A method to replace manual observation of thunderstorm days

Abstract

The present invention discloses a method for replacing manually observed thunderstorm days, comprising the following steps: S1: acquiring lightning data through a lightning location system; S2: performing quality control on the acquired lightning data; S3: establishing a linear model and a power model for the annual average cloud-to-ground lightning density and the number of manually observed thunderstorm days; S4: analyzing the consistency of the lightning location data and the number of manually observed thunderstorm days; S5: verifying the reliability of the lightning location data in replacing the number of manually observed thunderstorm days. The present invention establishes a linear model and a power model for the annual average cloud-to-ground lightning density and the number of manually observed thunderstorm days after quality control of the lightning location data, performs a consistency analysis of the lightning location data and the number of thunderstorm days, and then verifies the reliability of the lightning location data in replacing the number of manually observed thunderstorm days, thereby concluding that the lightning location data is feasible in replacing the number of manually observed thunderstorm days.

Description

Technical field:

[0001] The invention belongs to the technical field of meteorological observation, and in particular relates to a method for replacing manual observation of thunderstorm days. Background technology:

[0002] Thunderstorm days are a physical parameter that reflects the frequency and intensity of local lightning activity. Data on thunderstorm days has traditionally come from manual observations, which have limitations. Manual observations are easily affected by factors such as the meteorological station environment, background noise, and differences in the observer's hearing.

[0003] With increasing modernization, lightning location systems are gradually being applied to meteorological observations. The ADTD lightning location system detects cloud-to-ground lightning by measuring the electromagnetic radiation field generated by the return stroke during lightning. However, due to the influence of factors such as the surrounding electromagnetic environment and topography, the detection results often have a certain degree of uncertainty. Utilizing the VLF / LF lightning detection network, we can accurately measure the arrival time of VLF / LF electromagnetic pulses generated by thunderstorm discharges through GPS satellites. Combining broadband network communication technology with the principle of multi-station TOA time difference positioning, we can achieve three-dimensional positioning of the lightning VLF / LF radiation source, including key parameters such as time, position, altitude, intensity, and polarity. The application of this method can improve positioning accuracy and detection efficiency, achieving comprehensive detection of cloud-to-ground lightning, lightning flashes, and lightning height, overcoming the influence of factors such as the surrounding environment and topography on detection results, and reducing uncertainty.

[0004] However, there is currently a lack of relevant quality control for the raw data obtained by the lightning location system, and directly using the raw data will affect the accuracy of the analysis. On the other hand, there is also a lack of analytical research on the consistency between the data of the lightning location system and the data of manually observed thunderstorm days. Therefore, the data obtained by the current lightning location system is still difficult to replace the data of manually observed thunderstorm days.

[0005] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention:

[0006] The object of the present invention is to provide a method for replacing manual observation of thunderstorm days, thereby overcoming the defects in the above-mentioned prior art.

[0007] In order to achieve the above object, the present invention provides a method for replacing manual observation of thunderstorm days, characterized in that it comprises the following steps:

[0008] S1: Obtain lightning data through the lightning location system;

[0009] S2: Perform quality control on the acquired lightning data;

[0010] S3: Establish linear and power models for the annual average cloud-to-ground flash density and the number of manually observed thunderstorm days;

[0011] S4: Analyze the consistency between lightning location data and manually observed thunderstorm days;

[0012] S5: Verify the reliability of lightning location data as a substitute for manually observed thunderstorm days.

[0013] Furthermore, preferably, the S1 lightning location system adopts a VLF-LF lightning detection network.

[0014] Furthermore, preferably, the S2 performs quality control on the acquired lightning data, including eliminating abnormal data and small-amplitude lightning currents in the lightning data.

[0015] Furthermore, preferably, the range of the small-amplitude lightning current is 0-5KA.

[0016] Furthermore, preferably, the S3 manually observed thunderstorm days are defined as any day in which thunder is heard or lightning is observed once or more, and the day is counted as a thunderstorm day.

[0017] Furthermore, as an optimization, the linear model relationship between the annual average cloud-to-ground lightning density S3 and the number of manually observed thunderstorm days is N g =aT d , where N g is the annual average cloud-to-ground lightning density, T d is the number of days with manually observed thunderstorms, and a is the linear coefficient.

[0018] Furthermore, preferably, the N g Satisfies the following relationship: N g =N / S, N is the number of lightning strikes per year, and S is the local administrative area.

