A method for correcting precipitation numerical forecasts based on adaptive spatiotemporal scale selection

Abstract

This application discloses a precipitation numerical forecast correction method based on adaptive spatiotemporal scale selection. Spatially, it examines the target area and its surrounding areas. Temporally, it designs first and second real-time data for the input of the NWP model, and designs first and second future time periods for the output of the NWP model. During the execution of this application's technical solution, a computer can execute a computer program based on the method steps described in this application, thus achieving automated precipitation numerical forecast correction based on adaptive spatiotemporal scale selection. Data selection is based on sound criteria at both the temporal and spatial scales, avoiding errors and distortions caused by excessive human intervention due to personal preferences. It also enables the application of digital data processing technology in the field of precipitation numerical correlation technology.

Description

Technical Field

[0001] This application relates to the field of digital data processing technology, and more particularly to a method for digital computing or data processing specifically applicable to a particular application, specifically a precipitation numerical forecast correction method based on adaptive spatiotemporal scale selection. Background Technology

[0002] The NWP (Numerical Weather Prediction) model is a mathematical modeling system based on physical laws. It is a numerical model that predicts weather by solving atmospheric dynamic equations. Its core lies in transforming physical laws into mathematical equations and combining them with initial observational data for numerical simulation. Its core principle is to directly solve atmospheric physical equations (such as fluid dynamics and thermodynamics equations) and use supercomputers for numerical integration to calculate future weather conditions. It does not require training with historical data, relying instead on real-time observational data (satellites, radar, etc.) as initial conditions, but the model itself does not "learn" patterns from data. This makes the output of the NWP model highly dependent on the input data; determining which data to use for more accurate output relies heavily on human experience. With the intensification of El Niño, the losses caused by the inability to accurately predict extreme precipitation events could be severe. Therefore, improving the correction efficiency of numerical precipitation forecasts and achieving spatiotemporal adaptation while transitioning from manual to automated methods is a pressing issue.

[0003] For example, patent announcement number CN111832828B, titled "Intelligent Precipitation Prediction Method Based on Fengyun-4 Meteorological Satellite" (main classification number: G06F30 / 27), achieves intelligent prediction of precipitation based on data collected by the satellite. This demonstrates, on the one hand, the great potential of electronic digital data processing technology in generating precipitation numerical data; on the other hand, it also shows that there is a broad prospect for further technological development in this field. Summary of the Invention

[0004] This application provides a precipitation numerical forecast correction method based on adaptive spatiotemporal scale selection, which at least partially solves the above-mentioned technical problems.

[0005] The embodiments of this application adopt the following technical solutions:

[0006] In a first aspect, embodiments of this application provide a method for correcting numerical precipitation forecasts based on adaptive spatiotemporal scale selection, the method comprising:

[0007] Acquire first historical data and second historical data for the target area; the first historical data is data collected for the target area when extreme precipitation events occurred in the target area in the past; the second historical data is data collected for the target area and the surrounding areas when extreme precipitation events occurred in the surrounding areas of the target area in the past and affected the target.

[0008] The first initial field and first boundary conditions are constructed based on the first real-time data across the entire range;

[0009] Using the NWP model based on the first initial field and the first boundary conditions, the trend weather forecast results for the entire range within the first future time period are obtained;

[0010] If the trend indicated by the trend weather forecast result is greater than a preset trend threshold, and the first real-time data of the target area matches the first historical data, then the NWP model is used to obtain the first predicted precipitation result of the target area in the second future time period based on the second real-time data of the target area; and a second predicted precipitation result is obtained based on the second real-time data of the target area and the surrounding area; the time period to which the second real-time data belongs is shorter than the time period to which the first real-time data belongs;

[0011] The first predicted precipitation result is corrected using the second predicted precipitation result.

