CN111126718A - Typhoon path prediction method - Google Patents
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Abstract
The invention discloses a typhoon path prediction method, which comprises the following steps: 1. importing motion path data of typhoon in the decade into a database, and taking a section of typhoon motion track A; 2. collecting the motion path data of the typhoon which is happening at present, and selecting a section of typhoon motion track B; 3. judging the correlation between the trajectories C and D of the two segments of typhoon motion, entering the step 4 if the trajectory C is correlated with the trajectory D, and returning to reselect the trajectory segment if the trajectory C is not correlated with the trajectory D; 4. and (3) extracting the overall motion trail of the typhoon belonging to the trail C, fitting the motion trail with the motion trail of the typhoon collected in the step (2), and extracting the motion trail of the typhoon in the future time period. The method can predict the trend of the typhoon path in advance, and has high accuracy.
Description
Technical Field
The invention belongs to the technical field of oceans, and particularly relates to a typhoon path prediction method.
Background
China is wide in territory, most of China is close to the sea, the sea area is wide, and many people living and working on the sea and surrounding cities are often attacked by typhoons, so that the economic development and social progress of China are hindered, and therefore, the typhoon forecasting research is very important.
Traditionally, a conceptual typhoon model based on a physical process is mostly used for describing a typhoon process, and the method is mature and can achieve a good prediction effect. However, the models are complex, the adaptability of the models for different regions is poor, and the difficulty in calibrating the model parameters is high. Therefore, data-driven typhoon process prediction methods are increasingly developed. In recent years, a relatively perfect information monitoring network is established in China, a large amount of typhoon related data is collected, the data contains the internal rules of the typhoon process, and how to improve the accuracy of hydrologic process prediction by using a data-driven prediction model established by a data mining technology is an important research direction.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the typhoon path prediction method can predict the trend of the typhoon path in advance and has high accuracy.
The technical scheme is as follows: in order to solve the above technical problem, the present invention provides a typhoon path prediction method, which comprises the following steps:
(1) importing motion path data of typhoon in the decade into a database, and taking a section of typhoon motion track C;
(2) collecting the motion path data of the typhoon which is happening at present, and selecting a section of typhoon motion track D;
(3) judging the correlation between the tracks C and D of the two segments of typhoon motions, entering the step (4) if the track C is correlated with the track D, and returning to the step (1) if the track C is not correlated with the track D;
(4) and (3) extracting the overall motion track of the typhoon belonging to the track C, fitting the motion track with the motion track of the typhoon collected in the step (2), and extracting the motion track of the typhoon in the future time period.
Further, the specific steps of determining the correlation between the trajectories C and D of the two segments of typhoon motions in the step (3) are as follows:
(3.1) converting the longitude and latitude coordinates into a universal transverse-axis mercator projection coordinate system, and performing filtering processing on the track data by using a Kalman filtering algorithm;
(3.2) calculating the space distance between each point in the two sections of tracks C and D, and correcting;
(3.3) calculating the predicted distance between each point in the two sections of the tracks C and D;
(3.4) calculating the distance between each section of the two sections of tracks C and D;
(3.5) calculating the accumulated distance of the two tracks C and D;
(3.6) subjecting the result obtained in step (3.5) to a normalization process, i.e. normalizing the cumulative distance to [0,1], wherein 0 means that the two tracks are completely unrelated, and 1 means that the two tracks are completely similar, as long as the value is 0.5-1, there is a correlation.
Further, the specific step of calculating the spatial distance between each point in the two sections of trajectories C and D in the step (3.2) is as follows:
and selecting the ith track point in the track C and the jth track point in the track D, judging whether the distance between the point segment from the ith track point to the jth track point is equal to the distance between the point segment from the jth track point to the ith track point, and if not, correcting.
