Data processing method, device, equipment and medium for precipitation forecasting

Abstract

The present disclosure provides a data processing method, device, equipment and medium for precipitation forecasting. The data processing method for precipitation forecasting includes: determining an inverted precipitation rule based on historical precipitation data, and determining at least one precipitation index based on current precipitation data; obtaining at least one current inverted precipitation data corresponding to at least one precipitation index based on the inverted precipitation rule; and processing the current inverted precipitation data to determine the average precipitation forecast data corresponding to the current precipitation data. It can be seen that the above method can be based on the objective precipitation forecast method of the terrain following coordinate system of at least one precipitation index numerical forecast, give full play to the prediction potential of the numerical model, improve the accuracy of the numerical model precipitation forecast, so as to improve the precipitation forecast level or ability in steep mountainous areas and river basins.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a data processing method, device, equipment and medium for precipitation forecasting. Background Art

[0002] At present, the accuracy of precipitation forecast in steep terrain areas and river basins is relatively low. In addition to the lack of observations, there is also a lack of effective forecasting methods. At present, the main technical means of precipitation forecasting in the meteorological department are numerical model forecasting and nowcasting forecasting. Among them, numerical model is the main tool for predicting the occurrence and development of weather systems. With the development of computer technology and the continuous improvement of numerical models, the level of precipitation forecasting has been significantly improved. Numerical model forecasting and nowcasting forecasting have their own unique advantages in specific aspects. Numerical models can provide grid forecast products with high temporal and spatial resolution, and nowcasting can predict the development and movement of severe convection in 0-2 hours. These two forecasting methods also have certain shortcomings. Nowcasting mainly relies on radar satellite observations for extrapolation forecasting. Based on the linear development of weather systems, it can provide nowcast products for severe convective weather, but it cannot provide high-quality forecasts of severe convective systems for more than 2 hours, and it cannot predict the generation and disappearance of severe convection. There is a large uncertainty in numerical forecasting models. For example, model precipitation forecasts include resolvable scale precipitation and sub-grid scale precipitation. Resolvable scale precipitation is mainly determined by cloud microphysical processes; while sub-grid scale precipitation is mainly calculated by cumulus convection parameterization schemes. Actual precipitation is a very complex physical process. The cloud microphysical parameterization scheme and cumulus convection parameterization scheme in numerical models are both empirical and subjective. These parameterization schemes do not fully describe the physical process, resulting in low accuracy of numerical model precipitation forecasts, which often cannot meet the needs of the public. Steep terrain excites gravity waves, but the gravity wave parameterization scheme in the model is not perfect. In addition, complex terrain leads to inaccurate calculations of pressure gradient forces in numerical models. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In order to solve the bottleneck technical problem in the prior art that the complexity of numerical model prediction increases due to complex terrain, resulting in insufficient precipitation forecasting ability of the model in steep mountainous areas, the present disclosure provides a data processing method, device, equipment and medium for precipitation forecasting.

[0005] (II) Technical solution

[0006] A first aspect of the present disclosure provides a data processing method for precipitation forecasting, which includes: determining an inverted precipitation rule based on historical precipitation data, and determining at least one precipitation index based on current precipitation data; based on the inverted precipitation rule, obtaining at least one current inverted precipitation data corresponding to at least one precipitation index; and processing the current inverted precipitation data to determine average precipitation forecast data corresponding to the current precipitation data.

[0007] According to an embodiment of the present disclosure, historical precipitation data include historical precipitation observation data and precipitation index forecast data corresponding to historical numerical patterns. In determining the inversion precipitation rule based on the historical precipitation data, it includes: corresponding to at least one precipitation index, performing fitting analysis on the precipitation index forecast data and the historical precipitation observation data, and determining the inversion precipitation rule based on the least squares method.

[0008] According to an embodiment of the present disclosure, determining at least one precipitation index based on current precipitation data includes: determining forecast output data of a current numerical model based on the current precipitation data; extracting basic precipitation element data based on the forecast output data; and determining at least one precipitation index based on a quality terrain tracking coordinate system through the basic precipitation element data.

[0009] According to an embodiment of the present disclosure, basic precipitation element data include: at least one of temperature, air pressure, humidity and wind speed; at least one precipitation index includes: at least one of frontogenesis index, vorticity index, potential vorticity index, potential vorticity gradient index, helicity index, thermal helicity index, divergence index, shear index, deformation index and vertical velocity index.

[0010] According to an embodiment of the present disclosure, in acquiring at least one current inverted precipitation data corresponding one-to-one to at least one precipitation index based on an inverted precipitation rule, it includes: determining at least one current inverted precipitation data based on at least one precipitation index and at least one exponential coefficient corresponding to at least one precipitation index.

