Extended period quantitative rainfall forecasting method, system, equipment and medium

Abstract

The invention provides an extension period quantitative rainfall forecasting method, system, equipment and medium, and relates to the field of weather forecasting. The method comprises the following steps: on the basis of the current forecast timeliness of a certain grid point, by taking an ensemble average value of real-time forecast as an object, in a re-forecast data sample library subjected to ensemble average processing in the same forecast timeliness, sorting according to the similarity between re-forecast data subjected to ensemble average processing and the real-time forecast, the sorted first predefined number of re-forecast data is taken as a mode forecast similar sample set; obtaining a historical live rainfall similar set corresponding to the QPE data based on the mode forecast similar sample set; and on the basis of the rainfall similar set, a probability matching average technology and a bilinear interpolation technology are adopted, and an extension period rainfall grid forecasting product of a predefined number of days is obtained through calculation. In order to solve the problems of long extended period forecasting time efficiency, few required samples in the same period, few extended period modes, large forecasting uncertainty and the like, deep research is carried out, and a new-generation extended period precipitation forecasting model technology is established.

Description

Technical Field

[0001] The present invention belongs to the field of weather forecasting, and in particular relates to an extended period quantitative precipitation forecasting method, system, equipment and medium. Background Art

[0002] Precipitation is the main major weather disaster affecting my country and is an important cause of severe floods, geological disasters and urban waterlogging. The key to reducing precipitation and secondary disasters is to improve the accuracy of precipitation forecasts and extend the forecast time. At present, numerical models, as the basis of modern weather forecasting services, play a very important role in precipitation forecasting. However, due to the large number of influencing factors of precipitation forecasting, it is considered to be one of the most difficult challenges of numerical model forecasting, and the improvement of forecasting skills is relatively slow. In addition, as the forecast time is extended, the uncertainty of the model's precipitation forecast increases rapidly, and its forecast reliability is further significantly reduced.

[0003] In recent years, in order to improve the effect of long-term precipitation forecast, weather forecast centers in many countries around the world have been vigorously developing ensemble model technology while developing high-resolution numerical model systems. The advantage of ensemble models is that they reveal nonlinear uncertainty factors in weather processes, which is particularly applicable to extended-term forecasts. The advantages of ensemble models also lead to new difficulties. One of them is how forecasters can quickly and efficiently extract high-value information from the massive data of ensemble models to improve precipitation forecasting skills. In view of this, a variety of ensemble model statistical post-processing technology products have been established in the real-time quantitative precipitation forecast business of the China Meteorological Administration, such as probability matching average, quantile mapping, as well as machine learning technologies such as logistic regression, support vector machine, Bayesian, etc.; in addition, there are also studies on the application of deep learning and other methods in precipitation forecasting, which currently have good results in short-term / short-term forecasts. Some studies have classified model forecast post-processing technologies and pointed out that different methods should be used for deviation correction based on different correction purposes and application scenarios. In actual business applications, the above-mentioned correction products are mostly concentrated in the forecast period of 0-10 days. As for the forecast period of 11-30 days, the uncertainty of model forecast increases with the extension of forecast period, and the forecast difficulty further increases. At present, there is still a lack of mature and effective objective forecast. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a technical solution of an extended-term quantitative precipitation forecast method, system, equipment and medium. For the 11-30-day extended-term precipitation grid forecast business, the European Center (EC) ensemble model real-time forecast and re-forecast data, multi-fusion live grid product (QPE) and other basic data are used, and similar ensemble forecast technology ideas and probability matching technology are adopted to develop a national extended-term grid precipitation forecast correction method with daily updates, a time resolution of 11-30 days at 24-hour intervals, and a spatial resolution of 5km. In order to solve the above technical problems.

