Tropical cyclone induced area heavy rainfall event identification and unsteady state characteristic analysis method

Abstract

The invention discloses a tropical cyclone induced area heavy rainfall event identification and unsteady state characteristic analysis method. The method comprises the following steps: data acquisition; identifying a regional heavy rainfall event; associating a tropical cyclone with a regional heavy rainfall event; identifying the characteristics of regional heavy rainfall caused by the tropical cyclone; analyzing the regional heavy rainfall characteristic change rate caused by the tropical cyclone; unsteady-state frequency analysis of regional heavy rainfall characteristics caused by tropical cyclones is carried out; the action mechanism and the influence of shortening of the recurrence period of a heavy rainfall event in an extreme region caused by tropical cyclone; the method has the beneficial effects that the space-time evolution and movement characteristics of the regional extreme rainfall caused by the tropical cyclone can be fully identified, the change rule of the recurrence period of the severe rainfall event caused by the tropical cyclone in the extreme region is clarified, and the accuracy of forecasting the severe rainfall caused by the tropical cyclone can be further improved; and a scientific basis is provided for resisting disasters caused by tropical cyclones and making decisions.

Description

Technical Field

[0001] The invention relates to a method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics. Background Art

[0002] Heavy precipitation events are defined as precipitation exceeding a specified threshold. They have attracted widespread attention due to their significant impact on society and the economy, and are an important research topic in the field of earth science. Regional heavy precipitation events are defined as heavy precipitation occurring simultaneously over a large spatial range. In the context of climate warming, tropical cyclones tend to become stronger, decay more slowly, and last longer, increasing precipitation variability in monsoon regions, increasing precipitation intensity and range, and increasing regional heavy precipitation events, increasing the risk of triggering large-scale severe floods and waterlogging disasters, posing a huge threat to regional agriculture, social economy, and personal and property safety.

[0003] Tropical cyclones are an important source of heavy precipitation, contributing more than 30% of the heavy precipitation in coastal areas around the world and more than 50% of the heavy precipitation in China. However, current research on the identification of heavy precipitation events is based on single-point data (i.e., stations or grid points), ignoring the spatial connectivity and temporal continuity of heavy precipitation events, and lacks discussion on the role of tropical cyclones in regional heavy precipitation events. Therefore, it is necessary to accurately identify the movement and evolution of regional heavy precipitation caused by tropical cyclones. This will further improve the accuracy of forecasts of heavy precipitation caused by tropical cyclones and provide a scientific basis for preventing disasters and making decisions caused by tropical cyclones. Summary of the invention

[0004] The purpose of the present invention is to propose a method for identifying and analyzing non-steady-state characteristics of regional heavy rainfall events caused by tropical cyclones, so as to solve the technical problem of insufficient research methods on the impact of tropical cyclones on regional heavy rainfall events.

[0005] The present invention provides a method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics, comprising the following steps:

[0006] S1. Data collection: Collect observation-based gridded precipitation data, tropical cyclone best track data, and meteorological reanalysis data;

[0007] S2, identification of regional heavy precipitation events: according to the definition of heavy precipitation and the method for determining regional heavy precipitation events, regional heavy precipitation events are identified in combination with the grid precipitation data collected in step S1;

[0008] S3. Correlation between tropical cyclones and regional heavy precipitation events: Based on the regional heavy precipitation events obtained in step S2, a multi-temporal clustering algorithm is used to obtain a mapping relationship between tropical cyclones and the regional heavy precipitation events they cause;

[0009] S4. Identification of the characteristics of regional heavy rainfall caused by tropical cyclones: combining the mapping relationship between the tropical cyclone and the regional heavy rainfall events caused by it obtained in step S3, quantifying the spatiotemporal evolution of the characteristics of regional heavy rainfall events caused by tropical cyclones from four aspects: duration, cumulative affected area, cumulative average precipitation depth, and cumulative total precipitation;

[0010] S5. Analysis of the variability of regional heavy rainfall characteristics caused by tropical cyclones: combining the characteristics of regional heavy rainfall events caused by tropical cyclones obtained in step S4, quantifying the variability of event characteristics, and analyzing the mechanism that affects the variability of characteristics;

[0011] S6. Non-steady-state frequency analysis of regional heavy precipitation characteristics caused by tropical cyclones: Combined with the regional heavy precipitation events caused by tropical cyclones obtained in step S3, the extreme value theory is used to estimate the annual maximum cumulative impact area, cumulative area average precipitation depth and cumulative total precipitation recurrence period of the regional heavy precipitation events in step S4, to test whether the probability of occurrence of extreme regional heavy precipitation events caused by tropical cyclones has changed;

[0012] S7. Mechanism and impact of shortening the return period of extreme regional heavy rainfall events caused by tropical cyclones: Combined with the changes in the characteristic return period of regional heavy rainfall events caused by tropical cyclones obtained in step S6, the influencing factors of shortening the return period are analyzed.

[0013] A storage medium stores instructions and data for realizing a method for identifying heavy rainfall events in a tropical cyclone-induced area and analyzing non-steady-state characteristics.

[0014] A device for identifying heavy rainfall events in a tropical cyclone-induced area and analyzing non-steady-state characteristics, comprising: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a method for identifying heavy rainfall events in a tropical cyclone-induced area and analyzing non-steady-state characteristics.

[0015] The beneficial effects provided by the present invention are:

[0016] (1) The present invention discloses a method for identifying regional heavy precipitation events caused by tropical cyclones, determines the mapping relationship between tropical cyclones and regional heavy precipitation events caused by tropical cyclones, and quantifies the spatiotemporal evolution characteristics of this type of heavy precipitation events in historical periods from four aspects: duration, cumulative affected area, cumulative average precipitation depth, and cumulative total precipitation. The method of the present invention can fully identify the spatiotemporal evolution and movement characteristics of regional extreme precipitation caused by tropical cyclones, which will further improve the accuracy of heavy precipitation forecasts caused by tropical cyclones.

