A method and system for characterizing and calculating extreme low wind speed events on lake surfaces

Abstract

The present invention discloses a method and system for characterizing and calculating extreme low wind speed events on a lake surface, and belongs to the technical field of meteorological monitoring and climate change technology. The method comprises the following steps: obtaining basic geographic information of the lake, screening meteorological stations to obtain meteorological information; using percentile method as a method for characterizing and calculating thresholds for extreme low wind speeds; determining a method for extreme low wind speed events, extracting an extreme low wind speed event matrix; quantifying characteristic indicators of extreme low wind speed events, wherein the characteristic indicators include at least four parameters: frequency, initial time, end time, and intensity; receiving user query instructions on the system web page, updating, displaying, and downloading extreme low wind speed thresholds, four characteristic indicators, and long-term change trends. The present invention provides a method and system for characterizing and calculating extreme low wind speed events on a lake surface, which improves users' understanding of extreme low wind speed events and reduces the difficulty for users to explore the impact of extreme meteorological events on lakes.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological monitoring and climate change technology, and specifically to a method and system for characterizing and calculating extreme low wind speed events on a lake surface. Background Art

[0002] Wind speed is a key meteorological indicator and plays a crucial role in terrestrial and aquatic ecosystems. Decreased wind speeds have a range of direct and indirect ecological impacts on ecosystems, with the impact on lake ecosystems being particularly profound. As one of the driving forces of mixing in the water column, wind speed plays a significant role in the transport of matter and energy within the water column, particularly in deep lakes and eutrophic shallow lakes. However, reduced near-surface wind speeds can result in reduced wind and waves, directly impacting the thermal regime of the water column. This can intensify water stratification, hinder the downward transport of dissolved oxygen, heat, and nutrients from the surface layer, and create anoxic or anaerobic conditions at the water-sediment interface, thereby triggering the release of nutrients from sediments. Furthermore, reduced wind speeds reduce sediment disturbance, which can increase water transparency to some extent. However, this can also trigger the accumulation of surface cyanobacterial blooms and affect underwater light intensity. Therefore, reduced wind speeds can have multiple positive and negative impacts on lake ecosystems.

[0003] However, current attention on wind speed is primarily focused on extreme high wind events such as typhoons, with insufficient attention paid to low wind speed events, particularly those at lake surfaces and their impacts on lake ecosystems. Furthermore, the definition of extreme low wind speed events at lake surfaces remains unsettled, and the indicators used to quantify these events are yet to be standardized. Summary of the Invention

[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method and system for characterizing and calculating extreme low wind speed events on a lake surface.

[0005] The present invention is implemented by the following technical solution: a method for characterizing and calculating extreme low wind speed events on a lake surface, comprising the following steps:

[0006] Collect basic geographic information of lakes and meteorological stations around the lakes Select representative meteorological stations , and obtain the representative meteorological stations Weather information;

[0007] A two-dimensional daily average wind speed matrix is obtained based on the meteorological information , and determine the extreme low wind speed threshold set and wind speed climatological mean value set , based on the two-dimensional daily average wind speed matrix and extreme low wind speed threshold set Get the extreme low wind speed day matrix ; The extreme low wind speed day matrix The occurrence of a predetermined number of consecutive days of extremely low wind speed is recorded as an extreme low wind speed event, and an extreme low wind speed event matrix is generated. ;

[0008] The wind speed climatological mean value set Based on the characteristic index, the extreme low wind speed event matrix Perform data quantification to obtain characteristic data of extreme low wind speed events;

[0009] The extreme low wind speed event characteristic data is compared with the corresponding lakes and representative meteorological stations. , Extremely low wind speed threshold set The data are integrated with the extreme low wind speed event to obtain a quantitative result, and the quantitative result is updated and displayed in real time.

[0010] In a further embodiment, the basic geographic information includes at least: a lake vector file, lake elevation, lake area, and lake perimeter;

[0011] The representative meteorological stations The screening process is as follows:

[0012] Query the meteorological stations around the lake separately Latitude and longitude , based on latitude and longitude Generate meteorological station vector layer and lake vector layer with lake vector file, and calculate meteorological stations around the lake using meteorological station vector layer and lake vector layer Distance to lake border ;

[0013] At distance Filter out the minimum value , the minimum value Corresponding meteorological stations around the lake Selected as a representative meteorological station : ,in, , is a preset distance threshold.

