A precipitation forecast correction method and device

Abstract

The application discloses a precipitation forecast correction method, and relates to the technical field of weather forecast, which comprises the following steps: acquiring cloud top brightness temperature data and cloud cluster information data in a previous adjacent time period of a target area to be forecast; dividing the cloud cluster into multiple observation areas; statistically counting temperature type information and quantity in each observation area based on the cloud top brightness temperature data; respectively calculating the proportion of the low-temperature type quantity in the observation area; calculating the proportion of the heavy precipitation area in the cloud cluster; judging whether the cloud cluster area is increased; judging the change trend of the observation area with the low-temperature type proportion exceeding a threshold value; and correcting the precipitation grade forecast at the current time. In addition, the application also discloses a precipitation forecast correction device, which comprises a collection module, an image processing module, a calculation module, a comparison module and a correction module. The precipitation forecast correction method and device adopt satellite infrared cloud images as a data source, are less affected by other limiting conditions, have little interference on the final result, can be corrected, and can obtain a precipitation forecast with high accuracy.

Description

Technical Field

[0001] This invention relates to the field of meteorological forecasting technology, and in particular to a method and apparatus for correcting precipitation forecasts. Background Technology

[0002] Precipitation forecasting is one of the most important meteorological forecasting services, including forecasts of precipitation amount and precipitation location. Due to the inherent uncertainties in the initial values ​​of numerical models and the models themselves, precipitation forecasts are subject to certain errors. Consequently, the guiding significance of the risk level corresponding to precipitation amount is reduced. Therefore, it is essential to make corrections based on the risk registration corresponding to the precipitation forecasts from numerical models.

[0003] In recent years, precipitation forecasting based on traditional statistical methods has made some progress. Current precipitation statistics are mostly based on radar echo data and surface precipitation observations. Machine learning methods are used to establish the relationship between precipitation forecasts and radar echo data, and big data processing technology is used to quickly obtain minute-by-minute precipitation forecasts from weather radars, which serve as a scheme to correct the risk level of precipitation forecasts. However, there are shortcomings in using radar echoes to predict precipitation amount and precipitation areas. The main issues are: during precipitation, due to water droplet evaporation, atmospheric movement, and phase changes in water, the radar reflectivity varies significantly in the vertical direction. Simultaneously, the path of radar electromagnetic waves (even horizontally emitted paths) moves away from the ground surface with increasing distance; the greater the horizontal distance, the greater the vertical distance. Therefore, the difference between radar-observed precipitation and actual precipitation on the ground increases, leading to unreliable precipitation forecasts. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a precipitation forecast correction method with high accuracy. Additionally, a precipitation forecast correction device is also provided.

[0005] The technical solution adopted by the present invention to solve its technical problem is: the precipitation forecast correction method includes S1, acquiring cloud top brightness temperature data, cloud cluster information data and station observation precipitation data in the previous adjacent time period of the target area to be forecasted;

[0006] S2. Divide the cloud cluster into multiple observation areas, and statistically analyze the temperature type information and quantity in each observation area based on the cloud top brightness temperature data; wherein, the temperature type information includes at least low temperature type, medium temperature type and high temperature type;

[0007] S3. Calculate the proportion of low-temperature types in the observation area and determine whether the proportion exceeds the threshold. If so, determine that the current observation area is a heavy precipitation area.

[0008] S4. Repeat step S3 until all the segmented observation areas are traversed, calculate the proportion of heavy precipitation area in the cloud cluster, and determine whether the proportion exceeds the threshold. If so, it is judged as a heavy precipitation cloud cluster.

[0009] S5. Obtain the cloud cluster information data at the current moment and compare it with the cloud cluster information data in the previous adjacent time period to determine whether the cloud cluster area has increased. If so, proceed to step S6.

[0010] S6. Statistically analyze the changing trend of the number of low-temperature types in each observation area from the current time to the previous adjacent time period, determine the changing trend of the observation area where the proportion of low-temperature types exceeds the threshold, and correct the precipitation level forecast at the current time.