[0011] According to an embodiment of the present disclosure, before processing the current inverted precipitation data to determine the average precipitation forecast data corresponding to the current precipitation data, it also includes: determining the total number of correlation coefficients between at least one inverted precipitation and the observed precipitation corresponding to at least one precipitation index; determining the sequence number of the correlation coefficient of the inverted precipitation corresponding to at least one precipitation index in the total number of correlation coefficients; and determining the inverted precipitation weight of each precipitation index corresponding to at least one precipitation index through the total number of correlation coefficients and the sequence number.

[0012] According to an embodiment of the present disclosure, in processing the current inverted precipitation data to determine the average precipitation forecast data corresponding to the current precipitation data, it includes: linearly superimposing the current inverted precipitation data according to the inverted precipitation weight of at least one index to determine the corresponding average precipitation forecast data.

[0013] The second aspect of the present disclosure provides a data processing device for precipitation forecasting, which includes a rule index determination module, an inversion data acquisition module and a data processing module. The rule index determination module is used to determine the inversion precipitation rule according to the historical precipitation data, and determine at least one precipitation index according to the current precipitation data; the inversion data acquisition module is used to obtain at least one current inversion precipitation data corresponding to at least one precipitation index based on the inversion precipitation rule; and the data processing module is used to process the current inversion precipitation data and determine the average precipitation forecast data corresponding to the current precipitation data.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned data processing method applied to precipitation forecasting.

[0015] A fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned data processing method for precipitation forecasting.

[0016] The fifth aspect of the present disclosure further provides a computer program product, including a computer program, which implements the above-mentioned data processing method for precipitation forecasting when executed by a processor.

[0017] (III) Beneficial effects

[0018] The present disclosure provides a data processing method, device, equipment and medium for precipitation forecasting. The data processing method for precipitation forecasting includes: determining an inverted precipitation rule based on historical precipitation data, and determining at least one precipitation index based on current precipitation data; obtaining at least one current inverted precipitation data corresponding to at least one precipitation index based on the inverted precipitation rule; and processing the current inverted precipitation data to determine the average precipitation forecast data corresponding to the current precipitation data. It can be seen that the above method can be based on the objective precipitation forecast method of the terrain following coordinate system of at least one precipitation index numerical forecast, give full play to the prediction potential of the numerical model, improve the accuracy of the numerical model precipitation forecast, so as to improve the precipitation forecast level or ability in steep mountainous areas and river basins. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1A flowchart of a data processing method applied to precipitation forecast according to an embodiment of the present disclosure is schematically shown;

[0020] Figure 2 A flowchart of an application scenario of a data processing method for precipitation forecasting according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 3 A schematic diagram showing an architecture of a data processing device applied to precipitation forecast according to an embodiment of the present disclosure; and

[0022] Figure 4 The electronic device architecture diagram applicable to the above-mentioned data processing method for precipitation forecasting according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0024] It should be noted that the implementation methods not shown or described in the drawings or the text of the specification are all forms known to ordinary technicians in the relevant technical field and are not described in detail. In addition, the above definitions of various elements and methods are not limited to the various specific structures, shapes or methods mentioned in the embodiments, and ordinary technicians in the field can simply change or replace them.

[0025] It should also be noted that the directional terms mentioned in the embodiments, such as "upper", "lower", "front", "back", "left", "right", etc., are only reference directions of the drawings and are not intended to limit the scope of protection of the present disclosure. Throughout the drawings, the same elements are represented by the same or similar reference numerals. Conventional structures or configurations will be omitted when they may cause confusion in the understanding of the present disclosure.

[0026] Moreover, the shapes and sizes of the components in the figures do not reflect the real size and proportion, but only illustrate the contents of the embodiments of the present disclosure. In addition, in the claims, any reference symbols between brackets shall not be constructed as limiting the claims.

[0027] Furthermore, the word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.

[0028] The ordinal numbers used in the specification and claims, such as "first", "second", "third", etc., to modify the corresponding elements, do not themselves mean that the elements have any ordinal numbers, nor do they represent the order of one element and another element or the order of manufacturing methods. The use of these ordinal numbers is only used to clearly distinguish a component with a certain name from another component with the same name.

[0029] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose. Furthermore, in a unit claim that lists several devices, several of these devices may be embodied by the same hardware item.

[0030] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various disclosed aspects, in the above description of the exemplary embodiments of the present disclosure, the various features of the present disclosure are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: the claimed disclosure requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the disclosed aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present disclosure.