[0014] In a further embodiment, the meteorological information is daily meteorological data with a year span of at least 20 years, and the daily meteorological data includes at least: daily maximum wind speed and daily average wind speed;

[0015] The daily average wind speed is defined with the year as the row angle and the Julian day of the corresponding year as the column angle, and a two-dimensional daily average wind speed matrix is constructed. :

[0016] ;

[0017] in, 、 and All are from previous years and , ; , Indicates the year Zhongru Lue Ri The average daily wind speed, Indicates the year Zhongru Lue Ri The average daily wind speed, Indicates the year Zhongru Lue Ri The average daily wind speed;

[0018] In the two-dimensional daily average wind speed matrix Filter and obtain the calculation matrix , based on the calculation matrix The extreme low wind speed threshold sets are obtained respectively and wind speed climatological mean value set .

[0019] In a further embodiment, the calculation matrix Perform the following steps for all columns in the

[0020] The same Julian day in different years The daily average wind speeds are sorted in ascending order and numbered from 0 to Get the dataset , the data set is calculated using the following formula The tenth place of the sequence number : ,in, To calculate the matrix Year span;

[0021] When the serial number When it is an integer, select the data set The The daily average wind speed is the extreme low wind speed threshold ; When the serial number When it is a non-integer, the rounding function is used Match serial number Perform rounding and select the data set The The daily average wind speed is the extreme low wind speed threshold ;

[0022] The extreme low wind speed threshold The extreme low wind speed threshold set is obtained by integration : , are the extreme low wind speed thresholds corresponding to Julian days.

[0023] In a further embodiment, the calculation matrix Perform the following steps for all columns in the

[0024] The same Julian day in different years The climatological average value of wind speed is obtained by averaging the daily average wind speed. ; The wind speed climate state average value The wind speed climatological average value set is obtained by integration : , are the climatological average wind speeds on the corresponding Julian days.

[0025] In a further embodiment, the extreme low wind speed day matrix The acquisition process is as follows:

[0026] The two-dimensional daily average wind speed matrix Each row is associated with an extreme low wind speed threshold set Compare them one by one, when When , the corresponding Julian day is marked as an extremely low wind speed day;

[0027] Extract meteorological information corresponding to extremely low wind speed days to form an extremely low wind speed day matrix :

[0028] ;

[0029] in, , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in .

[0030] In a further embodiment, the extreme low wind speed event matrix The generation process is:

[0031] Extract the corresponding extreme low wind speed days and corresponding meteorological information from the extreme low wind speed events to form the extreme low wind speed event matrix , expressed as follows:

[0032] ;

[0033] Where, 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed; 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed, 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed.

[0034] In a further embodiment, the characteristic indicators include at least: the frequency, initial time, end time, and intensity of extreme low wind speed events;

[0035] Correspondingly, the characteristic data of extreme low wind speed events include: frequency matrix , initial time matrix , end time matrix and intensity matrix .

[0036] In a further embodiment, the calculation matrix The screening process is as follows:

[0037] Get the span of the year ,like , then the two-dimensional daily average wind speed matrix All the data in are used as calculation data, and the extreme low wind speed threshold and the wind speed climatological average value are calculated based on the calculation data;

[0038] like , then select the two-dimensional daily average wind speed matrix The first 30 rows of data in are used as calculation data, and the extreme low wind speed threshold and the wind speed climatological average value are calculated based on the calculation data.