[0011] Furthermore, the low-temperature type has a temperature range of -79℃ to -54℃, the medium-temperature type has a temperature range of -53℃ to -21℃, and the high-temperature type has a temperature range of -20℃ to -9℃.

[0012] Furthermore, the trend of changes in the observation area where the proportion of low-temperature precipitation exceeds a threshold is used to correct the precipitation level forecast at the current moment, including...

[0013] If the observed areas showing an increasing trend in cloud area and an increasing trend in the proportion of low-temperature precipitation exceeding the threshold, then the precipitation level should be revised upwards; or...

[0014] If the observed area of ​​cloud clusters shows an increasing trend, and the proportion of low-temperature precipitation types exceeding the threshold shows a decreasing trend, then the precipitation level should be revised downward.

[0015] Furthermore, given the increasing trend in cloud area and the unchanged proportion of observation areas with low-temperature precipitation exceeding the threshold, the current precipitation forecast is revised, including...

[0016] Obtain the generation time of the cloud cluster and determine its current lifecycle.

[0017] If the current cloud cluster is a non-long-life cloud cluster, the precipitation level will be revised upwards; among which, the non-long-life cloud cluster is defined as 0.5 hours to 14 hours.

[0018] Furthermore, the method of determining the changing trend of observation areas where the proportion of low-temperature precipitation exceeds a threshold and correcting the precipitation level forecast at the current moment also includes...

[0019] Obtain the center location information of the cloud cluster;

[0020] Based on the observation area to which the center location information belongs, the neighboring observation areas are determined, and the changing trend of the observation areas with a low temperature type ratio exceeding the threshold in the neighboring observation areas is judged, so as to correct the precipitation level forecast at the current time.

[0021] Based on the same idea, a precipitation forecast correction device is also provided, including...

[0022] The data acquisition module is used to acquire cloud top brightness temperature data, cloud cluster information data, and station-observed precipitation data for the target area to be forecasted in the previous adjacent time period.

[0023] The image processing module is used to divide the cloud cluster into multiple observation areas and to statistically analyze the temperature type information and quantity in each observation area based on the cloud top brightness temperature data; wherein, the temperature type information includes at least low temperature type, medium temperature type and high temperature type;

[0024] The calculation module is used to calculate the proportion of low-temperature types in the observation area and determine whether the proportion exceeds the threshold. If so, the current observation area is determined to be a heavy precipitation area.

[0025] Repeat the process until all the segmented observation areas are traversed, calculate the proportion of heavy precipitation areas in the cloud cluster, and determine whether the proportion exceeds the threshold. If so, it is determined to be a heavy precipitation cloud cluster.

[0026] The comparison module is used to obtain cloud cluster information data at the current moment and compare it with the cloud cluster information data in the previous adjacent time period to determine whether the cloud cluster area has increased.

[0027] The correction module is used to statistically analyze the changing trend of the number of low-temperature types in each observation area from the current time to the previous adjacent time period, determine the changing trend of the observation area where the proportion of low-temperature types exceeds the threshold, and correct the precipitation level forecast at the current time.

[0028] Furthermore, the low-temperature type has a temperature range of -79℃ to -54℃, the medium-temperature type has a temperature range of -53℃ to -21℃, and the high-temperature type has a temperature range of -20℃ to -9℃.

[0029] Furthermore, the correction module is also used to,

[0030] If the observed areas showing an increasing trend in cloud area and an increasing trend in the proportion of low-temperature precipitation exceeding the threshold, then the precipitation level should be revised upwards; or...

[0031] If the observed area of ​​cloud clusters shows an increasing trend, and the proportion of low-temperature precipitation types exceeding the threshold shows a decreasing trend, then the precipitation level should be revised downward.

[0032] Furthermore, the correction module is also used to,

[0033] Obtain the generation time of the cloud cluster and determine its current lifecycle.

