A monthly scale climate dynamic prediction method based on sea temperature anomalies

Abstract

A monthly scale climate dynamic prediction method based on sea temperature anomaly includes S1: generating a normal value sea temperature data file based on historical sea temperature data; generating a sea temperature anomaly data file for the previous month based on the sea temperature data and the normal value sea temperature data of the previous month; determining the sea temperature sensitive area of ​​the previous month by using the sea temperature anomaly threshold; S2: generating an abnormal sea temperature data file based on the content of step S1; S3: obtaining a model normal value circulation field data file and a model abnormal circulation field data file by running a climate dynamic model according to the content of steps S1 and S2; S4: processing each file of step S3 to obtain a predicted height field anomaly map; receiving historical precipitation data and historical height field data, generating a composite map of height field anomalies for precipitation anomalies in many years and a composite map of height field anomalies for precipitation anomalies in young years; S5: analyzing and comparing the results of step S4 to obtain corresponding results, and then obtaining a monthly scale precipitation prediction result. The prediction method of the present application can achieve good prediction results.

Description

Technical Field

[0001] The present invention relates to the technical field of climate prediction, and in particular to a monthly scale climate dynamic prediction method based on sea temperature anomaly. Background Art

[0002] A large number of studies have shown that atmospheric circulation has a relatively obvious response to sea temperature. Traditional statistical analysis methods make it difficult to separate the impact of sea temperature in different sea areas on regional climate from the entire sea-air coupling system, while climate models can use sensitivity tests of different sea temperature anomalies in different sea areas to analyze the impact of different abnormal sea temperature values ​​on atmospheric circulation in different regions. At the same time, the current climate model's forecasting skills for precipitation are still relatively low, while the forecasting skills for atmospheric circulation, especially the height field, are significantly higher than the forecasting skills for precipitation.

[0003] Based on the above two points, a monthly-scale climate dynamic prediction method based on sea temperature anomalies can be developed, which can flexibly select sensitive sea areas and predict height field anomalies through sea temperature anomalies in sensitive sea areas; on the other hand, it can make full use of the historical height field data and the corresponding precipitation values ​​in the prediction period of the target prediction area, and compare them with the height field model results with higher model prediction skills to achieve good forecasting results. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a monthly-scale climate dynamic prediction method based on sea temperature anomalies; by using different sea temperature data (abnormal sea temperature and normal sea temperature) to drive the atmospheric model to run, different model height fields (abnormal height field and normal height field) are generated, and the two are subtracted (abnormal height field minus normal height field) to obtain the model-predicted height field anomaly. The advantage of using the model-predicted height field for prediction is that the systematic error of the model can be removed by subtracting the abnormal height field from the normal height field. At the same time, the prediction skill of the model height field is significantly higher than the prediction skill of precipitation. Combined with the historical precipitation and historical height field data for the same period in the prediction period of the prediction area, it is used to solve the technical problem of low precipitation forecasting skill directly output by the current climate model.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A monthly scale climate dynamic prediction method based on sea temperature anomaly comprises the following steps:

[0007] S1: Receive historical sea temperature data, calculate and generate the normal sea temperature data file for each month; receive the sea temperature data of last month, combine it with the normal sea temperature data, calculate and generate the sea temperature anomaly data file of last month; use the anomaly threshold to determine the longitude and latitude range of the sea temperature sensitive area of ​​last month from the sea temperature anomaly data, and record the sea temperature anomaly value of each grid point in the sea temperature sensitive area;

[0008] S2: According to the latitude and longitude range of the SST sensitive area last month and the SST anomaly value of each grid point in the SST sensitive area, the SST anomaly is assumed to be persistent, and the SST anomaly value in the SST sensitive area is superimposed on the annual SST data to generate the abnormal SST data files for this month and next month;

[0009] S3: Read the normal sea temperature data and abnormal sea temperature data of this month and next month, generate the model normal sea temperature data file and model abnormal sea temperature data file with the same resolution as the climate dynamic model; read the model normal sea temperature data, and obtain the model normal circulation field data file by running the climate dynamic model; read the model abnormal sea temperature data, and obtain the model abnormal circulation field data file by running the climate dynamic model;

[0010] S4: Read the model annual circulation field data and the model abnormal circulation field data, and process them to obtain the predicted height field anomaly map; receive the historical precipitation data and the historical height field anomaly data, and generate the precipitation anomaly multi-year height field anomaly composite map and the precipitation anomaly juvenile height field anomaly composite map;

[0011] S5: Read the predicted height field anomaly map, the precipitation anomaly multi-year height field anomaly composite map and the precipitation anomaly juvenile height field anomaly composite map, compare the three to obtain the results, and obtain the monthly precipitation forecast results based on the comparison results.

