Extreme weather index calculation method and system based on convective scale ensemble forecast

Abstract

The present invention discloses a method and system for calculating an extreme weather index based on a convective-scale ensemble forecast, and relates to the technical field of extreme weather data processing, including: obtaining a weather initial value set, and obtaining a forecast result set by a convective-scale ensemble forecast system according to the weather initial value set; obtaining weather initial value set data, comprehensively analyzing to obtain an error evaluation index of each initial value unit, obtaining an initial value set error evaluation index, and obtaining an extreme weather warning threshold according to the initial value set error evaluation index; extracting weather data of each forecast result from the forecast result set, processing the weather data of each forecast result, obtaining an extreme weather evaluation index of each forecast result, and screening each extreme weather forecast result according to the initial value unit error evaluation index and the extreme weather evaluation index; counting the number of extreme weather forecast results, comprehensively analyzing to obtain an extreme weather warning evaluation value, and performing extreme weather warning feedback according to the extreme weather warning evaluation value.

Description

Technical Field

[0001] The present invention relates to the technical field of extreme weather data processing, and in particular to a method and system for calculating an extreme weather index based on a convective-scale ensemble forecast. Background Art

[0002] As human activities continue to increase, greenhouse gas emissions continue to rise, causing the average temperature of the earth to continue to rise. This climate change is causing the frequency and intensity of extreme weather events to increase.

[0003] Existing extreme weather forecasts are achieved through steps such as data collection and processing, extreme weather index calculation, ensemble forecasting and result analysis, and result verification and application.

[0004] For example, the invention patent with publication number CN115688547A is for simulating weather scenarios and extreme weather prediction. A computer-implemented method for generating predictive weather events includes generating a training model by artificial intelligence by a computer processor. The training model is based on climate data processed by a variable autoencoder. A geographical location for climate research is selected. Historical weather measurements associated with the selected geographical location are retrieved from a climate knowledge database. The retrieved historical weather measurements are processed using the training model. Threshold parameters defining weather extremes are received by the training model. The extremes are based on the degree to which a weather intensity data point deviates from the norm more than it approaches the norm. Synthetic weather data is generated for the selected location, wherein the synthetic weather data predicts weather events that meet the extreme threshold parameters.

[0005] For example, the invention patent with publication number CN116894390A is a method and system for predicting wind speed in extreme weather conditions. It includes the following steps: using ViT to extract features from the wind speed subsequence, using LSTM to establish the connection between the extracted features and the output wind speed, and building a ViT-LSTM model for predicting wind speed in extreme weather conditions; training the built ViT-LSTM model for predicting wind speed in extreme weather conditions; and predicting the wind speed at the next moment based on the trained model.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, the acquisition of extreme weather indices mainly relies on weather forecast models. However, since the initial weather values ​​may have errors and the weather forecast models have many prediction results, it is difficult to timely discover the extreme weather indices that need attention, which may lead to the inability to effectively respond to the occurrence of extreme weather. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention provides a method and system for calculating an extreme weather index based on a convective-scale ensemble forecast, which can effectively solve the problems involved in the above-mentioned background technology.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a method for calculating extreme weather index based on convective scale ensemble forecast, including: obtaining a set of initial weather values, and according to the set of initial weather values, a convective scale ensemble forecast system processes to obtain a set of forecast results.

[0010] Obtain weather initial value set data, conduct comprehensive analysis to obtain the error assessment index of each initial value unit, further process to obtain the initial value set error assessment index, and obtain the extreme weather warning threshold based on the initial value set error assessment index.

[0011] The weather data of each forecast result is extracted from the forecast result set, and the weather data of each forecast result is processed to obtain the extreme weather assessment index of each forecast result, and each extreme weather forecast result is obtained by screening according to the initial value unit error assessment index and the extreme weather assessment index.

[0012] Count the number of extreme weather forecast results, and based on the extreme weather assessment index of each extreme weather forecast result, conduct a comprehensive analysis to obtain the extreme weather warning assessment value, and provide extreme weather warning feedback based on the extreme weather warning assessment value.

[0013] As a further method, the weather initial value set is obtained, and based on the weather initial value set, the convective scale ensemble forecast system is processed to obtain a forecast result set. The specific analysis process is: the weather data includes the air pressure change rate, wind speed, precipitation and affected range; the weather initial value set data includes each meteorological parameter of each initial value unit.

[0014] As a further method, the comprehensive analysis obtains the error evaluation index of each initial value unit, and further processing obtains the initial value set error evaluation index. The specific analysis process is: obtaining the actual value of each meteorological parameter in the weather initial value set data, and obtaining the error evaluation index of each initial value unit based on the weather initial value set data through comprehensive analysis; marking the average value of each initial value unit error evaluation index as the initial value set error evaluation index.

[0015] As a further method, the extreme weather warning threshold is obtained according to the initial value set error evaluation index processing. The specific analysis process is: matching the initial value set error evaluation index with the extreme weather warning threshold correction parameters corresponding to each initial value set error evaluation index interval preset in the extreme weather database to obtain the extreme weather warning threshold correction parameters; obtaining the initial extreme weather warning threshold from the extreme weather database; adding the initial extreme weather warning threshold and the extreme weather warning threshold correction parameters to obtain the extreme weather warning threshold.

