CN116341287B - Track self-adaptive gridding processing method based on optimal typhoon track data - Google Patents
Track self-adaptive gridding processing method based on optimal typhoon track data Download PDFInfo
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Abstract
The invention relates to a track self-adaptive gridding processing method based on optimal typhoon track data, which is characterized in that the calendar year optimal typhoon track data of a target area is processed to obtain the grid size of the target area so as to obtain the grid of the target area, the calendar year optimal typhoon track data is processed in a thinning way by utilizing the grid of the target area, the thinned typhoon tracks corresponding to the calendar year optimal typhoon track data of each year are respectively obtained, a statistical experience model is constructed according to the grid of the target area and all the thinned typhoon tracks, and finally, the typhoon track distribution characteristics of typhoons in the target area on the grid of the target area are obtained according to the statistical experience model, so that the self-adaptive gridding processing of calendar year typhoon data of the target area is realized, the interference of human factors is avoided, and the distribution characteristics of the typhoon tracks finally obtained are more true due to the fact that the calendar year typhoon data processing based on the real target area and the typhoon tracks pass through the same grid for a plurality of times.
Description
Technical Field
The invention relates to the field of typhoon track processing, in particular to a track self-adaptive gridding processing method based on optimal typhoon track data.
Background
Typhoons are a natural phenomenon in nature, which are mainly characterized by high wind speeds and strong precipitation when occurring, and also generate direct or indirect natural disasters in the pathway regions thereof.
In order to master the typhoon track condition of the target area, the conventional typhoon track gridding processing method mainly processes typhoon track data by manually defining the gridding size, so as to outline the track condition of the target area when typhoon occurs.
However, the conventional typhoon track meshing processing method has the following defects: the manual definition of the grid size has great subjectivity, and the situation that the typhoon track passes through the same grid for multiple times can be ignored, the typhoon track cannot be objectively and truly represented, and further the distribution characteristics of the typhoon track are difficult to completely represent by utilizing the grid processing mode, so that the typhoon track condition is not beneficial to research.
Disclosure of Invention
The invention aims to provide a typhoon track self-adaptive gridding processing method based on optimal typhoon track data aiming at the prior art. The typhoon track self-adaptive gridding processing method can objectively and truly represent the typhoon track and completely represent the distribution characteristics of the typhoon track.
The technical scheme adopted for solving the technical problems is as follows: a track self-adaptive gridding processing method based on optimal typhoon track data is characterized by comprising the following steps of S1-S7:
step S1, selecting a target area in which typhoon tracks need to be counted;
s2, acquiring annual optimal typhoon track data of a target area;
step S3, obtaining the grid size of the target area corresponding to the target area according to the acquired annual optimal typhoon track data;
step S4, performing gridding treatment on the target area based on the obtained target area grid size to obtain a target area grid corresponding to the target area;
s5, refining the acquired annual optimal typhoon track data by utilizing the target area grid to respectively obtain refined typhoon tracks corresponding to the annual optimal typhoon track data;
s6, constructing a statistical experience model for counting the passing frequency of each grid in the target area grid by the typhoon track according to the target area grid and all the obtained refined typhoon tracks;
s7, obtaining typhoon track distribution characteristics of typhoons in the target area on the grid of the target area according to the statistical experience model; the typhoon track distribution characteristics comprise the frequency of each grid in the target area grid to be passed by the typhoon track.
In the typhoon track self-adaptive gridding processing method based on the optimal typhoon track data, in step S2, the historical optimal typhoon track data of the target area is acquired from a tropical cyclone data center of the China meteorological office.