[0039] A lake surface extreme low wind speed event characterization and calculation system, used to implement the above characterization and calculation method, comprising:

[0040] The first module is set up to collect basic geographic information of the lake and meteorological stations around the lake. Select representative meteorological stations , and obtain the representative meteorological stations Weather information;

[0041] The second module is configured to obtain a two-dimensional daily average wind speed matrix based on the meteorological information. , and determine the extreme low wind speed threshold set and wind speed climatological mean value set , based on the two-dimensional daily average wind speed matrix and extreme low wind speed threshold set Get the extreme low wind speed day matrix ; The extreme low wind speed day matrix The occurrence of a predetermined number of consecutive days of extremely low wind speed is recorded as an extreme low wind speed event, and an extreme low wind speed event matrix is generated. ;

[0042] The third module is set to the average value of wind speed climate state Based on the characteristic index, the extreme low wind speed event matrix Perform data quantification to obtain characteristic data of extreme low wind speed events;

[0043] The fourth module is configured to compare the extreme low wind speed event characteristic data with the corresponding lakes and representative meteorological stations. , Extremely low wind speed threshold set The data are integrated with the extreme low wind speed event to obtain a quantitative result, and the quantitative result is updated and displayed in real time.

[0044] Beneficial effects of the present invention: The present method determines the threshold value of extreme low wind speed through the percentile method (that is, the percentile method of the present invention is used to determine the threshold value, and the quintile position, the deciles position and other reasonable positions can be selected), identifies extreme low wind speed days and extreme low wind speed events, quantifies at least four characteristic indicators of extreme low wind speed events, improves users' understanding of extreme meteorological events, popularizes knowledge of extreme low wind speed events, and reduces the difficulty for users to explore the impact of extreme meteorological events on lakes.

[0045] This method can realize the update, query, statistics, upload and download of extreme low wind speed events on the lake surface during extreme meteorological events, ensuring that users obtain accurate and more extreme low wind speed event information, and assisting users in making decisions and judgments on the implementation of lake environmental governance strategies.

[0046] This method provides a method for characterizing and calculating extreme low wind speed events on the lake surface. By screening the meteorological stations closest to the lake and their meteorological data, the representativeness of lake surface meteorological data and the accuracy of low wind speed event monitoring are improved. A threshold extraction method for extreme low wind speed is provided through the percentile method. A method and system are provided for characterizing and calculating the frequency, start Julian day, end Julian day and intensity characteristics of extreme low wind speed events on the lake surface. The method overcomes the shortcomings of previous quantification methods for extreme low wind speed events on the lake surface and provides a research method for the characterization and quantification of extreme wind speed events under regional atmospheric stillness conditions.

[0047] This method supports interactive query on a web interface, and supports and assists users in obtaining information on extreme low wind speed events of their interest without being disturbed by other information. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of the method for characterizing and calculating extreme low wind speed events on the lake surface in Example 1.

[0049] Figure 2 This is the Taihu Lake vector map and the surrounding meteorological station distribution map of Example 1. DETAILED DESCRIPTION

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0051] Example 1

[0052] In response to the problems raised in the background technology, this embodiment discloses a method for characterizing and calculating extreme low wind speed events on a lake surface, including the following steps:

[0053] Collect basic geographic information of lakes and meteorological stations around the lakes Select representative meteorological stations , and obtain the representative meteorological stations Weather information;

[0054] A two-dimensional daily average wind speed matrix is obtained based on the meteorological information , and determine the extreme low wind speed threshold set and wind speed climatological mean value set , based on the two-dimensional daily average wind speed matrix and extreme low wind speed threshold set Get the extreme low wind speed day matrix ; The extreme low wind speed day matrix The occurrence of a predetermined number of consecutive days of extremely low wind speed (2 days in this embodiment is taken as an example) is recorded as an extreme low wind speed event, and an extreme low wind speed event matrix is generated. ;

[0055] The wind speed climatological mean value set Based on the characteristic index, the extreme low wind speed event matrix Perform data quantification to obtain characteristic data of extreme low wind speed events;

[0056] The extreme low wind speed event characteristic data is compared with the corresponding lakes and representative meteorological stations. , Extremely low wind speed threshold set The data are integrated with the extreme low wind speed event to obtain a quantitative result, and the quantitative result is updated and displayed in real time.

[0057] In a further embodiment, the basic geographic information includes at least: lake vector file, lake elevation, lake area and lake perimeter.