[0012] To optimize the above technical solutions, the specific measures taken also include:

[0013] Furthermore, the specific content of step S1 is:

[0014] Obtain historical sea temperature data from the sea temperature data storage area of ​​the local computer, call the historical sea temperature data processing program with the start and end years as parameters, process the historical sea temperature data, obtain the annual value sea temperature data file, and save it in the local computer sea temperature data storage area;

[0015] The sea temperature data of last month is obtained from the sea temperature data storage area of ​​the local computer, the annual sea temperature data of the month corresponding to the sea temperature of last month is retrieved, the value of the sea temperature data of each grid point last month is subtracted from the annual sea temperature data value of the corresponding month of the corresponding grid point, and the sea temperature anomaly data file of last month is calculated and generated, and the longitude and latitude range of the sea temperature sensitive area of ​​last month is determined by anomaly threshold analysis, and the longitude and latitude range of the sea temperature sensitive area and the sea temperature anomaly data of each grid point in the sea temperature sensitive area are saved in the sea temperature sensitive area parameter storage area of ​​the local computer.

[0016] Furthermore, the specific content of calling the historical sea temperature data processing program to process the historical sea temperature data and obtain the annual value sea temperature data is:

[0017] Step A01: Based on the historical sea temperature data, the average sea temperature value data of each grid point in the start and end year time period is calculated month by month to obtain the average sea temperature value of each grid point in each month;

[0018] Step A02: The average sea temperature value of each grid point in each month calculated in step A01 is stored as a 12-month sea temperature annual value data file, which is called the annual value sea temperature data.

[0019] Furthermore, the specific steps of determining the longitude and latitude range of the sea temperature sensitive area last month by using the anomaly threshold analysis, and storing the longitude and latitude range of the sea temperature sensitive area and the sea temperature anomaly data of each grid point in the sea temperature sensitive area in the local computer sea temperature sensitive area parameter storage area are as follows:

[0020] Step A11: Set the anomaly threshold, and select the grid points whose anomaly values ​​are greater than the anomaly threshold from the sea temperature anomaly data of the previous month;

[0021] Step A12: Filter out the grid points within the longitude and latitude range of the equatorial central and eastern Pacific Ocean, the equatorial western Pacific Ocean and the equatorial Indian Ocean in the area selected in step A11;

[0022] Step A13: setting a grid point number threshold, further screening out connected grid points from the grid points screened in step A12, so that the number of grid points in the connected area is greater than or equal to the grid point number threshold;

[0023] Step A14: Determine the number of regions selected in step A13. If the number of regions is greater than three, select three regions with larger areas. If the number of regions is less than or equal to three, select all regions. The determined regions are used as the sea temperature sensitive areas of the previous month.

[0024] Step A15: Record the values ​​of the starting longitude, ending longitude, starting latitude and ending latitude in each sea temperature sensitive zone in step A14 to obtain the longitude and latitude range of the sea temperature sensitive zone, and at the same time record the sea temperature anomaly data of each grid point within the longitude and latitude range of the sea temperature sensitive zone and save it in the local computer sea temperature sensitive zone parameter storage area.

[0025] Furthermore, the specific content of step S2 is:

[0026] Step A21: Assuming that the number of SST sensitive areas last month was 0, directly predict that the precipitation next month will be normal;

[0027] Step A22: Assuming that the number of SST sensitive areas last month is greater than 0, read the latitude and longitude range of the SST sensitive areas last month and the anomaly value of each grid point within the range, and at the same time read the corresponding annual SST data of this month and next month within the SST sensitive areas, and add them to the anomaly value of the corresponding grid point in the SST sensitive areas;

[0028] Step A23: Save the new sea temperature data calculated in step A22 into a file to obtain the abnormal sea temperature data files for this month and next month.