[0034] If the current cloud cluster is a non-long-life cloud cluster, the precipitation level will be revised upwards; among which, the non-long-life cloud cluster is defined as 0.5 hours to 14 hours.

[0035] Furthermore, the correction module is also used to,

[0036] Obtain the center location information of the cloud cluster;

[0037] Based on the observation area to which the center location information belongs, the neighboring observation areas are determined, and the changing trend of the observation areas with a low temperature type ratio exceeding the threshold in the neighboring observation areas is judged, so as to correct the precipitation level forecast at the current time.

[0038] This invention's precipitation forecast correction method, after acquiring cloud top brightness temperature data and cloud cluster information data, segments and numbers the cloud cluster data. Then, it calculates the temperature type of each observation area and uses it as an evaluation of the precipitation type for that area. Next, it evaluates the precipitation type of the entire cloud cluster based on each observation area, using this as a reference for precipitation level warnings. Furthermore, it uses the changing trends of cloud cluster area and specific temperature type over the current and previous time periods as influencing factors for precipitation level warning correction. Based on the changes in growing cloud clusters and specific temperature types, it corrects the precipitation level warning accordingly. This invention's precipitation forecast correction method uses satellite infrared cloud images as the data source. Compared to existing technologies that use radar echoes, these infrared cloud images are less affected by other limiting conditions, have less interference with the final result, and correct precipitation level warnings within a preset period, resulting in highly accurate precipitation forecasts. Furthermore, the precipitation forecast correction device based on the same idea has the same technical effect as the correction method of the present invention. The device uses satellite infrared cloud images as the data source. Compared with the radar echo used in the prior art, the infrared cloud images are less affected by other limiting conditions, have less interference with the acquisition of the final result, and correct precipitation level alarms within a preset period, thus obtaining precipitation forecasts with higher accuracy. Attached Figure Description

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0040] Figure 1 This is a flowchart of the precipitation forecast correction method according to an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of satellite infrared cloud images over a historical time period, as described in an embodiment of the present invention.

[0042] Figure 3 This is a schematic diagram of the satellite infrared cloud image at the current moment in an embodiment of the present invention;

[0043] Figure 4 This is a structural block diagram of the precipitation forecast correction device according to an embodiment of the present invention. Detailed Implementation

[0044] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0045] like Figure 1As shown, this precipitation forecast correction method includes the following steps.

[0046] S1. Obtain cloud top brightness temperature data, cloud cluster information data, and station-observed precipitation data for the target area to be forecasted within the preceding adjacent time period. The preceding adjacent time period can be adjusted by removing the cloud top, such as 10 minutes, 5 minutes, or other longer time periods like 60 minutes or 40 minutes. Cloud top brightness temperature data can be obtained through meteorological satellite systems. Cloud cluster data includes information such as cloud size, cloud origin or entry point, cloud scale, and lifespan.

[0047] S2. Divide the cloud cluster into multiple observation areas, and statistically analyze the temperature type information and quantity within each observation area based on cloud top brightness temperature data. The temperature type information includes at least three categories: low temperature, medium temperature, and high temperature. The low temperature range is -79℃ to -54℃, the medium temperature range is -53℃ to -21℃, and the high temperature range is -20℃ to -9℃. Cloud cluster segmentation can employ existing image processing techniques, cutting the image into pixels of the required size, and then combining the cloud top brightness temperature data to statistically analyze specific temperature types. In this embodiment, the temperature type information includes at least three categories: low temperature, medium temperature, and high temperature. The low temperature range is -79℃ to -54℃, the medium temperature range is -53℃ to -21℃, and the high temperature range is -20℃ to -9℃. This embodiment sets the temperature type information into three categories for convenient statistics without excessive computation. Of course, if the computational capacity meets the requirements, the three temperature types can be further classified into two levels to determine different temperature grades. In this embodiment, the low temperature type is used as a reference for determining the area of ​​heavy precipitation. This is mainly based on the analysis of typhoons No. 6, 7, 14, 15 and 30 in 1994. The results were obtained through experimental analysis. The cloud top temperature above the rainstorm area is generally ≤-54℃.