[0058] The representative meteorological stations The screening process is as follows:

[0059] Query the meteorological stations around the lake separately Latitude and longitude , based on latitude and longitude Generate meteorological station vector layer and lake vector layer with lake vector file, and calculate meteorological stations around the lake using meteorological station vector layer and lake vector layer Distance to lake border ;

[0060] At distance Filter out the minimum value , the minimum value Corresponding meteorological stations around the lake Selected as a representative meteorological station :

[0061] ,in, , is a preset distance threshold. In this embodiment The value is 25 km ,Right now .

[0062] In this embodiment, the meteorological station vector layer and the lake vector layer are ArcGIS Version 10.3 and above ArcmapGenerated in the software. Further, select "Analysis Tools" in the "Tools" first-level menu, and then select the "Point to Polygon" tool in the second-level "Proximity" submenu to calculate the distance from the meteorological station to the lake boundary. The unit is " km ", the output data is the meteorological station around the lake in this embodiment. Distance to lake border .

[0063] In a further embodiment, the meteorological information is daily meteorological data with a span of at least 20 years, and the daily meteorological data at least includes: daily maximum wind speed and daily average wind speed. Furthermore, the sources of the daily meteorological data in this embodiment at least include: daily meteorological data provided by China Meteorological Data Sharing Network and ERA 5 Daily reanalysis meteorological data provided by .

[0064] Based on the above description, the daily average wind speed is defined with the year as the row angle and the Julian day of the corresponding year as the column angle, and a two-dimensional daily average wind speed matrix is constructed. :

[0065] ;

[0066] in, 、 and All are from previous years and , ; , Indicates the year Zhongru Lue Ri The average daily wind speed, Indicates the year Zhongru Lue Ri The average daily wind speed, Indicates the year Zhongru Lue Ri The Julian calendar is a calendar system that records days continuously, mainly used in literature and space measurement.

[0067] In this embodiment, taking the monitoring of the extreme low wind speed events in Taihu Lake from 1980 to 2020 as an example, four weather stations around Taihu Lake and their longitudes and latitudes are found: Weather Station 1 、Weather Station 2 、Weather Station 3 and Weather Station 4 , calculate the shortest distance to the vector boundary of Taihu Lake based on latitude and longitude for each station ,like Figure 2 According to the minimum principle, Meteorological Station 2 was finally selected as the representative meteorological station for monitoring extreme low wind speed events on the surface of Taihu Lake.

[0068] The daily meteorological data of Meteorological Station 2 from 1980 to 2020 were downloaded from the China Meteorological Data Sharing Network, including data such as daily maximum wind speed, daily average wind speed, daily maximum temperature, and daily average temperature. The two-dimensional daily average wind speed matrix from 1980 to 2020 was constructed with the year as the row and the Julian day of the corresponding year as the column. :

[0069] .

[0070] In combination with the example further, in this embodiment For 1980, For 2020, therefore, It means that the average daily wind speed on the first Julian day of 1980 was 3.5; The average wind speed on the 365th Julian day in 2020 is 7.2. m / s .

[0071] In order to improve the accuracy of the data, the two-dimensional daily average wind speed matrix Filter and obtain the calculation matrix , based on the calculation matrix The extreme low wind speed threshold sets are obtained respectively and wind speed climatological mean value set .

[0072] Furthermore, the matrix The screening process is as follows:

[0073] Use to get the span of the year ,like , then the two-dimensional daily average wind speed matrix All the data in are used as calculation data, and the extreme low wind speed threshold and the wind speed climatological average value are calculated based on the calculation data; wherein, .

[0074] like , then select the two-dimensional daily average wind speed matrix The first 30 rows of data in are used as calculation data, and the extreme low wind speed threshold and the wind speed climatological average value are calculated based on the calculation data.

[0075] In the embodiment, since the selected data is 40 years from 1980 to 2020, the current In the case of The daily average wind speed data from 1980 to 2010 is selected.

[0076] In another embodiment, the calculation matrix Perform the following steps for all columns in the

[0077] The same Julian day in different years The daily average wind speeds are sorted in ascending order and numbered from 0 to Get the dataset , the data set is calculated using the following formula The tenth place of the sequence number : ,in, To calculate the matrix The percentile rule here takes the tenth place as an example.