[0005] The first aspect of the present invention discloses an extended period quantitative precipitation forecasting method, the method comprising: Step S1, based on the current forecast time of a certain grid point, taking the ensemble average of the real-time forecast as the object, in the re-forecast data sample library with the same forecast time and subjected to ensemble average processing, sorting the re-forecast data according to the similarity between the ensemble average processing and the real-time forecast, taking the first predefined number of re-forecast data in the sorting as the model forecast similarity sample set; Step S2, based on the model forecast similar sample set, corresponding to the QPE data, obtain the historical actual precipitation similarity set; Step S3: Based on the historical actual precipitation similarity set, the probability matching average technology and the bilinear interpolation technology are used to calculate the extended-range precipitation grid forecast product for a predefined number of days.

[0006] According to the method of the first aspect of the present invention, in step S1, the calculation of the similarity includes: in, It is the forecast timeliness; is the forecast time in the reforecast data, and its value is equal to the forecast time in the real-time forecast data ; Real-time forecast for a certain point and a certain forecast time period by ensemble average processing; It is the reforecast data processed by ensemble average of the same points and time periods in the past; is the number of predictor variables; is the weight coefficient; The standard deviation of the reforecast data series processed by ensemble mean at the same point; is the forecast time step; to is the time window, such as the current As the benchmark time limit, is the length of time for sliding before and after the benchmark time limit, that is, the sliding time window is to ,in =24 hours; is the i-th predictor variable, i.e. The forecast value of the forecast validity; is the same point i in the past Similarity prediction value of time.

[0007] According to the method of the first aspect of the present invention, in step S1, the predefined value is equal to 20; The number of predicted variables is 25 grid points in the range (x-2, x+2, y-2, y+2) around the current grid point (x, y); The weight coefficients are equal weights; The forecast time step is 12 hours; The time window is from -24h to 24h.

[0008] According to the method of the first aspect of the present invention, in step S3, the predefined number of days is 11-30 days.

[0009] The second aspect of the present invention discloses an extended period quantitative precipitation forecast system, the system comprising: The first processing module is configured to, based on the current forecast time of a certain grid point, take the ensemble average of the real-time forecast as the object, sort the re-forecast data processed by the ensemble average according to the similarity between the real-time forecast and the ensemble average, and take the first predefined number of re-forecast data in the sorting as the model forecast similarity sample set; The second processing module is configured to obtain a historical actual precipitation similarity set based on the model forecast similarity sample set corresponding to the QPE data; The third processing module is configured to calculate the extended-range precipitation grid forecast product for a predefined number of days based on the historical actual precipitation similarity set by using the probability matching average technology and the bilinear interpolation technology.

[0010] According to the system of the second aspect of the present invention, the first processing module is specifically configured such that the calculation of the similarity includes: in, It is the forecast timeliness; is the forecast time in the reforecast data, and its value is equal to the forecast time in the real-time forecast data ; Real-time forecast for a certain point and a certain forecast time period by ensemble average processing; It is the reforecast data processed by ensemble average of the same points and time periods in the past; is the number of predictor variables; is the weight coefficient; The standard deviation of the reforecast data series processed by ensemble mean at the same point; is the forecast time step; to is the time window, such as the current As the benchmark time limit, is the length of time for sliding before and after the benchmark time limit, that is, the sliding time window is to ,in =24 hours; is the i-th predictor variable, i.e. The forecast value of the forecast validity; is the same point i in the past Similarity prediction value of time.

[0011] According to the system of the second aspect of the present invention, the first processing module is specifically configured such that the predefined value is equal to 20; The number of predicted variables is 25 grid points in the range (x-2, x+2, y-2, y+2) around the current grid point (x, y); The weight coefficients are equal weights; The forecast time step is 12 hours; The time window is from -24h to 24h.

[0012] According to the system of the second aspect of the present invention, the third processing module is specifically configured such that the predefined number of days is 11-30 days.

[0013] The third aspect of the present invention discloses an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the extended-term quantitative precipitation forecasting methods in the first aspect of the present disclosure are implemented.

[0014] The fourth aspect of the present invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any one of the extended-term quantitative precipitation forecasting methods in the first aspect of the present disclosure are implemented.