Further, in the typhoon track self-adaptive gridding processing method based on the optimal typhoon track data, in step S3, the process of obtaining the target area grid size according to the acquired calendar optimal typhoon track data includes the following steps S31 to S36:
step S31, track point information of any optimal typhoon track in the annual optimal typhoon track data is obtained; the track point information comprises track point positions and track point recording time for recording the track points; marking the total number of track points of any optimal typhoon track asTwo adjacent track points on any optimal typhoon track are respectivelyAnd->，/>Is track point->Longitude coordinates of>Is track point->Dimension coordinates of>Is track point->Longitude coordinates of>Is track point->Dimension coordinates of (2), track points->The track point recording time mark of (2) is +.>Track point->The track points recorded time marks are as follows，/>；
Step S32, obtaining the distance between two adjacent track points according to the track point information of the two adjacent track points on any optimal typhoon track; wherein, adjacent track pointsAnd->The distance between them is marked->：
The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is the earth radius;
step S33, obtaining the time interval for generating the two adjacent track points according to the track point recording time of the two adjacent track points; wherein the two adjacent track pointsAnd->The time interval of (2) is marked->，；
Step S34, according to the obtained distance between adjacent track points and the corresponding time interval, obtaining the typhoon average moving speed of typhoons corresponding to any optimal typhoon track between the adjacent track points; wherein typhoons corresponding to any optimal typhoon track are positioned at the adjacent track pointsAnd->The typhoon average moving speed between is marked as +.>，；
Step S35, traversing and executing step S31 and step S34 on each piece of optimal typhoon track data in the annual optimal typhoon track data to obtain a plurality of average typhoon moving speeds, and searching to obtain an average moving speed median in the plurality of average typhoon moving speeds; wherein the average moving speed median of the plurality of typhoons is marked as；
Step S36, obtaining the grid size of the target area according to the median of the obtained average moving speed, the preset duration and the distance represented by the preset grid unit; wherein the mesh size of the target area is marked as，/>The method comprises the steps of carrying out a first treatment on the surface of the T is a preset duration, and L is a distance represented by a preset grid cell.
Still further, in the typhoon track adaptive gridding processing method based on the optimal typhoon track data, the preset duration T is 6 hours, and the preset grid unit is 1 ° Grid, 1 ° The grid characterizes a distance L of 110km.
In the typhoon track self-adaptive gridding processing method based on the optimal typhoon track data, in step S4, the target area grid is processed as follows:
performing gridding treatment on the target area to obtain a target area grid with a plurality of small grids; wherein the total number of small grids in the target area grid isThe parameters of the target area grid are +.>，The method comprises the steps of carrying out a first treatment on the surface of the The>Longitude range of each small grid isFirst->The latitude of the individual cells is +.>，/>。
Further, in the typhoon track self-adaptive gridding processing method based on the optimal typhoon track data, in step S5, a refined background wind track processing mode corresponding to the optimal typhoon track data of each year in the calendar year is obtained as follows:
calculating the quotient of the maximum average moving speed value in the plurality of typhoons average moving speeds and the median of the average moving speeds, and obtaining the rounding value of the quotient; wherein a maximum average moving speed value of the typhoons average moving speeds is marked as；
Taking the 4 times value of the obtained rounding value as an equal score value for carrying out track equal score processing on each typhoon track; wherein, the dividing number value of the track dividing treatment of each typhoon track is marked as，/>。
In a typhoon track self-adaptive gridding processing method based on the optimal typhoon track data, the construction process of the statistical experience model comprises the following steps S61-S69:
step S61, track point information on any thinned background wind track is obtained; wherein the total number of tracks on any one of the refined background wind tracks is marked asThe track points on any thinned background wind track are marked asRecord the generation of the trace point->The track point recording time mark of (2) is +.>；/>；
Step S62, according to all the obtained track point information, track points of any thinned background wind track falling on each small grid in the target area grid and track point recording time corresponding to the track points are obtained; the judging condition that the track points of any thinned background wind track fall on the small grids in the target area grid is as follows:
when the coordinates of the track points are satisfiedAt this time, the trace point is judged +.>Falling on a small grid within the target area grid; otherwise, determining the track pointNot falling on the small grid within the target area grid;
step S63, obtaining a track point recording time set corresponding to each small grid in the target area grid according to all track point recording times obtained in the step S62, and calculating the time interval of the thinned typhoon track falling on the small grid; wherein the time interval of the thinned background wind track falling on the small grid is marked as，/>；
Step S64, judging and obtaining similar time diversity and different time diversity corresponding to the small grid according to the time interval of the thinned background wind track falling on the small grid and the time interval between the recording times of two adjacent track points; wherein:
when the time interval between the recording time of two adjacent track points is larger than the time interval of the thinned typhoon track falling on the small grid, namelyDetermining trace point recordRecording time->And->Belongs to different types of time diversity; otherwise, determine track point recording time +.>And->Belongs to the similar time diversity;
step S65, according to the obtained similar time diversity and different time diversity, respectively calculating the average time of the similar time diversity and the average time of the different time diversity corresponding to the small grid;
step S66, calculating the average time total number of the same kind of time diversity according to the same kind of time diversity and the average time of the same kind of time diversity; and calculating the average time total number of the different types of time diversity according to the different types of time diversity and the average time of the different types of time diversity; wherein the average total number of times of the same kind of time diversity is marked asThe +.f in the time-sharing of the same class>The average time is marked->；/>；
Step S67, judging processing is carried out according to the average time of the obtained time sets:
when the average time between two adjacent times in the same class time set is greater than 1 day, i.eDay, determine->And->Belongs to the same small grid in the grid of the target area when typhoon tracks pass through at different times; otherwise, determine->Andbelongs to the same small grid in the grid of the target area, through which the typhoon track passes in continuous time;
step S68, obtaining the real frequency of the any thinned background wind track passing through the small grid according to the judging result of the step S67;
and step S69, performing traversal circulation on each thinned background wind track according to the mode of steps S61-S68 to obtain the real frequency of each small grid in the target area grid passing by the typhoon track.
