An index method for characterizing the intensity of cold snaps

Abstract

The exponential method for characterizing the intensity of cold snaps in the present invention first proposes the CWSI index. By using an original method to calculate the intensity of cold snaps, it can quantitatively give the intensity index of cold snaps, which is convenient for the public and relevant decision-making departments to understand and judge the intensity of cold snaps. Moreover, the indicators are specific and the information standards are unified, which is conducive to improving the accuracy of the characterization results, enhancing the reference value of the characterization results, and having good applicability.

Description

Technical Field

[0001] The present invention relates to the technical field of cold wave intensity characterization, and particularly to an index method for characterizing the intensity of cold waves. Background Art

[0002] Cold waves are one of the important disastrous weather events affecting Jiangsu Province in winter half-year. In addition to causing drastic temperature drops, they are often accompanied by severe disastrous weather such as frost, strong winds, heavy snow, and freezing rain, often resulting in freezing damage and causing great losses to industrial and agricultural production and people's livelihood.

[0003] Currently, there is a cold wave index used to measure cold waves at the climate scale. However, since it is applicable to numerical climate models, it has no guiding significance for conventional weather forecasts, and there are no other characteristic indices for characterizing the intensity of cold waves. In the existing national standards, the intensity of cold waves is only divided into three levels: cold wave, strong cold wave, and super strong cold wave.

[0004] Among them, the definition of the "cold wave" level is "the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥8°C within 24 hours, or by ≥10°C within 48 hours, or by ≥12°C within 72 hours, and makes the daily minimum temperature in this place ≤4°C"; the definition of the "strong cold wave" level is "the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥10°C within 24 hours, or by ≥12°C within 48 hours, or by ≥14°C within 72 hours, and makes the daily minimum temperature in this place ≤2°C"; the definition of the "super strong cold wave" level is "the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥12°C within 24 hours, or by ≥14°C within 48 hours, or by ≥16°C within 72 hours, and makes the daily minimum temperature in this place ≤0°C".

[0005] The existing national standards only make a general judgment, unable to accurately reflect the temperature drop situation, nor can they compare and obtain the magnitude of the cold wave intensity between two places. Moreover, the "Names, Icons and Standards of Cold Wave Disaster Warning Signals" published on the official website of the China Meteorological Administration also has similar problems. In the existing published literature, the cold wave index is mainly defined using data from climate models: "The daily minimum temperature is more than 5 degrees above the reference average value, and the longest continuous time is more than 5 days". This definition can only characterize the duration of the cold wave, but does not characterize the cooling intensity of the cold wave. And because it is defined as a comparison with the reference average value, it can only reflect the duration of the cold wave compared with the same historical period, and cannot judge the cooling intensity of a single cold wave.

[0006] It can be seen that the traditional cold wave index only provides the duration of the cold wave, but does not have a quantitative characterization and analysis of the cooling intensity of the cold wave, etc., cannot quantitatively give the intensity index of the cold wave, and is not convenient for the public and relevant decision-making departments to understand and judge the intensity of the cold wave.

[0007] Therefore, those skilled in the art are committed to developing an index method for characterizing the intensity of cold snaps to address the deficiencies of the above-mentioned prior art. Summary of the Invention

[0008] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is that in the current prior art, the traditional cold snap index only provides the duration of the cold snap, but there is no quantitative characterization and analysis of the cooling intensity of the cold snap, etc., and it cannot quantitatively give the intensity index of the cold snap, which is not convenient for the public and relevant decision-making departments to understand and judge the intensity of the cold snap.

[0009] To achieve the above object, an index method for characterizing the intensity of cold snaps according to the present invention includes the following steps:

[0010] Step 1: Collect numerical climate prediction model data and numerical weather prediction model data;

[0011] Step 2: Preprocess the numerical climate prediction model data and numerical weather prediction model data;

[0012] Step 3: Perform model fusion on the preprocessed numerical climate prediction model data and numerical weather prediction model data to obtain a weather-climate integrated numerical prediction sequence;

[0013] Step 4: Calculate the cold snap intensity index CWSI (CWSI, Cold Wave Strength Index) through the cold snap intensity index calculation formula;

[0014] Step 5: According to the calculation result obtained in Step 4, perform cold snap intensity index analysis according to the cold snap warning standard;

[0015] Further, in Step 1, the collection of the data includes data collection by ordinary personnel and data collection by meteorological department users;

[0016] Further, in Step 1, the data collection by ordinary personnel includes collecting numerical climate prediction model data and numerical weather prediction model data by downloading through the official website or applying to the local meteorological department, etc., and the acquisition frequency is once a day;