[0048] S3. Calculate the proportion of low-temperature clouds in the observation area and determine whether the proportion exceeds the threshold. If so, determine that the current observation area is a heavy precipitation area. S4. Repeat step S3 until all the divided observation areas are traversed, calculate the proportion of heavy precipitation areas in the cloud clusters, and determine whether the proportion exceeds the threshold. If so, determine that it is a heavy precipitation cloud cluster.

[0049] The cloud cluster is segmented, and calculations are performed on each segmented observation area to facilitate parallel processing. After segmentation, the center of the cloud cluster can be determined based on satellite infrared cloud images. Then, adjacent or within a certain range of the center can be identified. Since rainfall from cloud clusters is generally heavier near the center, the precipitation area affected by the center can be determined, and the number of observation areas centered on the center can be expanded as needed. Compared to directly defining a radius range based on the center and then acquiring the affected area, this processing step directly retrieves the processing data, significantly saving computational resources. In this embodiment, if 15%–60% of the pixels in the cloud cluster are ≤-54℃, it is identified as a heavy precipitation cloud cluster.

[0050] S5. Obtain the current cloud cluster information data and compare it with the cloud cluster information data in the previous adjacent time period to determine whether the cloud cluster area has increased. If so, proceed to step S6. The life cycle of a cloud cluster, from its growth to its dissipation, is inevitable. Cloud cluster growth has a significant impact on rainfall. The precipitation of the same cloud cluster at different development stages will vary greatly. If the cloud cluster area continues to increase and the number of low-temperature types continues to increase, then the cloud cluster is developing; conversely, it is dissipating.

[0051] S6. Statistically analyze the changing trend of the number of low-temperature types in each observation area from the current time to the previous adjacent time period, determine the changing trend of the observation area where the proportion of low-temperature types exceeds the threshold, and correct the precipitation level forecast at the current time.

[0052] Step S6 includes: if the area of ​​cloud clusters shows an increasing trend and the proportion of low-temperature types exceeds the threshold in the observation area, then the precipitation level is corrected to be increased.

[0053] Alternatively, if the observed area shows an increasing trend in cloud area and a decreasing trend in the proportion of low-temperature precipitation exceeding the threshold, the precipitation level should be revised downward.

[0054] Of course, in other cases, namely when the cloud area shows a tendency to increase and the proportion of low-temperature precipitation exceeding the threshold remains the same, the correction to the current precipitation forecast mainly takes into account the cloud's life cycle, including...

[0055] Obtain the generation time of the cloud cluster and determine its current lifecycle.

[0056] If the current cloud cluster is a non-long-life cloud cluster, the precipitation level will be revised upwards; among which, the non-long-life cloud cluster is defined as 0.5 hours to 14 hours.

[0057] Combining steps S3 and S4, to differentiate precipitation forecasts for different regions, it is necessary to distinguish the location of cloud clusters, accurately assess influencing factors, and obtain information on the center location of cloud clusters.

[0058] Based on the observation area to which the center location information belongs, the neighboring observation areas are determined, and the changing trend of the observation areas with a low temperature type ratio exceeding the threshold in the neighboring observation areas is judged, so as to correct the precipitation level forecast at the current time.

[0059] The precipitation forecast correction method, after acquiring cloud top brightness temperature data and cloud cluster information data, segments and numbers the cloud cluster data. Then, it calculates the temperature type of each observation area and uses it as an evaluation of the precipitation type for that area. Next, it evaluates the precipitation type of the entire cloud cluster based on each observation area, using this as a reference for precipitation level warnings. Furthermore, it uses the trends in cloud cluster area change and specific temperature type change over the current and previous time periods as influencing factors for precipitation level warning correction. Based on the changes in growing cloud clusters and specific temperature types, it corrects the precipitation level warning accordingly. This invention's precipitation forecast correction method uses satellite infrared cloud images as the data source. Compared to existing technologies that use radar echoes, these infrared cloud images are less affected by other limiting conditions, have less interference with the final result, and correct precipitation level warnings within a preset period, resulting in highly accurate precipitation forecasts.