[0031] In order to better describe the dynamic and thermal effects of terrain, numerical weather forecast models mostly use terrain-following coordinate systems. Compared with the local rectangular coordinate system, its lower boundary condition is simple, which makes it easier to introduce the impact of terrain on the atmosphere. Physical quantities used for rainstorm diagnosis in the past, such as divergence and vorticity, are all defined in local rectangular coordinate systems or isobaric coordinate systems, and terrain effects cannot be directly considered. During calculations, the model output data needs to be interpolated onto contour surfaces or isobaric surfaces. Doing so will substitute the errors generated during the interpolation process into the calculation of physical quantities, thereby affecting the accuracy of the analysis. In addition, if the coordinate surface (contour surface or isobaric surface) is lower than the terrain height, the coordinate surface intersects with the terrain surface, resulting in inaccurate calculations of near-ground physical quantities.

[0032] In order to solve the bottleneck technical problem in the prior art that the complexity of numerical model prediction increases due to complex terrain, resulting in insufficient precipitation forecasting ability of the model in steep mountainous areas, the present disclosure provides a data processing method, device, equipment and medium for precipitation forecasting.

[0033] like Figure 1 As shown, the first aspect of the present disclosure provides a data processing method for precipitation forecasting, which includes steps S101-S103.

[0034] In step S101, an inversion precipitation rule is determined according to historical precipitation data, and at least one precipitation index is determined according to current precipitation data;

[0035] In step S102, based on the inverted precipitation rule, at least one current inverted precipitation data corresponding to at least one precipitation index is obtained; and

[0036] In step S103, the current inverted precipitation data is processed to determine average precipitation forecast data corresponding to the current precipitation data.

[0037] In the disclosed embodiment, precipitation may include snowfall, rain, or even hail, a natural phenomenon in which water vapor in the atmosphere condenses and falls to the ground in liquid or solid form. To monitor precipitation, it is usually necessary to collect precipitation data from various regions, and after comprehensive processing, to provide the average precipitation in a certain period of time in the region. For precipitation forecasting, predictions can be made based on the average precipitation.

[0038] The historical precipitation data is the precipitation data in one or more specific areas in the historical time period. According to the historical precipitation data, the inversion precipitation rule can be determined, and the inversion precipitation rule is the relationship between the precipitation index, the correlation coefficient corresponding to the precipitation index and the inverted cumulative precipitation. The current precipitation data is the precipitation data in one or more specific areas in the current period. Among them, the current precipitation data can obtain a series of precipitation macro-indices as the above-mentioned precipitation index according to the mass terrain tracking coordinates. The precipitation index is a coefficient based on the above-mentioned mass terrain tracking coordinates used to characterize the environmental conditions such as dynamics and thermodynamics that are conducive to the occurrence and development of precipitation. According to the inversion precipitation rule, the current inversion precipitation data corresponding to each precipitation index can be determined, and the current inversion precipitation data is the inversion precipitation data of one or more specific areas at the current moment corresponding to each precipitation index in at least one precipitation index.

[0039] By processing the current inverted precipitation data, the average precipitation forecast data corresponding to the current precipitation data at the current moment can be determined. The average precipitation forecast data can be applied to precipitation forecasts for special complex terrains including plains and mountains. Among them, multiple precipitation indices can refer to multiple different types of precipitation indices, or can refer to precipitation indices with several of the same type and precipitation indices with other different types at the same time. The type of precipitation index can be determined based on the mass terrain tracking coordinates.

[0040] It can be seen that the above method can be used as an objective precipitation forecasting method based on the terrain following coordinate system of at least one precipitation index numerical forecast, which can give full play to the forecasting potential of numerical models, improve the accuracy of precipitation forecasting of numerical models, introduce terrain effects, so as to improve the precipitation forecasting level or ability in steep mountainous areas and river basins, effectively avoid the intersection of isobaric surfaces or isohyper surfaces in the lower troposphere with the ground, and overcome the problem of not being able to accurately analyze and forecast the intensity and movement of mountain precipitation.

[0041] like Figure 1 and Figure 2 As shown, according to an embodiment of the present disclosure, the historical precipitation data includes historical precipitation observation data and precipitation index forecast data corresponding to the historical numerical model. In step S101, the inversion precipitation rule is determined according to the historical precipitation data, including:

[0042] Corresponding to at least one precipitation index, a fitting analysis is performed on the precipitation index forecast data and the historical precipitation observation data, and an inverted precipitation rule is determined based on the least squares method.

[0043] In the embodiments of the present disclosure, the historical precipitation data mainly include historical precipitation observation data and precipitation index forecast data corresponding to historical numerical patterns.