[0017] (2) The non-steady-state characteristics of regional heavy precipitation events caused by tropical cyclones were analyzed. The non-steady-state frequency of extreme regional heavy precipitation events caused by tropical cyclones was quantified from four aspects: duration, cumulative affected area, cumulative average precipitation depth and cumulative total precipitation. The extreme value theory was used to predict the future recurrence period of extreme regional heavy precipitation events caused by tropical cyclones, and the mechanism and impact of the shortening of the recurrence period were explored, providing a scientific basis for preventing disasters caused by tropical cyclones and making decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of the method of the present invention;

[0019] Figure 2 It is a framework diagram for identifying regional heavy precipitation events caused by tropical cyclones;

[0020] Figure 3 This is the characteristic distribution map of regional heavy precipitation events in the eastern monsoon region of China;

[0021] Figure 4 This is a skewed distribution map of the characteristics of regional heavy precipitation events in the eastern monsoon region of China;

[0022] Figure 5 It is a temporal variation characteristic diagram of regional heavy precipitation events caused by tropical cyclones. The 95th threshold is used to identify regional heavy precipitation events;

[0023] Figure 6 It is a temporal variation characteristic diagram of regional heavy precipitation events caused by tropical cyclones. A 50mm threshold is used to identify regional heavy precipitation events;

[0024] Figure 7 This is a temporal variation characteristic diagram of regional heavy rainfall events in northeast China caused by tropical cyclones;

[0025] Figure 8 This is a time-varying characteristic diagram of regional heavy rainfall events in southeastern China caused by tropical cyclones;

[0026] Fig. 9 is the QQ diagram of the characteristics of regional heavy precipitation events caused by tropical cyclones, as observed and simulated by the generalized extreme value model;

[0027] Fig.10 is the QQ diagram of the characteristics of regional heavy precipitation events caused by tropical cyclones, as observed and simulated by the generalized extreme value model;

[0028] Fig.11 It is a diagram showing the relationship between the characteristics of regional heavy rainfall events caused by tropical cyclones and tropical cyclones;

[0029] Fig.12 It is a working schematic diagram of the hardware device of the present invention. DETAILED DESCRIPTION

[0030] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0031] Before formally describing the present invention, the scheme of the present invention is first generally described for easy understanding.

[0032] Please refer to Figure 1 , Figure 1 It is a schematic diagram of the process of the present invention.

[0033] The present invention provides a method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics, comprising the following steps:

[0034] S1. Data collection: Collect observation-based gridded precipitation data, tropical cyclone best track data, and meteorological reanalysis data;

[0035] As an exemplary embodiment, step S1 in the present invention is specifically as follows: the tropical cyclone optimal path data includes the latitude, longitude and maximum wind speed of the center of the tropical cyclone once every 3 hours; the precipitation data is grid precipitation data with high temporal and spatial resolution, with a time resolution of 3 hours and a spatial resolution of 0.25°×0.25°; the meteorological reanalysis data includes wind fields, latitudinal and longitudinal water vapor transport.

[0036] S2, identification of regional heavy precipitation events: according to the definition of heavy precipitation and the method for determining regional heavy precipitation events, regional heavy precipitation events are identified in combination with the grid precipitation data collected in step S1;

[0037] As an exemplary embodiment, step S2 in the present invention is specifically as follows:

[0038] S21: The definition of heavy precipitation by the China Meteorological Administration, that is, the total amount of precipitation is not less than 50 mm, is used as the threshold for heavy precipitation defined in the present invention. Due to the large spatial variability of precipitation in the monsoon region, the 95th percentile (95th) of the non-zero precipitation value is also used as the threshold (critical value) for each grid. Grids whose daily precipitation values ​​exceed the threshold are extracted, and adjacent grids are merged into one event. In order to ensure that large-scale heavy precipitation events can cover a large enough area, only events with an area greater than 10,000 square kilometers are considered;

[0039] S22: Method for determining regional heavy precipitation events - multi-temporal clustering algorithm; Heavy precipitation events can change continuously in time and space. It consists of multiple time states, and at least one heavy precipitation event will occur in each time state. However, for two adjacent time states, the distance moved by the same regional heavy precipitation event is very short and there is overlap in space. Therefore, if the spatial topological structures of multiple heavy precipitation events in adjacent time states intersect (that is, they share an overlapping area and at least one grid overlaps), they are considered to belong to the same regional heavy precipitation event. These search processes will continue until there are no overlapping areas for regional heavy precipitation events. For each regional heavy precipitation event, its transfer, fission and fusion processes can be tracked in each time state, so that the identified regional heavy precipitation events have spatiotemporal continuity and a high degree of consistency with the facts.

[0040] S3. Correlation between tropical cyclones and regional heavy precipitation events: Based on the regional heavy precipitation events obtained in step S2, a multi-temporal clustering algorithm is used to obtain a mapping relationship between tropical cyclones and the regional heavy precipitation events they cause;

[0041] As an exemplary embodiment, in step S3 of the present invention, a multi-temporal clustering algorithm is used to associate tropical cyclones with regional heavy precipitation events. The specific steps are as follows:

[0042] S31: Treat daily regional heavy precipitation events as rain belts;

[0043] S32: Calculate the distance between the tropical cyclone center and the weighted precipitation center of the regional heavy precipitation event (i.e., the grid point with the largest cumulative precipitation during the entire regional heavy precipitation event). If any distance is less than a certain threshold (e.g., 500 km, which is widely selected in previous studies), the regional heavy precipitation event is defined as a heavy precipitation event triggered by a tropical cyclone. Multiple regional heavy precipitation events triggered by the same tropical cyclone are merged into one event;

[0044] S33: Obtain a one-to-one mapping relationship between tropical cyclones and regional heavy rainfall events caused by tropical cyclones.

[0045] S4. Identification of the characteristics of regional heavy rainfall caused by tropical cyclones: combining the mapping relationship between the tropical cyclone and the regional heavy rainfall events caused by it obtained in step S3, quantifying the spatiotemporal evolution of the characteristics of regional heavy rainfall events caused by tropical cyclones from four aspects: duration, cumulative affected area, cumulative average precipitation depth, and cumulative total precipitation;

[0046] As an exemplary embodiment, step S4 of the present invention is specifically as follows:

[0047] S41: For each regional heavy rainfall event caused by a tropical cyclone, four indices are defined to evaluate its characteristics: duration, cumulative affected area, cumulative average precipitation depth, and cumulative total precipitation. The calculation formulas for the four indices are as follows:

[0048] (1) Duration (D; number of days): D = end date - start date + 1.

[0049] (2) Cumulative affected area (A; km 2 ), the sum of the daily affected areas during the entire duration:

[0050]

[0051] Among them, A t is the affected area on day t, and T is the total duration of the event.

[0052] (3) Cumulative mean precipitation depth (P; mm), the sum of the daily area-weighted mean precipitation depths of all affected grids during the entire duration:

[0053]

[0054] Among them, P t is the precipitation depth on day t, A t is the affected area on day t, A 总 It is the cumulative affected area during the entire duration.

[0055] (4) Total cumulative precipitation (V; km 3 ), the daily area-weighted average precipitation depth for the entire duration multiplied by the sum of the daily affected areas:

[0056]

[0057] Among them, P t is the precipitation depth on day t, A t is the affected area on day t.