[0012] In an optional embodiment of this specification, the first predicted precipitation result is corrected using the second predicted precipitation result, including:

[0013] When the precipitation amount in the target area indicated by the first predicted precipitation result is not greater than the precipitation amount in the target area indicated by the second predicted precipitation result, the precipitation amount in the target area indicated by the first predicted precipitation result is weighted by a preset second weight; the second weight is positively correlated with the trend indicated by the trend weather forecast result and positively correlated with the precipitation synergy between the target area and the surrounding area; the precipitation synergy is negatively correlated with the area ratio of the target area and the surrounding area and positively correlated with the ratio of extreme precipitation events occurring in both areas in history to extreme precipitation events occurring in one of them.

[0014] In an optional embodiment of this specification, the method further includes:

[0015] If the trend indicated by the trend weather forecast result is greater than the trend threshold, and the first real-time data of the target area matches the first historical data, and the first real-time data of the target area and the first real-time data of the surrounding area both match the second historical data, then the precipitation amount of the target area indicated by the first predicted precipitation result is weighted using the second weight and a preset third weight; the third weight is positively correlated with the precipitation amount indicated by the first historical data that matches the first real-time data of the target area, and is also positively correlated with the precipitation synergy.

[0016] In an optional embodiment of this specification, the method further includes:

[0017] If the duration of the second future time period is no more than 6 hours, and the precipitation amount of the target area indicated by the first predicted precipitation result is greater than the precipitation amount of the target area indicated by the second predicted precipitation result, then the precipitation amount of the target area indicated by the first predicted precipitation result shall be taken as the target precipitation amount of the target area.

[0018] In an optional embodiment of this specification, the method further includes:

[0019] When the precipitation synergy is greater than a preset synergy threshold, the NWP model is used to obtain the third trend precipitation result of the target area in the first future time period based on the first real-time data of the target area.

[0020] If the precipitation in the target area indicated by the third trend precipitation result is higher than the precipitation in the same period of the previous cycle;

[0021] The second weight and the preset fourth weight are used to weight the precipitation in the target area represented by the first predictive precipitation result. The fourth weight is positively correlated with the increase in precipitation in the target area compared with the same period of the previous cycle, and is positively correlated with the degree of deviation of the dynamic field elements and thermal field elements in the trend weather forecast results obtained based on the first real-time data of the target area from the historical average.

[0022] In an optional embodiment of this specification, the method further includes:

[0023] The first real-time data includes at least one of the following: conventional ground observation data, upper-air sounding data, satellite remote sensing data, radar observation data, and aircraft observation data.

[0024] In an optional embodiment of this specification, the method further includes:

[0025] The first real-time data falls within a time range of 6 to 12 hours; the second real-time data falls within a time range of 3 to 6 hours.

[0026] In an optional embodiment of this specification, the method further includes:

[0027] The synergy threshold is positively correlated with the duration of the second future time period.

[0028] In an optional embodiment of this specification, the method further includes:

[0029] If the duration of the second future time period is no more than 6 hours, the trend weather forecast result is obtained by weighting with a first weight; the first weight is positively correlated with the frequency of El Niño events occurring within a first historical time period specified from the current time.

[0030] Secondly, embodiments of this application also provide a precipitation numerical forecast correction device based on adaptive spatiotemporal scale selection, the device being used to implement the method steps in the first aspect.

[0031] Thirdly, embodiments of this application also provide an electronic device, including:

[0032] Processor; and

[0033] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method described in the first aspect.

[0034] Fourthly, embodiments of this application also provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device including multiple applications, cause the electronic device to perform the steps of the method described in the first aspect.

[0035] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0036] The method provided in this application examines the target area and its surrounding areas at a spatial scale. At a temporal scale, it designs first and second real-time data for the input of the NWP model, and designs first and second future time periods for the output of the NWP model. During the execution of the technical solution in this application, a computer can execute a computer program (e.g., a script) based on the method steps described in this application, thus achieving automated precipitation numerical forecast correction based on adaptive spatiotemporal scale selection. Data selection is based on evidence at both the temporal and spatial scales, avoiding errors and distortions caused by excessive human intervention due to personal preferences. Furthermore, addressing the issue that NWP models cannot accumulate knowledge or optimize their capabilities through learning like artificial intelligence models, the method provided in this application intervenes in both the input and output at both the temporal and spatial scales. Even if the NWP model cannot accumulate knowledge, the output obtained through the method described in this specification reflects the impact of differences at different macroscopic and microscopic levels on accuracy, which helps improve the accuracy of the NWP model output. This enables the application of digital data processing technology in the field of precipitation numerical correlation technology. Attached Figure Description