Further, the specific steps of calculating the predicted distance between each point in the two sections of trajectories C and D in the step (3.3) are as follows:
(3.3.1) taking the ith track point P in the track Ci(xi,yi) J-th track point SP in track Dj(xj,yj) (ii) a Comparison point PiAnd point SPjThe time sequence relation of (1) is that the point with large time is A and the point with small time is B; with time difference Δ t ═ tA-tB；
(3.3.2) calculating the predicted position B' of the point B, assuming that the point B is a moving point, traversing the time of each point, and searching tBAt + Δ t point B lies between which two trace points, i.e. where its predicted position is, assuming at tB+ Δ t point B is between the i-1 st and i-th points, the spatial coordinates (x) of the predicted position B' for point BB′,yB′) The calculation formula is as follows:
assuming that the motion between any two points on the track is uniform linear motion, the motion speed between the two points can be obtained, and the solving formula is as follows:
if time tB+ Δ t is absent on the B trace, then B' is calculated as follows:
wherein N is the total number of points of the track where the B point is located;
(3.3.3) calculating the predicted distance between A and B by the following formula:
distt(A,B)＝dist(A,B′)
where dist (A, B ') is the Euclidean distance of A and B' in space coordinates.
Further, the specific steps of calculating the distance between each of the two sections of the trajectories C and D in the step (3.4) are as follows:
(3.4.1) wherein SiSection i of the track C, SSjIs the jth segment of the trajectory D; calculating SiAnd SSjAngle theta, S ofiTwo end points of (A) are Pi(xi,yi) And Pi+1(xi+1,yi+1)，SSjAre SPj(xj,yj) And SPj+1(xj+1,yj+1) The calculation formula of the angle theta is as follows:
θ＝|arctan2(yi+1-yi,xi+1-xi)-arctan2(yj+1-yj,xj+1-xj)|
(3.4.2) the spatio-temporal distance between segments is the sum of the spatio-temporal distances of the two end points of the segment, so SiAnd SSjInter-segment distance dist ofs(Si,SSj) The calculation formula of (2) is as follows:
dists(Si,SSj)＝f(θ)(distst(Pi,SPj)+distst(Pi+1,SPj+1))
(3.4.3) solving f (theta), wherein the calculation formula is as follows:
wherein: ω is the negative factor of the adjustable parameter shape, with larger ω distances being less sensitive to the shape factor, where ω is 1, distsmid(Si,SSj) Is the midpoint SiAnd SSjSpace-time distance of midpoints, distmax(R, S) is the maximum spatiotemporal distance between any two points of the trajectory C and the trajectory D.
Compared with the prior art, the invention has the advantages that:
according to the method, the next step trend of the typhoon path can be predicted in advance better by improving the accuracy of the track similarity, and the accuracy is higher.
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FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is further elucidated with reference to the drawings and the detailed description. The described embodiments of the present invention are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by a person of ordinary skill in the art without any creative effort belong to the protection scope of the present invention.
A typhoon path prediction method comprises the following steps:
(1) importing motion path data of typhoon in the decade into a database, and taking a section of typhoon motion track C;
(2) collecting the motion path data of the typhoon which is happening at present, and selecting a section of typhoon motion track D;
(3) judging the correlation between the tracks C and D of the two segments of typhoon motions, entering the step (4) if the track C is correlated with the track D, and returning to the step (1) if the track C is not correlated with the track D;
(4) and (3) extracting the overall motion track of the typhoon belonging to the track C, fitting the motion track with the motion track of the typhoon collected in the step (2), and extracting the motion track of the typhoon in the future time period.
Further, the specific steps of determining the correlation between the trajectories C and D of the two segments of typhoon motions in the step (3) are as follows:
(3.1) converting the longitude and latitude coordinates into a universal transverse-axis mercator projection coordinate system, and performing filtering processing on the track data by using a Kalman filtering algorithm;
(3.2) calculating the space distance between each point in the two sections of tracks C and D, and correcting;
(3.3) calculating the predicted distance between each point in the two sections of the tracks C and D;
(3.4) calculating the distance between each section of the two sections of tracks C and D;
(3.5) calculating the accumulated distance of the two tracks C and D;
(3.6) subjecting the result obtained in step (3.5) to a normalization process, i.e. normalizing the cumulative distance to [0,1], wherein 0 means that the two tracks are completely unrelated, and 1 means that the two tracks are completely similar, as long as the value is 0.5-1, there is a correlation.
Further, the specific step of calculating the spatial distance between each point in the two sections of trajectories C and D in the step (3.2) is as follows:
and selecting the ith track point in the track C and the jth track point in the track D, judging whether the distance between the point segment from the ith track point to the jth track point is equal to the distance between the point segment from the jth track point to the ith track point, and if not, correcting.