[0015] In summary, the solution proposed in the present invention can conduct in-depth discussions on the two problems of long extended-term forecast validity and small number of samples required for the same period; and small number of extended-term models and large forecast uncertainty, and study the establishment of a new generation of extended-term precipitation forecast model technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of an extended-period quantitative precipitation forecast method according to an embodiment of the present invention; Figure 2 A structural diagram of an extended-period quantitative precipitation forecast system according to an embodiment of the present invention; Figure 3 The figure is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 making creative work are within the scope of protection of the present invention.

[0019] Similarity ensemble forecasting technology is based on the comparison of similar features between deterministic forecasts and model forecasts of various historical periods, and selects the actual observations corresponding to the most similar historical model forecasts as the forecast members after error correction. This method was proposed in 2010 by the point-based similarity ensemble forecasting technology (point-based AnEn technique) constructed by scientists such as NCAR Monache. Compared with traditional ensemble forecasting, it saves computing resources, avoids the uncertainty of EPS itself, and can provide spatial and temporal uncertainty estimates. It does not require post-processing, but requires a large amount of observation and historical forecast data. The required time varies depending on the forecast object, but at least it includes several months of a season, which is used to improve the short-term time-effectiveness forecast effect.

[0020] In order to solve the bottleneck problems faced in the construction of extended-term precipitation forecast model: first, the extended-term forecast has a long validity period, and the required samples of the same period are relatively small; second, there are few extended-term models and the forecast uncertainty is large. When constructing the extended-term similar ensemble forecast model, we followed the method and ideas of the above-mentioned similar ensemble forecast technology and made adaptive improvements, which are mainly reflected in: first, the original method based on deterministic forecast is changed to real-time forecast based on ensemble forecast (ensemble average), which filters high-frequency information to a certain extent and reduces forecast uncertainty; second, the model forecast of each historical period is changed to ensemble forecast and then forecast data (ensemble average), that is, on the one hand, a large number of historical samples of the same period are added, and at the same time, the use of model historical data is also conducive to correcting the model system error.

[0021] The first aspect of the present invention discloses an extended-period quantitative precipitation forecasting method. Figure 1 FIG. 4 is a flow chart of a method for quantitative precipitation forecasting for an extended period according to an embodiment of the present invention. Figure 1 As shown, the method includes: Step S1, based on the current forecast time of a certain grid point, taking the ensemble average of the real-time forecast as the object, in the re-forecast data sample library with the same forecast time and subjected to ensemble average processing, sorting the re-forecast data according to the similarity between the ensemble average processing and the real-time forecast, taking the first predefined number of re-forecast data in the sorting as the model forecast similarity sample set; Step S2, based on the model forecast similar sample set, corresponding to the QPE data, obtain the historical actual precipitation similarity set; Step S3: Based on the historical actual precipitation similarity set, the probability matching average technology and the bilinear interpolation technology are used to calculate the extended-range precipitation grid forecast product for a predefined number of days.

[0022] In step S1, based on the current forecast time of a certain grid point, taking the ensemble mean of the real-time forecast as the object, in the re-forecast data sample library with the same forecast time that has been processed by ensemble mean, the re-forecast data processed by ensemble mean are sorted according to the similarity between the real-time forecast, and the first predefined number of re-forecast data in the sorting are taken as the model forecast similarity sample set.

[0023] In some embodiments, in step S1, the calculation of the similarity includes: in, It is the forecast timeliness; is the forecast time in the reforecast data, and its value is equal to the forecast time in the real-time forecast data ; Real-time forecast for a certain point and a certain forecast time period by ensemble average processing; It is the reforecast data processed by ensemble average of the same points and time periods in the past; is the number of predictor variables; is the weight coefficient; The standard deviation of the reforecast data series processed by ensemble mean at the same point; is the forecast time step; to is the time window, such as the current As the benchmark time limit, is the length of time for sliding before and after the benchmark time limit, that is, the sample sliding time window in this project is to ,in =24 hours; is the i-th predictor variable, i.e. The forecast value of the forecast validity; is the same point i in the past Similarity prediction value of time.