[0060] like Figure 3 and Figure 4 As shown, where Figure 3 The distance shown in the satellite infrared cloud image Figure 4 The satellite infrared cloud image shown is displayed at 40-minute intervals. The formation and dissipation of the cloud clusters in the square and circular areas in the upper left corner of the image are clearly visible. Figure 3 At the time shown, local stations recorded rainfall of 18 millimeters per hour, classified as a rainstorm, with 42% of the rainfall occurring at low temperatures. The cloud cover gradually dissipated after 40 minutes, and the precipitation gradually ceased. Figure 4 The rainfall observed at the local station in the area shown was 14 mm per hour at that time, lasting for 6 hours, with low-temperature rainfall accounting for 32%-40% of the total.

[0061] like Figure 2 As shown, the precipitation forecast correction device includes...

[0062] The data acquisition module is used to obtain cloud top brightness temperature data, cloud cluster information data, and station-observed precipitation data for the target area to be forecasted within the preceding adjacent time period. The preceding adjacent time period can be adjusted by removing the cloud top, such as 10 minutes, 5 minutes, or other longer time periods like 60 minutes or 40 minutes, depending on specific needs. Cloud top brightness temperature data can be obtained through meteorological satellite systems. Cloud cluster data includes information such as cloud size, cloud origin or entry point, cloud scale, and lifespan.

[0063] The image processing module is used to divide the cloud cluster into multiple observation areas and statistically analyze the temperature type information and quantity in each observation area based on the cloud top brightness temperature data. The temperature type information includes at least low temperature, medium temperature and high temperature. The low temperature range is -79℃ to -54℃, the medium temperature range is -53℃ to -21℃, and the high temperature range is -20℃ to -9℃.

[0064] The low-temperature type has a temperature range of -79℃ to -54℃, the medium-temperature type has a temperature range of -53℃ to -21℃, and the high-temperature type has a temperature range of -20℃ to -9℃. Cloud segmentation can be performed using existing image processing techniques, cutting the image into pixels of the required size, and then combining the cloud top brightness and temperature data to statistically determine the specific temperature type. In this embodiment, the temperature type information includes at least low-temperature, medium-temperature, and high-temperature types, where the low-temperature type has a temperature range of -79℃ to -54℃, the medium-temperature type has a temperature range of -53℃ to -21℃, and the high-temperature type has a temperature range of -20℃ to -9℃. This embodiment sets the temperature type information into three categories, which is convenient for statistics and will not cause excessive computation. Of course, if the computing power meets the requirements, the three temperature types can be further classified into two levels to divide different temperature grades. In this embodiment, the low temperature type is used as a reference for determining the area of ​​heavy precipitation. This is mainly based on the analysis of typhoons No. 6, 7, 14, 15 and 30 in 1994. The results were obtained through experimental analysis. The cloud top temperature above the rainstorm area is generally ≤-54℃.

[0065] The calculation module is used to calculate the proportion of low-temperature types in the observation area and determine whether the proportion exceeds the threshold. If so, the current observation area is determined to be a heavy precipitation area.

[0066] Repeat the process until all the segmented observation areas are traversed, calculate the proportion of heavy precipitation areas in the cloud cluster, and determine whether the proportion exceeds the threshold. If so, it is determined to be a heavy precipitation cloud cluster.

[0067] The cloud cluster is segmented, and calculations are performed on each segmented observation area to facilitate parallel processing. After segmentation, the center of the cloud cluster can be determined based on satellite infrared cloud images. Then, adjacent or within a certain range of the center can be identified. Since rainfall from cloud clusters is generally heavier near the center, the precipitation area affected by the center can be determined, and the number of observation areas centered on the center can be expanded as needed. Compared to directly defining a radius range based on the center and then acquiring the affected area, this processing step directly retrieves the processing data, significantly saving computational resources. In this embodiment, if 15%–60% of the pixels in the cloud cluster are ≤-54℃, it is identified as a heavy precipitation cloud cluster.

