CN111766642A - Login tropical cyclone precipitation forecasting system - Google Patents
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Abstract
The invention relates to a system for forecasting rainfall of a login tropical cyclone day, which comprises: the generalized initial value building module is used for receiving the paths of the historical TCs, acquiring forecast paths of the target TC at the starting time of a certain path, and merging observation paths of the forecast paths of the target TC before the starting time of the path into target TC paths; processing to obtain TC moving speed information of a target TC and a historical TC in a specific daily scale time period; the initial value similarity judging module constructs a day scale similarity region, identifies the closest point and the shortest distance of the historical TC, calculates the similarity degree of the historical TC path and the target TC path, marks the historical TC with the moving speed difference value reaching a threshold value and marks the historical TC with the shortest distance being greater than a certain threshold value, and selects m optimal similar historical TCs to send to the ensemble forecasting module; the ensemble forecasting module acquires and aggregates the specific daily rainfall fields of the optimal historical TCs. The invention has good forecasting performance on TC day rainfall of landing China.
Description
Technical Field
The invention relates to a weather forecasting technology, in particular to a login tropical cyclone daily rainfall forecasting system based on a dynamic-statistic-similar ensemble forecasting model.
Background
Aiming at weather forecast, a scholars puts forward a dynamic statistics similarity ensemble forecasting (DSAEF) theory (model) based on an accurate mode, and the theory has the ideas of generalized initial value construction, initial value similarity judgment and ensemble forecasting. Subsequently, a DSAEF forecasting technique for landing tropical cyclone process precipitation (DSAEF _ LTP _ a 1.0 system) was developed and the examples demonstrate that the system forecasts well. However, there is currently a lack of forecasting techniques for the DSAEF model for landing tropical cyclonic daily precipitation. The logging tropical cyclone daily rainfall forecasting system is a forecasting technology (DSAEF _ LTP _ D1.0 system) of a DSAEF model aiming at logging tropical cyclone daily rainfall.
Disclosure of Invention
The invention aims to provide a system for forecasting precipitation of tropical cyclone solar landing, which is used for solving the problems in the prior art.
The invention relates to a system for forecasting precipitation of tropical cyclone on the landing, which comprises: the generalized initial value building module is used for receiving the paths of the historical TCs, acquiring forecast paths of the target TC at the starting time of a certain path, and merging observation paths of the forecast paths of the target TC before the starting time of the path into target TC paths; processing to obtain TC moving speed information of a target TC and a historical TC in a specific daily scale time period; the initial value similarity judging module constructs a day scale similarity region, identifies the closest point and the shortest distance of the historical TC, calculates the similarity degree of the historical TC path and the target TC path, marks the historical TC with the moving speed difference value reaching a threshold value and marks the historical TC with the shortest distance being greater than a certain threshold value, and selects m optimal similar historical TCs to send to the ensemble forecasting module; the ensemble forecasting module acquires and aggregates the specific daily rainfall fields of the optimal historical TCs.
According to an embodiment of the login tropical cyclone daily rainfall forecasting system, the generalized initial value construction module comprises a TC path acquisition module and a TC daily speed acquisition module; the TC path acquisition module is used for acquiring a forecast path of a target TC at a path starting time from a numerical weather forecast mode, merging the forecast path of the target TC and an observation path before the path starting time into a target TC path, acquiring paths of all historical TCs by the TC path acquisition module, and sending the target TC path and the historical TC path to the initial value similarity judgment module; the TC daily shift speed acquisition module is used for acquiring TC shift speed information of a target TC and a historical TC in a certain 24-hour time period.
According to an embodiment of the login tropical cyclone daily precipitation forecast system, the initial value similarity judging module comprises a daily scale similarity region building module, the target TC daily precipitation forecast starting point is taken from any point on the target TC path, one end point a of a diagonal line of the similarity region slides at a point a1 before the end point a and a plurality of TC positions before the end point a, and the other end point B of the diagonal line of the similarity region slides at a TC position after the point a1 and is farthest to the point at which the maximum forecast time is located.