Further, the specific steps of calculating the predicted distance between each point in the two sections of trajectories C and D in the step (3.3) are as follows:
(3.3.1) taking the ith track point P in the track Ci(xi,yi) J-th track point SP in track Dj(xj,yj) (ii) a Comparison point PiAnd point SPjThe time sequence of (1) is defined as A, hourThe dots with small intervals are B; with time difference Δ t ═ tA-tB；
(3.3.2) calculating the predicted position B' of the point B, assuming that the point B is a moving point, traversing the time of each point, and searching tBAt + Δ t point B lies between which two trace points, i.e. where its predicted position is, assuming at tB+ Δ t point B is between the i-1 st and i-th points, the spatial coordinates (x) of the predicted position B' for point BB′,yB′) The calculation formula is as follows:
assuming that the motion between any two points on the track is uniform linear motion, the motion speed between the two points can be obtained, and the solving formula is as follows:
if time tE+ Δ t is absent on the B trace, then B' is calculated as follows:
wherein N is the total number of points of the track where the B point is located;
(3.3.3) calculating the predicted distance between A and B by the following formula:
distt(A,B)＝dist(A,B′)
where dist (A, B ') is the Euclidean distance of A and B' in space coordinates.
Further, the specific steps of calculating the distance between each of the two sections of the trajectories C and D in the step (3.4) are as follows:
(3.4.1) wherein SiSection i of the track C, SSjIs the jth segment of the trajectory D; calculating SiAnd SSjAngle theta, S ofiTwo end points of (A) are Pi(xi,yi) And Pi+1(xi+1,yi+1)，SSjTwo of (2)End point is SPj(xj,yj) And SPj+1(xj+1,yj+1) The calculation formula of the angle theta is as follows:
θ＝|arctan2(yi+1-yi,xi+1-xi)-arctan2(yj+1-yj,xj+1-xj)|
(3.4.2) the spatio-temporal distance between segments is the sum of the spatio-temporal distances of the two end points of the segment, so SiAnd SSjInter-segment distance dist ofs(Si,SSj) The calculation formula of (2) is as follows:
dists(Si,SSj)＝f(θ)(distst(Pi,SPj)+distst(Pi+1,SPj+1))
(3.4.3) solving f (theta), wherein the calculation formula is as follows:
wherein: ω is the negative factor of the adjustable parameter shape, with larger ω distances being less sensitive to the shape factor, where ω is 1, distsmid(Si,SSj) Is the midpoint SiAnd SSjSpace-time distance of midpoints, distmax(R, S) is the maximum spatiotemporal distance between any two points of the trajectory C and the trajectory D.
Claims (5)
1. A typhoon path prediction method is characterized by comprising the following steps:
(1) importing motion path data of typhoon in the decade into a database, and taking a section of typhoon motion track C;
(2) collecting the motion path data of the typhoon which is happening at present, and selecting a section of typhoon motion track D;
(3) judging the correlation between the tracks C and D of the two segments of typhoon motions, entering the step (4) if the track C is correlated with the track D, and returning to the step (1) if the track C is not correlated with the track D;
(4) and (3) extracting the overall motion track of the typhoon belonging to the track C, fitting the motion track with the motion track of the typhoon collected in the step (2), and extracting the motion track of the typhoon in the future time period.
2. The method for predicting the typhoon path according to claim 1, wherein the specific steps of judging the correlation between the trajectories C and D of the two segments of typhoon motion in the step (3) are as follows:
(3.1) converting the longitude and latitude coordinates into a universal transverse-axis mercator projection coordinate system, and performing filtering processing on the track data by using a Kalman filtering algorithm;
(3.2) calculating the space distance between each point in the two sections of tracks C and D, and correcting;
(3.3) calculating the predicted distance between each point in the two sections of the tracks C and D;
(3.4) calculating the distance between each section of the two sections of tracks C and D;
(3.5) calculating the accumulated distance of the two tracks C and D;
(3.6) subjecting the result obtained in step (3.5) to a normalization process, i.e. normalizing the cumulative distance to [0,1], wherein 0 means that the two tracks are completely unrelated, and 1 means that the two tracks are completely similar, as long as the value is 0.5-1, there is a correlation.