[0019] Furthermore, as an optimization, the power model relationship between the annual average cloud-to-ground lightning density S3 and the number of manually observed thunderstorm days is N g =aT d b , where N g is the annual average cloud-to-ground lightning density, T d is the number of days with manually observed thunderstorms, a is the linear coefficient, and b is the power coefficient.

[0020] Furthermore, preferably, when analyzing the consistency between the lightning location data and the number of manually observed thunderstorm days, S4 intends to determine that a natural day with more than 10 lightning strikes monitored by more than three lightning location system stations is a machine-observed thunderstorm day.

[0021] Compared with the prior art, one aspect of the present invention has the following beneficial effects:

[0022] After quality control of lightning location data, the present invention established a linear model and a power model for the annual average cloud-to-ground lightning density and the number of manually observed thunderstorm days, completed a consistency analysis of the lightning location data and the number of thunderstorm days, and then verified the reliability of the lightning location data in replacing the manually observed thunderstorm days, thereby concluding the feasibility of using lightning location data to replace the manually observed thunderstorm days. Description of the drawings:

[0023] Figure 1 A schematic flow chart of a method for replacing manual observation of thunderstorm days according to the present invention;

[0024] Figure 2 This is the effect diagram of the normal distribution fitting performed using SPSS software after excluding the 0-5kA samples, taking the lightning locator data of 2012 as an example in the present invention. Specific implementation method:

[0025] The specific embodiments of the present invention are described in detail below, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0026] The following is a brief summary of one or more aspects to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceivable aspects and is neither intended to identify key or critical elements of all aspects nor to define the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that will be provided later.

[0027] Example:

[0028] A method for replacing manual observation of thunderstorm days, characterized by comprising the following steps:

[0029] S1: Obtain lightning data through the lightning location system;

[0030] The VLF / LF lightning detection network can accurately measure the arrival time of VLF / LF electromagnetic pulses generated by thunderstorm discharges via GPS satellites. Combined with broadband network communication technology and the principle of multi-station TOA time difference positioning, it can achieve three-dimensional positioning of lightning VLF / LF radiation sources, including key parameters such as time, position, altitude, intensity, and polarity. This can improve positioning accuracy and detection efficiency, enabling comprehensive detection of cloud-to-ground flashes and lightning heights, overcoming the influence of factors such as the surrounding environment and topography on detection results, and reducing uncertainty.

[0031] S2: Perform quality control on the acquired lightning data;

[0032] Currently, there is little research on quality control of raw data from lightning location systems. For example, in Suzhou, there are no quality control measures for lightning location data. Directly using raw data for business and scientific research will affect the accuracy of research and analysis. Therefore, it is necessary to pre-process the location data by removing abnormal data and small-amplitude lightning current data.

[0033] S21: Eliminate abnormal data

[0034] Thunderstorm activity and lightning in Suzhou occur primarily from April to October. Statistical lightning location data reveals that on many observation days throughout the year, Suzhou only experiences a few single ground-to-ground flash return strokes. Some examples are shown in Table 1.

[0035] Table 1 Example of single ground-to-ground lightning return stroke process records of lightning location data in Suzhou City

[0036]

[0037]

[0038] Lightning usually occurs with strong convection, so the frequency of cloud-to-ground flashes in a thunderstorm is usually high. If it is a systemic strong thunderstorm, it can last for several hours and the frequency of lightning can even reach thousands of times. In contrast, local thunderstorms in mountainous areas are often smaller and weaker, usually lasting only tens of minutes, and the frequency of lightning is only over 100. When the convection intensity is insufficient, the probability of lightning is very small, and the possibility of sporadic lightning in this case is very small. In addition, domestic and foreign researchers have found through studying the correlation between radar echo parameters and lightning occurrence that when the radar echo top height exceeds 7 kilometers and the echo intensity reaches above 40dbz, there is usually a good correlation with lightning occurrence. In urban areas, due to complex changes in the electromagnetic environment, lightning locators often produce measurement errors.

[0039] Therefore, when the observation data only shows a single ground-to-ground lightning return stroke, the reliability of the return stroke is determined by comparing the manually observed thunderstorm day in Suzhou with the weather radar observation data. Specifically, if there is no relevant thunderstorm record on the manually observed thunderstorm day, and the echo intensity in the area does not exceed 40 decibels and the echo top height does not exceed 7 kilometers, then the lightning location data is considered unreliable and is discarded.