According to an embodiment of the system for forecasting landing tropical cyclone daily rainfall, the initial similarity determination module further comprises: sending the constructed similar area information to a path similarity index calculation module and a shortest distance identification module; the path similarity index calculation module is used for calculating path similarity area indexes of the target TC and all historical TCs one by one in the similar region, arranging the historical TCs from low to high according to the size of the TSAI, and sending the sequencing result to the optimal similar historical TC determination module; the shortest distance identification module is used for identifying an observation point, closest to the starting point of the target TC daily rainfall forecast, of each historical TC in the similar area and a corresponding shortest distance, and eliminating the historical TCs of which the shortest distances are greater than a set value; the TC moving speed similarity judging module receives moving speed information of the target TC and the historical TC, which is generated by the TC daily moving speed obtaining module; then calculating the difference between all historical TCs and the moving speed of the target TC one by one, marking the historical TCs with the absolute values of the differences larger than a certain threshold, and sending the marked historical TC numbers to an optimal similar historical TC determining module; the best similar historical TC determining module firstly receives the TSAI value sequencing results of all the historical TCs generated in the TC path similarity calculating module, receives the serial numbers of the historical TCs and rejects all the marked historical TCs; and determining the rest historical TCs as the best similar historical TCs, and sending the TC numbers to a TC ensemble forecasting module.
According to an embodiment of the login tropical cyclone daily rainfall forecasting system, the shortest distance identification module comprises a closest point identification module and a shortest distance judgment module; the closest point identification module sequentially calculates the distances between all observation points of all historical TCs and a target TC daily rainfall forecast starting point in a selected similar area, sends the calculated closest point information of all historical TCs to the TC daily rainfall acquisition module, and sends the shortest distance information to the shortest distance judgment module; and the shortest distance judging module marks the historical TC after receiving the shortest distance information and sends the marked historical TC number to the optimal similar historical TC determining module.
According to an embodiment of the login tropical cyclone daily rainfall forecasting system, the TC rainfall ensemble forecasting module comprises a similar TC daily rainfall acquisition module and a rainfall ensemble module; the similar TC daily rainfall acquisition module is used for receiving the serial number of the optimal similar historical TC generated in the optimal similar historical TC determination module, calculating 24-hour accumulated rainfall after the closest point of each serial number, then performing TC rainfall identification by using an objective weather map analysis method, thereby acquiring a corresponding similar TC daily rainfall field and sending the corresponding similar TC daily rainfall field to the rainfall aggregation module; the precipitation aggregation module is used for aggregating the TC-day precipitation fields generated in the similar TC-day precipitation acquisition module into one precipitation field, the aggregation scheme is that the maximum value is taken at each station, and the precipitation field obtained by aggregation is the precipitation forecast result of the target TC within 24 hours after the starting point of the daily precipitation forecast.
According to an embodiment of the login tropical cyclone daily rainfall forecasting system, the TC daily moving speed obtaining module receives the closest point information of all historical TCs generated by the closest point identification module, calculates an average moving speed of the target TC within 24 hours after a starting point of the target TC daily rainfall forecast as a moving speed of the target TC, calculates an average moving speed of all historical TCs within 24 hours after the closest point as respective moving speeds, and sends the moving speed information of the target TC and all historical TCs to the TC moving speed similarity judging module.
According to an embodiment of the login tropical cyclone daily rainfall forecasting system, the TC moving speed similarity judging module receives moving speed information of a target TC and all historical TCs generated in the TC moving speed acquiring module, calculates the moving speed difference between all historical TCs and the target TC, marks the historical TCs of which the difference meets a certain condition, and sends the marked historical TC numbers to the optimal similar historical TC determining module.
According to an embodiment of the login tropical cyclone daily rainfall forecasting system, the best similar historical TC determining module receives TSAI value sequencing results of all historical TCs generated in the TC path similarity calculation module, receives all marked historical TC numbers in the shortest distance distinguishing module and the TC moving speed similarity distinguishing module, eliminates all marked historical TCs, determines the remaining top m historical TCs as the best similar historical TCs, and sends the TC numbers to the TC ensemble forecasting module.
According to an embodiment of the login tropical cyclone daily rainfall forecasting system, the similar TC daily rainfall acquisition module receives serial numbers of m optimal similar historical TCs generated in the optimal similar historical TC determination module, calculates 24-hour accumulated rainfall after the closest point of each serial number, then conducts TC rainfall recognition on the serial numbers by using an objective weather map analysis method, accordingly obtains m corresponding similar TC daily rainfall fields, and sends the m similar TC daily rainfall fields to the TC rainfall collection forecasting module.