[0078] When the serial number When it is an integer, select the data set The N The daily average wind speed is the extreme low wind speed threshold ; When the serial number When it is a non-integer, the rounding function is used Match serial number N Perform rounding and select the data set The The daily average wind speed is the extreme low wind speed threshold ;

[0079] The extreme low wind speed threshold The extreme low wind speed threshold set is obtained by integration : , are the extreme low wind speed thresholds corresponding to Julian days, and For example, it represents the extreme low wind speed threshold on the second Julian day.

[0080] Correspondingly, the calculation matrix Perform the following steps for all columns in the

[0081] The same Julian dates in different years The climatological average value of wind speed is obtained by averaging the daily average wind speed. ; The wind speed climate state average value The wind speed climatological average value set is obtained by integration : ,by For example, it represents the climatological mean of wind speed on the 365th Julian day.

[0082] In an embodiment, the calculation matrix Each column is sorted in ascending order. , that is, the third daily average wind speed after sorting each column is selected as the extreme low wind speed threshold. Integrate 365 days a year to obtain the extreme low wind speed threshold set : In other words , , . To calculate the matrix The average value of each column is calculated to obtain the climatological average value of wind speed, and the climatological average value of wind speed is obtained by integrating 365 days a year. : ,Right now , , .

[0083] In a further embodiment, the extreme low wind speed day matrix The acquisition process is as follows: the two-dimensional daily average wind speed matrix Each row is associated with an extreme low wind speed threshold set Compare them one by one, when When , the corresponding Julian day is marked as an extremely low wind speed day;

[0084] Extract meteorological information corresponding to extremely low wind speed days to form an extremely low wind speed day matrix :

[0085] ;

[0086] in, , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year It should be noted that the number of extremely low wind speed days and Julian days in each year may be different. Therefore, the extreme low wind speed day matrix of this embodiment is There is no limit on the number of columns in each row.

[0087] Therefore, the occurrence of at least two consecutive days of extremely low wind speeds is recorded as an extreme low wind speed event, and the extreme low wind speed event matrix is generated. . It is further expressed as extracting the corresponding extreme low wind speed day and corresponding meteorological information in the extreme low wind speed event to form the extreme low wind speed event matrix , expressed as follows:

[0088] ;

[0089] Where, 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed; 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed, 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed. For the Tabulation of the last extreme heatwave day during the last extreme low wind event in 2018. For the List of the first extreme heatwave day during the first extreme low wind event in 2019. For the A list of any extreme heat wave day during any extreme low wind event in a year.

[0090] In this embodiment, the two-dimensional daily average wind speed matrix Each row is associated with an extreme low wind speed threshold set Compare them one by one, extract the extremely low wind speed days and the corresponding meteorological information, and generate the extremely low wind speed day matrix W :

[0091] .

[0092] Further, , the 121st and 328th Julian days in 1980 were extremely low wind days, with wind speeds of 1.5 and 1.3 respectively; and similarly, the 4th and 357th Julian days in 2010 were extremely low wind days, with wind speeds of 1.0 and 1.2 respectively.

[0093] In the matrix W The occurrence of at least two consecutive days of extremely low wind speed is recorded as an extreme low wind speed event. The corresponding extreme low wind speed days and corresponding meteorological information are extracted from the extreme low wind speed event to form an extreme low wind speed event matrix. K :

[0094] .

[0095] Furthermore, the characteristic indicators of this embodiment include at least: the frequency, initial time, end time and intensity of extreme low wind speed events.

[0096] Therefore, the characteristic data of extreme low wind speed events include: frequency matrix , initial time matrix , end time matrix and intensity matrix .

[0097] Based on the above characteristic indicators, the extreme low wind speed event matrix The quantification process is as follows: First, the extreme low wind speed day matrix is statistically analyzed. The frequency of occurrence of extreme low wind speed events in each row is calculated through the frequency matrix Quantitative reflection:

[0098] Where, Indicates the year The frequency of extremely low wind speed events, .

[0099] In this embodiment, the frequency matrix of extreme low wind speed events on the Taihu Lake from 1980 to 2020 is obtained by statistics. :

[0100] .