[0016] As a further method, the weather data of each forecast result is processed to obtain the extreme weather assessment index of each forecast result. The specific analysis process is: extracting the critical air pressure change rate, critical wind speed, critical precipitation and critical impact range from the extreme weather database; based on the weather data of each forecast result, a comprehensive analysis is performed to obtain the extreme weather assessment index of each forecast result.

[0017] As a further method, the extreme weather forecast results are obtained by screening according to the initial value unit error evaluation index and the extreme weather evaluation index. The specific analysis process is: obtaining each initial value unit error evaluation index, obtaining the initial value unit error evaluation index of each forecast result according to the one-to-one correspondence between each forecast result and each initial value unit, comparing the initial value unit error evaluation index of each forecast result with the initial value unit error evaluation index threshold preset in the extreme weather database, and marking the forecast result corresponding to the initial value unit error evaluation index being less than the initial value unit error evaluation index threshold as a qualified forecast result, and counting the qualified forecast results; comparing the extreme weather evaluation index of each qualified forecast result with the extreme weather evaluation index threshold, if the extreme weather evaluation index of a qualified forecast result is greater than or equal to the extreme weather evaluation index threshold, marking the qualified forecast result as an extreme weather forecast result; and obtaining the extreme weather forecast results by statistics.

[0018] As a further method, the comprehensive analysis obtains an extreme weather warning assessment value. The specific analysis process is: based on the number of extreme weather forecast results and the extreme weather assessment index of each extreme weather forecast result, a comprehensive analysis is performed to obtain an extreme weather warning assessment value, and the extreme weather warning assessment value is used to quantitatively assess the degree of need for extreme weather warnings.

[0019] As a further method, the extreme weather warning feedback is performed according to the extreme weather warning evaluation value. The specific analysis process is: comparing the extreme weather warning evaluation value with the extreme weather warning threshold. If the extreme weather warning evaluation value is greater than or equal to the extreme weather warning threshold, an extreme weather warning is issued.

[0020] The second aspect of the present invention provides an extreme weather index calculation system based on convective scale ensemble forecast, including: a data acquisition module for acquiring a set of initial weather values, and a convective scale ensemble forecast system processes the set of initial weather values ​​to obtain a set of forecast results.

[0021] The initial value set analysis module is used to obtain the weather initial value set data, comprehensively analyze and obtain the error evaluation index of each initial value unit, further process to obtain the initial value set error evaluation index, and obtain the extreme weather warning threshold based on the initial value set error evaluation index.

[0022] The forecast result evaluation module is used to extract the weather data of each forecast result from the forecast result set, process the weather data of each forecast result, obtain the extreme weather evaluation index of each forecast result, and screen each extreme weather forecast result according to the initial value unit error evaluation index and the extreme weather evaluation index.

[0023] The weather warning module is used to count the number of extreme weather forecast results, and to obtain the extreme weather warning evaluation value based on the extreme weather evaluation index of each extreme weather forecast result through comprehensive analysis, and to provide extreme weather warning feedback based on the extreme weather warning evaluation value.

[0024] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0025] (1) The present invention provides an extreme weather index calculation method and system based on convective-scale ensemble forecasts to evaluate extreme weather, improve the accuracy and timeliness of extreme weather forecasts, provide extreme weather probability information, help implement extreme weather prevention strategies, help improve the efficiency of resource allocation, and help understand extreme weather mechanisms and improve forecast models.

[0026] (2) The present invention obtains the extreme weather warning threshold by matching the initial value set error evaluation index, can reasonably match the threshold according to the error, improve the accuracy of extreme weather warning, reduce false alarms and missed alarms, make the warning more scientific and reasonable, make the warning threshold more in line with the actual situation, and effectively deal with extreme weather hazards.

[0027] (3) The present invention can comprehensively consider the factors affecting extreme weather and improve the accuracy of the extreme weather assessment index by conducting a comprehensive analysis of the air pressure change rate, wind speed, precipitation and impact range of extreme weather.

[0028] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0030] Figure 2 It is a schematic diagram of system module connection of the present invention.

[0031] Figure 3 It is a functional relationship diagram of the extreme weather assessment index and the extreme weather warning assessment value of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0034] Reference Figure 1 As shown, the first aspect of the present invention provides a method for calculating an extreme weather index based on a convective-scale ensemble forecast, comprising: obtaining a set of initial weather values, and obtaining a set of forecast results by processing the set of initial weather values ​​using a convective-scale ensemble forecast system.

[0035] Specifically, a set of initial weather values ​​is obtained, and based on the set of initial weather values, the convective-scale ensemble forecast system is processed to obtain a set of forecast results. The specific analysis process is as follows: weather data includes air pressure change rate, wind speed, precipitation and affected range; the weather initial value set data includes various meteorological parameters of each initial value unit, wherein each meteorological parameter includes temperature, humidity, air pressure, cloud thickness, precipitation and wind speed.