Compared with the prior art, the invention has the advantages that: according to the typhoon track self-adaptive gridding processing method, the historical optimal typhoon track data of the target area are processed to obtain the grid size of the target area, gridding processing is carried out on the target area based on the grid size of the target area to obtain the target area grid corresponding to the target area, the historical optimal typhoon track data are further refined by the target area grid, refined typhoon tracks corresponding to the historical optimal typhoon track data in each year are respectively obtained, further a statistical empirical model for counting the frequency of each grid in the target area grid passing by the typhoon track is constructed according to the target area grid and all the obtained refined typhoon tracks, finally typhoon track distribution characteristics of typhoons in the target area on the target area grid are obtained according to the statistical empirical model, self-adaptive gridding processing of the historical typhoon data of the target area is realized, interference of human factors is avoided, and due to the fact that the actual historical typhoon data processing is carried out on the basis of the target area, the condition that the typhoon track passes through the same grid for multiple times is considered, so that finally obtained typhoon track distribution characteristics are more true and reasonable.
Drawings
FIG. 1 is a schematic flow chart of a track self-adaptive gridding processing method based on optimal typhoon track data in an embodiment of the invention;
FIG. 2 is a schematic diagram of typhoon trajectories of 2018 typhoon BERINCA after typhoon trajectory adaptive meshing processing in this embodiment;
FIG. 3 is a graph showing statistics of typhoon trajectories shown in FIG. 2 on a grid of a target area after being processed by a statistical empirical model.
Detailed Description
The invention is described in further detail below with reference to the embodiments of the drawings.
The embodiment provides a track self-adaptive gridding processing method based on optimal typhoon track data. Referring to fig. 1, the typhoon track self-adaptive gridding processing method based on the optimal typhoon track data comprises the following steps S1 to S7:
step S1, selecting a target area in which typhoon tracks need to be counted; for example, in this embodiment, north south China sea (105 DEG E-121 DEG E,8 DEG N-25 DEG N) is selected as the target area;
s2, acquiring annual optimal typhoon track data of a target area; specifically, the historical optimal typhoon track data of the target area, such as the optimal typhoon track data of the current last 70 years, can be obtained from a tropical cyclone data center of the national weather service; by the acquisition, 933 typhoon file data of the past year, namely 933 optimal typhoon track data, are acquired in the embodiment;
step S3, obtaining the grid size of the target area corresponding to the target area according to the acquired annual optimal typhoon track data;
step S4, performing gridding treatment on the target area based on the obtained target area grid size to obtain a target area grid corresponding to the target area;
s5, refining the acquired annual optimal typhoon track data by utilizing the target area grid to respectively obtain refined typhoon tracks corresponding to the annual optimal typhoon track data; referring to fig. 2, in the grid, the dots in fig. 2 are typhoon tracks;
s6, constructing a statistical experience model for counting the passing frequency of each grid in the target area grid by the typhoon track according to the target area grid and all the obtained refined typhoon tracks;
s7, obtaining typhoon track distribution characteristics of typhoons in the target area on the grid of the target area according to the statistical experience model; the typhoon track distribution characteristics comprise the frequency of each grid in the target area grid to be passed by the typhoon track.