The logging-in tropical cyclone precipitation forecast system based on the dynamic-statistic-similar ensemble forecast model has good forecast performance for TC precipitation in logging-in China (particularly TC precipitation in the day before logging-in).
Drawings
FIG. 1 is a block diagram of a tropical cyclone precipitation forecast system according to the present invention based on a dynamic-statistical-similar ensemble forecasting model;
FIG. 2a is a schematic view of construction of a Japanese scale similarity region;
fig. 2b is a schematic diagram illustrating the shortest distance recognition.
Detailed Description
In order to make the objects, contents, and advantages of the present invention clearer, the following detailed description of the embodiments of the present invention will be made in conjunction with the accompanying drawings and examples.
Fig. 1 is a block diagram of a system for forecasting precipitation of tropical cyclone landing (landing TC) days based on a dynamic-statistical-similar ensemble forecasting model, as shown in fig. 1, the system for forecasting precipitation of tropical cyclone landing days of the present invention includes: the system comprises a generalized initial value construction module 1, an initial value similarity judgment module 2 and a TC precipitation ensemble forecasting module 3.
As shown in fig. 1, the generalized initial value building module 1 receives a path of a history TC, and is configured to process and obtain TC shift speed information of a target TC path, the target TC, and the history TC in a specific day scale time period, where a generalized initial value of the system (DSAEF _ LTP _ D1.0) of the present invention includes two factors, namely, a TC path and a TC shift speed. Further, the generalized initial value construction module 1 includes a TC path obtaining module 11 and a TC daily shift speed obtaining module 12; the TC path obtaining module 11 is configured to obtain a forecast path of the target TC at a certain path starting time from the numerical weather forecast mode, merge the forecast path of the target TC and an observation path before the path starting time into a complete path, which is a target TC path, and obtain all paths of the historical TCs (such as 1960, so far), and send the target TC path and the historical TC path to the initial value similarity judging module 2; the TC daily shift rate obtaining module 12 is configured to obtain TC shift rate information of the target TC and the historical TC in a certain 24-hour time period, which is subject to the recognition result of the shortest distance recognition module 23 in the initial value similarity determination module 2.
As shown in fig. 1, the initial value similarity determining module 2 is configured to determine the similarity between the TC path included in the generalized initial value and the TC shift speed, and finally select 5 (the setting of 5 is merely an example) best similar historical TCs to send to the TC precipitation ensemble forecasting module 3. Further, the initial value similarity determination module 2 includes a daily scale similarity region construction module 21, a path similarity index calculation module 22, a shortest distance identification module 23, a TC moving speed similarity determination module 24, and an optimal similarity history TC determination module 25.
The daily scale similarity region construction module 21 shown in fig. 1 constructs a similarity region (rectangular box) of a daily scale (a scale indicating a moving distance of a TC within one day) through a target TC path, where the region is restricted by a target TC daily precipitation forecast starting point and a path farthest forecast point, but the selection of the region should also include various possibilities. Specifically, as shown in fig. 2a, the daily scale similarity region is constructed as a schematic diagram, and in principle, the target TC daily precipitation forecast starting point (a1) may be any point (TC observation or forecast positions spaced by several hours, such as six hours) on the target TC path (graph center line), depending on the forecast demand. One end of the diagonal of the similar area (point a) may slide at point a1 and several TC locations before it (solid point), and the other end (point B) may slide at TC locations after point a1 (open point), and the farthest is the location of the maximum forecast time (point B1, i.e. the farthest forecast point of the path), for example, the a end may take 0, 12 or 24 hours before point a1, the a end may take 18, 24, 30, 36 hours after point a1 or 0, 6, 12 hours before point B1, depending on the actual forecast demand (the similar area shown in fig. 2a is only an example). Finally, the constructed similar area information is sent to the path similarity index calculation module 22 and the shortest distance identification module 23.
The path similarity index calculation module 22 shown in fig. 1 is configured to calculate path similarity area indexes (TSAI) for the target TC and all history TCs one by one in the similar region, arrange the history TCs from low to high according to the size of TSAI, and send the ordering result to the optimal similar history TC determination module 25.