[0058] S42: The trend of the characteristics of regional heavy rainfall events caused by tropical cyclones is calculated by the least squares method. The slope obtained by fitting the time series of the research variables by the least squares method is the trend of change. The least squares fitting formula of the slope is as follows:

[0059]

[0060] In the formula, b is the slope, n is the total number of years in the study period, x represents the year, and x i is the i-th year in the study period, is the mean of the years in the research period; y represents the value of the research variable, y iis the value of the study variable in the i-th year during the study period, is the mean value of the research variable during the research period.

[0061] S5. Analysis of the variability of regional heavy rainfall characteristics caused by tropical cyclones: combining the characteristics of regional heavy rainfall events caused by tropical cyclones obtained in step S4, quantifying the variability of event characteristics, and analyzing the mechanism that affects the variability of characteristics;

[0062] As an exemplary embodiment, step S5 of the present invention is specifically as follows:

[0063] The maximum values ​​of the three variables of the cumulative affected area, cumulative average precipitation depth and cumulative total precipitation of regional heavy rainfall events caused by tropical cyclones in each year are extracted; the maximum values ​​of these three variables in each year are then sorted from large to small, "Max10" refers to the data corresponding to the first 10 years in this sorting, and "Min10" refers to the data corresponding to the last 10 years in this sorting; based on the above two groups of 10-year data of these three variables (one group takes the maximum value and the other group takes the minimum value), the positive and negative anomalies of these three variables in the corresponding 10 years are calculated to analyze the mechanism of the characteristic variability that may affect the regional heavy rainfall caused by tropical cyclones.

[0064] S6. Non-steady-state frequency analysis of regional heavy precipitation characteristics caused by tropical cyclones: Combined with the regional heavy precipitation events caused by tropical cyclones obtained in step S3, the extreme value theory is used to estimate the annual maximum cumulative impact area, cumulative area average precipitation depth and cumulative total precipitation recurrence period of the regional heavy precipitation events in step S4, to test whether the probability of occurrence of extreme regional heavy precipitation events caused by tropical cyclones has changed;

[0065] As an exemplary embodiment, step S6 in the present invention is specifically as follows:

[0066] S61: Use Akaike Information Criterion to select the best model;

[0067] Akaike Information Criterion (AIC) is a statistic used for model selection, which aims to select the best statistical model by balancing the goodness of fit of the model and the complexity of the model. The calculation formula of Akaike Information Criterion (AIC) is as follows:

[0068] AIC=2k-2ln(L)

[0069] Where: k is the number of parameters in the model. L is the maximum likelihood estimate of the model, which indicates the probability of the data occurring under given parameters. The lower the AIC value, the better the model fits the data, that is, the better the model.

[0070] S62: Select extreme value distribution model;

[0071] In order to evaluate the frequency changes of regional heavy precipitation event characteristics caused by tropical cyclones, the extreme value theory is used according to the Akaike information criterion to estimate the annual maximum recurrence period (i.e., once every n years on average) of the cumulative affected area A, the cumulative average precipitation depth P, and the cumulative total precipitation V. The formula of the cumulative distribution function of the generalized extreme value distribution is as follows:

[0072]

[0073] Where z is the annual maximum time series of the cumulative affected area A, the cumulative average precipitation depth P, and the cumulative total precipitation V; μ, σ, and ξ are the location parameter, scale parameter, and shape parameter, respectively. If the three parameters remain unchanged, the generalized extreme value distribution is a steady-state model. To establish a non-steady-state model, the location parameter (μ) and scale parameter (σ) can be obtained by a linear function of the covariate that changes over time:

[0074]

[0075] where x is a time-varying covariate; μ 0 , σ 0 , α 1 , β 1 is the regression parameter, μ 0 represents the initial value of the position parameter, σ 0 represents the initial value of the scale parameter, α 1 represents the rate at which the location parameter changes with the independent variable, β 1 It represents the rate at which the scale parameter changes with the independent variable, and is calculated using linear regression analysis methods such as the least squares method. 0 , σ 0 , α 1 , β 1 The estimated value minimizes the error between the fitted straight line and the actual data; the shape parameter ξ is set to a constant due to the large uncertainty in the estimate. Therefore, three models are constructed: M 0 (α 1 =0,β 1 =0); M 1 (α 1 ≠0, β 1 =0); and M 2 (α 1 ≠0, β 1 ≠0). Model M 0 is a steady-state model. Model M 1 is a non-stationary model in which only the location parameter changes with time. 2 Both position and scale parameters vary with time.

[0076] To assess the changes in the frequency of regional heavy precipitation events caused by tropical cyclones, using the time of year in tropical cyclone data, tropical cyclone intensity, and calculated tropical cyclone movement speed as covariates.

[0077] S63: Identify covariates;

[0078] The duration of a tropical cyclone's movement is the time difference between the first and last positions of a tropical cyclone in the study area, expressed in hours. The moving speed of a tropical cyclone is the average of all 6-hour moving speeds of a tropical cyclone in the study area, where the 6-hour moving speed is obtained by calculating the spherical distance between the centers of two consecutive tropical cyclones and dividing it by 6 hours. The angular deviation of a tropical cyclone is defined as the average of the angular deviations of all consecutive 6-hour path vectors of a tropical cyclone in the study area.

[0079] The calculation formula for the moving speed of a tropical cyclone is as follows:

[0080] L (n-1)n = R arccos[cos(α n-1 -α n )cosβ n-1 cosβ n +sinβ n-1 sinβ n ]

[0081]

[0082] Since the time resolution of tropical cyclones is 6 hours, where n is the number of path positions of a tropical cyclone in the study area every 6 hours, L (n-1)n For tropical cyclones in p n-1 、p n The moving distance between two points, R is the radius of the earth, α n-1 , α n For p n-1 、p n Longitude of two points, β n-1 , β n For p n-1 、p n The latitude of two points, For tropical cyclones in p n-1 、p n The speed of movement between two points, For tropical cyclones in p 1 、p 2 The speed of movement between two points, For tropical cyclones in p 2 、p 3 The speed of movement between two points, is the moving speed of a tropical cyclone in the study area.

[0083] S64: Model validation;

[0084] Quantile-Quantile (QQ) plots can be used to compare the similarity of two distributions to test the normality of data or verify the fitting effect of the model. In the QQ plot, the horizontal axis usually represents the quantile of the theoretical distribution, and the vertical axis represents the quantile of the actual data. If the simulated quantile and the observed quantile are distributed along a 1:1 straight line, the non-stationary model can fit the observed regional heavy precipitation event index well.

