance test;
5.2, calculating the synchronous correlation between the surge index lag of 24 hours and the surge energy flux density on each grid point of the sea area to obtain a correlation coefficient field distribution diagram, and only displaying the area of which the correlation coefficient passes the significance test;
5.3, respectively calculating lags 48, 72, 96, 120, 144 and 168 \ 8230, wherein the \ 8230n h is synchronously related to the surge energy flow density of each grid point of the sea area in n hours, correspondingly obtaining a correlation coefficient field distribution diagram, and only displaying the area of which the correlation coefficient passes significance test;
5.4, highlighting the large-value center of each correlation coefficient field obtained in the step 5.1 to the step 5.3, connecting all highlighted areas by adopting a solid line, pointing the highlighted areas to the attention area, and representing the surge propagation direction by arrows to obtain a surge propagation path; the same method can be adopted to obtain the propagation paths of the surge in each season;
sixthly, solving a surge source;
based on the second step, when the surge index lags for n hours and is not significantly related to the proximity of the surge energy flow density on each grid point of the sea area, the area of which the relevant coefficient at the previous moment passes the significance test is the surge source of the concerned area;
step seven, calculating the time required by surge propagation;
after the surge source is obtained, calculating the synchronous correlation, the advance correlation and the delay correlation of the surge index and the source surge energy flow density, wherein when the surge index lags for i hours, the correlation coefficient of the surge index and the source surge energy flow density can reach the peak value, and the surge index and the source surge energy flow density are subjected to significance test, namely the surge needs i hours to be transmitted to a concerned area from the source;
eighthly, calculating the attenuation rate in the surge propagation process;
the method for calculating the surge energy attenuation rate is as follows:
in the formula: d t The attenuation rate of surge energy at a certain time t is expressed in percentage; WPD t The unit of the surge energy flow density at a certain moment t source is kW/m; WPD (t-i) The unit of the surge energy flow density of an attention area lags behind a source for i hours is kW/m; the value standard of i is as follows: when the delay of the concerned area is i hours later than the source, the surge energy flow density correlation coefficient of the concerned area and the source reaches the peak value;
ninth, calculating the seasonal period of the surge energy flow density of the concerned region and the source by utilizing wavelet analysis and the surge energy flow density data, and proving that the seasonal oscillation characteristics of the two regions have commonality;
step ten, calculating the phase difference of the surge energy flow density of the concerned area and the source in a common period by using the cross wavelet and the surge energy flow density data, and proving the correctness of the propagation time calculated by using the lead-lag correlation;
and eleventh, repeating the third step to the tenth step to realize the surge propagation characteristic analysis in each season.
2. The surge monitoring and early warning method according to claim 1, wherein the analysis of the surge propagation characteristics in the eleventh step includes the source of the surge, the propagation path, the propagation time and the attenuation during the propagation of the surge in the region of interest.
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