The shortest distance identification module 23 is shown in fig. 1, and is configured to identify, within the similar area, an observation point (C1) of each historical TC that is closest to the target TC daily precipitation forecast starting point (a1), and a corresponding shortest distance (d0), and to eliminate historical TCs whose shortest distances are greater than a set value. Specifically, the shortest distance identifying module 23 includes a closest point identifying module 231 and a shortest distance distinguishing module 232; as shown in fig. 2b, in the shortest distance identification schematic diagram, the closest point identification module 231 is configured to sequentially calculate distances between all observation points (blue color points) of all historical TCs (a line is an example historical TC path) and a target TC daily rainfall forecast starting point (a1) in a selected similar area, where the observation point with the closest distance is denoted as a point C1, a straight-line distance d0 between a point C1 and the point a1 is the shortest distance between the historical TC and the target TC, and finally send the calculated closest point (C1) information of all historical TCs to the TC daily rainfall acquisition module 12 and send the shortest distance (d0) information to the shortest distance discrimination module 232; the shortest distance determining module 232 marks the history TC with the d0 being greater than 190km (the setting of greater than 190km is merely an example, and may also be set to be different from 0km to 500km, or set to be infinite) after receiving the shortest distance (d0) information, and sends the marked history TC number to the best similar history TC determining module 25.
The TC daily shift speed obtaining module 12 shown in fig. 1, which first receives the closest point information of all the historical TCs generated by the closest point identifying module 231; then, calculating the average moving speed of the target TC within 24 hours after the target TC day precipitation forecast starting point (point A1 in FIG. 2 b) as the moving speed of the target TC, and calculating the average moving speed of all historical TCs within 24 hours after the closest point (point C1 in FIG. 2 b) as the moving speeds of the target TC and the historical TCs; finally, the shift speed information of the target TC and all the historical TCs is sent to the TC shift speed similarity determination module 24.
The TC shift speed similarity determination module 24 shown in fig. 1 firstly receives the shift speed information of the target TC and all the historical TCs, which is generated by the TC daily shift speed acquisition module 12; then calculating the difference between all historical TCs and the moving speed of the target TC one by one, and marking the historical TCs with the difference absolute value larger than 8km/h (the setting that the difference absolute value exceeds 8km/h is only an example, the setting that the difference absolute value is larger than 0km/h to the difference absolute value is larger than 50km/h is not equal, or the setting that the difference is larger than zero, the difference is smaller than zero or the difference is infinite); the marked history TC numbers are finally sent to the best similar history TC determination module 25.
The best similar history TC determining module 25 shown in fig. 1 firstly receives TSAI value sorting results of all history TCs generated in the TC path similarity calculating module 22, and receives all marked history TC numbers in the shortest distance judging module 232 and the TC moving speed similarity judging module 24; then, rejecting all marked historical TCs; finally, the remaining top 5 (the top 5 are set as an example only) historical TCs are determined as the best similar historical TCs and their TC numbers are sent to the similar TC daily precipitation acquisition module 31 in the TC ensemble forecasting module 3.
The TC precipitation ensemble forecasting module 3 shown in fig. 1 is used to obtain the daily precipitation field of the best historical TC and aggregate it using a suitable aggregation scheme. Further, the ensemble forecasting module 3 includes a similar TC day precipitation obtaining module 31 and a precipitation ensemble module 32; the similar TC daily precipitation acquiring module 31 is configured to receive the numbers of the 5 best similar historical TCs generated in the best similar historical TC determining module 25, calculate 24 hours of accumulated precipitation (original precipitation field) after the closest point (point C1 in fig. 2 b) of the 5 best similar historical TCs, perform TC precipitation recognition on the accumulated precipitation (original precipitation field) by using an objective weather map analysis method (OSAT), thereby obtaining corresponding 5 similar TC daily precipitation fields, and send the 5 similar TC daily precipitation fields to the precipitation aggregating module 32; the precipitation aggregation module 32 is configured to aggregate the 5 TC day precipitation farms generated in the similar TC day precipitation obtaining module 31 into one precipitation farm, where an aggregation scheme is that a maximum value is taken at each station (the setting of taking the maximum value at each station is only an example, and may also be a reasonable averaging scheme at each station), and the obtained precipitation farm by aggregation is a precipitation forecast result of the target TC within 24 hours after a point a1 (a start point of daily precipitation forecast).