[0085] S7. Mechanism and impact of shortening the return period of extreme regional heavy rainfall events caused by tropical cyclones: Combined with the changes in the characteristic return period of regional heavy rainfall events caused by tropical cyclones obtained in step S6, the influencing factors of shortening the return period are analyzed.

[0086] As an exemplary embodiment, in step S7 of the present invention: the Spearman correlation coefficient is used to analyze the correlation between the intensity and movement speed of tropical cyclones and regional heavy rainfall events caused by landfalling tropical cyclones; the t-test is used to test the significance of the obtained correlation; and the relationship between the regional heavy rainfall events caused by tropical cyclones and tropical cyclones is analyzed based on the size of the correlation and its significance.

[0087] In order to better explain the present invention, a specific embodiment is listed in the present invention.

[0088] The embodiment takes the tropical cyclones that landed in China from 1961 to 2018 as an example, limits the research area to the monsoon region in eastern China, and uses an objective recognition method combining a multi-temporal clustering algorithm and objective weather analysis technology to identify and study regional heavy precipitation events caused by tropical cyclones in the region.

[0089] The implementation flow chart of the method of the present invention is as follows Figure 1 As shown, the specific steps are as follows:

[0090] (1) Collection of basic data;

[0091] In this embodiment, the tropical cyclone best track data uses the tropical cyclone best track data set of the International Climate Management Best Track Archive, including the cyclone center position and near-center maximum wind speed of the tropical cyclone every 3 hours, and the data set covers the period from 1961 to 2018; the precipitation data uses the grid data set (CN05.1) from 1961 to 2018 developed based on the observation data of 2416 meteorological stations across the country, with a time resolution of 3 hours and a horizontal spatial resolution of 0.25°×0.25°; the reanalysis data collects the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), with a time resolution of monthly and a spatial resolution of 0.25°×0.25°, and the data includes wind field, latitudinal water vapor flux, and longitudinal water vapor flux. The vertical wind shear is calculated based on the wind field between 200hPa and 850hPa, and the wind field of 500hPa is used to guide the airflow.

[0092] (2) Identification of regional heavy precipitation events:

[0093] like Figure 2 As shown, the method of step S2 is used to identify and extract regional heavy precipitation events from the grid precipitation data obtained in step S1 according to the definition of heavy precipitation and the multi-temporal clustering algorithm.

[0094] The main process of the multi-temporal clustering algorithm for identifying regional heavy precipitation events includes: setting the 95th percentile of daily precipitation not less than 50 mm and non-zero precipitation values ​​as thresholds to define heavy precipitation respectively, identifying heavy precipitation events, and extracting grid points that exceed the set thresholds in each time state. Only adjacent super-threshold grid points are considered to be merged into an area larger than 10,000 square kilometers to ensure that the coverage of heavy precipitation events is large enough; according to the spatial topological intersection of multiple heavy precipitation events in adjacent time states, identify whether they belong to the same regional heavy precipitation event. If two events overlap in space (that is, at least one grid point overlaps), they are considered to be the same event. Repeat this process until there is no event overlap; for each regional heavy precipitation event, track its transmission, fission and fusion in each time state.

[0095] (3) Association between tropical cyclones and regional heavy rainfall events:

[0096] like Figure 2As shown, the method of step S3 is adopted to associate the tropical cyclone with the regional heavy precipitation event obtained in step S2. First, the regional heavy precipitation event of each day is regarded as a rain belt, and the distance between the center of the tropical cyclone and the weighted precipitation center of the regional heavy precipitation event (i.e., the grid point with the largest cumulative precipitation during the entire regional heavy precipitation event) is calculated. If any distance is less than 500 kilometers, the regional heavy precipitation event is identified as an event caused by the center of the tropical cyclone. Multiple regional heavy precipitation events caused by the same tropical cyclone center are merged into one event. Finally, the mapping relationship between the tropical cyclone and the regional heavy precipitation event guided by the tropical cyclone is obtained.

[0097] For each regional heavy precipitation event caused by a tropical cyclone, four indices are used to evaluate its characteristics, see Figure 2 , D, A, P and V refer to the duration, cumulative affected area, cumulative average precipitation depth and cumulative total precipitation of regional heavy precipitation events caused by tropical cyclones, respectively.

[0098] Based on this identification framework, if 50 mm / 95th is used as the critical value, in this example, 472 / 571 regional heavy precipitation events caused by tropical cyclones in the eastern monsoon region of China between 1961 and 2018 were extracted.

[0099] (4) Identification of the characteristics of regional heavy rainfall caused by tropical cyclones:

[0100] like Figures 3 to 8 As shown, the method of step S4 is used to calculate four indexes of the characteristics of each regional heavy rainfall event: duration (D; number of days), cumulative affected area (A; km 2 ), cumulative average precipitation depth (P; mm), cumulative total precipitation (V; km 3 ). The time series of the characteristics (four indexes) of regional heavy rainfall events caused by tropical cyclones are statistically analyzed, and the changing trend of the characteristics of regional heavy rainfall events caused by tropical cyclones is calculated by the least squares method. The slope obtained by fitting the time series of the research variables by the least squares method is the changing trend.

[0101] like Figure 3 As shown in Figure 2, the four characteristics of regional heavy rainfall events caused by tropical cyclones all show a skewed distribution. Specifically, about 92.5% / 77.9% of the total events last no longer than 4 days. Although 88.3% / 63.0% of the events have a cumulative impact area of ​​no more than 20×10 4 km 2 However, the maximum cumulative impact area of ​​regional heavy rainfall events caused by tropical cyclones reached 192.7×10 4 km 2The cumulative average precipitation depth of events not exceeding 200 mm accounted for 53.2% / 55.3%, while the maximum cumulative average precipitation depth reached 1139.5 mm. Similarly, the cumulative total precipitation of regional heavy precipitation events caused by tropical cyclones in 83.7% / 67.1% did not exceed 20 km. 3 , but its maximum value is 246.7km 3 These results indicate that the spatial extent and total precipitation of regional heavy precipitation events induced by tropical cyclones show significant heterogeneity in these events.

[0102] Figure 4 The seasonal characteristics of regional heavy precipitation events caused by tropical cyclones are shown. In this embodiment, tropical cyclones within a radius of 500 kilometers from the coastline are counted, and it is found that 79.3% of tropical cyclones occur from June to September. Correspondingly, during the period from June to September, the characteristic values ​​of regional heavy precipitation events caused by tropical cyclones are 86.7% / 88.5% events, 87.7% / 91.1% cumulative affected area, 86.0% / 86.6% cumulative average precipitation depth, and 87.2% / 89.5% cumulative total precipitation. These characteristics reach their peak values ​​from June to September, with the highest values ​​in July. If attention is turned to the characteristics of regional heavy precipitation events caused by a single tropical cyclone, the inverted "V" shape of the seasonal distribution becomes flatter. The cumulative affected area, cumulative average precipitation depth, and cumulative total precipitation of regional heavy precipitation events of a single tropical cyclone increase sharply to about 10×10 4 km 2 , 80mm and 6km 3 . It began to fluctuate steadily on the 120th day, then remained stable until the 300th day, and then dropped sharply to near zero. Therefore, the characteristics of regional heavy precipitation events caused by a single tropical cyclone show weak seasonality, which poses a greater challenge to the prediction and prevention of regional heavy precipitation disasters caused by tropical cyclones.