[0036] In this embodiment, the convective scale ensemble system is an ensemble forecasting method for weather forecasting in the field of meteorology. Ensemble forecasting is to combine multiple different forecast results together. The convective scale ensemble system is mainly aimed at convective scale weather phenomena. The convective scale usually refers to a weather system with a horizontal scale ranging from several kilometers to tens of kilometers, a vertical scale ranging from hundreds of meters to more than ten kilometers, and a time scale ranging from tens of minutes to hours. The convective scale ensemble system is obtained by integrating multiple meteorological models. By deeply mining and analyzing historical weather data, a mathematical model is established. The convective scale ensemble system is constructed based on mathematical models, physical principles and preset numerical methods. For example, by assimilating data based on the WRF three-dimensional variational module and actual observations, the initial analysis value is obtained, wherein WRF (Weather Research and Forecasting Model, Weather Research and Forecasting Model) is a mesoscale numerical weather forecast model jointly developed by the National Center for Atmospheric Research and the National Center for Environmental Prediction of the United States; such as using the MICAPS4 (Meteorological Information Comprehensive Analysis and Processing System 4, the fourth generation of the meteorological information comprehensive analysis and processing system) client ensemble forecasting function module, the initial value is output to obtain the forecast result.

[0037] It should be understood that in this embodiment, the weather initial value set refers to a set of initial values ​​with differences generated in numerical weather forecasting. The convective scale set system is achieved by inputting the weather initial value set data in the numerical model. The weather initial value set is within the allowable range of observation error, and then using the numerical model to output a forecast result set with differences. The forecaster estimates the probability of extreme weather based on the forecast result set. The forecast result set refers to a set of different forecast results obtained for the same effective forecast time. The weather initial value set includes initial value units, and the initial value unit includes meteorological parameters. The initial value unit is a component of the weather initial value set. A set of meteorological parameters is collected through actual observation, and each meteorological parameter is changed within a preset allowable deviation range to obtain each initial value unit.

[0038] Obtain weather initial value set data, conduct comprehensive analysis to obtain the error assessment index of each initial value unit, further process to obtain the initial value set error assessment index, and obtain the extreme weather warning threshold based on the initial value set error assessment index.

[0039] Specifically, a comprehensive analysis is performed to obtain the error evaluation index of each initial value unit, which is then further processed to obtain the initial value set error evaluation index. The specific analysis process is: obtaining the actual value of each meteorological parameter in the weather initial value set data, and obtaining the error evaluation index of each initial value unit through a comprehensive analysis based on the weather initial value set data.

[0040] It should be understood that the initial value set error evaluation index in this embodiment is used to quantify the degree of deviation between the initial value set and the actual real situation. The actual deviation value of the meteorological parameter refers to the absolute difference between the actual value of the meteorological parameter and the meteorological parameter of each initial value unit. The actual value of the meteorological parameter is obtained through ground meteorological station observation, high-altitude meteorological detection and satellite remote sensing observation.

[0041] In a specific embodiment, the initial value unit error evaluation index is obtained as follows:

[0042] ;

[0043] In the formula, represents the error evaluation index of the tth initial value unit, represents the jth meteorological parameter of the tth initial value unit, represents the average value of the jth meteorological parameter, represents the actual value of the jth meteorological parameter, represents the actual deviation value of the jth meteorological parameter of the tth initial value unit, Indicates the error assessment impact weight corresponding to the preset maximum deviation value of the meteorological parameter, It represents the error assessment impact weight corresponding to the actual deviation value of the preset meteorological parameter, t represents the number of each initial value unit, t=1, 2, 3, ..., m, m represents the total number of weather initial value units, j represents the number of each meteorological parameter, j=1, 2, 3, ..., n, n represents the total number of meteorological parameters.

[0044] In this embodiment and It is the error assessment impact weight corresponding to the meteorological parameters preset in the extreme weather database and the actual deviation value of the meteorological parameters, which represents the numerical value of the influence degree of the error assessment corresponding to the meteorological parameters and the actual deviation value of the meteorological parameters on the error assessment index of the initial value set, and can be directly obtained from the extreme weather database when used. For example, the average temperature of extreme weather and the error assessment impact weight corresponding to the meteorological parameters preset in the extreme weather database and the actual deviation value of the meteorological parameters form a mapping set, and the error assessment impact weight corresponding to the preset meteorological parameters and the actual deviation value of the meteorological parameters is obtained according to the average temperature of extreme weather input mapping set, wherein the mapping relationship is a one-to-one relationship. The value range of the impact weight in this embodiment is between 0 and 1.

[0045] In this embodiment, the initial value unit error evaluation index is used to judge the degree of deviation between each initial value unit data (meteorological parameter) in the weather initial value set and the actual situation. The larger the actual deviation value of the meteorological parameter, the larger the initial value unit error evaluation index, indicating that the error between the initial value unit data and the actual value is greater.