[0017] Further, in Step 1, the data collection by meteorological department users includes obtaining climate model prediction data and numerical weather prediction model data through the internal network of the China Meteorological Administration, and the acquisition frequency is once a day;

[0018] Further, in step 2, the preprocessing method for the forecast data generated by the numerical climate prediction model is to perform spatial linear interpolation downscaling using the gridded minimum temperature forecast product of the second-generation dynamical extended range regional forecast model DERF (Dynamical Extension Regional Forecast Model) issued by the China Meteorological Administration, to obtain the DERF gridded minimum temperature forecast product with a spatial resolution of 10 km. If calculating the cold wave index only for a single-point location, then extract the single-point minimum temperature forecast product corresponding to the longitude and latitude in the gridded minimum temperature forecast product; in actual operation practice, products of other similar weather forecast models can be selected;

[0019] Further, in step 2, the preprocessing method for the forecast data generated by the numerical weather prediction model is to perform spatial linear interpolation downscaling using the forecast gridded minimum temperature forecast product of the China Meteorological Administration–Global Forecast System (CMA-GFS), to obtain the CMA-GFS gridded minimum temperature forecast product with a spatial resolution of 5 km; in actual operation practice, products of other similar weather forecast models can be selected;

[0020] Further, in step 2, if calculating the cold wave index only for a single-point location in the preprocessing, then extract the single-point minimum temperature forecast product corresponding to the longitude and latitude in the gridded minimum temperature forecast product;

[0021] Further, in step 3, the model fusion adopts the time-division series fusion method, using the preprocessed minimum temperature forecast products of CMA-GFS with a forecast period of days 1 to 10 and the preprocessed minimum temperature forecast products of DERF with a forecast period of days 11 to 30 to form a fused minimum temperature forecast product with a forecast period of 30 days;

[0022] Further, in step 3, if calculating the cold wave index only for a single-point location in the model fusion, then extract the single-point fused minimum temperature forecast product corresponding to the longitude and latitude in the gridded fused minimum temperature forecast product;

[0023] Further, in step 4, the calculation formula for the cold wave strength index CWSI is

[0024]

[0025] where CWSI n is the cold wave strength index on the nth day;

[0026] is the difference between the lowest temperature on the (n - 1)-th day and the lowest temperature on the next day (the lowest temperature on the n-th day);

[0027] is the lowest temperature on the n-th day;

[0028] Furthermore, in the step 4, the calculation formula is

[0029]

[0030] where is the difference between the lowest temperature on the (n - 1)-th day and the lowest temperature on the next day (the lowest temperature on the n-th day);

[0031] is the lowest temperature on the n-th day;

[0032] is the lowest temperature on the (n - 1)-th day;

[0033] Furthermore, in the step 5, the cold wave warning standard includes the recognition threshold for a blue cold wave warning;

[0034] Furthermore, in the step 5, the recognition threshold for a blue cold wave warning is that the temperature drop between the lowest temperatures of two adjacent days is greater than or equal to 8 °C or the lowest temperature drops to 4 °C;

[0035] Furthermore, in the step 5, the cold wave warning standard is that the CWSI value corresponding to the recognition threshold for a blue cold wave warning is 0.8; the CWSI value corresponding to the recognition threshold for a yellow cold wave warning is 1; the CWSI value corresponding to the recognition threshold for an orange cold wave warning is 1.5; the CWSI value corresponding to the recognition threshold for a red cold wave warning is 2;

[0036] Furthermore, in the step 5, the greater the CWSI value, the greater the cold wave intensity;

[0037] Adopting the above scheme, the index method for characterizing the cold wave intensity disclosed by the present invention has the following advantages:

[0038] (1) For the index method for characterizing the cold wave intensity of the present invention, the CWSI index is proposed for the first time, and the cold wave intensity is calculated by an original method, which can quantitatively give the intensity index of the cold wave, facilitating the public and relevant decision-making departments to understand and judge the cold wave intensity. For example, although it reaches the national standard "cold wave" level, there are still differences in the cooling intensity. Traditional warnings only give qualitative conclusions, but the CWSI index can give quantitative conclusions;

[0039] (2) The index method for characterizing the cold wave intensity of the present invention has specific indicators and unified information standards, which is beneficial to improving the accuracy of the characterization results, enhancing the reference value of the characterization results, and has good applicability.