[0103] From the perspective of spatial distribution, regional heavy precipitation events caused by tropical cyclones gradually decrease from the coast to the inland. Southeast China is the region with the most frequent regional heavy precipitation events caused by tropical cyclones (86.8 / 96.1 times on average). When divided by the 95th threshold, the high-value areas and areas with longer duration of regional heavy precipitation events caused by tropical cyclones are significantly greater than the 50mm threshold. However, in the EMC region, the contribution of regional heavy precipitation events caused by tropical cyclones to the total regional heavy precipitation events under the two thresholds is basically the same, except for the Northeast. In the southeast coastal area, the contribution rates under the two thresholds are 48.4% and 42.4%, respectively, which is almost equal to the proportion of fixed-point heavy precipitation caused by tropical cyclones. For the Northeast, when the 95th threshold is used, regional heavy precipitation events caused by tropical cyclones account for less than 20% of the total events, while when the 50mm threshold is used, this proportion can exceed 40%. This shows that tropical cyclones play an important role in the large-scale heavy rains exceeding 50mm and the subsequent large-scale floods in Northeast China.

[0104] The characteristics of regional heavy precipitation events caused by these tropical cyclones are particularly significant in southeastern China, manifested by a significant increase in frequency and duration. For regional heavy precipitation events caused by tropical cyclones under the two thresholds, there are obvious differences in the trend direction in Northeast China. When the threshold is 50 mm, the frequency and duration of regional heavy precipitation events caused by tropical cyclones in Northeast China increase, while the opposite is true when the threshold is 95th.

[0105] Figure 5 The temporal variation characteristics of regional heavy precipitation events caused by tropical cyclones with the 95th threshold are shown. The annual frequency and duration of various characteristics of regional heavy precipitation events caused by tropical cyclones show a slight downward trend, with a decrease of -0.59% / 10 years and -1.16% / 10 years, respectively. The cumulative affected area also showed a downward trend (-2.72% / 10 years). However, the cumulative average precipitation depth and the cumulative total precipitation both showed an upward trend, with an increase rate of 1.22% / 10 years and 1.12% / 10 years, respectively. The trend signals of all characteristics of regional heavy precipitation events caused by tropical cyclones are not strongly stable, as none of them passed the statistical significance test. The 10-year interannual variation rate of these characteristics with a period of about 20 years is more significant. Overall, for all characteristics of regional heavy precipitation events triggered by tropical cyclones, this interdecadal variation shows a consistent cycle of decrease and increase, namely, decrease from 1961 to 1980, increase from 1980 to 1995, decrease from 1995 to 2005, and increase again from 2005 to 2019.

[0106] Figure 6The temporal variation characteristics of regional heavy precipitation events caused by tropical cyclones with a threshold of 50 mm are shown. All characteristics of regional heavy precipitation events caused by tropical cyclones have increased. The increase rates of frequency, duration, cumulative affected area, cumulative average precipitation depth and cumulative total precipitation are 1.56% / decade, 3.15% / decade, 5.15% / decade, 3.41% / decade and 5.45% / decade respectively. Regional heavy precipitation events caused by tropical cyclones exceeding 50 mm mainly occur in southeastern China, so large-scale floods caused by tropical cyclones in the region tend to be more frequent, more extensive and more intense.

[0107] Figures 7-8 The authors focused on the characteristic changes of regional heavy precipitation events caused by tropical cyclones in Northeast China and the southeastern coastal areas. From 1961 to 2018, the regional heavy precipitation event index caused by tropical cyclones in Northeast China showed a downward trend ( Figure 7 ), which is consistent with Figure 5 The significant downward trends in frequency and duration shown in c and d are consistent; while the regional heavy precipitation event indices caused by tropical cyclones in the southeastern coastal areas of China mostly show positive trends, although these positive trends are not as obvious as the negative trends in northeastern China ( Figure 8 The differences in the trends of regional heavy precipitation events caused by tropical cyclones in Northeast China and the southeastern coastal areas indicate that the risks associated with regional heavy precipitation events caused by tropical cyclones have different regional patterns.

[0108] (5) Analysis of the mechanism of the characteristic variability of regional heavy rainfall caused by tropical cyclones:

[0109] As shown in Tables 1 to 2, the method of step S5 is used to analyze the mechanism of action of the characteristic variability of regional heavy precipitation caused by tropical cyclones. In this embodiment, two groups of 10-year data of regional heavy precipitation events caused by 472 / 571 tropical cyclones in the eastern monsoon region of China from 1961 to 2018 are selected to explore the mechanism of action of the triggered variability, one group takes the maximum value and the other group takes the minimum value (see Table 1). The recognition threshold of regional heavy precipitation events is 50mm / 95th; based on the above two groups of 10-year data, this embodiment constructs a table of 10-year positive and negative anomalies (see Table 2), and compares and analyzes the synthetic graphs of positive and negative anomalies.

[0110] Comparison and analysis show the interannual variation of 850hPa horizontal wind, vertical wind shear, guiding airflow and integrated water vapor transport from June to September. These large-scale environmental anomalies have been proven to strongly affect the frequency, duration and path of hot cyclones. In the 10 years with positive anomalies, easterly winds prevail over the western Pacific warm pool and the Chinese land, pushing the Pacific warm pool westward, and then moving the tropical cyclone generation area westward. Therefore, these prevailing easterly winds provide favorable dynamic conditions for the development of tropical cyclones to the east of China. At the same time, the westward guiding airflow over the central and eastern Pacific helps typhoons move toward land. An anomalous guiding airflow appeared over southeastern China, and this anomalous cyclone is conducive to more tropical cyclones passing through southeastern China. Southeast winds blow on the east side of the monsoon trough, forming a guiding airflow, which in turn guides tropical cyclones to land in coastal areas of China. In positive anomaly years, vertical wind shear will also weaken, which is conducive to the formation and maintenance of the warm core structure of tropical cyclones, and thus to the maintenance of the intensity of tropical cyclones after landing. In positive anomaly years, the integrated water vapor transport is characterized by a water vapor flux path from the western Pacific Ocean (WNP), the South China Sea, and the Bay of Bengal to southeastern China. In particular, an anomaly anticyclone is observed near the eastern waters of China, which enhances the water vapor transport from the western Pacific Ocean to the land. This intensive water vapor flux provides abundant water vapor supply. On the one hand, the warm and humid air flow can provide energy for maintaining the intensity of tropical cyclones, and on the other hand, it also provides favorable conditions for heavy precipitation caused by tropical cyclones. In negative anomaly years, the above atmospheric circulation patterns are not observed or show opposite characteristics.