[0046] The algorithm of this embodiment combines various meteorological parameters, and comprehensively analyzes to obtain the initial value unit error evaluation index. In this formula, various meteorological parameters will affect each other. Various meteorological parameters are interconnected through physical processes. For example, there is a close relationship between air pressure and wind speed. According to the principle of pressure gradient force, the spatial difference of air pressure is the main driving force for generating wind. When the air pressure value of a certain area in the initial value set of air pressure is relatively low relative to the surrounding area, a pressure gradient force pointing to the low pressure area will be formed, thereby causing the initial value of wind speed to increase in this direction. Similarly, temperature and air pressure are also interrelated, and changes in temperature will cause changes in air pressure. Humidity will also affect other meteorological parameters. During the rising process of high humidity air, water vapor condenses and releases latent heat, affecting the vertical distribution of temperature, thereby affecting the vertical movement of air pressure and air, and producing a chain reaction on meteorological parameters such as wind speed and precipitation. The mutual correlation between meteorological parameters shows that the weather system is an organic whole. In the initial value set, the mutual influence of various meteorological parameter data reflects the integrity of the weather system. By comprehensively analyzing the data of various meteorological parameters, the initial value unit error evaluation index can be accurately obtained to quantitatively describe the degree of error between the initial value unit data and the true value.

[0047] Specifically, the average value of the initial value unit error evaluation index is marked as the initial value set error evaluation index.

[0048] It should be understood that, in this embodiment, the initial value set error evaluation index is used to quantitatively evaluate the degree of deviation between the initial value set and the actual true situation.

[0049] Specifically, the extreme weather warning threshold is obtained according to the initial value set error evaluation index processing. The specific analysis process is: matching the initial value set error evaluation index with the extreme weather warning threshold correction parameters corresponding to each initial value set error evaluation index interval preset in the extreme weather database to obtain the extreme weather warning threshold correction parameters.

[0050] It should be understood that in the extreme weather database in this embodiment, the preset initial value set error evaluation index interval is to divide the error evaluation index into different ranges. These intervals are determined based on historical data, meteorological model simulation results, expert experience and other factors. For example, a low error interval (such as 0-0.2), a medium error interval (0.2-0.5) and a high error interval (0.5-1) may be divided, and each interval represents a different degree of quality of the initial value set data. The low error interval indicates that the initial data is closer to the actual situation and the data quality is higher; the high error interval means that the initial data may have a large deviation, which may have a greater impact on the accuracy of subsequent extreme weather warnings and forecasts. These intervals help to classify and evaluate the initial value set data. The extreme weather warning threshold correction parameter is a value used to adjust the extreme weather warning threshold. The extreme weather warning threshold correction parameter is obtained based on the matching result of the initial value set error evaluation index and the preset interval in the extreme weather database. The purpose is to correct the initial extreme weather warning threshold to better reflect the actual situation of possible extreme weather.

[0051] Obtain the initial extreme weather warning threshold from the extreme weather database.

[0052] It should be understood that the initial extreme weather warning threshold in this embodiment is a critical value preliminarily preset in the extreme weather database, which is used to preliminarily determine whether extreme weather is likely to occur and is a basic reference standard for extreme weather warnings.

[0053] The initial extreme weather warning threshold is added to the extreme weather warning threshold correction parameter to obtain the extreme weather warning threshold.

[0054] It should be understood that the extreme weather warning threshold in this embodiment is a critical value used to determine whether to issue an extreme weather warning and to determine the level of the warning.

[0055] The weather data of each forecast result is extracted from the forecast result set, and the weather data of each forecast result is processed to obtain the extreme weather assessment index of each forecast result, and each extreme weather forecast result is obtained by screening according to the initial value unit error assessment index and the extreme weather assessment index.

[0056] Specifically, the weather data of each forecast result is processed to obtain the extreme weather assessment index of each forecast result. The specific analysis process is: extracting the critical air pressure change rate, critical wind speed, critical precipitation and critical impact range from the extreme weather database.

[0057] It should be understood that the critical air pressure change rate in this embodiment refers to the critical value of the air pressure change rate at which the air pressure change may trigger or intensify extreme weather phenomena (such as storms, hurricanes, tornadoes, etc.). The critical wind speed refers to the critical value of the wind speed at which the wind may trigger extreme weather events (such as strong wind disasters) or have a significant impact. The critical precipitation refers to the critical value of precipitation that may trigger extreme weather events (such as floods, mudslides, landslides, etc.) or have a significant catastrophic impact. The critical impact range refers to the critical value of the impact area of ​​extreme weather events, including the areas directly impacted and the critical value of the impact range of surrounding areas that may be indirectly affected.

[0058] Based on the weather data of each forecast result, a comprehensive analysis is conducted to obtain the extreme weather assessment index of each forecast result.

[0059] In a specific embodiment, the extreme weather assessment index is obtained as follows:

[0060] ;

[0061] In the formula, represents the extreme weather assessment index of the i-th forecast result, represents the rate of change of air pressure of the i-th forecast result, represents the wind speed of the i-th forecast result, represents the precipitation of the i-th forecast result, represents the influence range of the i-th forecast result, represents the critical pressure change rate, represents the critical wind speed, represents the critical precipitation, represents the critical influence range, Indicates the preset air pressure change rate influence weight, Indicates the preset wind speed influence weight, Indicates the preset precipitation impact weight, represents the preset impact weight of the impact range, i represents the number of the extreme weather forecast result, i=1, 2, 3, ..., k, k represents the total number of extreme weather forecast results.