[0024] The predefined value is equal to 20; The number of predicted variables is 25 grid points in the range (x-2, x+2, y-2, y+2) around the current grid point (x, y); The weight coefficients are equal weights; The forecast time step is 12 hours; The time window is from -24h to 24h.

[0025] The ensemble mean forecast (EC_M) uses equal weights to calculate the mathematical average of all ECMWF ensemble forecast members (E), which can reflect the overall trend of the results of all ensemble forecast members. The calculation formula is as follows: in, Forecast each member for the assembly.

[0026] In step S3, based on the historical actual precipitation similarity set, the probability matching average technology and the bilinear interpolation technology are used to calculate the extended-range precipitation grid forecast product for the predefined number of days.

[0027] In some embodiments, in step S3, the predefined number of days is 11-30 days.

[0028] Probability Matching Average Quantitative precipitation forecasts can rarely accurately predict the spatial distribution of precipitation. Ensemble mean calculations can indicate the most likely location of the precipitation center, but there is a magnitude deviation in the precipitation ensemble mean. This is reflected in the fact that the ensemble mean product smoothes the precipitation distribution, that is, the heavy precipitation value decreases, while the range of small-scale rainfall expands.

[0029] In order to correct the precipitation magnitude bias of the ensemble mean product, the probability matching technique is used. The probability matching technique is used to fuse data sources with different temporal and spatial distributions. Usually one data source has a better spatial distribution, while the other data has better accuracy. This technique is implemented by setting the probability distribution function (PDF) of the low-accuracy data to a high-accuracy PDF. Practical examples include the fusion between radar and rain gauge observations, and precipitation estimates from polar orbiting or geostationary satellites. In ensemble forecasts, this technique is used to combine the ensemble mean field with better spatial distribution and the ensemble member forecasts with better magnitude accuracy.

[0030] Select a certain area, arrange all forecasts of n members in the area from large to small, and then retain the forecast values ​​of every n / 2 intervals; arrange the ensemble mean field from large to small; match the sequence retained in the first step with the ensemble mean sequence to obtain the probability matching ensemble mean product.

[0031] Experiment: May to September 2024 was selected as the test period, and the TS score test method commonly used in forecasting business was adopted to conduct forecast test and comparison between the revised product and the original model product. The results showed that the revised product has a significant improvement over the original model in the 11-30 day forecast period in each precipitation level (such as: ≥0.1 mm, ≥10 mm, ≥25 mm, ≥50 mm), especially in the heavy precipitation level (i.e. ≥25 mm, ≥50 mm), the TS score is more than 2 times higher than the original model.

[0032] In summary, the scheme proposed in this invention can address the two problems of long extended-term forecast validity and few samples required for the same period; and few extended-term models and large forecast uncertainty. It has carried out in-depth discussions and studied the establishment of a new generation of extended-term precipitation forecast model technology. It was finally determined that based on similar ensemble forecast technology, probability matching average, bilinear interpolation and other technologies, the model was constructed using ensemble model real-time forecast data, re-forecast data and actual data, and a national precipitation refined business forecast product with daily updates and a spatiotemporal resolution of 1 day and 5 kilometers was developed and generated. Among them, the study made adaptive improvements to the similar ensemble forecast technology, which is mainly reflected in: first, the original method based on deterministic forecast is changed to real-time forecast based on ensemble forecast (ensemble average), which filters high-frequency information to a certain extent and reduces forecast uncertainty; second, the model forecasts of each historical period are changed to ensemble forecast re-forecast data, that is, on the one hand, a large number of historical samples of the same period are added, and at the same time, due to the use of model historical data, it is conducive to correcting the model system error.