[0044] The historical precipitation observation data refers specifically to the historical precipitation observation data set. Figure 2 As shown, in step S202, a historical 6-hour cumulative observed precipitation data set can be established by collecting, specifically, by collecting and collating the basic precipitation observation data of the past three years to generate the above 6-hour cumulative observed precipitation data; then interpolated to the numerical model grid points to form a gridded 6-hour cumulative observed precipitation data set, that is, as the historical precipitation observation data. At the same time, the data set of the historical precipitation observation data is updated in real time, and the latest precipitation observation data is continuously included in the data set to expand the data set capacity and realize the real-time characteristics of the data.

[0045] In addition, the precipitation index forecast data of the historical numerical model is specifically a precipitation index forecast data dataset of the historical numerical model. Figure 2 As shown, in step S201, basic meteorological element data such as temperature, humidity, air pressure, and wind speed of precipitation forecasts of historical numerical models and multiple different types of precipitation indices are collected, so that a precipitation index forecast data set corresponding to the historical numerical model can be established. Basic forecast data such as temperature, humidity, air pressure, wind field, and precipitation forecast data of the numerical model in the past three years can be collected and sorted, and multiple precipitation indices can be calculated using these basic forecast data to generate a gridded multiple precipitation index forecast data set as the precipitation index forecast data of the historical numerical model mentioned above; at the same time, the precipitation index forecast data set is also updated in real time, and the corresponding multiple precipitation indices are continuously calculated using the latest numerical model forecast data, and included in the forecast data set (for details, please refer to Figure 2 It should be noted that the horizontal grid of the forecast dataset is completely consistent with that of the 6-hour cumulative precipitation observation dataset, and the time is also completely corresponding.

[0046] like Figure 2 As shown, in step S203, in the process of obtaining the inverted precipitation rule, the historical data set of long-term precipitation observation and precipitation index forecast is used to perform fitting analysis on the precipitation index forecast data and the 6-hour precipitation observation data, and the inverted precipitation rule is determined according to the least squares method, which can be specifically used to determine the correlation coefficient a of the inverted precipitation rule. i and b i .

[0047] like Figure 2 As shown, in step S208, the long-term historical precipitation observation data set and the historical numerical model precipitation index forecast data set established above can be used to perform linear fitting statistical analysis on the observed precipitation and each precipitation index, establish the inversion precipitation rule of each precipitation index and the 6-hour cumulative observed precipitation, and realize the precipitation inversion of precipitation index. Specifically, the inversion precipitation rule can be embodied as follows:

[0048] R i =a iX i +b i (1)

[0049] Among them, X i is the ith precipitation index calculated using the numerical model forecast data, a i is the coefficient of the ith index, b i is a constant, R i is the 6-hour cumulative precipitation inverted by the i-th precipitation index.

[0050] Therefore, the inverted precipitation rules can be acquired through historical precipitation data, so that the inverted precipitation rules can improve the accuracy of the numerical model precipitation forecast of the above-mentioned data processing method of the embodiment of the present disclosure, give full play to the forecasting potential of the numerical model, and ensure that the intensity and movement of mountain precipitation can be accurately analyzed and forecasted.

[0051] It should be particularly noted that, in order to enable the technical solution of the present disclosure to be clearly and completely described, the specific time values ​​such as 3 years and 6 hours mentioned in the embodiments of the present disclosure are only used for illustration, and the protection scope of the present disclosure is not limited thereto. Those skilled in the art should be aware that the corresponding time values ​​can be replaced or modified according to specific application examples, which will not be elaborated here.

[0052] like Figure 1 and Figure 2 As shown, according to an embodiment of the present disclosure, in step S101, determining at least one precipitation index according to current precipitation data includes:

[0053] Determine the forecast output data of the current numerical model based on the current precipitation data;

[0054] Extract basic precipitation element data based on forecast output data;

[0055] At least one precipitation index is determined based on the basic precipitation element data and the quality terrain following coordinate system.

[0056] According to an embodiment of the present disclosure, the basic precipitation element data includes: at least one of temperature, air pressure, humidity and wind speed;

[0057] The at least one precipitation index includes: at least one of a frontogenesis index, a vorticity index, a potential vorticity index, a potential vorticity gradient index, a helicity index, a thermal helicity index, a divergence index, a shear index, a deformation index and a vertical velocity index.