The invention relates to a power-statistics-similar Ensemble forecasting model-based system for forecasting Precipitation of tropical cyclone days during landing (DSAEF _ LTP _ D, dynamic-Statistical-Analog Ensemble Forecast for Landfalling tropical cyclone cycles Precipitation), which is a generation version (DSAEF _ LTP _ D1.0) of an application technology of the DSAEF model for forecasting Precipitation of the tropical cyclone days during landing. The DSAEF model comprises a generalized initial value construction module, an initial value similarity judgment module and an ensemble forecasting module, wherein in DSAEF _ LTP _ D1.0, the generalized initial value construction module is used for acquiring path information of a target Tropical Cyclone (TC) and a historical TC and acquiring TC moving speed information of the target TC and the historical TC in a specific daily scale time period, and the constructed generalized initial value comprises two physical factors (called factors or variables for short) of the TC path and the TC moving speed; the initial value similarity judging module is used for constructing a day scale similarity region, identifying the closest point (C1) and the shortest distance (d0) of the historical TCs, calculating the similarity of the historical TC paths and the target TC paths, marking the historical TCs with the moving speed difference value of the target TC meeting a certain condition, marking the historical TCs with the shortest distance (d0) being larger than a certain value, and finally selecting m optimal similar historical TCs to send to the ensemble forecasting module; the ensemble forecasting module (TC precipitation ensemble forecasting module) is used for acquiring and aggregating a specific daily precipitation field of the optimal historical TC.
The logging-in tropical cyclone precipitation forecast system based on the dynamic-statistic-similar ensemble forecast model has good forecast performance for TC precipitation in logging-in China (particularly TC precipitation in the day before logging-in). The invention can forecast TC day rainfall of landing more accurately.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A system for forecasting precipitation of a tropical cyclone on the day when landing, which is characterized by comprising:
the generalized initial value building module is used for receiving the paths of the historical TCs, acquiring forecast paths of the target TC at the starting time of a certain path, and merging observation paths of the forecast paths of the target TC before the starting time of the path into target TC paths; processing to obtain TC moving speed information of a target TC and a historical TC in a specific daily scale time period;
the initial value similarity judging module constructs a day scale similarity region, identifies the closest point and the shortest distance of the historical TC, calculates the similarity degree of the historical TC path and the target TC path, marks the historical TC with the moving speed difference value reaching a threshold value and marks the historical TC with the shortest distance being greater than a certain threshold value, and selects m optimal similar historical TCs to send to the ensemble forecasting module; the ensemble forecasting module acquires and aggregates the specific daily rainfall fields of the optimal historical TCs.
2. The login tropical cyclone daily rainfall forecasting system of claim 1, wherein the generalized initial value building module comprises a TC path obtaining module and a TC daily speed obtaining module; the TC path acquisition module is used for acquiring a forecast path of a target TC at a path starting time from a numerical weather forecast mode, merging the forecast path of the target TC and an observation path before the path starting time into a target TC path, acquiring paths of all historical TCs by the TC path acquisition module, and sending the target TC path and the historical TC path to the initial value similarity judgment module; the TC daily shift speed acquisition module is used for acquiring TC shift speed information of a target TC and a historical TC in a certain 24-hour time period.
3. The system for forecasting landing tropical cyclonic solar precipitation as claimed in claim 1, wherein the initial value similarity discrimination module comprises a day scale similarity region construction module, which takes the target TC day precipitation forecast starting point to any point on the target TC path, one end point a of a diagonal line of the similarity region slides at a point a1 before the end point a and several TC positions before the end point a, and the other end point B of the diagonal line of the similarity region slides at a TC position after the point a1 and is farthest to the point of the maximum forecast time.
4. The system for forecasting precipitation on tropical cyclone day of landing according to claim 3, wherein the initial similarity discrimination module further comprises:
sending the constructed similar area information to a path similarity index calculation module and a shortest distance identification module; the path similarity index calculation module is used for calculating path similarity area indexes of the target TC and all historical TCs one by one in the similar region, arranging the historical TCs from low to high according to the size of the TSAI, and sending the sequencing result to the optimal similar historical TC determination module; the shortest distance identification module is used for identifying an observation point, closest to the starting point of the target TC daily rainfall forecast, of each historical TC in the similar area and a corresponding shortest distance, and eliminating the historical TCs of which the shortest distances are greater than a set value;
the TC moving speed similarity judging module receives moving speed information of the target TC and the historical TC, which is generated by the TC daily moving speed obtaining module; then calculating the difference between all historical TCs and the moving speed of the target TC one by one, marking the historical TCs with the absolute values of the differences larger than a certain threshold, and sending the marked historical TC numbers to an optimal similar historical TC determining module;
the best similar historical TC determining module firstly receives the TSAI value sequencing results of all the historical TCs generated in the TC path similarity calculating module, receives the serial numbers of the historical TCs and rejects all the marked historical TCs; and determining the rest historical TCs as the best similar historical TCs, and sending the TC numbers to a TC ensemble forecasting module.