[0037] Figure 1 A schematic diagram illustrating the process of a precipitation numerical forecast correction method based on adaptive spatiotemporal scale selection provided in the embodiments of this specification;

[0038] Figure 2 This is a schematic diagram of the structure of an electronic device in an embodiment of this specification. Detailed Implementation

[0039] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Similar elements in different embodiments are referred to by related similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the present application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to the present application are not shown or described in the specification. This is to avoid obscuring the core parts of the present application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0040] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0041] The serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. The terms "connection" and "linkage" used in this application, unless otherwise specified, include both direct and indirect connections (linkages).

[0042] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0043] like Figure 1 As shown, the precipitation numerical forecast correction method based on adaptive spatiotemporal scale selection in this specification includes the following steps:

[0044] S100: Obtain the first and second historical data of the target area.

[0045] In this manual, "target area" and "surrounding area" refer to geographical locations. Their meanings may differ in different scenarios. For example, in a provincial-level meteorological forecast scenario (such as precipitation), the provincial meteorological bureau may use the province itself as the target area and neighboring provinces as the surrounding area. In a meteorological forecast scenario for a specific location, the target area may be the region containing that specific location (which could be a high-value target, such as a location with a high risk of landslides, or a location with high-value astronomical observation facilities that may be affected by severe precipitation). In other words, the size of both the target area and the surrounding area can be determined based on the actual circumstances.

[0046] To enhance the specificity of data processing and model output, the surrounding area in this specification does not include all areas surrounding the target area. Only areas that have historically occurred extreme precipitation events (precipitation events that caused disasters) that have impacted the target area (e.g., both area A and the target area experienced extreme precipitation events simultaneously; the target area's own disaster resilience was insufficient to cause damage, but area A suffered a disaster due to the extreme precipitation event, which then affected the target area; in this case, area A is considered a surrounding area of ​​the target area. Otherwise, area A is not a surrounding area of ​​the target area. There may also be cases where area A experienced extreme precipitation events, but the target area did not; the judgment criteria are the same) and are adjacent to the target area, are considered surrounding areas. The definition of "impact" can be determined based on actual business needs; for example, damage to buildings might be considered an "impact," or simply traffic disruption might be considered an "impact."

[0047] The first historical data in this specification refers to data collected on the target area during historical extreme precipitation events (including at least one of the following: conventional ground observation data, upper-air sounding data, satellite remote sensing data, radar observation data, and aircraft observation data. These data characterize at least one of the following attributes: air pressure, temperature, humidity and wind speed and direction, precipitation, atmospheric vertical temperature and humidity profile, ozone, trace gases, soil temperature, snow cover, and depth). The second historical data refers to data collected on the target area and the surrounding area during historical extreme precipitation events that affected the target (i.e., extreme precipitation events in the surrounding area affected the target area). The second historical data contains the same or similar items as the first historical data.

[0048] As can be seen, the purpose of correcting precipitation numerical forecasts using the method described in this specification is based on the potential impacts of precipitation. The data input to the model is selected on a spatial scale according to the degree of potential impacts, in order to accurately prevent potential damage, especially given the increasingly severe El Niño phenomenon, where such damage cannot be ignored.

[0049] In practical applications, the target area may not be unique. The description in this manual uses only one target area as an example, and other target areas can also be described using the same method. The surrounding area may also not be unique. If two surrounding areas are not adjacent, these two target areas can be examined separately.

[0050] S102: Construct the first initial field and the first boundary conditions based on the first real-time data of the full range.

[0051] The "full range" in this specification refers to the maximum geographical area from which data can be obtained. To improve global meteorological data processing capabilities, multiple countries have reached agreements to share at least some meteorological data; therefore, the specific size of the full range depends on the countries supporting data sharing. Ideally, meteorological data collection stations are located all over the world, allowing for global coverage of the full range. In the methodology of this specification, meteorological data collected from both the full range and target areas are included in the scope of consideration, which helps improve the globality of weather forecasting.