[0062] In this embodiment , , and They are respectively the influence weights of the extreme weather forecast results corresponding to the pressure change rate, wind speed, precipitation and impact range preset in the extreme weather database, and respectively represent the numerical values ​​of the influence degree of the pressure change rate, wind speed, precipitation and impact range on the extreme weather assessment index, and can be directly obtained from the extreme weather database when used. For example, the average temperature of extreme weather and the influence weights of the extreme weather forecast results corresponding to the pressure change rate, wind speed, precipitation and impact range preset in the extreme weather database form a mapping set, and the mapping set is input according to the average temperature of extreme weather to obtain the influence weights of the extreme weather forecast results corresponding to the pressure change rate, wind speed, precipitation and impact range, wherein the mapping relationship is a one-to-one relationship. In this embodiment, the value range of the influence weight is between 0 and 1.

[0063] In this embodiment, the extreme weather assessment index is used to quantitatively assess the impact of extreme weather events. The greater the rate of change of air pressure, wind speed, precipitation and impact range, the larger the extreme weather assessment index, indicating that the impact of weather data on extreme weather is greater.

[0064] The algorithm of this embodiment combines the pressure change rate, wind speed, precipitation and influence range, and comprehensively analyzes to obtain the extreme weather assessment index. In this formula, the pressure change rate, wind speed, precipitation and influence range will affect each other. The greater the pressure change rate, the stronger the pressure gradient force. The pressure gradient force pushes the air from the high pressure area to the low pressure area, which leads to an increase in wind speed, and the air pressure rises rapidly, the air sinks, the weather is generally dry, and the precipitation will decrease, and the pressure gradient around it will cause the air to flow in a wide area. Strong winds can promote the transport of water vapor. At the same time, wind can also affect the distribution of precipitation. For example, the windward slope of a mountain range is often more abundant due to the forced lifting effect of the wind. The faster the wind speed, the more the impact of extreme weather can be spread to a farther place. By comprehensively analyzing the pressure change rate, wind speed, precipitation and influence range, the extreme weather assessment index can be accurately obtained, and the degree of influence of weather data on extreme weather can be quantitatively described.

[0065] Specifically, each extreme weather forecast result is obtained by screening according to the initial value unit error evaluation index and the extreme weather evaluation index. The specific analysis process is: obtain each initial value unit error evaluation index, obtain the initial value unit error evaluation index of each forecast result according to the one-to-one correspondence between each forecast result and each initial value unit, compare the initial value unit error evaluation index of each forecast result with the initial value unit error evaluation index threshold preset in the extreme weather database, and mark the forecast result corresponding to the initial value unit error evaluation index less than the initial value unit error evaluation index threshold as a qualified forecast result, and count the qualified forecast results.

[0066] It should be understood that the forecast results in this embodiment are obtained based on the meteorological parameters in the initial value unit, and each forecast result has its corresponding initial value unit. By comparing the initial value unit error evaluation index of each forecast result with the initial value unit error evaluation index threshold preset in the extreme weather database, if the initial value unit error evaluation index of a forecast result is greater than or equal to the initial value unit error evaluation index threshold preset in the extreme weather database, the forecast result is marked as an unqualified forecast result; if the initial value unit error evaluation index of a forecast result is less than the initial value unit error evaluation index threshold preset in the extreme weather database, the forecast result is marked as a qualified forecast result, and each qualified forecast result is counted.

[0067] The extreme weather assessment index of each qualified forecast result is compared with the extreme weather assessment index threshold. If the extreme weather assessment index of a qualified forecast result is greater than or equal to the extreme weather assessment index threshold, the qualified forecast result is marked as an extreme weather forecast result.

[0068] It should be understood that in this embodiment, the extreme weather assessment index corresponding to each forecast result is compared with the extreme weather assessment index threshold to determine the forecast result that meets the extreme weather conditions. If the extreme weather assessment index of a forecast result is greater than or equal to the extreme weather assessment index threshold, it can be determined that the current weather conditions belong to extreme weather events, and the forecast result is marked as an extreme weather forecast result; if the extreme weather assessment index of a forecast result is less than the extreme weather assessment index threshold, it can be determined that the current weather conditions belong to general weather conditions, and the forecast result is marked as a general weather forecast result.

[0069] The extreme weather forecast results are obtained by statistics.

[0070] It should be understood that the extreme weather forecast results in this embodiment refer to the forecast results for the same area and the same time.

[0071] Count the number of extreme weather forecast results, and based on the extreme weather assessment index of each extreme weather forecast result, conduct a comprehensive analysis to obtain the extreme weather warning assessment value, and provide extreme weather warning feedback based on the extreme weather warning assessment value.

[0072] Specifically, a comprehensive analysis is performed to obtain an extreme weather warning assessment value, and the specific analysis process is: based on the number of extreme weather forecast results and the extreme weather assessment index of each extreme weather forecast result, a comprehensive analysis is performed to obtain an extreme weather warning assessment value, and the extreme weather warning assessment value is used to quantitatively assess the degree of need for extreme weather warnings.