[0101] Furthermore, the initial time is represented by the initial time matrix Quantification refers to the Julian day of the first extreme low wind speed day in each year; the end time is measured using the end time matrix For quantification, it refers to the Julian day of the last extreme low wind speed day of the last extreme low wind speed event each year.

[0102] In a further embodiment, is the extreme low wind speed event matrix The first extreme heat wave day during the first extreme low wind event in the table is:

[0103] ,in, 、 、 、 The Julian day for the start of an extremely low wind day.

[0104] Correspondingly, is the extreme low wind speed event matrix Tabular note for the last extreme heatwave day during the last extreme low wind event in , denoted as:

[0105] ,in, 、 、 、 Julian day indicating the end of the extremely low wind day.

[0106] In this embodiment, ; ,in, Indicates that no extreme low wind speed events occurred.

[0107] Furthermore, intensity refers to the difference between the daily average wind speed during the extreme low wind event and the climatological average wind speed during the same period. deviation; and through the intensity matrix Quantify.

[0108] Based on the wind speed climatological mean value set , in the extreme low wind speed event matrix Extract the average wind speed climatological state of the same column label row by row, which is the same column label matrix , and expressed as:

[0109] ;

[0110] in, and Year Julian Day Julian Day Julian Day The corresponding climatological average of wind speed.

[0111] Then, the intensity matrix Expressed as: ,in, Indicates absolute value;

[0112] .

[0113] Furthermore, the user displays and downloads the results of extreme low wind speed days and extreme low wind speed events according to different time scales and user query instructions until receiving the user's end query instruction.

[0114] This method calculates the threshold of extreme low wind speed through the percentile method, identifies extreme low wind speed days and extreme low wind speed events, and quantifies the four characteristic indicators of extreme low wind speed events. It improves users' understanding of extreme meteorological events, popularizes knowledge about extreme low wind speed events, and reduces the difficulty for users to explore the impact of extreme meteorological events on lakes.

[0115] Example 2

[0116] This embodiment discloses a system for characterizing and calculating extreme low wind speed events on a lake surface, which is used to implement the characterization and calculation method described in Example 1, including:

[0117] The first module is set up to collect basic geographic information of the lake and meteorological stations around the lake. Select representative meteorological stations , and obtain the representative meteorological stations Weather information;

[0118] The second module is configured to obtain a two-dimensional daily average wind speed matrix based on the meteorological information. , and determine the extreme low wind speed threshold set and wind speed climatological mean value set , based on the two-dimensional daily average wind speed matrix and extreme low wind speed threshold set Get the extreme low wind speed day matrix ; The extreme low wind speed day matrix The occurrence of a predetermined number of consecutive days of extremely low wind speed is recorded as an extreme low wind speed event, and an extreme low wind speed event matrix is generated. ;

[0119] The third module is set to the average value of wind speed climate state Based on the characteristic index, the extreme low wind speed event matrix Perform data quantification to obtain characteristic data of extreme low wind speed events;

[0120] The fourth module is configured to compare the extreme low wind speed event characteristic data with the corresponding lakes and representative meteorological stations. , Extremely low wind speed threshold set The data are integrated with the extreme low wind speed event to obtain a quantitative result, and the quantitative result is updated and displayed in real time.

[0121] To further illustrate, the basic information collection module is used to receive basic information of the lake, including at least basic geographic information such as latitude and longitude, elevation, lake area, and perimeter;

[0122] The meteorological data input module is used to select suitable meteorological stations and download or upload daily meteorological data from the meteorological stations; the module at least calculates the Euclidean distance between the lake and the meteorological station based on the longitude and latitude of the two, and selects the appropriate meteorological station based on the principle of the closest distance; directly downloads daily image data from the China Meteorological Data Sharing Network based on the selected meteorological stations, or uploads daily meteorological data from a designated meteorological station based on user needs; the daily meteorological data at least includes daily measured meteorological data and reanalyzed daily meteorological data;

[0123] The web page display module is used to receive and display user query requirements and the frequency, initial time, end time, duration, intensity and long-term change trend results of extreme low wind speed events.