[0040] In summary, the index method for characterizing the cold wave intensity disclosed by the present invention first proposes the CWSI index. The cold wave intensity is calculated through an original method, and the intensity index of the cold wave can be quantitatively given, which is convenient for the public and relevant decision-making departments to understand and judge the cold wave intensity; moreover, the indicators are specific and the information standards are unified, which is beneficial to improving the accuracy of the characterization results, enhancing the reference value of the characterization results, and has good applicability.

[0041] The following will further illustrate the concept, specific technical solutions and technical effects of the present invention in combination with specific embodiments to fully understand the purpose, features and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of the CWSI index method for characterizing the cold wave intensity of the present invention;

[0043] Figure 2 It is a CWSI diagram showing the variation of the CWSI for characterizing the cold wave intensity of the present invention with the "change value of the minimum temperature in 24 hours" and the "minimum temperature"; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The following introduces multiple preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and these embodiments are described by way of example. The protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0045] Example 1. Characterize and analyze the cold wave intensities of places A and B through the index method for characterizing the cold wave intensity as Figure 1 、 Figure 2 shown,

[0046] Step 1: Obtain the temperature forecast products of the numerical climate forecast model (the second-generation dynamical extended-range regional forecast model DERF (Dynamical Extension Regional Forecast Model)) forecasted on September 1, 20** through the internal transmission method within the meteorological department; obtain the temperature forecast products of the numerical weather forecast model (China Meteorological Administration - Global Forecast System CMA-GFS (China Meteorological Administration - Global Forecast System)); among them, the forecast period of DERF is from September 1 to September 30; the forecast period of CMA-GFS is from September 1 to September 10, and the spatial range selects the 10°×10° space enclosed by four points (30°N, 115°E), (30°N, 125°E), (40°N, 115°E), and (40°N, 125°E) as the case;

[0047] The forecast period of the numerical model will be adjusted with the development of technology, but it does not affect the implementation of this method; at the same time, the forecast model can also be replaced by DERF with other similar climate prediction models, and CMA-GFS can be replaced by other similar weather forecast models;

[0048] Step 2: Since the current spatial resolution of DERF is 2.5°×2.5°, linear interpolation is used to preprocess the DERF forecast products and increase its resolution to 0.05°×0.05°. Since the current spatial resolution of CMA-GFS is 0.1°×0.1°, linear interpolation is used to preprocess the CMA-GFS forecast products and increase its resolution to 0.05°×0.05°;

[0049] Before preprocessing, within the 10°×10° area range, the DERF forecast products are grid forecast products of a 4×4 matrix; after preprocessing, they are grid forecast products of a 200×200 matrix;

[0050] After preprocessing, within the same 10°×10° area range, the CMA-GFS forecast products are grid forecast products of a 100×100 matrix; after preprocessing, they are grid forecast products of a 200×200 matrix;

[0051] After preprocessing, the DERF grid forecast products and the CMA-GFS grid forecast products have the same matrix size, thus forming two sets of spatially matched forecast products.

[0052] Step 3: Through the time-division series fusion method, fuse the preprocessed numerical climate prediction model temperature prediction products and the numerical weather prediction model temperature prediction products to obtain a sequence of weather-climate integrated temperature prediction products with a prediction period of 1 to 30 days;

[0053] Step 4: As can be seen from the weather-climate integrated numerical prediction sequence in Step 3, after Step 3, the formed weather-climate integrated temperature prediction product sequence is a three-dimensional matrix, and the three dimensions are: longitude, latitude, and time. In the example data, the longitude dimension is 200, the latitude dimension is 200, and the time dimension is 30.

[0054] In the specific calculation process, it is necessary to calculate the CWSI of each of the 40,000 points of 200*200 one by one for each day. Taking the minimum temperature predictions of Place A and Place B represented by two of the 40,000 points on the 3rd and 4th days as an example for calculation.

[0055] In the 10-day investigation in the first stage, the minimum temperature on the 4th day is: for Place A: 4°C; for Place B: 5°C; the minimum temperature on the 3rd day is: for Place A: 12°C; for Place B: 20°C;

[0056] In the 10-day investigation in the second stage, the minimum temperature on the 8th day is: for Place A: 3°C; for Place B: -3°C; the minimum temperature on the 7th day is: for Place A: 11°C; for Place B: 4°C;

[0057] Calculate the cold wave intensity index CWSI (CWSI, Cold Wave Strength Index) through the cold wave intensity index calculation formula; the cold wave intensity index CWSI calculation formula is

[0058]

[0059] where, CWSI n is the cold wave intensity index on the nth day;