[0052] This specification sets up a first real-time data and a second implementation data for the model input on different time scales. The duration of the first real-time data is longer than the duration of the second implementation data. In an optional embodiment of this specification, the time range of the first real-time data is 6 to 12 hours (used for medium-term trends of 3-10 days, such as large-scale weather processes like cold waves and widespread rain and snow, which are more predictable. The accuracy of medium-term forecasts depends more on the accuracy of the initial state of global large-scale circulation patterns (such as the westerly winds and subtropical high pressure). A 12-hour window can utilize more global observation data (especially valuable radiosonde data) to better analyze and correct these large-scale systems, which is beneficial for providing accurate large-scale circulation background fields and boundary conditions as the primary task of global models. The 12-hour window achieves a good balance between data and computational costs); the time range of the second real-time data is 3 to 6 hours (used for forecasting rapidly changing weather such as short-duration heavy precipitation, thunderstorms, and fine structures of typhoons, which have short lifespans and evolve quickly. A "fresher" initial field can more accurately capture key information such as the trigger point of convection and the location of mesoscale vortices).

[0053] In related technologies, the technical means used to construct the first initial field and the first boundary conditions are applicable to this specification, where conditions permit. In this step, these are observational data from around the world, which are fused into a complete, physically self-consistent three-dimensional initial field, i.e., the first initial field, through a data assimilation system.

[0054] S104: Using the NWP model based on the first initial field and the first boundary conditions, obtain the trend weather forecast results for the entire range within the first future time period.

[0055] The trend weather forecasts in this manual represent the magnitude of the trend toward El Niño, which may be a continuation of historical El Niño events. The longer the trend is expected to last in the first future time period, the stronger it will be.

[0056] The NWP model outputs specific data representing future weather conditions, and this trend can be reflected by comparing the model's output data with historical averages. The magnitude of the difference between the overall data (exemplarily represented by eigenvectors) and the mean of historical data can be used to represent this trend.

[0057] The first future time period should be longer than the second future time period. Optionally, the first future time period should be at least 15 days (medium-term forecast). The second future time period should not exceed 24 hours, preferably 6 hours.

[0058] The trend weather forecasts in this manual include not only precipitation forecasts, but also other forecasts.

[0059] In an optional embodiment of this specification, the trend weather forecast results include a multidimensional data snapshot of the atmospheric state across the entire range. This snapshot contains predicted values ​​of various meteorological elements from the ground to dozens of layers of upper atmosphere. This data is typically stored and represented in a computer as grid point data. For example, the trend weather forecast results include: dynamic field elements (e.g., wind field (horizontal wind, U component (east-west direction), V component (north-south direction). This is the most critical data describing atmospheric motion; vertical velocity, ω or W component, representing the rising or sinking motion of air, which is crucial for precipitation formation, etc.), pressure field (e.g., sea level pressure; pressure values ​​of isobaric surfaces at various levels, used to analyze high-pressure, low-pressure, and other weather systems), thermal field elements (e.g., temperature, humidity, geopotential height, etc.), hydrological elements (e.g., precipitation, cloud water / rainwater content, ice / snow content, etc.), and near-surface elements (e.g., 2-meter temperature, 2-meter humidity, 10-meter wind, surface precipitation rate, etc.).

[0060] The method described in this specification relies on the natural law-based data processing capabilities of the NWP model, and on this basis, achieves spatiotemporal scale adaptation through correction. The NWP model is essentially a set of coupled, nonlinear partial differential equations, which are discretized and solved on a supercomputer. This set of equations is commonly referred to as the primal equations. The natural laws involved include, but are not limited to:

[0061] 1. Newton's Second Law: Force is the cause of acceleration. The motion of the atmosphere (wind) is determined by various forces acting on air particles. This enables the application of the Navier-Stokes equations in the atmosphere, i.e., the equations of motion. A specific application is the Coriolis force, generated by the Earth's rotation, which causes moving objects to deflect to the right in the Northern Hemisphere and to the left in the Southern Hemisphere. It determines that the direction of wind is parallel to isobars, rather than blowing directly from one point to another.