[0111] Table 1: Maximum and minimum values ​​of duration (D), cumulative affected area (A), cumulative average precipitation depth (P) and cumulative total precipitation (V) of regional heavy precipitation events caused by tropical cyclones in the past 10 years

[0112]

[0113] Note: The first / second value of " / " is used for regional heavy precipitation events caused by tropical cyclones, with a threshold of 50 mm / 95th.

[0114] Table 2: Anomalies of the two sets of data in Table 2. The last row is the standard deviation of the entire time series of these indices for regional heavy precipitation events caused by tropical cyclones.

[0115]

[0116] Note: The first / second value of " / " is used for regional heavy precipitation events caused by tropical cyclones, with a threshold of 50 mm / 95th.

[0117] (6) Non-stationary frequency analysis of the characteristics of regional heavy rainfall caused by tropical cyclones;

[0118] like Figures 9-10 As shown in Table 3, the generalized extreme value theory of step S6 is used to estimate the extreme values ​​of the four indexes of the characteristics of regional heavy precipitation events caused by tropical cyclones within a fixed return period, and to clarify the law of the occurrence probability and intensity of extreme regional heavy precipitation events caused by tropical cyclones over time. The specific steps are as follows:

[0119] 1) Data preparation:

[0120] Extract the maximum values ​​of the three variables of the cumulative impact area, cumulative average precipitation depth and cumulative total precipitation of regional heavy precipitation events caused by tropical cyclones from 1961 to 2018 in each year (a total of 58 data);

[0121] 2) Select the extreme value distribution model:

[0122] In this example, the generalized extreme value distribution is used to describe the annual extreme value data, and the model parameters are set to change over time, and a non-stationary model of the time series of three characteristic indices of regional heavy precipitation events (i.e., cumulative affected area A, cumulative average precipitation depth P, and cumulative total precipitation V) is established. The location parameter and scale parameter change over time, μ = a 0 +a 1 x,σ=β 0 +β 1 x, the shape parameters remain unchanged. In this embodiment, the time, tropical cyclone intensity and tropical cyclone moving speed obtained by calculation in the tropical cyclone data of the International Climate Management Best Track Archive are used as covariates;

[0123] 3) Model Validation:

[0124] This example uses a quantile-quantile (QQ) plot to evaluate the goodness of fit. The fitting results are shown in Fig. 9 and Fig.10 As shown in the figure, the X-axis is the quantile of the regional heavy precipitation event index simulated by the optimal non-steady-state model, and the Y-axis is the quantile of the observed regional heavy precipitation event index. Fig. 9 and Fig.10 It can be seen that the quantiles of the observed and simulated values ​​are almost on the 1:1 straight line, indicating that the simulation results of the optimal model are in good agreement with the observed values, and the generalized extreme value distribution can well fit the annual maximum time series of regional heavy precipitation events caused by tropical cyclones;

[0125] 4) Find the extreme value in the specified return period:

[0126] The 58 years were divided into two 29-year time periods (1961-1989 and 1990-2018), and the 10-year, 20-year and 50-year extreme values ​​(i.e., return periods of 10, 20 and 50 years) of the cumulative impact area, cumulative average precipitation depth and cumulative total precipitation of tropical cyclone-induced regional heavy precipitation events in the two sub-periods were estimated using the generalized extreme value distribution function. The return period level is inferred from the generalized extreme value distribution function:

[0127]

[0128] Compared with 1961-1989, regional heavy precipitation events triggered by tropical cyclones in the last 29 years showed significant increases in all three of the above characteristics (see Table 3). For example, the cumulative affected area, cumulative mean precipitation depth, and cumulative total precipitation in the 20-year period increased by 58.9% / 15.5%, 16.4% / 30.1%, and 59.0% / 46.0%, respectively. In addition, these increases were more pronounced for more extreme regional heavy precipitation events triggered by tropical cyclones. Specifically, for the 50-year values, these percentages were 83.7% / 15.2%, 16.1% / 39.4%, and 76.3% / 58.3%, respectively. The increase in the extreme values ​​of the regional heavy precipitation event index within a fixed decadal period also represents a shortening of the return period of extreme regional heavy precipitation events of specific size triggered by tropical cyclones.

[0129] Table 3: Changes in the cumulative impact area (A), cumulative mean precipitation depth (P), and cumulative total precipitation (V) of TC-induced RHPE within 10-, 20-, and 50-year return periods

[0130]

[0131] Note: The first / second value of " / " is applicable to regional heavy precipitation events caused by tropical cyclones, with a threshold of 50 mm / 95th.

[0132] (7) Mechanism and impact of shortened return period of extreme regional heavy rainfall events caused by tropical cyclones:

[0133] In this example, tropical cyclones are classified according to their intensity into tropical depression, tropical storm, severe tropical storm, typhoon, severe typhoon and super typhoon. The median of the tropical cyclone intensity and the regional heavy precipitation event characteristics caused by tropical cyclones in each classification are extracted and paired ( Fig.11 ac). Tropical cyclones are divided into ten quantiles according to their moving speed. In every two adjacent quantiles, the median of the tropical cyclone moving speed and the regional heavy rainfall event characteristics caused by the tropical cyclone are extracted and paired ( Fig.11 df).

[0134] As the intensity of tropical cyclones increases, the three characteristic indices of regional heavy rainfall events caused by tropical cyclones will become larger, showing a positive correlation. The situation is similar for the speed of tropical cyclones, but when the tropical cyclone moves faster, the cumulative average precipitation depth becomes smaller ( Fig.11 e) It is worth noting that the characteristics of regional heavy rainfall events caused by tropical cyclones are not linearly related to the intensity and movement speed of the tropical cyclones.