[0060] is the difference between the minimum temperature on the (n - 1)th day and the minimum temperature on the next day, the nth day (the minimum temperature on the nth day);

[0061] is the minimum temperature on the nth day;

[0062] The calculation formula is

[0063]

[0064] where, is the difference between the minimum temperature on the (n - 1)-th day and the minimum temperature on the next day (the minimum temperature on the n-th day);

[0065] is the minimum temperature on the n-th day;

[0066] is the minimum temperature on the (n - 1)-th day;

[0067] It can be obtained that

[0068] In the 10-day investigation in the first stage,

[0069] Area A Area B

[0070] CWSI of Area A n = 0.8; CWSI of Area B n = 1.39;

[0071] In the 10-day investigation in the second stage,

[0072] Area A Area B

[0073] CWSI of Area A n = 0.97; CWSI of Area B n = 1.06;

[0074] In the investigations of the first stage and the second stage, Area A has reached the level of issuing a blue cold wave warning signal, while Area B fails to meet the requirements for issuing a blue cold wave warning signal. However, in the first-stage investigation, the temperature drop in Area B reached 15°C in 24 hours, and in the second-stage investigation, the temperature in Area B dropped to -3°C. Both of these situations have a greater impact on production and life, but according to the existing requirements for cold wave warning signals, relevant warnings cannot be issued. However, from the perspective of the cold wave intensity index CWSI, the CWSI of Area B in both the first stage and the second stage is greater than that of Area A, indicating that both the large temperature drop in the first stage and the relatively low minimum temperature in the second stage in Area B are reflected by CWSI. It can be seen that the CWSI proposed by the present invention can effectively make up for the deficiencies in the existing warning signal standards;

[0075] Step 5: According to the cold wave warning standard, conduct an analysis of the cold wave intensity index; the cold wave warning standard is that when 0.8 ≤ CWSI < 1 in 24 hours, it corresponds to a blue cold wave warning; when 1 ≤ CWSI < 1.5, it corresponds to a yellow cold wave warning; when 1.5 ≤ CWSI < 2, it corresponds to an orange cold wave warning; when CWSI ≥ 2, it corresponds to a red cold wave warning;

[0076] According to the calculation results obtained in Step 4,

[0077] In the 10-day investigation of the first stage,

[0078] Area A

[0079] Area B

[0080] CWSI of Area A n = 0.8; CWSI of Area B n = 1.39;

[0081] Corresponding to the blue cold wave warning for Area A; the yellow cold wave warning for Area B;

[0082] In the 10-day investigation of the second stage,

[0083] In the 10-day investigation of the second stage,

[0084] Area A

[0085] Area B

[0086] CWSI of Area A n = 0.97; CWSI of Area B n = 1.06;

[0087] Corresponding to the blue cold wave warning for Area A; the yellow cold wave warning for Area B;

[0088] It shows that according to the index method of cold wave intensity in Embodiment 1 of the present invention, a quantitative cold wave intensity index is obtained and a corresponding warning is given; the result is simple and intuitive, facilitating the understanding and judgment of cold wave intensity by the public and relevant decision-making departments;

[0089] Comparative Example 2. Characterize and analyze the cold wave intensity of Areas A and B through existing national standards

[0090] The investigation time period in Comparative Example 2 is the same as that in Example 1;

[0091] Step 1. Obtain the temperature data of Areas A and B for 10 days and sort out the temperature drop amplitude and minimum temperature data of Areas A and B;

[0092] In the 10-day investigation of the first stage, the 24-hour temperature drop amplitudes of the daily minimum temperatures in Areas A and B are 8°C and 15°C respectively, and the daily minimum temperatures are 4°C and 5°C respectively;

[0093] In the 10-day investigation of the second stage, the 24-hour temperature drop amplitudes of the daily minimum temperatures in Areas A and B are 8°C and 7°C respectively, and the daily minimum temperatures are 3°C and -3°C respectively;

[0094] Step 2. Judge whether Areas A and B are cold waves according to the cold wave definition;

[0095] The cold snaps are divided into cold snap, severe cold snap, and extreme cold snap.

[0096] The cold snap refers to the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥8°C within 24 hours, or by ≥10°C within 48 hours, or by ≥12°C within 72 hours, and the daily minimum temperature in that place is ≤4°C.

[0097] The severe cold snap refers to the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥10°C within 24 hours, or by ≥12°C within 48 hours, or by ≥14°C within 72 hours, and the daily minimum temperature in that place is ≤2°C.