[0062] 2. Laws of Thermodynamics: The change in internal energy of a micro-particle of air equals the heat it absorbs minus the work it does on its surroundings. A specific application is the calculation of local temperature variations in air, primarily considering adiabatic heating / cooling. Adiabatic heating / cooling refers to the phenomenon where air expands and cools as it rises due to decreased air pressure, and is compressed and warms as it sinks due to increased air pressure. This is the most significant factor contributing to temperature changes in the vertical direction.

[0063] 3. Law of Conservation of Mass: Matter cannot be created or destroyed. A specific application is as follows: For the atmosphere, it describes the "compressibility" of air. If there is a net inflow of air into a region, the air density in that region will increase (and the air pressure may rise); conversely, the airflow will decrease. It ensures that atmospheric motion is conserved in terms of mass.

[0064] 4. Ideal Gas Law: Describes the relationship between the pressure, volume, and temperature of an ideal gas. Specific applications include: it connects the three most fundamental physical quantities of the atmosphere—pressure (P), density (ρ), and temperature (T)—allowing dynamic and thermodynamic processes to be coupled together for solution.

[0065] In an optional implementation of this specification, when the duration of the second future time period is no more than 6 hours, the trend weather forecast result is obtained after weighting with a first weight. This first weight is positively correlated with the frequency of El Niño events occurring within a first historical time period (an empirical value, for example, 10 years) from the current time. When the duration of the second future time period is no more than 6 hours, it indicates that higher accuracy is needed for short-term forecasts, allowing for timely warnings in case of disaster risks. However, the NWP model, unlike the LSTM model, lacks memory capabilities, and its macroscopic predictions may overlook changes or shifts in patterns. Meteorological characteristics are not instantaneous, and their persistent impact cannot be ignored, especially now that El Niño events are frequent and prolonged. The start and end times of El Niño events can be defined based on expert experience through observations of sea surface temperature. The method in this specification, through the design of the first weight, allows for the reflection of such historical influences and shifts in patterns.

[0066] S106: If the trend indicated by the trend weather forecast result is greater than the preset trend threshold, and the first real-time data of the target area matches the first historical data, then the NWP model is used to obtain the first predicted precipitation result of the target area in the second future time period based on the second real-time data of the target area; and a second predicted precipitation result is obtained based on the second real-time data of the target area and the surrounding area.

[0067] The trend threshold in this specification may be an empirical value based on expert experience, and in optional embodiments, it may be positively correlated with the maximum risk that the target area can withstand.

[0068] The method for collecting the first real-time data of the target area can be the same as or similar to the method for collecting the first real-time data of the entire range.

[0069] The first real-time data for the target area matches the first historical data (however, the first real-time data for the surrounding area and the first real-time data for the target area do not match the second historical data, indicating that precipitation is likely to have an impact on the target area, but the target area is less likely to be affected by the surrounding area. This situation is not very serious, but the risks it brings should not be ignored. Since the matching occurs over a relatively wide time range, it is necessary to refine the timing of this risk).

[0070] In related technologies, the technical means used to determine the matching degree of data are applicable to this specification, where conditions permit. A match is considered complete if the determined matching degree is greater than a preset matching degree threshold (empirical value).

[0071] The time period to which the second real-time data belongs in this specification is shorter than the time period to which the first real-time data belongs.

[0072] The duration of the time period to which the first historical data and the second historical data belong may not match that of the first real-time data. Therefore, the first historical data and the second historical data can be filtered based on the time dimension to match the time period to which the first real-time data belongs. Then, the matching degree can be calculated based on the filtering results.

[0073] In practical applications, there may be situations where the trend indicated by the trend weather forecast results is not greater than the preset trend threshold. In such cases, there is no need to follow the steps in this manual, and conventional methods can be used for processing.

[0074] This step involves using the NWP model to obtain the corresponding output based on the second real-time data. First, the corresponding second initial field and second boundary conditions are obtained based on the second real-time data. Then, prediction is achieved based on the second initial field and second boundary conditions. The method for constructing the initial field and boundary conditions is similar to the process described above.