[0073] It should be understood that the number of extreme weather forecast results in this embodiment reflects the probability of extreme weather events. If there are a large number of extreme weather forecast results in a certain time period or region, this indicates that the possibility of extreme weather is relatively high. Then, when calculating the extreme weather warning evaluation value, this factor will cause the evaluation value to have an increasing trend. For example, if there are 5 extreme weather forecast results in a city in one day, it is more important to pay attention to it than to have only 1 extreme weather forecast result, and the warning evaluation value will also increase accordingly. The extreme weather evaluation index of each extreme weather forecast result reflects the severity of the extreme weather event. The higher the evaluation index, the greater the intensity of the extreme weather and the greater the possible harm. In the comprehensive analysis, a high extreme weather evaluation index will significantly increase the extreme weather warning evaluation value. The initial value set error evaluation index reflects the quality of the initial data used for weather forecasting. The smaller the initial value set error evaluation index, the closer the initial data is to the actual situation, and the higher the credibility of the forecast result; conversely, a larger error evaluation index means that the uncertainty of the forecast result increases. In the calculation of extreme weather warning assessment values, the smaller the initial value set error assessment index, the more inclined the extreme weather warning assessment value will be to believe the forecast results, which may improve the warning assessment value.

[0074] In a specific embodiment, the extreme weather warning evaluation value is obtained as follows:

[0075] ;

[0076] In the formula, represents the extreme weather warning assessment value, represents the extreme weather assessment index of the qth extreme weather forecast result, Indicates the preset extreme weather assessment index impact weight, Indicates the total number of forecast results in the forecast result set, Indicates the number of extreme weather forecast results, Indicates the influence weight of the preset number of extreme weather forecasts, Indicates the number of extreme weather events that occurred during the monitoring period. Indicates the critical value of the number of extreme weather events in the preset time period in the database. represents the influence weight of the preset number of extreme weather historical forecast results, q represents the number of each extreme weather forecast result, q=1, 2, 3, ..., k, k represents the total number of extreme weather forecast results.

[0077] It should be understood that the number of extreme weather forecast results in this embodiment refers to the number of forecast results in the same area at the same time. The critical value refers to the critical value of the number of extreme weather events allowed to occur within a preset time period.

[0078] In a specific embodiment, Figure 3 As shown, = =0.3, =0.4, =1, =5, =30, when =15, the functional relationship between the extreme weather assessment index and the extreme weather warning assessment value is shown in curve a; =10, the functional relationship between the extreme weather assessment index and the extreme weather warning assessment value is shown in curve b; =6, the functional relationship between the extreme weather assessment index and the extreme weather warning assessment value is shown in curve c.

[0079] It should be understood that in this embodiment , and The extreme weather warning impact weights corresponding to the extreme weather assessment index preset in the extreme weather database, the number of extreme weather forecasts, and the number of historical occurrences of extreme weather in the monitoring period, respectively, represent the numerical values ​​of the degree of influence of the extreme weather assessment index, the number of extreme weather forecasts, and the number of historical occurrences of extreme weather in the monitoring period on the extreme weather warning assessment value, and can be directly obtained from the extreme weather database when used. For example, the average temperature of extreme weather and the environmental interference degree impact weights corresponding to the extreme weather assessment index, the number of extreme weather forecasts, and the number of historical occurrences of extreme weather in the monitoring period preset in the extreme weather database form a mapping set, and the mapping set is input according to the average temperature of extreme weather to obtain the extreme weather warning assessment value impact weight corresponding to the extreme weather assessment index, the number of extreme weather forecasts, and the number of historical occurrences of extreme weather in the monitoring period, wherein the mapping relationship is a one-to-one relationship. In this embodiment, the value range of the impact weight is between 0 and 1.

[0080] In this embodiment, the number of extreme weather events in history during the monitoring period refers to the total number of various extreme weather events that actually occurred in a certain area during a specific monitoring period. If the number of extreme weather events in history during the monitoring period is greater than or equal to the critical number of extreme weather events in history during the monitoring period preset in the database, it means that the probability of extreme weather occurring in the monitoring period is high; if the number of extreme weather events in history during the monitoring period is less than the critical number of extreme weather events in history during the monitoring period preset in the database, it means that the probability of extreme weather occurring in the monitoring period is low. The extreme weather warning assessment value is used to quantitatively assess the need for extreme weather warnings. The greater the extreme weather assessment index and the number of extreme weather forecasts, the greater the extreme weather warning assessment value, indicating that the need for extreme weather warnings is greater.

[0081] The algorithm of this embodiment combines the extreme weather assessment index, the number of extreme weather forecasts, and the number of historical occurrences of extreme weather during the monitoring period, and comprehensively analyzes to obtain the extreme weather warning assessment value. In this formula, the extreme weather assessment index, the number of extreme weather forecasts, and the number of historical occurrences of extreme weather during the monitoring period will affect each other. The extreme weather assessment index is used to quantitatively assess the degree of extreme weather events. The larger the extreme weather assessment index, the higher the quality of the initial data, the number of extreme weather forecasts will increase accordingly, and the number of extreme weather historical forecast results will also increase accordingly. By comprehensively analyzing the extreme weather assessment index, the number of extreme weather forecasts, and the number of historical occurrences of extreme weather during the monitoring period, the extreme weather warning assessment value can be accurately obtained, and the degree of demand for extreme weather warnings can be quantitatively described.