[0033] The second aspect of the present invention discloses an extended-period quantitative precipitation forecasting system. Figure 2 is a structural diagram of an extended period quantitative precipitation forecast system according to an embodiment of the present invention; Figure 2As shown, the system 100 includes: The first processing module 101 is configured to, based on the current forecast time effectiveness of a certain grid point, take the ensemble average of the real-time forecast as the object, sort the ensemble average processed re-forecast data in the re-forecast data sample library with the same forecast time effectiveness according to the similarity between the ensemble average processed re-forecast data and the real-time forecast, and take the first predefined value of the sorted re-forecast data as the model forecast similarity sample set; The second processing module 102 is configured to obtain a historical actual precipitation similarity set based on the model forecast similarity sample set corresponding to the QPE data; The third processing module 103 is configured to calculate the extended-range precipitation grid forecast product for a predefined number of days by using the probability matching average technology and the bilinear interpolation technology based on the historical actual precipitation similarity set.

[0034] According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured such that the calculation of the similarity includes: in, It is the forecast timeliness; is the forecast time in the reforecast data, and its value is equal to the forecast time in the real-time forecast data ; Real-time forecast for a certain point and a certain forecast time period by ensemble average processing; It is the reforecast data processed by ensemble average of the same points and time periods in the past; is the number of predictor variables; is the weight coefficient; The standard deviation of the reforecast data series processed by ensemble mean at the same point; is the forecast time step; to is the time window, such as the current As the benchmark time limit, is the length of time for sliding before and after the benchmark time limit, that is, the sample sliding time window in this project is to ,in =24 hours; is the i-th predictor variable, i.e. The forecast value of the forecast validity; is the same point i in the past Similarity prediction value of time.

[0035] The predefined value is equal to 20; The number of predicted variables is 25 grid points in the range (x-2, x+2, y-2, y+2) around the current grid point (x, y); The weight coefficients are equal weights; The forecast time step is 12 hours; The time window is from -24h to 24h.

[0036] The ensemble mean forecast (EC_M) uses equal weights to calculate the mathematical average of all ECMWF ensemble forecast members (E), which can reflect the overall trend of the results of all ensemble forecast members. The calculation formula is as follows: in, Forecast each member for the assembly.

[0037] According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured such that the predefined number of days is 11-30 days.

[0038] Probability Matching Average Quantitative precipitation forecasts can rarely accurately predict the spatial distribution of precipitation. Ensemble mean calculations can indicate the most likely location of the precipitation center, but there is a magnitude deviation in the precipitation ensemble mean. This is reflected in the fact that the ensemble mean product smoothes the precipitation distribution, that is, the heavy precipitation value decreases, while the range of small-scale rainfall expands.

[0039] In order to correct the precipitation magnitude bias of the ensemble mean product, the probability matching technique is used. The probability matching technique is used to fuse data sources with different temporal and spatial distributions. Usually one data source has a better spatial distribution, while the other data has better accuracy. This technique is implemented by setting the probability distribution function (PDF) of the low-accuracy data to a high-accuracy PDF. Practical examples include the fusion between radar and rain gauge observations, and precipitation estimates from polar orbiting or geostationary satellites. In ensemble forecasts, this technique is used to combine the ensemble mean field with better spatial distribution and the ensemble member forecasts with better magnitude accuracy.

[0040] Select a certain area, arrange all forecasts of n members in the area from large to small, and then retain the forecast values ​​of every n / 2 intervals; arrange the ensemble mean field from large to small; match the sequence retained in the first step with the ensemble mean sequence to obtain the probability matching ensemble mean product.

[0041] The third aspect of the present invention discloses an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the extended-term quantitative precipitation forecasting methods disclosed in the first aspect of the present invention are implemented.

[0042] Figure 3 is a structural diagram of an electronic device according to an embodiment of the present invention, such as Figure 3As shown, the electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, near field communication (NFC) or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the housing of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0043] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a structural diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the technical solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0044] The fourth aspect of the present invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any one of the steps in the extended-term quantitative precipitation forecast method disclosed in the first aspect of the present invention are implemented.

[0045] Please note that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above embodiments only express several implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all belong to the scope of protection of the present application. Therefore, the scope of protection of the patent in this application shall be based on the attached claims.