[0075] The first and second predicted precipitation results fall within the same timeframe, for example, both within the next 6 hours. The second predicted precipitation result corresponds to the target area and its surrounding areas. The first predicted precipitation result corresponds to the target area. The second real-time data can be obtained from the first real-time data, filtered based on temporal distribution and spatial location. The second real-time data for the target area is the implementation data collected specifically for that area. Both the first and second real-time data should ideally cover timeframes as close as possible to the current moment.

[0076] S108: Correct the first predicted precipitation result using the second predicted precipitation result.

[0077] The corrections in this manual are based on the impact of the surrounding area on the target area. In other words, this impact must be reflected in order to link the predicted precipitation values ​​with the actual disasters.

[0078] The method provided in this application examines the target area and its surrounding areas at a spatial scale. At a temporal scale, it designs first and second real-time data for the input of the NWP model, and designs first and second future time periods for the output of the NWP model. During the execution of the technical solution in this application, a computer can execute a computer program (e.g., a script) based on the method steps described in this application, thus achieving automated precipitation numerical forecast correction based on adaptive spatiotemporal scale selection. Data selection is based on evidence at both the temporal and spatial scales, avoiding errors and distortions caused by excessive human intervention due to personal preferences. Furthermore, addressing the issue that NWP models cannot accumulate knowledge or optimize their capabilities through learning like artificial intelligence models, the method provided in this application intervenes in both the input and output at both the temporal and spatial scales. Even if the NWP model cannot accumulate knowledge, the output obtained through the method described in this specification reflects the impact of macroscopic and microscopic differences in time on accuracy, which helps improve the accuracy of the NWP model output.

[0079] The specific methods of correction will be explained below.

[0080] In an optional embodiment of this specification, when the duration of the second future time period is no more than 6 hours, if the precipitation amount in the target area indicated by the first predicted precipitation result is greater than the precipitation amount in the target area indicated by the second predicted precipitation result, the precipitation amount in the target area indicated by the first predicted precipitation result is taken as the target precipitation amount for the target area. In this embodiment, because the duration of the second future time period is relatively short, its prediction accuracy and precision are relatively high, and users have higher requirements for its precision. This is reflected in users being more sensitive to potential risks. The difference between the two prediction results is caused by spatial scale. When comprehensively considering the target area and the surrounding area, local precipitation may be regarded as precipitation over a larger area. The method in this specification can identify such relatively concentrated regional precipitation.

[0081] In a further optional embodiment of this specification, when the precipitation amount in the target area indicated by the first predicted precipitation result is not greater than the precipitation amount in the target area indicated by the second predicted precipitation result (indicating that although a smaller area to which the target area belongs can obtain a more targeted output for the target area under limited consideration of factors, the target area is also affected by the surrounding areas, and the trend of the surrounding areas' influence on the target area cannot be ignored, for example, cumulonimbus clouds tend to move from the surrounding areas to the target area), the precipitation amount in the target area indicated by the first predicted precipitation result is weighted with a second weight (a value greater than 1, optionally an empirical value). Optionally, the second weight is positively correlated with the trend indicated by the trend weather forecast result (to reflect the characteristic that the effect of macro factors is stronger) and positively correlated with the precipitation synergy between the target area and the surrounding areas (precipitation synergy refers to the consistency of precipitation conditions between the two. Since some terrains and landforms prone to disasters such as debris flows are more sensitive to precipitation, synergy can also quantify the range of disaster occurrence). The precipitation synergy is negatively correlated with the ratio of the area of ​​the target area to the area of ​​the surrounding area (when the ratio is too large, the two areas may be considered as one geographically, but not necessarily administratively; this inherent characteristic of synergy makes the second weight approach 1), and positively correlated with the ratio of historical extreme precipitation events occurring in both areas to extreme precipitation events occurring in one of them. Therefore, the weighted data can reflect the predictability of risks, helping users to increase their awareness.