5. The login tropical cyclone daily precipitation forecast system of claim 4, wherein the shortest distance identification module comprises a closest point identification module and a shortest distance discrimination module; the closest point identification module sequentially calculates the distances between all observation points of all historical TCs and a target TC daily rainfall forecast starting point in a selected similar area, sends the calculated closest point information of all historical TCs to the TC daily rainfall acquisition module, and sends the shortest distance information to the shortest distance judgment module; and the shortest distance judging module marks the historical TC after receiving the shortest distance information and sends the marked historical TC number to the optimal similar historical TC determining module.
6. The login tropical cyclonic solar precipitation forecast system of claim 4, wherein the TC precipitation ensemble forecast module comprises a similar TC-day precipitation acquisition module and a precipitation ensemble module; the similar TC daily rainfall acquisition module is used for receiving the serial number of the optimal similar historical TC generated in the optimal similar historical TC determination module, calculating 24-hour accumulated rainfall after the closest point of each serial number, then performing TC rainfall identification by using an objective weather map analysis method, thereby acquiring a corresponding similar TC daily rainfall field and sending the corresponding similar TC daily rainfall field to the rainfall aggregation module; the precipitation aggregation module is used for aggregating the TC-day precipitation fields generated in the similar TC-day precipitation acquisition module into one precipitation field, the aggregation scheme is that the maximum value is taken at each station, and the precipitation field obtained by aggregation is the precipitation forecast result of the target TC within 24 hours after the starting point of the daily precipitation forecast.
7. The login tropical cyclone daily precipitation forecast system of claim 2, wherein the TC daily shift rate obtaining module receives the closest point information of all historical TCs generated by the closest point identifying module, calculates an average shift rate of the target TC within 24 hours after the target TC daily precipitation forecast starting point as a shift rate of the target TC, calculates an average shift rate of all historical TCs within 24 hours after the closest point as respective shift rates, and sends the shift rate information of the target TC and all historical TCs to the TC shift rate similarity judging module.
8. The login tropical cyclone daily rainfall forecasting system of claim 2, wherein the TC moving speed similarity determining module receives moving speed information of the target TC and all historical TCs generated in the TC daily moving speed acquiring module, calculates a difference between all historical TCs and the moving speed of the target TC, marks the historical TCs of which the differences satisfy a certain condition, and sends the marked historical TC numbers to the optimal similar historical TC determining module.
9. The login tropical cyclone daily rainfall forecasting system of claim 2, wherein the optimal similar history TC determining module receives TSAI value sorting results of all history TCs generated in the TC path similarity calculating module, receives all marked history TC numbers in the shortest distance discriminating module and the TC moving speed similarity discriminating module, eliminates all marked history TCs, determines the remaining top m history TCs as the optimal similar history TCs, and sends the TC numbers to the TC ensemble forecasting module.
10. The system for forecasting landing tropical cyclonic solar precipitation of claim 9, wherein the similar TC solar precipitation acquiring module receives numbers of m best similar historical TCs generated in the best similar historical TC determining module, calculates 24-hour cumulative precipitation after the closest point of the m best similar historical TCs, and then performs TC precipitation recognition on the cumulative precipitation 24 hours after the closest point of the cumulative precipitation by using an objective weather map analysis method, so as to obtain m corresponding similar TC solar precipitation fields, and sends the m similar TC solar precipitation fields to the TC precipitation ensemble forecasting module.
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| CN113805252A (en) * | 2021-09-15 | 2021-12-17 | 中国气象科学研究院 | System for forecasting gale in tropical cyclone landing process based on ensemble forecasting model |
| CN114202104A (en) * | 2021-11-17 | 2022-03-18 | 国家海洋环境预报中心 | Method for determining similarity degree of tropical cyclone path and storage medium |
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| CN114202104B (en) * | 2021-11-17 | 2022-06-03 | 国家海洋环境预报中心 | Method for determining similarity degree of tropical cyclone path and storage medium |
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