[0029] Furthermore, the specific content of step S3 is:

[0030] Upload the annual sea temperature data files of this month and next month from the local computer sea temperature data storage area to the high-performance server sea temperature data storage area, and convert the annual sea temperature data into a data file consistent with the resolution of the climate dynamic model, called the model annual sea temperature data, as the input data of the climate dynamic model in the high-performance server, and then run the climate dynamic model to obtain the model annual circulation field data file, and download it to the local computer circulation field data storage area;

[0031] The abnormal sea temperature data files of this month and next month are uploaded from the local computer sea temperature data storage area to the high-performance server sea temperature data storage area, and the abnormal sea temperature data is converted into a data file consistent with the resolution of the climate dynamic model, called model abnormal sea temperature data, as the input data of the climate dynamic model in the high-performance server, and then the climate dynamic model is run to obtain the model abnormal circulation field data file, and downloaded to the local computer circulation field data storage area.

[0032] Furthermore, the specific content of step S4 is:

[0033] Obtaining the model annual value circulation field data and the model abnormal circulation field data from the local computer circulation field data storage area, calling the height field anomaly data generation program, generating the predicted height field anomaly data, and drawing the data into a predicted height field anomaly map, and saving it to the height field anomaly map storage area;

[0034] The historical precipitation data is obtained from the local computer historical precipitation data storage area, the historical height field anomaly data is obtained from the local computer historical height anomaly field data storage area, the precipitation abnormal year height field anomaly synthetic data generation program is called, the precipitation abnormal year multi-year height field anomaly synthetic data and the precipitation abnormal year height field anomaly synthetic data are generated, and the data are drawn into the precipitation abnormal year multi-year height field anomaly synthetic map and the precipitation abnormal year height field anomaly synthetic map, and saved to the height field anomaly map storage area.

[0035] Furthermore, the specific content of calling the height field anomaly data generation program to generate the predicted height field anomaly data is:

[0036] Step A31: Select the grid point data of the 500 hPa height layer of the next month from the model normal circulation field data; select the grid point data of the 500 hPa height layer of the next month from the model abnormal circulation field data;

[0037] Step A32: Subtract the corresponding grid point data in the model normal circulation field data selected in step A31 from each grid point data in the model abnormal circulation field data selected in step A31 to obtain new grid point data, which is called predicted height field anomaly data.

[0038] Furthermore, the specific contents of calling the precipitation abnormal year height field anomaly synthesis data generation program to generate the precipitation abnormal multi-year height field anomaly synthesis data and the precipitation abnormal juvenile height field anomaly synthesis data are as follows:

[0039] Step A41: selecting annual precipitation data of the required predicted month from the historical precipitation data, sorting them from large to small according to the precipitation amount, selecting the first three years in the sequence as the years with abnormal precipitation, and selecting the last three years in the sequence as the years with abnormal precipitation;

[0040] Step A42: selecting the height field anomaly data that is consistent with the years in the multi-year precipitation anomaly in step A41 from the historical height field anomaly data, and then extracting the grid point data of the required prediction month from the grid point data of multiple years according to the required prediction month, and calculating the average value as the composite data of the height field anomaly of the multi-year precipitation anomaly;

[0041] Step A43: Select the height field anomaly data that is consistent with the year of the precipitation anomaly teenager in step A41 from the historical height field anomaly data, and then extract the grid data of the required prediction month from the grid data of multiple years according to the required prediction month, and calculate the average value as the synthetic data of the height field anomaly of the precipitation anomaly teenager.

[0042] Further, the specific content of step S5 is: obtaining the predicted height field anomaly map and the precipitation abnormal multi-year height field anomaly composite map from the local computer height field anomaly map storage area, comparing whether the predicted height field anomaly map and the precipitation abnormal multi-year height field anomaly composite map are similar, if the two are similar, the probability of the predicted month's precipitation being high is large; if the two are not similar, obtaining the precipitation abnormal juvenile height field anomaly composite map from the local computer height field anomaly map storage area, comparing whether the predicted height field anomaly map and the precipitation abnormal juvenile height field anomaly composite map are similar, if the two are similar, the probability of the predicted month's precipitation being low is large; if the two are not similar, then the probability of the predicted month's precipitation being normal is large.