[0135] This example further uses a non-stationary model to explore the impact of tropical cyclone intensity and speed on the probability of regional heavy precipitation events. The 10 years with the largest and the 10 years with the smallest tropical cyclone intensity and speed are introduced into the model as covariates, and the impact of another variable on the probability of regional heavy precipitation events is analyzed when the intensity or speed is fixed. As shown in Table 3, compared with low-intensity tropical cyclone and speed events, the probability of occurrence of high-intensity tropical cyclone and speed events changes with Fig.11 The results are basically consistent with those shown in the table. For example, when the tropical cyclone moves fast, the 50-year cumulative impact area and cumulative total precipitation increase by 2.2% / 3.4% and 1.5% / 13.4%, respectively, while the 50-year cumulative average precipitation depth decreases by -5.5% / -9.6%. Strong tropical cyclones also increase the 50-year cumulative impact area and cumulative total precipitation values ​​by 1.6% / 6.9% and 2.5% / 1.7%, respectively. Numerical simulations of climate models predict that under artificial warming, the movement speed of tropical cyclones will slow down, while the intensity of tropical cyclones will increase significantly. This indicates that the eastern monsoon region of China is more likely to be hit by rare and destructive regional heavy precipitation events caused by tropical cyclones in the future.

[0136] See also Fig.12 , Fig.12 4 is a schematic diagram of the working of the hardware device of an embodiment of the present invention, wherein the hardware device specifically comprises: a device 401 for identifying and analyzing non-steady-state characteristics of heavy rainfall events in a tropical cyclone-induced region, a processor 402 and a storage medium 403.

[0137] A tropical cyclone-induced regional heavy rainfall event identification and non-steady-state feature analysis device 401: The tropical cyclone-induced regional heavy rainfall event identification and non-steady-state feature analysis device 401 implements the tropical cyclone-induced regional heavy rainfall event identification and non-steady-state feature analysis method.

[0138] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the method for identifying heavy rainfall events in a tropical cyclone-induced area and analyzing non-steady-state characteristics.

[0139] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the method for identifying heavy rainfall events in a tropical cyclone-induced area and analyzing non-steady-state characteristics.

[0140] The beneficial effects of the present invention are:

[0141] (1) The present invention discloses a method for identifying regional heavy precipitation events caused by tropical cyclones, determines the mapping relationship between tropical cyclones and regional heavy precipitation events caused by tropical cyclones, and quantifies the spatiotemporal evolution characteristics of this type of heavy precipitation events in historical periods from four aspects: duration, cumulative affected area, cumulative average precipitation depth, and cumulative total precipitation. The method of the present invention can fully identify the spatiotemporal evolution and movement characteristics of regional extreme precipitation caused by tropical cyclones, which will further improve the accuracy of heavy precipitation forecasts caused by tropical cyclones.

[0142] (2) The non-steady-state characteristics of regional heavy precipitation events caused by tropical cyclones were analyzed. The non-steady-state frequency of extreme regional heavy precipitation events caused by tropical cyclones was quantified from four aspects: duration, cumulative affected area, cumulative average precipitation depth and cumulative total precipitation. The extreme value theory was used to predict the future recurrence period of extreme regional heavy precipitation events caused by tropical cyclones, and the mechanism and impact of the shortening of the recurrence period were explored, providing a scientific basis for preventing disasters caused by tropical cyclones and making decisions.

[0143] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics, characterized by: The method comprises the following steps: S1. Data collection: Collect observation-based gridded precipitation data, tropical cyclone best track data, and meteorological reanalysis data; S2. Identification of regional heavy precipitation events: Based on the definition of heavy precipitation and the method for determining regional heavy precipitation events, regional heavy precipitation events are identified in combination with the grid precipitation data collected in step S1; S3. Correlation between tropical cyclones and regional heavy precipitation events: Based on the regional heavy precipitation events obtained in step S2, a multi-temporal clustering algorithm is used to obtain a mapping relationship between tropical cyclones and the regional heavy precipitation events they cause; S4. Identification of the characteristics of regional heavy rainfall caused by tropical cyclones: combining the mapping relationship between the tropical cyclone and the regional heavy rainfall events caused by it obtained in step S3, quantifying the spatiotemporal evolution of the characteristics of regional heavy rainfall events caused by tropical cyclones from four aspects: duration, cumulative affected area, cumulative average precipitation depth, and cumulative total precipitation; S5. Analysis of the variability of regional heavy rainfall characteristics caused by tropical cyclones: combining the characteristics of regional heavy rainfall events caused by tropical cyclones obtained in step S4, quantifying the variability of event characteristics, and analyzing the mechanism that affects the variability of characteristics; S6. Non-steady-state frequency analysis of regional heavy precipitation characteristics caused by tropical cyclones: Combined with the regional heavy precipitation events caused by tropical cyclones obtained in step S3, the extreme value theory is used to estimate the annual maximum cumulative impact area, cumulative area average precipitation depth and cumulative total precipitation recurrence period of the regional heavy precipitation events in step S4, to test whether the probability of occurrence of extreme regional heavy precipitation events caused by tropical cyclones has changed; S7. Mechanism and impact of shortening the return period of extreme regional heavy rainfall events caused by tropical cyclones: Combined with the changes in the characteristic return period of regional heavy rainfall events caused by tropical cyclones obtained in step S6, the influencing factors of shortening the return period are analyzed.

2. The method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics according to claim 1, characterized in that: Step S1 is specifically as follows: The tropical cyclone optimal path data includes the latitude, longitude and maximum wind speed of the center of the tropical cyclone every 3 hours; the precipitation data is grid precipitation data with high temporal and spatial resolution, with a time resolution of 3 hours and a spatial resolution of 0.25°×0.25°; the meteorological reanalysis data includes wind field, latitudinal and longitudinal water vapor transport.

3. The method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics as claimed in claim 2, characterized in that: Step S2 is specifically as follows: S21: The heavy precipitation refers to a total precipitation of not less than 50 mm. Grids with daily precipitation values ​​exceeding a preset threshold are extracted, and adjacent grids are merged into one event. S22: Identify regional heavy precipitation events using a multi-temporal clustering algorithm.

4. The method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics as claimed in claim 3, characterized in that: Step S3 is as follows: A multi-temporal clustering algorithm is used to associate tropical cyclones with regional heavy precipitation events. The specific steps are as follows: S31: Treat daily regional heavy precipitation events as rain belts; S32: Calculate the distance between the center of the tropical cyclone and the weighted precipitation center of the regional heavy precipitation event; if any distance is less than a certain threshold, the regional heavy precipitation event is defined as a heavy precipitation event caused by the tropical cyclone; multiple regional heavy precipitation events caused by the same tropical cyclone are merged into one event; S33: Obtain a one-to-one mapping relationship between tropical cyclones and regional heavy rainfall events caused by tropical cyclones.