[0082] In a further optional embodiment of this specification, if the trend indicated by the trend weather forecast result is greater than the trend threshold, and the first real-time data of the target area matches the first historical data, and both the first real-time data of the target area and the first real-time data of the surrounding area match the second historical data (indicating that both the target area and the surrounding area are similar to the situation over a long period of time when extreme precipitation occurred in the past, indicating that this phenomenon is likely to continue, the possibility of misjudgment is low, and El Niño may exacerbate the consequences of this phenomenon, which are also likely to continue), then the second weight and the third weight are used to weight the precipitation amount of the target area indicated by the first predicted precipitation result. The third weight is positively correlated with the precipitation amount indicated by the first historical data that matches the first real-time data of the target area, and is also positively correlated with the precipitation synergy; the value of the third weight is greater than 1. Considering that the extreme precipitation events that may occur in the target area are likely to continue, and that the target area will also be affected by the surrounding areas, this risk is more worthy of attention.

[0083] In a further optional embodiment of this specification, when the precipitation synergy is greater than a preset synergy threshold (which can be an empirical value; in one optional embodiment of this specification, the synergy threshold is positively correlated with the duration of the second future time period, highlighting the possibility of the El Niño phenomenon causing extreme impacts in a short period of time, and the prediction of the impact of the surrounding area on the target area is beneficial to improving the warning effect against extreme risks), the NWP model is used to obtain the third trend precipitation result of the target area in the first future time period based on the first real-time data of the target area (since the time period to which the first real-time data belongs is relatively long, the regularity shown is more obvious, but the characteristics of sudden situations not reflected by regularity are not very obvious, and the output obtained by the model based on it is not the precipitation situation at a certain exact moment in the future, but the possible trend of precipitation, which is the possibility of the long-term effect of a certain event. The duration of the first future time period is also relatively long, and it cannot accurately represent the specific situation at a certain exact moment, but rather the possible impact of the long-term effect of the event).

[0084] If the precipitation in the target area indicated by the third trend precipitation result is higher than the precipitation in the same period of the previous cycle (the cycle length can be selected as one year), it indicates that the strengthening effect of El Niño on the specific region does exist. Then, the second weight and the fourth weight are used to weight the precipitation in the target area indicated by the first predictive precipitation result. The fourth weight is positively correlated with the increase in precipitation in the target area compared to the same period of the previous cycle, and positively correlated with the degree to which the dynamic field elements and thermodynamic field elements (these two elements are the most fundamental to characterizing the El Niño phenomenon and can be addressed from the perspective of the fundamental causes of the phenomenon) in the trend weather forecast results (including but not limited to the third trend precipitation result) obtained based on the first real-time data of the target area deviate from their historical averages. A value greater than 1 for the fourth weight indicates the manifestation of the strengthening effect of the El Niño phenomenon on precipitation.

[0085] Furthermore, this specification also provides a precipitation numerical forecast correction device based on adaptive spatiotemporal scale selection, which is used to implement the aforementioned method steps. This device can execute the methods in any of the foregoing embodiments and can obtain the same or similar technical effects, which will not be elaborated further here.

[0086] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0087] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 2 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0088] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0089] The processor reads the corresponding computer program from non-volatile memory into memory and then runs it, forming a precipitation numerical forecast correction device based on adaptive spatiotemporal scale selection at the logical level. The processor executes the program stored in memory and specifically performs any of the aforementioned precipitation numerical forecast correction methods based on adaptive spatiotemporal scale selection.

[0090] The above is as stated in this application. Figure 1 The precipitation numerical forecast correction method based on adaptive spatiotemporal scale selection disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0091] The electronic device can also perform Figure 1 A method for correcting precipitation numerical forecasts based on adaptive spatiotemporal scale selection is proposed and implemented. Figure 1 The functions of the embodiments shown are not described again in this application.

[0092] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, perform any of the aforementioned precipitation numerical forecast correction methods based on adaptive spatiotemporal scale selection.