[0082] Specifically, extreme weather warning feedback is performed based on the extreme weather warning evaluation value. The specific analysis process is: comparing the extreme weather warning evaluation value with the extreme weather warning threshold. If the extreme weather warning evaluation value is greater than or equal to the extreme weather warning threshold, an extreme weather warning is issued.

[0083] It should be understood that in this embodiment, when the extreme weather warning evaluation value is less than the extreme weather warning threshold, although it has not reached the threshold for triggering a formal warning, real-time data monitoring is still required. Once the extreme weather warning evaluation value is greater than or equal to the extreme weather warning threshold, it is necessary to conduct a comprehensive and formal extreme weather warning feedback, and issue warning information to the public through multiple communication channels to ensure that the information can be widely and timely communicated. When the extreme weather warning evaluation value far exceeds the extreme weather warning threshold, in addition to the conventional feedback measures when the warning threshold is reached, it is also necessary to strengthen deployment for key areas where extreme weather may cause serious impacts (such as densely populated areas, locations of important infrastructure, mountainous areas prone to geological disasters, etc.). For example, increase the reserve of rescue forces and materials in densely populated urban centers, organize personnel evacuation in advance in mountainous areas prone to mudslides, and arrange special personnel to monitor geological changes. Update extreme weather warning information more frequently so that the public can keep abreast of the latest developments in extreme weather. For example, the warning information originally updated every 2 hours is changed to be updated every 1 hour so that the public can adjust preventive measures in a timely manner according to the latest situation.

[0084] Reference Figure 2 As shown, the second aspect of the present invention provides a method and system for calculating extreme weather index based on convective-scale ensemble forecast, including: a data acquisition module, a forecast result evaluation module, an initial value ensemble analysis module and a weather warning module.

[0085] The data acquisition module is used to obtain a set of initial weather values. Based on the set of initial weather values, the convective scale ensemble forecast system processes the set to obtain a set of forecast results.

[0086] The forecast result evaluation module is used to extract the weather data of each forecast result from the forecast result set, obtain the extreme weather evaluation index of each forecast result after processing, and obtain each extreme weather forecast result based on the extreme weather evaluation index.

[0087] The initial value set analysis module is used to obtain the weather initial value set data, comprehensively analyze and obtain the error evaluation index of each initial value unit, further process to obtain the initial value set error evaluation index, and obtain the extreme weather warning threshold based on the initial value set error evaluation index.

[0088] The weather warning module is used to count the number of extreme weather forecast results, and to obtain the extreme weather warning evaluation value based on the extreme weather evaluation index of each extreme weather forecast result through comprehensive analysis, and to provide extreme weather warning feedback based on the extreme weather warning evaluation value.

[0089] The extreme weather database is used to store relevant data for extreme weather forecasts, including indicators such as critical pressure change rate, critical wind speed, critical precipitation, critical impact range, and initial value ensemble error assessment index. The data in the extreme weather database can be obtained through official meteorological department platforms such as the China Meteorological Parameters Network and international meteorological parameter platforms such as the World Meteorological Organization (WMO) data exchange platform.

[0090] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. A method for calculating extreme weather index based on convective scale ensemble forecast, characterized in that: include: Obtain a set of initial weather values, and obtain a set of forecast results based on the set of initial weather values ​​processed by the convective scale ensemble forecast system; Obtain weather initial value set data, conduct comprehensive analysis to obtain the error evaluation index of each initial value unit, further process to obtain the initial value set error evaluation index, and process to obtain the extreme weather warning threshold based on the initial value set error evaluation index; Extracting weather data of each forecast result from the forecast result set, processing the weather data of each forecast result to obtain the extreme weather assessment index of each forecast result, and screening each extreme weather forecast result according to the initial value unit error assessment index and the extreme weather assessment index; Count the number of extreme weather forecast results, and obtain the extreme weather warning assessment value through comprehensive analysis based on the extreme weather assessment index of each extreme weather forecast result, and provide extreme weather warning feedback based on the extreme weather warning assessment value; The comprehensive analysis obtains the error evaluation index of each initial value unit, and further processing obtains the error evaluation index of the initial value set. The specific analysis process is as follows: Obtain the actual values ​​of each meteorological parameter in the weather initial value set data, and obtain the error evaluation index of each initial value unit through comprehensive analysis based on the weather initial value set data; The average value of the initial value unit error evaluation index is marked as the initial value set error evaluation index; The comprehensive analysis obtains the extreme weather warning assessment value, and the specific analysis process is as follows: According to the number of extreme weather forecast results and the extreme weather assessment index of each extreme weather forecast result, a comprehensive analysis is performed to obtain an extreme weather warning assessment value, which is used to quantitatively assess the degree of need for extreme weather warnings; The extreme weather warning assessment value is obtained in the following manner: ； In the formula, represents the extreme weather warning assessment value, represents the extreme weather assessment index of the qth extreme weather forecast result, Indicates the preset extreme weather assessment index impact weight, Indicates the total number of forecast results in the forecast result set, Indicates the number of extreme weather forecast results, Indicates the influence weight of the preset number of extreme weather forecasts, Indicates the number of extreme weather events that occurred during the monitoring period. Indicates the critical value of the number of extreme weather events in the preset time period in the database. represents the influence weight of the preset number of extreme weather historical forecast results, q represents the number of each extreme weather forecast result, q=1, 2, 3, ..., k, k represents the total number of extreme weather forecast results; The initial value unit error evaluation index is obtained as follows: ； In the formula, represents the error evaluation index of the tth initial value unit, represents the jth meteorological parameter of the tth initial value unit, represents the average value of the jth meteorological parameter, represents the actual value of the jth meteorological parameter, represents the actual deviation value of the jth meteorological parameter of the tth initial value unit, Indicates the error assessment impact weight corresponding to the preset maximum deviation value of the meteorological parameter, It represents the error assessment impact weight corresponding to the actual deviation value of the preset meteorological parameter, t represents the number of each initial value unit, t=1, 2, 3, ..., m, m represents the total number of weather initial value units, j represents the number of each meteorological parameter, j=1, 2, 3, ..., n, n represents the total number of meteorological parameters.