[0043] The beneficial effects of the present invention are as follows: the monthly-scale climate dynamic prediction method based on sea temperature anomaly of the present invention uses the normal sea temperature and the abnormal sea temperature to drive the atmospheric model respectively, obtains two sets of height field model output values, and subtracts the two sets of height field output values ​​(abnormal height field minus normal height field) as the predicted height field anomaly map, so that the systematic error of the model can be removed. Comparing it with the synthetic map of the precipitation abnormal year, it provides the impact of the sea temperature in the sensitive area on the precipitation and anomaly in a certain area in the future for climate prediction. The method has clear ideas and high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of the overall technical solution of the present invention. DETAILED DESCRIPTION

[0045] The present invention will now be described in further detail with reference to the accompanying drawings.

[0046] refer to Figure 1 , the technical solution of this application is:

[0047] A monthly scale climate dynamic prediction method based on sea temperature anomaly comprises the following steps:

[0048] (1) Collect sea temperature data

[0049] (2) Generate annual sea temperature data file

[0050] (3) Generate the sea temperature anomaly data file for last month

[0051] (4) Create a model of annual sea temperature file

[0052] (5) Create abnormal sea temperature file

[0053] (6) Generate model annual value height field

[0054] (7) Generate model abnormal height field

[0055] (8) Create a predicted height field anomaly map

[0056] (9) Collect historical precipitation and altitude field anomaly data

[0057] (10) Produce composite maps of height field anomalies for abnormally high and low precipitation

[0058] (11) Predict regional precipitation.

[0059] The specific technical solutions are as follows:

[0060] A monthly scale climate dynamic prediction method based on sea temperature anomaly comprises the following steps:

[0061] S1: Receive historical sea temperature data, calculate and generate the normal sea temperature data file for each month; receive the sea temperature data of last month, combine it with the normal sea temperature data, calculate and generate the sea temperature anomaly data file of last month; use the anomaly threshold to determine the longitude and latitude range of the sea temperature sensitive area of ​​last month from the sea temperature anomaly data, and record the sea temperature anomaly value of each grid point in the sea temperature sensitive area;

[0062] S2: According to the latitude and longitude range of the SST sensitive area last month and the SST anomaly value of each grid point in the SST sensitive area, the SST anomaly is assumed to be persistent, and the SST anomaly value in the SST sensitive area is superimposed on the annual SST data to generate the abnormal SST data files for this month and next month;

[0063] S3: Read the normal sea temperature data and abnormal sea temperature data of this month and next month, generate the model normal sea temperature data file and model abnormal sea temperature data file with the same resolution as the climate dynamic model; read the model normal sea temperature data, and obtain the model normal circulation field data file by running the climate dynamic model; read the model abnormal sea temperature data, and obtain the model abnormal circulation field data file by running the climate dynamic model;

[0064] S4: Read the model annual circulation field data and the model abnormal circulation field data, and process them to obtain the predicted height field anomaly map; receive the historical precipitation data and the historical height field anomaly data, and generate the precipitation anomaly multi-year height field anomaly composite map and the precipitation anomaly juvenile height field anomaly composite map;

[0065] S5: Read the predicted height field anomaly map, the precipitation anomaly multi-year height field anomaly composite map and the precipitation anomaly juvenile height field anomaly composite map, compare the three to obtain the results, and obtain the monthly precipitation forecast results based on the comparison results.

[0066] The specific content of step S1 is:

[0067] Obtain historical sea temperature data from the sea temperature data storage area of ​​the local computer, call the historical sea temperature data processing program with the start and end years as parameters, process the historical sea temperature data, obtain the annual value sea temperature data file, and save it in the local computer sea temperature data storage area;

[0068] The sea temperature data of last month is obtained from the sea temperature data storage area of ​​the local computer, the annual sea temperature data of the month corresponding to the sea temperature of last month is retrieved, the value of the sea temperature data of each grid point last month is subtracted from the annual sea temperature data value of the corresponding month of the corresponding grid point, and the sea temperature anomaly data file of last month is calculated and generated, and the longitude and latitude range of the sea temperature sensitive area of ​​last month is determined by anomaly threshold analysis, and the longitude and latitude range of the sea temperature sensitive area and the sea temperature anomaly data of each grid point in the sea temperature sensitive area are saved in the sea temperature sensitive area parameter storage area of ​​the local computer.