[0040] S22: Small-amplitude lightning current data processing

[0041] The lightning current amplitude observation data usually conforms to the log-normal model. Taking the lightning locator data from 2012 as an example, after removing the 0-5kA samples, the normal distribution fitting and test were performed using SPSS software. It was found that the lightning current data after removing the 0-5kA interval had a better fitting effect, such as Figure 2 As shown; Therefore, this application regards 0-5KA as the error range and does not consider it; After quality control of Suzhou lightning location data, 270,373 cloud-to-ground lightning monitoring data were finally obtained;

[0042] S3: Establish linear and power models for the annual average cloud-to-ground flash density and the number of manually observed thunderstorm days;

[0043] There are two kinds of computational relationships between the annual average cloud-to-ground flash density and the number of thunderstorm days in a certain area, namely linear and power. In this embodiment, Suzhou City is taken as the basic unit, and the annual average cloud-to-ground flash density N in this city is obtained by statistics. g :

[0044] N g =N / S

[0045] N is the annual number of lightning strikes, which is obtained from lightning location data; S is the administrative area of ​​Suzhou City, which is obtained from the Suzhou Municipal Government; the annual average cloud-to-ground flash density N in Suzhou City is determined by g and the number of thunderstorm days T d , using T d Calculate N using the standard formula g , and compared the relative error with the actual cloud-to-ground lightning density value; SPSS software was used to fit the Suzhou N g and T d The computational model was used to analyze the results.

[0046] Statistics of the annual average cloud-to-ground flash density N in Suzhou from 2012 to 2014 and from 2020 to 2022 g and the number of thunderstorm days T d , respectively, using linear model N g =aT d and power model N g =aT d b The fitting was performed and the correlation coefficient R was used to test the fitting effect. The results are shown in Table 2:

[0047] Table 2 Fitting results of linear and power models for the annual average cloud-to-ground flash density and number of thunderstorm days in Suzhou

[0048]

[0049] Among them, the linear model N g =aT d Power model N g =aTd b The fitting effect is better and more suitable for describing the number of thunderstorm days T in Suzhou City d and the annual average cloud-to-ground lightning density N g relationship;

[0050] S4: Analyze the consistency between lightning location data and manually observed thunderstorm days;

[0051] Manually observed thunderstorm days are defined as days in which thunder is heard or lightning is observed at least once. The selection and statistics of thunderstorm day data follow the following rules:

[0052] (1) If a station records several thunderstorms within one day, it is counted as one thunderstorm day;

[0053] (2) According to the "Surface Meteorological Observation Specifications", the meteorological day boundary is 8:00 pm Beijing time. If a thunderstorm crosses 8:00 pm Beijing time, it will be counted as two thunderstorm days;

[0054] By studying the lightning locator data after quality control, it is proposed to define the natural day when three or more lightning locator stations monitor more than 10 lightning strikes in a single day as a machine-observed thunderstorm day. The daily lightning strike counts in Suzhou from 2020 to 2022 were statistically analyzed, and Table 3 was obtained;

[0055] Table 3 Number of thunderstorm days observed by humans and machines in Suzhou

[0056]

[0057] As can be seen from the table, the agreement rate between manually observed thunderstorm days and machine-observed thunderstorm days in the past three years has reached more than 88%. In 2021, there were 8 thunderstorm days with no lightning location data. Combined with the radar echo data of that day, there was no thunderstorm on that day. The manually observed information may be incorrect. It is comprehensively judged that the manually observed thunderstorm day on that day is an error term;

[0058] S5: Verify the reliability of lightning location data as a substitute for manually observed thunderstorm days.

[0059] After statistical analysis of Suzhou's lightning location data from 2015 to 2019, the number of machine-observed thunderstorm days and annual lightning strikes from 2015 to 2019 were obtained. Combined with the data from 2020 to 2022, a linear model was used for verification analysis. The results are shown in Table 4:

[0060] Table 4 Comprehensive analysis of lightning location data from 2015 to 2022

[0061]

[0062]

[0063] As can be seen from the above chart, the average annual number of lightning strikes from 2015 to 2022 is 22,711.13 times, and the average number of thunderstorm days is 47.9 days.