[0093] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0098] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0099] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0100] It should also be noted that 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 limitation, 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.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A precipitation numerical prediction correction method based on adaptive spatiotemporal scale selection, characterized in that, The method includes: Acquire first historical data and second historical data for the target area; the first historical data is data collected for the target area when extreme precipitation events occurred in the target area in the past; the second historical data is data collected for the target area and the surrounding areas when extreme precipitation events occurred in the surrounding areas of the target area in the past and affected the target. The first initial field and first boundary conditions are constructed based on the first real-time data across the entire range; Using the NWP model based on the first initial field and the first boundary conditions, the trend weather forecast results for the entire range within the first future time period are obtained; If the trend indicated by the trend weather forecast result is greater than a preset trend threshold, and the first real-time data of the target area matches the first historical data, then the NWP model is used to obtain the first predicted precipitation result of the target area in the second future time period based on the second real-time data of the target area; and a second predicted precipitation result is obtained based on the second real-time data of the target area and the surrounding area; the time period to which the second real-time data belongs is shorter than the time period to which the first real-time data belongs; The first predicted precipitation result is corrected using the second predicted precipitation result; The first predicted precipitation result is corrected using the second predicted precipitation result, including: When the precipitation amount in the target area indicated by the first predicted precipitation result is not greater than the precipitation amount in the target area indicated by the second predicted precipitation result, the precipitation amount in the target area indicated by the first predicted precipitation result is weighted by a preset second weight; the second weight is positively correlated with the trend indicated by the trend weather forecast result and positively correlated with the precipitation synergy between the target area and the surrounding area; the precipitation synergy is negatively correlated with the area ratio of the target area and the surrounding area and positively correlated with the ratio of extreme precipitation events occurring in both areas in history to extreme precipitation events occurring in one of them; If the trend indicated by the trend weather forecast result is greater than the trend threshold, and the first real-time data of the target area matches the first historical data, and the first real-time data of the target area and the first real-time data of the surrounding area both match the second historical data, then the precipitation amount of the target area indicated by the first predicted precipitation result is weighted using the second weight and a preset third weight; the third weight is positively correlated with the precipitation amount indicated by the first historical data that matches the first real-time data of the target area, and is also positively correlated with the precipitation synergy; When the precipitation synergy is greater than a preset synergy threshold, the NWP model is used to obtain the third trend precipitation result of the target area in the first future time period based on the first real-time data of the target area. If the precipitation in the target area indicated by the third trend precipitation result is higher than the precipitation in the same period of the previous cycle; The second weight and the preset fourth weight are used to weight the precipitation in the target area represented by the first predicted precipitation result; the fourth weight is positively correlated with the increase in precipitation in the target area compared with the same period of the previous cycle, and is positively correlated with the degree of deviation of the dynamic field elements and thermal field elements in the trend weather forecast results obtained based on the first real-time data of the target area from the historical average. The data collected in the target area includes at least one of the following: conventional ground observation data, upper-air sounding data, satellite remote sensing data, radar observation data, and aircraft observation data; these data characterize at least one of the following attributes: air pressure, temperature, humidity and wind speed and direction, precipitation, atmospheric vertical temperature and humidity profile, ozone, trace gases, soil temperature, snow cover and depth.

2. The method as described in claim 1, characterized in that, The method further includes: If the duration of the second future time period is no more than 6 hours, and the precipitation amount of the target area indicated by the first predicted precipitation result is greater than the precipitation amount of the target area indicated by the second predicted precipitation result, then the precipitation amount of the target area indicated by the first predicted precipitation result shall be taken as the target precipitation amount of the target area.

3. The method as described in claim 1, characterized in that, The method further includes: The first real-time data includes at least one of the following: conventional ground observation data, upper-air sounding data, satellite remote sensing data, radar observation data, and aircraft observation data.

4. The method as described in claim 1, characterized in that, The method further includes: The first real-time data falls within a time range of 6 to 12 hours; the second real-time data falls within a time range of 3 to 6 hours.

5. The method as described in claim 1, characterized in that, The method further includes: The synergy threshold is positively correlated with the duration of the second future time period.

6. The method as described in claim 1, characterized in that, The method further includes: If the duration of the second future time period is no more than 6 hours, the trend weather forecast result is obtained by weighting with a first weight; the first weight is positively correlated with the frequency of El Niño events occurring within a first historical time period specified from the current time.

7. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform any of the methods of claims 1 to 6.

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