[0068] The comparison module is used to acquire cloud cluster information data at the current moment and compare it with cloud cluster information data in the previous adjacent time period to determine whether the cloud cluster area has increased. The life cycle of a cloud cluster, from growth to dissipation, is inevitable. Cloud cluster growth has a significant impact on rainfall. The precipitation of the same cloud cluster can vary greatly at different development stages. If the cloud cluster area continues to increase and the number of low-temperature types continues to increase, then the cloud cluster is developing; conversely, it is dissipating.

[0069] The correction module is used to statistically analyze the changing trend of the number of low-temperature types in each observation area from the current time to the previous adjacent time period, determine the changing trend of the observation area where the proportion of low-temperature types exceeds the threshold, and correct the precipitation level forecast at the current time.

[0070] This correction module is also used to adjust the precipitation level if the observed area shows an increasing trend in cloud area and an increasing trend in the proportion of low-temperature precipitation exceeding the threshold; or,

[0071] If the observed area of ​​cloud clusters shows an increasing trend, and the proportion of low-temperature precipitation types exceeding the threshold shows a decreasing trend, then the precipitation level should be revised downward.

[0072] Of course, in other cases, such as when the cloud area is increasing and the proportion of low-temperature clouds exceeds the threshold in the observation area remains the same, the correction to the precipitation level forecast at the current moment mainly takes into account the life history of the cloud, including the time of cloud formation and the determination of the current cloud life cycle.

[0073] If the current cloud cluster is a non-long-life cloud cluster, the precipitation level will be revised upwards; among which, the non-long-life cloud cluster is defined as 0.5 hours to 14 hours.

[0074] Combining steps S3 and S4, to differentiate precipitation forecasts for different regions, it is possible to distinguish the location of cloud clusters, accurately assess influencing factors, and also includes information on the center location of cloud clusters.

[0075] Based on the observation area to which the center location information belongs, the neighboring observation areas are determined, and the changing trend of the observation areas with a low temperature type ratio exceeding the threshold in the neighboring observation areas is judged, so as to correct the precipitation level forecast at the current time.

[0076] The precipitation forecast correction device and method are based on the same concept and have the same technical effects as the correction method of this invention. Using satellite infrared cloud images as the data source, compared to the radar echo method used in the prior art, these infrared cloud images are less affected by other limiting conditions, have less interference with the final result, and correct precipitation level warnings within a preset period, thus obtaining precipitation forecasts with higher accuracy.

[0077] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0078] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for correcting precipitation forecasts, characterized in that, include S1. Obtain cloud top brightness temperature data, cloud cluster information data, and station observation precipitation data for the target area to be predicted in the previous adjacent time period. S2. Divide the cloud cluster into multiple observation areas, and statistically analyze the temperature type information and quantity in each observation area based on the cloud top brightness temperature data; wherein, the temperature type information includes at least low temperature type, medium temperature type and high temperature type; S3. Calculate the proportion of low-temperature types in the observation area and determine whether the proportion exceeds the threshold. If so, determine that the current observation area is a heavy precipitation area. S4. Repeat step S3 until all the segmented observation areas are traversed, calculate the proportion of heavy precipitation area in the cloud cluster, and determine whether the proportion exceeds the threshold. If so, it is judged as a heavy precipitation cloud cluster. S5. Obtain the cloud cluster information data at the current moment and compare it with the cloud cluster information data in the previous adjacent time period to determine whether the cloud cluster area has increased. If so, proceed to step S6. S6. Statistically analyze the changing trend of the number of low-temperature types in each observation area from the current time to the previous adjacent time period, determine the changing trend of the observation area where the proportion of low-temperature types exceeds the threshold, and correct the precipitation level forecast at the current time. The low-temperature type has a temperature range of -79℃ to -54℃, the medium-temperature type has a temperature range of -53℃ to -21℃, and the high-temperature type has a temperature range of -20℃ to -9℃. The trend of changes in the observation area where the proportion of low-temperature precipitation exceeds a threshold is used to correct the precipitation level forecast for the current time, including... If the observed areas showing an increasing trend in cloud area and an increasing trend in the proportion of low-temperature precipitation exceeding the threshold, then the precipitation level should be revised upwards; or... If the observed area of ​​cloud clusters shows an increasing trend, and the proportion of low-temperature precipitation types exceeding the threshold shows a decreasing trend, then the precipitation level should be revised downward.