[0098] The extreme cold snap refers to the cold air activity that causes the daily minimum temperature in a certain place to drop by ≥12°C within 24 hours, or by ≥14°C within 48 hours, or by ≥16°C within 72 hours, and the daily minimum temperature in that place is ≤0°C.

[0099] Result data:

[0100] In the 10-day investigation of the first stage, place A had a cold snap, while place B did not.

[0101] In the 10-day investigation of the second stage, place A had a cold snap, while place B did not.

[0102] Comparative Example 3: Use the "Names, Icons, and Standards of Cold Snap Disaster Warning Signals" published on the official website of the China Meteorological Administration to characterize and analyze the cold snap intensity in places A and B.

[0103] The investigation time period in Comparative Example 3 is the same as that in Example 1.

[0104] Step 1: Obtain the climate model data of places A and B for 10 days, and integrate to obtain the daily minimum temperatures and baseline average values of places A and B.

[0105] In the 10-day investigation of the first stage, the daily minimum temperatures of places A and B for 5 consecutive days were 4°C and 5°C respectively; the baseline average values were 8°C and 9°C respectively.

[0106] In the 10-day investigation of the first stage, the daily minimum temperatures of places A and B for 6 consecutive days were 4°C and 3°C respectively; the baseline average values were 11°C and 9°C respectively.

[0107] Step 2: Conduct a characterization and analysis of the cold snaps in places A and B according to the definition of the cold snap index.

[0108] The cold snap index is the longest time when the daily minimum temperature is more than 5 degrees lower than the baseline average value and lasts for more than 5 consecutive days.

[0109] Result data:

[0110] In the 10-day investigation of the first stage, there was no cold snap in Area A, and there was also no cold snap in Area B;

[0111] In the 10-day investigation of the second stage, there was a cold snap in Area A and a cold snap in Area B;

[0112] It shows that the result obtained in Comparative Example 3 can only characterize the duration of the cold snap, but does not characterize the cooling intensity of the cold snap. And since it is defined as a comparison with the reference average value, it can only reflect the duration of the cold snap compared with the same period in history, and cannot judge the cooling intensity of a single cold snap;

[0113] Test Example 4: Compare Example 1 with Comparative Example 2 and Comparative Example 3

[0114] In Example 1, according to an index method for characterizing the intensity of a cold snap disclosed in the present invention, the intensity index of the cold snap given quantitatively can be obtained, and multi-color early warnings are carried out according to the intensity index;

[0115] In Comparative Example 2, when the 24-hour temperature drop amplitudes of the daily minimum temperatures in Areas A and B were 8°C and 15°C respectively, and the daily minimum temperatures were 4°C and 5°C respectively, during the first stage of the investigation, there was a cold snap in Area A and there was no cold snap in Area B. Then, according to the existing national standard, there was a cold snap in Area A and no cold snap in Area B. However, from the actual situation, the temperature drop amplitude in Area B was very large, and the minimum temperature was only 1°C higher than that in Area B. The existing national standard could not reflect the cold degree situation in Area B;

[0116] When the 24-hour temperature drop amplitudes of the daily minimum temperatures in Area A were 8°C and 9°C respectively, and the daily minimum temperatures were 4°C and 3°C respectively, during the first stage and the second stage of the investigation, there was a cold snap in Area A and there was also a cold snap in Area B. Then, according to the existing national standard, the cold snap levels in Areas A and B were the same, both at the "cold snap" level. However, from the actual situation, the minimum temperature drop in Area A during the second stage was greater than that during the first stage of the investigation, and the minimum temperature was also lower than that in Area A during the first stage of the investigation. The cold snap in Area A during the second stage of the investigation was stronger than that in Area A during the first stage of the investigation, and the existing national standard could not reflect the information that the cold snap intensity in Area A during the second stage of the investigation was stronger than that in Area A during the first stage of the investigation;

[0117] In Comparative Example 3, the result obtained was the same as that in Comparative Example 2, which could only characterize the duration of the cold snap, but did not characterize the cooling intensity of the cold snap. And since it was defined as a comparison with the reference average value, it could only reflect the duration of the cold snap compared with the same period in history, and could not judge the cooling intensity of a single cold snap;

[0118] In summary, the patented technical solution first proposes the CWSI index and calculates the cold wave intensity through an original method, which can quantitatively give the intensity index of the cold wave, facilitating the public and relevant decision-making departments to understand and judge the cold wave intensity. Moreover, the indicators are specific and the information standards are unified, which is conducive to improving the accuracy of the characterization results, enhancing the reference value of the characterization results, and having good applicability.