[0064] The annual average cloud-to-ground flash density is close to the estimated value fitted by the linear model, with an average relative error of 4.0%. Due to objective reasons, the data on manually observed thunderstorm days from 2015 to 2019 were not obtained. Based on the above results, combined with the linear model of the recorded annual average cloud-to-ground flash density and the number of manually observed thunderstorm days, the number of manually observed thunderstorm days in each district of Suzhou during this period was inferred. The historical thunderstorm days in each district of Suzhou were statistically analyzed, and the results are shown in Table 5:

[0065] Table 5 Statistics of the average number of thunderstorm days in Suzhou

[0066]

[0067] The total refers to the dates on which thunderstorms occurred within the jurisdiction of each district and county-level meteorological agency, and the union of the dates is taken as the number of thunderstorm days in that year.

[0068] The reverse calculation results show that the number of thunderstorm days observed manually from 2015 to 2019 was 49 days, which is consistent with the 47.9 days of thunderstorm days observed by machines.

[0069] Therefore, the lightning data obtained by the lightning location system can be processed and used as a method to replace manual observation of thunderstorm days, which has a certain degree of reliability.

[0070] This embodiment establishes and improves a database of thunderstorm days and lightning location data in Suzhou City; completes data quality control of original lightning location data, obtains the annual average cloud-to-ground lightning density in Suzhou City, and establishes a linear model and a power model for the annual average cloud-to-ground lightning density and the number of thunderstorm days; completes consistency analysis of Suzhou City's lightning location data and thunderstorm days, and concludes that lightning data obtained by the lightning location system has a certain degree of reliability as a method to replace manually observed thunderstorm days; manually observed thunderstorm days and lightning location data can complement each other. Specifically, when both manual observation and lightning locator observation of thunderstorms on the same day are recorded as one thunderstorm day; when no thunderstorm is observed manually, but three or more lightning locator sites record more than 10 daily lightning strikes and radar echo data detect thunderstorms, it is recorded as one thunderstorm day; when a thunderstorm is observed manually but neither the instrument nor the radar echo data detects thunderstorms, it is not counted as a thunderstorm day.

[0071] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for replacing manual observation of thunderstorm days in Suzhou, characterized in that: The following steps are involved: S1: Obtain lightning data through the lightning location system; S2: Perform quality control on the acquired lightning data; S3: Establish linear and power models for the annual average cloud-to-ground flash density and the number of manually observed thunderstorm days; S4: Analyze the consistency between lightning location data and manually observed thunderstorm days; S5: Verify the reliability of lightning location data as a substitute for manually observed thunderstorm days; The S2 performs quality control on the acquired lightning data, including: removing abnormal data and removing small-amplitude lightning currents in the lightning data; When the observed data only indicates a single ground lightning return stroke, the reliability of the return stroke is determined by comparing the manually observed thunderstorm day in Suzhou with the weather radar observation data. If there is no relevant thunderstorm record on the manually observed thunderstorm day, and the echo intensity in the area does not exceed 40dBZ and the echo top height does not exceed 7 kilometers, then the lightning location data is considered abnormal and is discarded. The range of small-amplitude lightning current is 0-5KA. When S4 analyzes the consistency between the lightning location data and the number of manually observed thunderstorm days, it is intended to determine that the natural day with more than 10 lightning strikes monitored by more than three lightning location system stations is a machine-observed thunderstorm day, so as to analyze the consistency between the lightning location data and the number of manually observed thunderstorm days.

2. The method for replacing manual observation of thunderstorm days in Suzhou according to claim 1, characterized in that: The S1 lightning location system uses a VLF-LF lightning detection network.

3. The method for replacing manual observation of thunderstorm days in Suzhou according to claim 1, characterized in that: The S3 manually observed thunderstorm days are defined as any day in which thunder is heard or lightning is observed once or more.

4. The method for replacing manual observation of thunderstorm days in Suzhou according to claim 1, characterized in that: The linear model relationship between the annual average cloud-to-ground lightning density in S3 and the number of manually observed thunderstorm days is N g = aT d , where N g is the annual average cloud-to-ground lightning density, T d is the number of days with manually observed thunderstorms, and a is the linear coefficient.

5. The method for replacing manual observation of thunderstorm days in Suzhou according to claim 4, characterized in that: The N g Satisfies the following relationship: N g =N / S, where N is the number of lightning strikes per year and S is the local administrative area.

6. The method for replacing manual observation of thunderstorm days in Suzhou according to claim 1, characterized in that: The power model relationship between the annual average cloud-to-ground lightning density in S3 and the number of manually observed thunderstorm days is N g = aT d b , where N g is the annual average cloud-to-ground lightning density, T d is the number of days with manually observed thunderstorms, a is the linear coefficient, and b is the power coefficient.

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