[0069] The specific content of calling the historical sea temperature data processing program to process the historical sea temperature data and obtain the annual sea temperature data is:

[0070] Step A01: Based on the historical sea temperature data, the average sea temperature value data of each grid point in the start and end year time period is calculated month by month to obtain the average sea temperature value of each grid point in each month;

[0071] Step A02: The average sea temperature value of each grid point in each month calculated in step A01 is stored as a 12-month sea temperature annual value data file, which is called the annual value sea temperature data.

[0072] The specific steps of determining the longitude and latitude range of the sea temperature sensitive area last month by using the anomaly threshold analysis and storing the longitude and latitude range of the sea temperature sensitive area and the sea temperature anomaly data of each grid point in the sea temperature sensitive area in the local computer sea temperature sensitive area parameter storage area are as follows:

[0073] Step A11: Set the anomaly threshold, and select the grid points whose anomaly values ​​are greater than the anomaly threshold from the sea temperature anomaly data of the previous month;

[0074] Step A12: Filter out the grid points within the longitude and latitude range of the equatorial central and eastern Pacific Ocean, the equatorial western Pacific Ocean and the equatorial Indian Ocean in the area selected in step A11;

[0075] Step A13: setting a grid point number threshold, further screening out connected grid points from the grid points screened in step A12, so that the number of grid points in the connected area is greater than or equal to the grid point number threshold;

[0076] Step A14: Determine the number of regions selected in step A13. If the number of regions is greater than three, select three regions with larger areas. If the number of regions is less than or equal to three, select all regions. The determined regions are used as the sea temperature sensitive areas of the previous month.

[0077] Step A15: Record the values ​​of the starting longitude, ending longitude, starting latitude and ending latitude in each sea temperature sensitive zone in step A14 to obtain the longitude and latitude range of the sea temperature sensitive zone, and at the same time record the sea temperature anomaly data of each grid point within the longitude and latitude range of the sea temperature sensitive zone and save it in the local computer sea temperature sensitive zone parameter storage area.

[0078] The specific content of step S2 is:

[0079] Step A21: Assuming that the number of SST sensitive areas last month was 0, directly predict that the precipitation next month will be normal;

[0080] Step A22: Assuming that the number of SST sensitive areas last month is greater than 0, read the latitude and longitude range of the SST sensitive areas last month and the anomaly value of each grid point within the range, and at the same time read the corresponding annual SST data of this month and next month within the SST sensitive areas, and add them to the anomaly value of the corresponding grid point in the SST sensitive areas;

[0081] Step A23: Save the new sea temperature data calculated in step A22 into a file to obtain the abnormal sea temperature data files for this month and next month.

[0082] The specific content of step S3 is:

[0083] Upload the annual sea temperature data files of this month and next month from the local computer sea temperature data storage area to the high-performance server sea temperature data storage area, and convert the annual sea temperature data into a data file consistent with the resolution of the climate dynamic model, called the model annual sea temperature data, as the input data of the climate dynamic model in the high-performance server, and then run the climate dynamic model to obtain the model annual circulation field data file, and download it to the local computer circulation field data storage area;

[0084] The abnormal sea temperature data files of this month and next month are uploaded from the local computer sea temperature data storage area to the high-performance server sea temperature data storage area, and the abnormal sea temperature data is converted into a data file consistent with the resolution of the climate dynamic model, called model abnormal sea temperature data, as the input data of the climate dynamic model in the high-performance server, and then the climate dynamic model is run to obtain the model abnormal circulation field data file, and downloaded to the local computer circulation field data storage area.

[0085] The specific content of step S4 is:

[0086] Obtaining the model annual value circulation field data and the model abnormal circulation field data from the local computer circulation field data storage area, calling the height field anomaly data generation program, generating the predicted height field anomaly data, and drawing the data into a predicted height field anomaly map, and saving it to the height field anomaly map storage area;

[0087] The historical precipitation data is obtained from the local computer historical precipitation data storage area, the historical height field anomaly data is obtained from the local computer historical height anomaly field data storage area, the precipitation abnormal year height field anomaly synthetic data generation program is called, the precipitation abnormal year multi-year height field anomaly synthetic data and the precipitation abnormal year height field anomaly synthetic data are generated, and the data are drawn into the precipitation abnormal year multi-year height field anomaly synthetic map and the precipitation abnormal year height field anomaly synthetic map, and saved to the height field anomaly map storage area.