Specifically, in the step S3, the process of obtaining the mesh size of the target area according to the acquired annual optimal typhoon track data includes the following steps S31 to S36:
step S31, track point information of any optimal typhoon track in the annual optimal typhoon track data is obtained; the track point information comprises track point positions and track point recording time for recording the track points; marking the total number of track points of any optimal typhoon track asTwo adjacent track points on any optimal typhoon track are respectivelyAnd->，/>Is track point->Longitude coordinates of>Is track point->Dimension coordinates of>Is track point->Longitude coordinates of>Is track point->Dimension coordinates of (2), track points->The track point recording time mark of (2) is +.>Track point->The track points recorded time marks are as follows，/>；
Step S32, obtaining the distance between two adjacent track points according to the track point information of the two adjacent track points on any optimal typhoon track; wherein, adjacent track pointsAnd->The distance between them is marked->：
The method comprises the steps of carrying out a first treatment on the surface of the Wherein R is the earth radius, and the earth radius R is specificIs 6371.393km;
step S33, obtaining the time interval for generating the two adjacent track points according to the track point recording time of the two adjacent track points; wherein the two adjacent track pointsAnd->The time interval of (2) is marked->，；
Step S34, according to the obtained distance between adjacent track points and the corresponding time interval, obtaining the typhoon average moving speed of typhoons corresponding to any optimal typhoon track between the adjacent track points; wherein typhoons corresponding to any optimal typhoon track are positioned at the adjacent track pointsAnd->The typhoon average moving speed between is marked as +.>，；
Step S35, traversing and executing step S31 and step S34 on each piece of optimal typhoon track data in the annual optimal typhoon track data to obtain a plurality of average typhoon moving speeds, and searching to obtain an average moving speed median in the plurality of average typhoon moving speeds; wherein the average moving speed median of the plurality of typhoons is marked asThe method comprises the steps of carrying out a first treatment on the surface of the Obtained based on the embodimentCalculating the optimal typhoon track data of the past year to obtain the median of the average moving speeds in the plurality of typhoons>=4.0273m/s；
Step S36, obtaining the grid size of the target area according to the median of the obtained average moving speed, the preset duration and the distance represented by the preset grid unit; wherein the mesh size of the target area is marked as，/>The method comprises the steps of carrying out a first treatment on the surface of the T is a preset duration, and L is a distance represented by a preset grid cell. For example, in this embodiment, the preset time period T is 6 hours and the preset grid cell is 1 ° Grid, 1 ° The grid-characterized distance L is 110km, and the target area grid size G= 0.7908 is calculated ° 。
Of course, for the above step S4, the above target area mesh is obtained by processing as follows:
gridding the target area (namely north China south China sea) to obtain a target area grid with a plurality of small grids; wherein the total number of small grids in the target area grid is M, and the parameters of the target area grid are，/>The method comprises the steps of carrying out a first treatment on the surface of the The longitude range of the mth small grid in the target area grid is +.>The latitude range of the mth small grid is +.>，/>。
For the step S5, the processing manner of the refined typhoon track corresponding to the optimal typhoon track data of each year in the calendar year in this embodiment is as follows:
calculating the quotient of the maximum average moving speed value in the plurality of typhoons average moving speeds and the median of the average moving speeds, and obtaining the rounding value of the quotient; wherein the maximum average moving speed value of the typhoons average moving speeds is marked as；
Taking the 4 times value of the obtained rounding value as an equal score value for carrying out track equal score processing on each typhoon track; wherein, the equal number value of the track equal treatment for each typhoon track is marked as H,. After calculation, the equally divided number value H is 20, and the target area grid range is: lon=105:0.8:121, lat=8:0.8:25, and then dividing the target area grid range into 21×22 small grids based on the fractional magnitude H;
for the statistical empirical model, the construction process of the statistical empirical model in this embodiment includes the following steps S61 to S69:
step S61, track point information on any thinned background wind track is obtained; wherein the total number of tracks on any one of the thinned typhoon tracks is marked as J, and the track points on any one of the thinned typhoon tracks are marked as JRecord the generation of the trace point->The track point recording time mark of (2) is +.>；/>；
Step S62, according to all the obtained track point information, track points of any thinned background wind track falling on each small grid in the target area grid and track point recording time corresponding to the track points are obtained; the judging condition that the track points of any thinned background wind track fall on the small grids in the target area grid is as follows:
when the coordinates of the track points are satisfiedAt this time, the trace point is judged +.>Falling on a small grid within the target area grid; otherwise, determining the track pointNot falling on the small grid within the target area grid;
step S63, obtaining a track point recording time set corresponding to each small grid in the target area grid according to all track point recording times obtained in the step S62, and calculating the time interval of the thinned typhoon track falling on the small grid; wherein, the time interval of the thinned background wind track falling on the small grid is marked as h,；