3. The typhoon path prediction method according to claim 1, characterized in that, the specific steps of calculating the spatial distance between each point in the two sections of trajectories C and D in the step (3.2) are as follows:
and selecting the ith track point in the track C and the jth track point in the track D, judging whether the distance between the point segment from the ith track point to the jth track point is equal to the distance between the point segment from the jth track point to the ith track point, and if not, correcting.
4. The typhoon path prediction method according to claim 1, characterized in that, the specific steps of calculating the predicted distance between each point in the two sections of trajectories C and D in the step (3.3) are as follows:
(3.3.1) taking the ith track in the tracks CPoint Pi(xi,yi) J-th track point SP in track Dj(xj,yj) (ii) a Comparison point PiAnd point SPjThe time sequence relation of (1) is that the point with large time is A and the point with small time is B; with time difference Δ t ═ tA-tB；
(3.3.2) calculating the predicted position B' of the point B, assuming that the point B is a moving point, traversing the time of each point, and searching tBAt + Δ t point B lies between which two trace points, i.e. where its predicted position is, assuming at tB+ Δ t point B is between the i-1 st and i-th points, the spatial coordinates (x) of the predicted position B' for point BB′,yB′) The calculation formula is as follows:
assuming that the motion between any two points on the track is uniform linear motion, the motion speed between the two points can be obtained, and the solving formula is as follows:
if time tB+ Δ t is absent on the B trace, then B' is calculated as follows:
wherein N is the total number of points of the track where the B point is located;
(3.3.3) calculating the predicted distance between A and B by the following formula:
distt(A,B)＝dist(A,B′)
where dist (A, B ') is the Euclidean distance of A and B' in space coordinates.
5. The typhoon path prediction method according to claim 1, characterized in that, the specific steps of calculating the distance between each of the two sections of the trajectories C and D in the step (3.4) are as follows:
(3.4.1) wherein SiSection i of the track C, SSjIs the jth segment of the trajectory D; calculating SiAnd SSjAngle theta, S ofiTwo end points of (A) are Pi(xi,yi) And Pi+1(xi+1,yi+1)，SSjAre SPj(xj,yj) And SPj+1(xj+1,yj+1) The calculation formula of the angle theta is as follows:
θ＝|arctan2(yi+1-yi,xi+1-xi)-arctan2(yj+1-yj,xj+1-xj)|
(3.4.2) the spatio-temporal distance between segments is the sum of the spatio-temporal distances of the two end points of the segment, so SiAnd SSjInter-segment distance dist ofs(Si,SSj) The calculation formula of (2) is as follows:
dists(Si,SSj)＝f(θ)(distst(Pi,SPj)+distst(Pi+1,SPj+1))
(3.4.3) solving f (theta), wherein the calculation formula is as follows:
wherein: ω is the negative factor of the adjustable parameter shape, with larger ω distances being less sensitive to the shape factor, where ω is 1, distsmid(Si,SSj) Is the midpoint SiAnd SSjSpace-time distance of midpoints, distmax(R, S) is the maximum spatiotemporal distance between any two points of the trajectory C and the trajectory D.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111722306A (en) * | 2020-07-27 | 2020-09-29 | 郑州航空工业管理学院 | Typhoon landing intensity prediction method and system based on TCN network model |
| CN111898815A (en) * | 2020-07-21 | 2020-11-06 | 中远海运科技(北京)有限公司 | Typhoon track prediction method and device, electronic equipment and computer readable medium |
| CN116341287A (en) * | 2023-05-24 | 2023-06-27 | 自然资源部第二海洋研究所 | Track self-adaptive gridding processing method based on optimal typhoon track data |
-
2020
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111898815A (en) * | 2020-07-21 | 2020-11-06 | 中远海运科技(北京)有限公司 | Typhoon track prediction method and device, electronic equipment and computer readable medium |
| CN111722306A (en) * | 2020-07-27 | 2020-09-29 | 郑州航空工业管理学院 | Typhoon landing intensity prediction method and system based on TCN network model |
| CN116341287A (en) * | 2023-05-24 | 2023-06-27 | 自然资源部第二海洋研究所 | Track self-adaptive gridding processing method based on optimal typhoon track data |
| CN116341287B (en) * | 2023-05-24 | 2023-08-01 | 自然资源部第二海洋研究所 | Track self-adaptive gridding processing method based on optimal typhoon track data |
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