5. The method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics as claimed in claim 4, characterized in that: Step S4 is specifically as follows: S41: For each regional heavy rainfall event caused by a tropical cyclone, four indices are defined to evaluate its characteristics: duration, cumulative affected area, cumulative average precipitation depth, and cumulative total precipitation. The calculation formulas for the four indices are as follows: (1) Duration (D; number of days): D = end date - start date + 1; (2) Cumulative affected area, the sum of the daily affected areas during the entire duration: Among them, A t is the affected area on day t, and T is the total duration of the event; (3) Cumulative average precipitation depth, the sum of the daily area-weighted average precipitation depths of all affected grids during the entire duration: Among them, P t is the precipitation depth on day t, A t is the affected area on day t, A 总 is the cumulative affected area over the entire duration; (4) Total cumulative precipitation, which is the sum of the daily area-weighted average precipitation depth multiplied by the daily affected area during the entire duration: Among them, P t is the precipitation depth on day t, A t is the impact area on day t; S42: The trend of the characteristics of regional heavy rainfall events caused by tropical cyclones is calculated by the least squares method. The slope obtained by fitting the time series of the research variables by the least squares method is the trend of change. The least squares fitting formula of the slope is as follows: In the formula, b is the slope, n is the total number of years in the study period, x represents the year, and x i is the i-th year in the study period, is the mean of the years in the research period; y represents the value of the research variable, y i is the value of the study variable in the i-th year during the study period, is the mean value of the research variable during the research period.

6. A method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics as claimed in claim 5, characterized in that: Step S5 is specifically as follows: The maximum values ​​of the three variables of the cumulative affected area, cumulative average precipitation depth and cumulative total precipitation of regional heavy rainfall events caused by tropical cyclones in each year were extracted; the maximum values ​​of the three variables in each year were then sorted from large to small, with Max10 representing the data corresponding to the first 10 years in this sorting, and Min10 representing the data corresponding to the last 10 years in this sorting; based on the above two groups of 10-year data of these three variables, one group took the maximum value and the other group took the minimum value, and the positive and negative anomalies of the three variables in the corresponding 10 years were calculated to analyze the mechanism of the characteristic variability that may cause the regional heavy rainfall caused by tropical cyclones.

7. A method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics as claimed in claim 6, characterized in that: Step S6 is specifically as follows: S61: Use Akaike Information Criterion to select the best model; S62: Select the extreme value distribution model; use the extreme value theory to estimate the annual maximum return period of the cumulative impact area A, the cumulative average precipitation depth P and the cumulative total precipitation V according to the Akaike information criterion. The formula of the cumulative distribution function of the generalized extreme value distribution is as follows: Among them, z is the annual maximum time series of the cumulative affected area A, the cumulative average precipitation depth P and the cumulative total precipitation V; μ, σ and ξ are the location parameter, scale parameter and shape parameter respectively; if the three parameters remain unchanged, the generalized extreme value distribution is a steady-state model; to establish a non-steady-state model, the location parameter (μ) and scale parameter (σ) are obtained by linear functions of the covariates that change over time: Among them, x is a covariate that changes with time; μ0, σ0, α1, and β1 are regression parameters, μ0 represents the initial value of the location parameter, σ0 represents the initial value of the scale parameter, α1 represents the rate at which the location parameter changes with the independent variable, and β1 represents the rate at which the scale parameter changes with the independent variable. The estimated values ​​of μ0, σ0, α1, and β1 are calculated using linear regression analysis methods such as the least squares method to minimize the error between the fitted straight line and the actual data; the shape parameter ξ is set to a constant due to the large uncertainty in its estimation; therefore, three models are constructed: model M0, with parameters α1=0, β1=0; model M1 with parameters α1≠0, β1=0; and model M2 with parameters α1≠0, β1≠0; model M0 is a steady-state model; model M1 is a non-stationary model in which only the location parameter changes with time; and model M2 with both location and scale parameters changing with time; To assess the changes in the frequency of regional heavy precipitation events caused by tropical cyclones, using the time of year, tropical cyclone intensity and calculated tropical cyclone movement speed from tropical cyclone data as covariates; S63: Identify covariates; The duration of a tropical cyclone movement is the time difference between the first and last positions of a tropical cyclone in the study area, in hours; the moving speed of a tropical cyclone is the average of all the moving speeds of a tropical cyclone every 6 hours in the study area, where the 6-hour moving speed can be obtained by calculating the spherical distance between the center positions of two consecutive tropical cyclones and dividing it by 6 hours; the angular deviation of a tropical cyclone is defined as the average of the angular deviations of all consecutive 6-hour path vectors of a tropical cyclone in the study area; the calculation formula for the moving speed of a tropical cyclone is as follows: L (n-1)n =R arccos[cos(α n-1 -a n )cosβ n-1 cosβ n +sinβ n-1 sinβ n ] Since the time resolution of tropical cyclones is 6 hours, where n is the number of path positions of a tropical cyclone in the study area every 6 hours, L (n-1)n For tropical cyclones in p n-1 、p n The moving distance between two points, R is the radius of the earth, α n-1 , α n For p n-1 、p n Longitude of two points, β n-1 , β n For p n-1 、p n The latitude of two points, For tropical cyclones in p n-1 、p n The speed of movement between two points, is the moving speed of the tropical cyclone between points p1 and p2, is the moving speed of the tropical cyclone between points p2 and p3, is the moving speed of a tropical cyclone in the study area; S64: Model validation; The quantile-quantile QQ plot is used to test the normality of the data or verify the fitting effect of the model. In the QQ plot, the horizontal axis represents the quantile of the theoretical distribution, and the vertical axis represents the quantile of the actual data. If the simulated quantile and the observed quantile are distributed along a 1:1 straight line, it means that the non-steady-state model can fit the observed regional heavy precipitation event index.

8. A method for identifying heavy rainfall events in a tropical cyclone-induced region and analyzing non-steady-state characteristics as claimed in claim 7, characterized in that: In step S7: using the Spearman correlation coefficient to analyze the correlation between the intensity and speed of tropical cyclones and the regional heavy precipitation events caused by the landfalling tropical cyclones; The t-test was used to test the significance of the obtained correlation; the relationship between regional heavy rainfall events caused by tropical cyclones and tropical cyclones was analyzed based on the size of the correlation and its significance.

9. A storage medium, characterized in that: The storage medium stores instructions and data for implementing a method for identifying heavy rainfall events and analyzing non-steady-state characteristics in a tropical cyclone-induced region as described in any one of claims 1 to 8.

10. A device for identifying heavy rainfall events in a tropical cyclone-induced area and analyzing non-steady-state characteristics, characterized by: include: Processor and storage medium; the processor loads and executes instructions and data in the storage medium to implement a method for identifying heavy rainfall events caused by tropical cyclones and analyzing non-steady-state characteristics in a region as described in any one of claims 1 to 8.

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