Claims

1. A method for characterizing and calculating extreme low wind speed events on a lake surface, characterized in that: The following steps are involved: Collect basic geographic information of lakes and meteorological stations around the lakes Select representative meteorological stations , and obtain the representative meteorological stations Weather information; A two-dimensional daily average wind speed matrix is obtained based on the meteorological information , and determine the extreme low wind speed threshold set and wind speed climatological mean value set , based on the two-dimensional daily average wind speed matrix and extreme low wind speed threshold set Get the extreme low wind speed day matrix ; Extreme low wind speed day matrix The occurrence of a predetermined number of consecutive days of extremely low wind speed is recorded as an extreme low wind speed event, and an extreme low wind speed event matrix is generated. ; The wind speed climatological mean value set Based on the characteristic index, the extreme low wind speed event matrix Perform data quantification to obtain characteristic data of extreme low wind speed events; The characteristic data of the extreme low wind speed event include: frequency matrix , initial time matrix , end time matrix and intensity matrix ; The extreme low wind speed event characteristic data is compared with the corresponding lakes and representative meteorological stations. , Extremely low wind speed threshold set The data is integrated with the extreme low wind speed event to obtain quantitative results, which are updated in real time and displayed: The frequency matrix The quantitative expression of is: Where, Indicates the year The frequency of extremely low wind speed events, ; The initial time matrix The quantitative expression of is: ,in, 、 、 、 The Julian day for the start of the extremely low wind day; The end time matrix The quantization of is now: ,in, 、 、 、 Julian day indicating the end of the extremely low wind day; The intensity matrix The quantitative expression of is: ,in, Indicates the absolute value, Label the matrix for the same columns: Based on the set of climatological mean values of wind speed , in the extreme low wind speed event matrix Extract the average wind speed climatological state of the same column label row by row; ; in, and Year Julian Day Julian Day Julian Day The corresponding climatological average of wind speed.

2. A method for characterizing and calculating extreme low wind speed events on a lake surface according to claim 1, characterized in that: The basic geographic information includes at least: lake vector file, lake elevation, lake area and lake perimeter; The representative meteorological stations The screening process is as follows: Query the meteorological stations around the lake separately Latitude and longitude , based on latitude and longitude Generate meteorological station vector layer and lake vector layer with lake vector file, and calculate meteorological stations around the lake using meteorological station vector layer and lake vector layer Distance to lake border ; At distance Filter out the minimum value , the minimum value Corresponding meteorological stations around the lake Selected as a representative meteorological station : ,in, , is a preset distance threshold.

3. The method for characterizing and calculating extreme low wind speed events on a lake surface according to claim 1, characterized in that: The meteorological information is daily meteorological data with a year span of at least 20 years, and the daily meteorological data includes at least: daily maximum wind speed and daily average wind speed; The daily average wind speed is defined with the year as the row angle and the Julian day of the corresponding year as the column angle, and a two-dimensional daily average wind speed matrix is constructed. : ； in, 、 and All are from previous years and , ; , Indicates the year Zhongru Lue Ri The average daily wind speed, Indicates the year Zhongru Lue Ri The average daily wind speed, Indicates the year Zhongru Lue Ri The average daily wind speed; In the two-dimensional daily average wind speed matrix Filter and obtain the calculation matrix , based on the calculation matrix The extreme low wind speed threshold sets are obtained respectively and wind speed climatological mean value set .

4. A method for characterizing and calculating extreme low wind speed events on a lake surface according to claim 3, characterized in that: Calculate the matrix Perform the following steps for all columns in the The same Julian day in different years The daily average wind speeds are sorted in ascending order and numbered from 0 to Get the dataset , the data set is calculated using the following formula The tenth place of the sequence number : ,in, To calculate the matrix Year span; When the serial number When it is an integer, select the data set The The daily average wind speed is the extreme low wind speed threshold ; When the serial number When it is a non-integer, the rounding function is used Match serial number Perform rounding and select the data set The The daily average wind speed is the extreme low wind speed threshold ; The extreme low wind speed threshold The extreme low wind speed threshold set is obtained by integration : , are the extreme low wind speed thresholds corresponding to Julian days.