2. The precipitation forecast correction method according to claim 1, characterized in that, If the cloud area shows a trend of increasing size and the proportion of low-temperature precipitation exceeding the threshold remains the same, the precipitation level forecast for the current time will be revised, including... Obtain the generation time of the cloud cluster and determine its current lifecycle. If the current cloud cluster is a non-long-life cloud cluster, the precipitation level will be revised upwards; among which, the non-long-life cloud cluster is defined as 0.5 hours to 14 hours.

3. The precipitation forecast correction method according to claim 1, characterized in that, The method of determining the changing trend of observation areas where the proportion of low-temperature precipitation exceeds a threshold, and correcting the precipitation level forecast for the current moment, also includes... Obtain the center location information of the cloud cluster; Based on the observation area to which the center location information belongs, the neighboring observation areas are determined, and the changing trend of the observation areas with a low temperature type ratio exceeding the threshold in the neighboring observation areas is judged, so as to correct the precipitation level forecast at the current time.

4. A precipitation forecast correction device, characterized in that, include The data acquisition module is used to acquire cloud top brightness temperature data, cloud cluster information data, and station-observed precipitation data for the target area to be forecasted in the previous adjacent time period. The image processing module is used to divide the cloud cluster into multiple observation areas and to statistically analyze the temperature type information and quantity in each observation area based on the cloud top brightness temperature data; wherein, the temperature type information includes at least low temperature type, medium temperature type and high temperature type; The calculation module is used to calculate the proportion of low-temperature types in the observation area and determine whether the proportion exceeds the threshold. If so, the current observation area is determined to be a heavy precipitation area. Repeat the process until all the segmented observation areas are traversed, calculate the proportion of heavy precipitation areas in the cloud cluster, and determine whether the proportion exceeds the threshold. If so, it is determined to be a heavy precipitation cloud cluster. The comparison module is used to obtain cloud cluster information data at the current moment and compare it with the cloud cluster information data in the previous adjacent time period to determine whether the cloud cluster area has increased. The correction module is used to statistically analyze the changing trend of the number of low-temperature types in each observation area from the current time to the previous adjacent time period, determine the changing trend of the observation area where the proportion of low-temperature types exceeds the threshold, and correct the precipitation level forecast at the current time. The low-temperature type has a temperature range of -79℃ to -54℃, the medium-temperature type has a temperature range of -53℃ to -21℃, and the high-temperature type has a temperature range of -20℃ to -9℃. The correction module is also used for, If the observed areas showing an increasing trend in cloud area and an increasing trend in the proportion of low-temperature precipitation exceeding the threshold, then the precipitation level should be revised upwards; or... If the observed area where the cloud cluster area shows an increasing trend and the proportion of low-temperature types exceeds the threshold shows a decreasing trend, then the precipitation level should be corrected to decrease. The correction module is also used for, Obtain the generation time of the cloud cluster and determine its current lifecycle. If the current cloud cluster is a non-long-life cloud cluster, the precipitation level will be revised upwards; among which, the non-long-life cloud cluster is defined as 0.5 hours to 14 hours.

5. The precipitation forecast correction device according to claim 4, characterized in that, The correction module is also used for, Obtain the center location information of the cloud cluster; Based on the observation area to which the center location information belongs, the neighboring observation areas are determined, and the changing trend of the observation areas with a low temperature type ratio exceeding the threshold in the neighboring observation areas is judged, so as to correct the precipitation level forecast at the current time.

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