[0119] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field based on the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art shall fall within the protection scope determined by the claims.

Claims

1. An index method for characterizing the intensity of cold snaps, characterized in that, It includes the following steps: Step 1: Collect numerical climate prediction model data and numerical weather prediction model data; Step 2: Preprocess the numerical climate prediction model data and numerical weather prediction model data; Step 3: Perform model fusion on the preprocessed numerical climate prediction model data and numerical weather prediction model data to obtain a weather-climate integrated numerical prediction sequence; The model fusion adopts the time-division series fusion method, using the preprocessed minimum temperature prediction product with a prediction period of the 1st to 10th days of the Global Regional Integrated Assimilation and Prediction System Model CMA-GFS issued by the China Meteorological Administration and the preprocessed minimum temperature prediction product with a prediction period of the 11th to 30th days of the second-generation dynamic extended range regional prediction model DERF issued by the China Meteorological Administration to form a fused minimum temperature prediction product with a prediction period of 30 days; If the model fusion only calculates the cold wave index for a single point location, extract the single-point fused minimum temperature prediction product corresponding to the longitude and latitude in the grid-based fused minimum temperature prediction product; Step 4: Calculate the cold wave intensity index CWSI through the cold wave intensity index calculation formula; The calculation formula for the cold wave intensity index CWSI is Among them, CWSI n is the cold wave intensity index on the nth day; is the difference between the lowest temperature on the (n - 1)-th day and the lowest temperature on the n-th day; is the lowest temperature on the nth day; Step 5: According to the calculation result obtained in Step 4, conduct an analysis of the cold wave intensity index according to the cold wave warning standard.

2. The index method for characterizing the intensity of cold snaps as described in claim 1, wherein, In Step 1, the data collection includes data collection by ordinary personnel and data collection by meteorological department users; The data collection by ordinary personnel includes downloading through the official website or applying to the local meteorological department to collect numerical climate prediction model data and numerical weather prediction model data, with a frequency of once a day; The data collection by meteorological department users includes obtaining climate model prediction data and numerical weather prediction model data through the internal network of the China Meteorological Administration, with a frequency of once a day.

3. The index method for characterizing the intensity of cold snaps as described in claim 1, wherein, In Step 2, The preprocessing method for the prediction data generated by the numerical climate prediction model is to perform spatial linear interpolation downscaling processing using the grid-based minimum temperature prediction product of the second-generation dynamic extended range regional prediction model DERF issued by the China Meteorological Administration to obtain a DERF grid-based minimum temperature prediction product with a spatial resolution of 10 km. If only calculating the cold wave index for a single point location, extract the single-point minimum temperature prediction product corresponding to the longitude and latitude in the grid-based minimum temperature prediction product; In actual operation practice, select products of other similar climate prediction models; The preprocessing method for the prediction data generated by the numerical weather prediction model is to perform spatial linear interpolation downscaling processing using the grid-based minimum temperature prediction product of the Global Regional Integrated Assimilation and Prediction System Model CMA-GFS issued by the China Meteorological Administration to obtain a CMA-GFS grid-based minimum temperature prediction product with a spatial resolution of 5 km; In actual operation practice, select products of other similar weather prediction models; If the preprocessing only calculates the cold wave index for a single point location, extract the single-point minimum temperature prediction product corresponding to the longitude and latitude in the grid-based minimum temperature prediction product.

4. The exponential method for characterizing the intensity of cold snaps according to claim 1, wherein, In Step 4, The calculation formula is wherein, is the difference between the lowest temperature on the (n - 1)-th day and the lowest temperature on the n-th day; is the lowest temperature on the nth day; is the lowest temperature on the (n - 1)-th day.

5. The index method for characterizing the intensity of cold snaps as described in claim 1, wherein In Step 5, The cold wave warning standard includes the recognition threshold for a blue cold wave warning; The recognition threshold for a blue cold wave warning is that the drop in the lowest temperature between two adjacent days is greater than or equal to 8°C or the lowest temperature drops to 4°C; The cold wave warning standard is that the CWSI value corresponding to the recognition threshold for a blue cold wave warning is 0.8; the CWSI value corresponding to the recognition threshold for a yellow cold wave warning is 1; the CWSI value corresponding to the recognition threshold for an orange cold wave warning is 1.5; the CWSI value corresponding to the recognition threshold for a red cold wave warning is 2; The greater the CWSI value, the greater the intensity of the cold wave.

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