step S64, judging and obtaining similar time diversity and different time diversity corresponding to the small grid according to the time interval of the thinned background wind track falling on the small grid and the time interval between the recording times of two adjacent track points; wherein:
when the time interval between the recording time of two adjacent track points is larger than the time interval of the thinned typhoon track falling on the small grid, namelyDetermining track point recording time->And->Belongs to different types of time diversity; otherwise, determine track point recording time +.>And->Belongs to the similar time diversity;
step S65, according to the obtained similar time diversity and different time diversity, respectively calculating the average time of the similar time diversity and the average time of the different time diversity corresponding to the small grid;
step S66, calculating the average time total number of the same kind of time diversity according to the same kind of time diversity and the average time of the same kind of time diversity; and calculating the average time total number of the different types of time diversity according to the different types of time diversity and the average time of the different types of time diversity; wherein the total number of average times of the time-of-kind diversity is denoted as K, and the kth average time in the time-of-kind diversity is denoted as；/>The method comprises the steps of carrying out a first treatment on the surface of the That is, it is assumed that the K average times obtained are respectively、/>、…、/>And->；
Step S67, judging processing is carried out according to the average time of the obtained time sets:
when the average time between two adjacent times in the same class time set is greater than 1 day, i.eDay, determine->And->Belongs to the same small grid in the grid of the target area when typhoon tracks pass through at different times; otherwise, determine->Andbelongs to the same small grid in the grid of the target area, through which the typhoon track passes in continuous time;
step S68, obtaining the real frequency of the any thinned background wind track passing through the small grid according to the judging result of the step S67;
and step S69, performing traversal circulation on each thinned background wind track according to the mode of steps S61-S68 to obtain the real frequency of each small grid in the target area grid passing by the typhoon track. The statistics situation of typhoon tracks on the grid of the target area after being processed by the statistical empirical model is shown in fig. 3, and the frequency in fig. 3 refers to the number of times that the real and reasonable typhoon tracks pass through the small grid.
While the preferred embodiments of the present invention have been described in detail, it is to be clearly understood that the same may be varied in many ways by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A track self-adaptive gridding processing method based on optimal typhoon track data is characterized by comprising the following steps:
step S1, selecting a target area in which typhoon tracks need to be counted;
s2, acquiring annual optimal typhoon track data of a target area;
step S3, obtaining the grid size of the target area corresponding to the target area according to the acquired annual optimal typhoon track data;
step S4, performing gridding treatment on the target area based on the obtained target area grid size to obtain a target area grid corresponding to the target area;
s5, refining the acquired annual optimal typhoon track data by utilizing the target area grid to respectively obtain refined typhoon tracks corresponding to the annual optimal typhoon track data;
s6, constructing a statistical experience model for counting the passing frequency of each grid in the target area grid by the typhoon track according to the target area grid and all the obtained refined typhoon tracks;
s7, obtaining typhoon track distribution characteristics of typhoons in the target area on the grid of the target area according to the statistical experience model; the typhoon track distribution characteristics comprise the frequency of each grid in the target area grid to be passed by the typhoon track;
in step S3, the process of obtaining the mesh size of the target area according to the acquired annual optimal typhoon track data includes the following steps S31 to S36:
step S31, track point information of any optimal typhoon track in the annual optimal typhoon track data is obtained; the track point information comprises track point positions and track point recording time for recording the track points; marking the total number of track points of any optimal typhoon track as N, wherein two adjacent track points on any optimal typhoon track are respectivelyAnd->，/>Is track point->Longitude coordinates of>Is track point->Dimension coordinates of>Is track point->Longitude coordinates of>Is track point->Dimension coordinates of (2), track points->The track point recording time mark of (2) is +.>Track point->The track point recording time mark of (2) is +.>，/>；
Step S32, obtaining two adjacent tracks according to the track point information of the two adjacent track points on any optimal typhoon trackThe distance between the points; wherein, adjacent track pointsAnd->The distance between them is marked->：
The method comprises the steps of carrying out a first treatment on the surface of the Wherein,,is the earth radius;
step S33, obtaining the time interval for generating the two adjacent track points according to the track point recording time of the two adjacent track points; wherein the two adjacent track pointsAnd->The time interval of (2) is marked->，/>；
Step S34, according to the obtained distance between adjacent track points and the corresponding time interval, obtaining the typhoon average moving speed of typhoons corresponding to any optimal typhoon track between the adjacent track points; wherein typhoons corresponding to any optimal typhoon track are positioned at the adjacent track pointsAnd->The typhoon average moving speed between is marked as +.>，/>；
Step S35, traversing and executing step S31 and step S34 on each piece of optimal typhoon track data in the annual optimal typhoon track data to obtain a plurality of average typhoon moving speeds, and searching to obtain the average moving speed median in the plurality of average typhoon moving speeds; wherein the average moving speed median of the plurality of typhoons is marked as；