2. The method for calculating extreme weather index based on convective scale ensemble forecast according to claim 1, characterized in that: The weather initial value set is obtained, and according to the weather initial value set, the convective scale ensemble forecast system is processed to obtain a forecast result set. The specific analysis process is as follows: Weather data includes the rate of change of air pressure, wind speed, precipitation, and impact area; The weather initial value set data includes various meteorological parameters of each initial value unit.

3. The method for calculating extreme weather index based on convective scale ensemble forecast according to claim 1, characterized in that: The extreme weather warning threshold is obtained by processing the initial value set error evaluation index. The specific analysis process is as follows: The initial value set error evaluation index is matched with the extreme weather warning threshold correction parameter corresponding to each initial value set error evaluation index interval preset in the extreme weather database to obtain the extreme weather warning threshold correction parameter; Obtaining initial extreme weather warning thresholds from the extreme weather database; The initial extreme weather warning threshold is added to the extreme weather warning threshold correction parameter to obtain the extreme weather warning threshold.

4. The method for calculating extreme weather index based on convective scale ensemble forecast according to claim 1, characterized in that: The weather data of each forecast result is processed to obtain the extreme weather assessment index of each forecast result. The specific analysis process is as follows: Extract critical air pressure change rate, critical wind speed, critical precipitation and critical impact range from the extreme weather database; Based on the weather data of each forecast result, a comprehensive analysis is conducted to obtain the extreme weather assessment index of each forecast result.

5. The method for calculating extreme weather index based on convective scale ensemble forecast according to claim 3, characterized in that: The extreme weather forecast results are obtained by screening according to the initial value unit error evaluation index and the extreme weather evaluation index. The specific analysis process is as follows: Obtain the error evaluation index of each initial value unit, and obtain the initial value unit error evaluation index of each forecast result according to the one-to-one correspondence between each forecast result and each initial value unit, compare the initial value unit error evaluation index of each forecast result with the initial value unit error evaluation index threshold preset in the extreme weather database, and mark the forecast result corresponding to the initial value unit error evaluation index being less than the initial value unit error evaluation index threshold as a qualified forecast result, and count the qualified forecast results; The extreme weather assessment index of each qualified forecast result is compared with the extreme weather assessment index threshold value. If the extreme weather assessment index of a qualified forecast result is greater than or equal to the extreme weather assessment index threshold value, the qualified forecast result is marked as an extreme weather forecast result; The extreme weather forecast results are obtained by statistics.

6. The method for calculating extreme weather index based on convective scale ensemble forecast according to claim 1, characterized in that: The specific analysis process of providing extreme weather warning feedback based on the extreme weather warning evaluation value is as follows: The extreme weather warning assessment value is compared with the extreme weather warning threshold. If the extreme weather warning assessment value is greater than or equal to the extreme weather warning threshold, an extreme weather warning is issued.

7. A system using the extreme weather index calculation method based on convective scale ensemble forecast as claimed in any one of claims 1 to 6, characterized in that: include: Data acquisition module, initial value set analysis module, forecast result evaluation module and weather warning module; The data acquisition module is used to acquire a set of initial weather values, and the convective scale ensemble forecast system processes the set of initial weather values ​​to obtain a set of forecast results; The initial value set analysis module is used to obtain weather initial value set data, comprehensively analyze to obtain the error evaluation index of each initial value unit, further process to obtain the initial value set error evaluation index, and process to obtain the extreme weather warning threshold according to the initial value set error evaluation index; The forecast result evaluation module is used to extract the weather data of each forecast result from the forecast result set, process the weather data of each forecast result, obtain the extreme weather evaluation index of each forecast result, and screen each extreme weather forecast result according to the initial value unit error evaluation index and the extreme weather evaluation index; The weather warning module is used to count the number of extreme weather forecast results, and to obtain an extreme weather warning evaluation value based on a comprehensive analysis of the extreme weather evaluation index of each extreme weather forecast result, and to provide extreme weather warning feedback based on the extreme weather warning evaluation value.

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