Claims

1. An extended-period quantitative precipitation forecasting method, characterized in that: The method comprises: Step S1, based on the current forecast time of a certain grid point, taking the ensemble average of the real-time forecast as the object, in the re-forecast data sample library with the same forecast time and subjected to ensemble average processing, sorting the re-forecast data according to the similarity between the ensemble average processing and the real-time forecast, taking the first predefined number of re-forecast data in the sorting as the model forecast similarity sample set; Step S2, based on the model forecast similar sample set, corresponding to the QPE data, obtain the historical actual precipitation similarity set; Step S3: Based on the historical actual precipitation similarity set, the probability matching average technology and the bilinear interpolation technology are used to calculate the extended-range precipitation grid forecast product for a predefined number of days.

2. The extended-period quantitative precipitation forecasting method according to claim 1, characterized in that: In step S1, the calculation of the similarity includes: in, It is the forecast timeliness; is the forecast time in the reforecast data, and its value is equal to the forecast time in the real-time forecast data ; Real-time forecast for a certain point and a certain forecast time period by ensemble average processing; It is the reforecast data processed by ensemble average of the same points and time periods in the past; is the number of predictor variables; is the weight coefficient; The standard deviation of the reforecast data series processed by ensemble mean at the same point; is the forecast time step; to is the time window, such as the current As the benchmark time limit, is the length of time for sliding before and after the benchmark time limit, that is, the sliding time window is to ,in =24 hours; is the i-th predictor variable, i.e. The forecast value of the forecast validity; is the same point i in the past Similarity prediction value of time.

3. The extended-period quantitative precipitation forecasting method according to claim 2, characterized in that: In the step S1, the predefined value is equal to 20; The number of predicted variables is 25 grid points in the range (x-2, x+2, y-2, y+2) around the current grid point (x, y); The weight coefficients are equal weights; The forecast time step is 12 hours; The time window is from -24h to 24h.

4. The extended-period quantitative precipitation forecasting method according to claim 1, characterized in that: In the step S3, the predefined number of days is 11-30 days.

5. An extended-period quantitative precipitation forecast system, characterized in that: The system comprises: The first processing module is configured to, based on the current forecast time of a certain grid point, take the ensemble average of the real-time forecast as the object, sort the re-forecast data processed by the ensemble average according to the similarity between the real-time forecast and the ensemble average, and take the first predefined number of re-forecast data in the sorting as the model forecast similarity sample set; The second processing module is configured to obtain a historical actual precipitation similarity set based on the model forecast similarity sample set corresponding to the QPE data; The third processing module is configured to calculate the extended-range precipitation grid forecast product for a predefined number of days based on the historical actual precipitation similarity set by using the probability matching average technology and the bilinear interpolation technology.

6. The extended-period quantitative precipitation forecast system according to claim 5, characterized in that: The first processing module is specifically configured such that the calculation of the similarity includes: in, It is the forecast timeliness; is the forecast time in the reforecast data, and its value is equal to the forecast time in the real-time forecast data ; Real-time forecast for a certain point and a certain forecast time period by ensemble average processing; It is the reforecast data processed by ensemble average of the same points and time periods in the past; is the number of predictor variables; is the weight coefficient; The standard deviation of the reforecast data series processed by ensemble mean at the same point; is the forecast time step; to is the time window, such as the current As the benchmark time limit, is the length of time for sliding before and after the benchmark time limit, that is, the sliding time window is to ,in =24 hours; is the i-th predictor variable, i.e. The forecast value of the forecast validity; is the same point i in the past Similarity prediction value of time.

7. The extended-period quantitative precipitation forecast system according to claim 6, characterized in that: The first processing module is specifically configured such that the predefined value is equal to 20; The number of predicted variables is 25 grid points in the range (x-2, x+2, y-2, y+2) around the current grid point (x, y); The weight coefficients are equal weights; The forecast time step is 12 hours; The time window is from -24h to 24h.

8. The extended-period quantitative precipitation forecast system according to claim 5, characterized in that: The third processing module is specifically configured such that the predefined number of days is 11-30 days.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the extended-term quantitative precipitation forecast method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the extended-range quantitative precipitation forecast method according to any one of claims 1 to 4 are implemented.

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