Step S36, obtaining the grid size of the target area according to the median of the obtained average moving speed, the preset duration and the distance represented by the preset grid unit; wherein the mesh size of the target area is marked as，/>；/>For a preset time length, < >>The distance represented by the preset grid unit is calculated;
the construction process of the statistical experience model comprises the following steps S61-S69:
step S61, track point information on any thinned background wind track is obtained; wherein the total number of tracks on any one of the refined background wind tracks is marked asThe track point mark on any thinned background wind track is +.>Record the generation of the trace point->The track point recording time mark of (2) is +.>；/>；
Step S62, according to all the obtained track point information, track points of any thinned background wind track falling on each small grid in the target area grid and track point recording time corresponding to the track points are obtained; the judging condition that the track points of any thinned background wind track fall on the small grids in the target area grid is as follows:
when the coordinates of the track points are satisfiedAt this time, the trace point is judged +.>Falling on a small grid within the target area grid; otherwise, determining the track pointNot falling on the small grid within the target area grid;
step S63, obtaining a track point recording time set corresponding to each small grid in the target area grid according to all track point recording times obtained in the step S62, and calculating the time interval of the thinned typhoon track falling on the small grid; wherein the time interval of the thinned background wind track falling on the small grid is marked as，/>；
Step S64, judging and obtaining similar time diversity and different time diversity corresponding to the small grid according to the time interval of the thinned background wind track falling on the small grid and the time interval between the recording times of two adjacent track points; wherein:
when the time interval between the recording time of two adjacent track points is larger than the time interval of the thinned typhoon track falling on the small grid, namelyDetermining track point recording time->And->Belongs to different types of time diversity; otherwise, determine track point recording time +.>And->Belongs to the similar time diversity;
step S65, according to the obtained similar time diversity and different time diversity, respectively calculating the average time of the similar time diversity and the average time of the different time diversity corresponding to the small grid;
step S66, calculating the average time total number of the same kind of time diversity according to the same kind of time diversity and the average time of the same kind of time diversity; and calculating the average time total number of the different types of time diversity according to the different types of time diversity and the average time of the different types of time diversity; wherein the average total number of times of the same kind of time diversity is marked asThe +.f in the time-sharing of the same class>The average time is marked->；/>；
Step S67, judging processing is carried out according to the average time of the obtained time sets:
when the average time between two adjacent times in the same class time set is greater than 1 day, i.eDay, determine->And->Belongs to the same small grid in the grid of the target area when typhoon tracks pass through at different times; otherwise, determine->And->Belongs to the same small grid in the grid of the target area, through which the typhoon track passes in continuous time;
step S68, obtaining the real frequency of the any thinned background wind track passing through the small grid according to the judging result of the step S67;
and step S69, performing traversal circulation on each thinned background wind track according to the mode of steps S61-S68 to obtain the real frequency of each small grid in the target area grid passing by the typhoon track.
2. The track adaptive meshing processing method based on optimal typhoon track data according to claim 1, wherein in step S2, the annual optimal typhoon track data of the target area is acquired from a tropical cyclone data center of the national weather service.
3. The track self-adaptive gridding processing method based on optimal typhoon track data according to claim 1, wherein the preset duration is as followsFor 6 hours, the preset grid unit is 1 ° Grid, 1 ° Grid-characterized distance->110km.
4. The track adaptive meshing processing method based on the optimal typhoon track data according to claim 1, wherein in step S4, the target area mesh is obtained by processing in the following manner:
performing gridding treatment on the target area to obtain a target area grid with a plurality of small grids; wherein the total number of small grids in the target area grid isThe parameters of the target area grid are +.>， The method comprises the steps of carrying out a first treatment on the surface of the The>Longitude range of each small grid isFirst->The latitude of the individual cells is +.>，/>。
5. The track self-adaptive gridding processing method based on the optimal typhoon track data according to claim 4, wherein in step S5, a refined typhoon track processing mode for obtaining the optimal typhoon track data corresponding to each year of the calendar year is as follows:
calculating the quotient of the maximum average moving speed value in the plurality of typhoons average moving speeds and the median of the average moving speeds, and obtaining the rounding value of the quotient; wherein a maximum average moving speed value of the typhoons average moving speeds is marked as；
Taking the 4 times value of the obtained rounding value as an equal score value for carrying out track equal score processing on each typhoon track; wherein, the dividing number value of the track dividing treatment of each typhoon track is marked as，/>。
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