5. The method for characterizing and calculating extreme low wind speed events on a lake surface according to claim 3, characterized in that: Calculate the matrix Perform the following steps for all columns in the The same Julian day in different years The climatological average value of wind speed is obtained by averaging the daily average wind speed. ; The wind speed climatological average value The wind speed climatological average value set is obtained by integration : , are the climatological average wind speeds on the corresponding Julian days.

6. The method for characterizing and calculating extreme low wind speed events on a lake surface according to claim 3, characterized in that: Extremely low wind speed day matrix The acquisition process is as follows: The two-dimensional daily average wind speed matrix Each row is associated with an extreme low wind speed threshold set Compare them one by one, when When , the corresponding Julian day is marked as an extremely low wind speed day; Extract meteorological information corresponding to extremely low wind speed days to form an extremely low wind speed day matrix : ； in, , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in ; , is the year Julian day with extremely low wind speed, , indicating the year The wind speed corresponding to the extremely low wind day in .

7. The method for characterizing and calculating extreme low wind speed events on a lake surface according to claim 1, characterized in that: The extreme low wind speed event matrix The generation process is: Extract the corresponding extreme low wind speed days and corresponding meteorological information from the extreme low wind speed events to form the extreme low wind speed event matrix , expressed as follows: ； Where, 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed; 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed, 、 and Year Zhongru Lue Ri Julian Day Julian Day The corresponding wind speed.

8. The method for characterizing and calculating extreme low wind speed events on a lake surface according to claim 1, characterized in that: The characteristic indicators include at least: the frequency, initial time, end time and intensity of extreme low wind speed events.

9. The method for characterizing and calculating extreme low wind speed events on a lake surface according to claim 3, characterized in that: The calculation matrix The screening process is as follows: Get the span of the year ,like , then the two-dimensional daily average wind speed matrix All the data in are used as calculation data, and the extreme low wind speed threshold and the wind speed climatological average value are calculated based on the calculation data; like , then select the two-dimensional daily average wind speed matrix The first 30 rows of data in are used as calculation data, and the extreme low wind speed threshold and the wind speed climatological average value are calculated based on the calculation data.

10. A system for characterizing and calculating extreme low wind speed events on a lake surface, for implementing the characterization and calculation method according to any one of claims 1 to 9, characterized in that: include: The first module is set up to collect basic geographic information of the lake and meteorological stations around the lake. Select representative meteorological stations , and obtain the representative meteorological stations Weather information; The second module is configured to obtain a two-dimensional daily average wind speed matrix based on the meteorological information. , and determine the extreme low wind speed threshold set and wind speed climatological mean value set , based on the two-dimensional daily average wind speed matrix and extreme low wind speed threshold set Get the extreme low wind speed day matrix ; The extreme low wind speed day matrix The occurrence of a predetermined number of consecutive days of extremely low wind speed is recorded as an extreme low wind speed event, and an extreme low wind speed event matrix is generated. ; The third module is set to the average value of wind speed climate state Based on the characteristic index, the extreme low wind speed event matrix Perform data quantification to obtain characteristic data of extreme low wind speed events; The characteristic data of the extreme low wind speed event include: frequency matrix , initial time matrix , end time matrix and intensity matrix ; The fourth module is configured to compare the extreme low wind speed event characteristic data with the corresponding lakes and representative meteorological stations. , Extremely low wind speed threshold set and extremely low wind speed events to obtain quantitative results, and update and display the quantitative results in real time; the frequency matrix The quantitative expression of is: Where, Indicates the year The frequency of extremely low wind speed events, ; The initial time matrix The quantitative expression of is: ,in, 、 、 、 The Julian day for the start of the extremely low wind day; The end time matrix The quantization of is now: ,in, 、 、 、 Julian day indicating the end of the extremely low wind day; The intensity matrix The quantitative expression of is: ,in, Indicates the absolute value, Label the matrix for the same columns: Based on the set of climatological mean values of wind speed , in the extreme low wind speed event matrix Extract the average wind speed climatological state of the same column label row by row; ; in, and Year Julian Day Julian Day Julian Day The corresponding climatological average of wind speed.

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