[0088] The specific content of calling the height field anomaly data generation program to generate the predicted height field anomaly data is as follows:

[0089] Step A31: Select the grid point data of the 500 hPa height layer of the next month from the model normal circulation field data; select the grid point data of the 500 hPa height layer of the next month from the model abnormal circulation field data;

[0090] Step A32: Subtract the corresponding grid point data in the model normal circulation field data selected in step A31 from each grid point data in the model abnormal circulation field data selected in step A31 to obtain new grid point data, which is called predicted height field anomaly data.

[0091] The specific contents of calling the precipitation abnormal year height field anomaly synthesis data generation program to generate the precipitation abnormal multi-year height field anomaly synthesis data and the precipitation abnormal juvenile height field anomaly synthesis data are as follows:

[0092] Step A41: selecting annual precipitation data of the required predicted month from the historical precipitation data, sorting them from large to small according to the precipitation amount, selecting the first three years in the sequence as the years with abnormal precipitation, and selecting the last three years in the sequence as the years with abnormal precipitation;

[0093] Step A42: selecting the height field anomaly data that is consistent with the years in the multi-year precipitation anomaly in step A41 from the historical height field anomaly data, and then extracting the grid point data of the required prediction month from the grid point data of multiple years according to the required prediction month, and calculating the average value as the composite data of the height field anomaly of the multi-year precipitation anomaly;

[0094] Step A43: Select the height field anomaly data that is consistent with the year of the precipitation anomaly teenager in step A41 from the historical height field anomaly data, and then extract the grid data of the required prediction month from the grid data of multiple years according to the required prediction month, and calculate the average value as the synthetic data of the height field anomaly of the precipitation anomaly teenager.

[0095] Wherein, the specific content of step S5 is: obtaining the predicted height field anomaly map and the precipitation abnormal multi-year height field anomaly composite map from the local computer height field anomaly map storage area, comparing whether the predicted height field anomaly map and the precipitation abnormal multi-year height field anomaly composite map are similar, if the two are similar, the probability of more precipitation in the predicted month is large; if the two are not similar, obtaining the precipitation abnormal juvenile height field anomaly composite map from the local computer height field anomaly map storage area, comparing whether the predicted height field anomaly map and the precipitation abnormal juvenile height field anomaly composite map are similar, if the two are similar, the probability of less precipitation in the predicted month is large; if the two are not similar, the probability of normal precipitation in the predicted month is large.

[0096] In addition, the relevant concepts that need to be supplemented are:

[0097] 1. The meaning of grid points: the nodes where longitude and latitude lines intersect, which are similar to the coordinate points of longitude and longitude of the earth.

[0098] 2. Climate dynamics model: It is a complex numerical climate model currently available.

[0099] It should be noted that the terms such as "upper", "lower", "left", "right", "front", "back", etc. cited in the invention are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments in their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0100] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A monthly scale climate dynamic prediction method based on sea temperature anomaly, characterized in that: The following steps are involved: S1: Receive historical sea temperature data, calculate and generate the normal sea temperature data file for each month; receive the sea temperature data of last month, combine it with the normal sea temperature data, calculate and generate the sea temperature anomaly data file of last month; use the anomaly threshold to determine the longitude and latitude range of the sea temperature sensitive area of ​​last month from the sea temperature anomaly data, and record the sea temperature anomaly value of each grid point in the sea temperature sensitive area; S2: According to the latitude and longitude range of the SST sensitive area last month and the SST anomaly value of each grid point in the SST sensitive area, the SST anomaly is assumed to be persistent, and the SST anomaly value in the SST sensitive area is superimposed on the annual SST data to generate the abnormal SST data files for this month and next month; S3: Read the normal sea temperature data and abnormal sea temperature data of this month and next month, generate the model normal sea temperature data file and model abnormal sea temperature data file with the same resolution as the climate dynamic model; read the model normal sea temperature data, and obtain the model normal circulation field data file by running the climate dynamic model; read the model abnormal sea temperature data, and obtain the model abnormal circulation field data file by running the climate dynamic model; S4: Read the model annual circulation field data and the model abnormal circulation field data, and process them to obtain the predicted height field anomaly map; receive the historical precipitation data and the historical height field anomaly data, and generate the precipitation anomaly multi-year height field anomaly composite map and the precipitation anomaly juvenile height field anomaly composite map; S5: Read the predicted height field anomaly map, the precipitation anomaly multi-year height field anomaly composite map and the precipitation anomaly juvenile height field anomaly composite map, compare the three to get the result, and get the monthly precipitation forecast result based on the comparison result.

2. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 1 is characterized in that: The specific content of step S1 is: Obtain historical sea temperature data from the sea temperature data storage area of ​​the local computer, call the historical sea temperature data processing program with the start and end years as parameters, process the historical sea temperature data, obtain the annual value sea temperature data file, and save it in the local computer sea temperature data storage area; The sea temperature data of last month is obtained from the sea temperature data storage area of ​​the local computer, the annual sea temperature data of the month corresponding to the sea temperature of last month is retrieved, the value of the sea temperature data of each grid point last month is subtracted from the annual sea temperature data value of the corresponding month of the corresponding grid point, and the sea temperature anomaly data file of last month is calculated and generated, and the longitude and latitude range of the sea temperature sensitive area of ​​last month is determined by anomaly threshold analysis, and the longitude and latitude range of the sea temperature sensitive area and the sea temperature anomaly data of each grid point in the sea temperature sensitive area are saved in the sea temperature sensitive area parameter storage area of ​​the local computer.

3. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 2 is characterized in that: The specific contents of calling the historical sea temperature data processing program to process the historical sea temperature data and obtain the annual sea temperature data are as follows: Step A01: Based on the historical sea temperature data, the average sea temperature value data of each grid point in the start and end year time period is calculated month by month to obtain the average sea temperature value of each grid point in each month; Step A02: The average sea temperature value of each grid point in each month calculated in step A01 is stored as a 12-month sea temperature annual value data file, which is called the annual value sea temperature data.

4. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 2 is characterized in that: The specific steps of determining the latitude and longitude range of the sea temperature sensitive area last month by using the anomaly threshold analysis, and storing the latitude and longitude range of the sea temperature sensitive area and the sea temperature anomaly data of each grid point in the sea temperature sensitive area in the local computer sea temperature sensitive area parameter storage area are as follows: Step A11: Set the anomaly threshold, and select the grid points whose anomaly values ​​are greater than the anomaly threshold from the sea temperature anomaly data of the previous month; Step A12: Filter out the grid points within the longitude and latitude range of the equatorial central and eastern Pacific Ocean, the equatorial western Pacific Ocean and the equatorial Indian Ocean in the area selected in step A11; Step A13: setting a grid point number threshold, further screening out connected grid points from the grid points screened in step A12, so that the number of grid points in the connected area is greater than or equal to the grid point number threshold; Step A14: Determine the number of regions selected in step A13. If the number of regions is greater than three, select three regions with larger areas. If the number of regions is less than or equal to three, select all regions. The determined regions are used as the sea temperature sensitive areas of the previous month. Step A15: Record the values ​​of the starting longitude, ending longitude, starting latitude and ending latitude in each sea temperature sensitive zone in step A14 to obtain the longitude and latitude range of the sea temperature sensitive zone, and at the same time record the sea temperature anomaly data of each grid point within the longitude and latitude range of the sea temperature sensitive zone and save it in the local computer sea temperature sensitive zone parameter storage area.

5. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 4 is characterized in that: The specific content of step S2 is: Step A21: Assuming that the number of SST sensitive areas last month was 0, directly predict that the precipitation next month will be normal; Step A22: Assuming that the number of SST sensitive areas last month is greater than 0, read the latitude and longitude range of the SST sensitive areas last month and the anomaly value of each grid point within the range, and at the same time read the corresponding annual SST data of this month and next month within the SST sensitive areas, and add them to the anomaly value of the corresponding grid point in the SST sensitive areas; Step A23: Save the new sea temperature data calculated in step A22 into a file to obtain the abnormal sea temperature data files for this month and next month.

6. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 5 is characterized in that: The specific content of step S3 is: Upload the annual sea temperature data files of this month and next month from the local computer sea temperature data storage area to the high-performance server sea temperature data storage area, and convert the annual sea temperature data into a data file consistent with the resolution of the climate dynamic model, called the model annual sea temperature data, as the input data of the climate dynamic model in the high-performance server, and then run the climate dynamic model to obtain the model annual circulation field data file, and download it to the local computer circulation field data storage area; The abnormal sea temperature data files of this month and next month are uploaded from the local computer sea temperature data storage area to the high-performance server sea temperature data storage area, and the abnormal sea temperature data is converted into a data file consistent with the resolution of the climate dynamic model, called model abnormal sea temperature data, as the input data of the climate dynamic model in the high-performance server, and then the climate dynamic model is run to obtain the model abnormal circulation field data file, and downloaded to the local computer circulation field data storage area.

7. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 6 is characterized in that: The specific content of step S4 is: Obtaining the model annual value circulation field data and the model abnormal circulation field data from the local computer circulation field data storage area, calling the height field anomaly data generation program, generating the predicted height field anomaly data, and drawing the data into a predicted height field anomaly map, and saving it to the height field anomaly map storage area; The historical precipitation data is obtained from the local computer historical precipitation data storage area, the historical height field anomaly data is obtained from the local computer historical height anomaly field data storage area, the precipitation abnormal year height field anomaly synthetic data generation program is called, the precipitation abnormal year multi-year height field anomaly synthetic data and the precipitation abnormal year height field anomaly synthetic data are generated, and the data are drawn into the precipitation abnormal year multi-year height field anomaly synthetic map and the precipitation abnormal year height field anomaly synthetic map, and saved to the height field anomaly map storage area.

8. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 7 is characterized in that: The specific content of calling the height field anomaly data generation program to generate the predicted height field anomaly data is: Step A31: Select the grid point data of the 500 hPa height layer of the next month from the model normal circulation field data; select the grid point data of the 500 hPa height layer of the next month from the model abnormal circulation field data; Step A32: Subtract the corresponding grid point data in the model normal circulation field data selected in step A31 from each grid point data in the model abnormal circulation field data selected in step A31 to obtain new grid point data, which is called predicted height field anomaly data.

9. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 7, characterized in that: The specific contents of calling the precipitation abnormal year height field anomaly synthesis data generation program to generate the precipitation abnormal multi-year height field anomaly synthesis data and the precipitation abnormal juvenile height field anomaly synthesis data are as follows: Step A41: selecting annual precipitation data of the required predicted month from the historical precipitation data, sorting them from large to small according to the precipitation amount, selecting the first three years in the sequence as the years with abnormal precipitation, and selecting the last three years in the sequence as the years with abnormal precipitation; Step A42: selecting the height field anomaly data that is consistent with the years in the multi-year precipitation anomaly in step A41 from the historical height field anomaly data, and then extracting the grid point data of the required prediction month from the grid point data of multiple years according to the required prediction month, and calculating the average value as the composite data of the height field anomaly of the multi-year precipitation anomaly; Step A43: Select the height field anomaly data that is consistent with the year of the precipitation anomaly teenager in step A41 from the historical height field anomaly data, and then extract the grid data of the required prediction month from the grid data of multiple years according to the required prediction month, and calculate the average value as the synthetic data of the height field anomaly of the precipitation anomaly teenager.

10. The monthly scale climate dynamic prediction method based on sea temperature anomaly according to claim 7, characterized in that: The specific content of step S5 is: obtain the predicted height field anomaly map and the precipitation abnormal multi-year height field anomaly composite map from the local computer height field anomaly map storage area, compare the predicted height field anomaly map and the precipitation abnormal multi-year height field anomaly composite map to see whether they are similar, if the two are similar, the probability of the predicted monthly precipitation being high is large; if the two are not similar, obtain the precipitation abnormal juvenile height field anomaly composite map from the local computer height field anomaly map storage area, compare the predicted height field anomaly map and the precipitation abnormal juvenile height field anomaly composite map to see whether they are similar, if the two are similar, the probability of the predicted monthly precipitation being low is large; if the two are not similar, then the probability of the predicted monthly precipitation being normal is large.

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