[0058] like Figure 2As shown, in step S205, the current precipitation data may be the precipitation forecast output data of the current numerical model collected. In step S206, basic precipitation element data may be extracted according to the precipitation forecast output data of the current numerical model, and the basic precipitation element data may include basic element data such as temperature, air pressure, humidity, wind speed, etc. In step S207, at least one precipitation index of different types corresponding to the above-mentioned current precipitation data may be determined according to the quality terrain tracking coordinate system through these basic precipitation element data.

[0059] In the embodiment of the present disclosure, a quality terrain tracking coordinate system (x, y, η, t) may be introduced, wherein x is the east-west coordinate axis, y is the north-south coordinate axis, η is the quality terrain tracking vertical coordinate axis, and t is time. Specifically, η may be defined as:

[0060]

[0061] Among them, p dh is the static equilibrium pressure of dry atmosphere; p dht is the static equilibrium pressure of the top dry atmosphere in the numerical model; μ d =p dhs -p dht is the dry air mass of the air column, where p dhs The dry atmosphere is statically balanced with the surface air pressure.

[0062] Among them, μ d It is also called topographic influence factor, because the dry atmosphere statically balances the surface pressure p dhs It varies with the height of the terrain and can be used to reflect the terrain effect. Therefore, the above data processing method of the embodiment of the present disclosure can take into account the influence of terrain effect on precipitation forecast by introducing a quality terrain tracking coordinate system, so that it can be applied not only to precipitation forecast in plain areas with simple traditional terrain, but also to precipitation forecast in steep mountainous areas with complex terrain, effectively solving the problem of intersection of coordinate plane and terrain and inaccurate calculation of near-ground spatial difference.

[0063] Specifically, the transformation relationship between the local rectangular coordinate system (x, y, z, t) and the mass terrain following coordinate system (x, y, η, t) is:

[0064]

[0065]

[0066] Among them, S represents the coordinates (x, y, t), and A represents at least one basic meteorological element of wind, temperature, humidity and air pressure.

[0067] Furthermore, the definition of geopotential height is introduced:

[0068] φ=gz (5)

[0069] Where g is the acceleration due to gravity and Z is the height from sea level.

[0070] Therefore, using the potential height φ in formula (5), formulas (3) and (4) can be further rewritten as:

[0071]

[0072]

[0073] Therefore, according to the mass terrain following coordinate system, equations (2), (6) and (7) can be obtained by the surface pressure p dhs The terrain effect is introduced into the η coordinate plane, so that no matter in steep mountainous areas or plain areas, the η coordinate plane is always parallel to the terrain surface, and η = 1 on the ground and η = 0 at the top of the model. Therefore, the intersection of the η coordinate plane and the terrain surface is effectively avoided, and the spatial interpolation calculation formulas (6) and (7) near the ground are more accurate. At the same time, the terrain effect is also introduced into the physical quantity formula, making it easier to study the influence of terrain effect on meteorological elements.

[0074] Obviously, the above-mentioned data processing method of the embodiment of the present disclosure can develop a series of precipitation indices using the above-mentioned quality terrain tracking coordinate system. These precipitation indices can comprehensively characterize the dynamic and thermal environment that is conducive to the occurrence and development of rainfall, and introduce terrain effects. Therefore, they can be closely related to the precipitation system and have certain indicative and predictive significance for rainfall in mountainous areas and plains.

[0075] In the disclosed embodiment, at least one precipitation index includes: at least one of a frontogenesis index, a vorticity index, a potential vorticity index, a potential vorticity gradient index, a helicity index, a thermal helicity index, a divergence index, a shear index, a deformation index, and a vertical velocity index. Specifically, the definition and physical meaning of the above precipitation index are explained as follows:

[0076] 1) Frontogenesis Index

[0077] The frontogenesis index is defined as:

[0078]

[0079]

[0080] Among them, θ e is the equivalent potential temperature, λ is the potential temperature advection, and the other symbols are the same as those of the above formula (2). The frontogenesis index characterizes the frontogenesis function ( and ), atmospheric wet baroclinicity ( and ) and atmospheric potential stability coupling effect.

[0081] Precipitation processes often have strong frontogenesis, significant wet baroclinicity and potential instability. The frontogenesis index formula (8) is a physical quantity that comprehensively describes atmospheric frontogenesis, wet baroclinicity and potential instability, and is closely related to the precipitation system.

[0082] It should be noted that in formula (8), and Representing the terrain effect, in mountainous areas with steep terrain, these two items can be large, which can increase the abnormality of the frontogenesis index; in plain areas, these two items can be zero, and have almost no contribution to the frontogenesis index. Therefore, the frontogenesis index has indicative forecasting significance for the development and movement of rainfall in mountainous areas and plains.

[0083] 2) Vorticity Index

[0084] The vorticity index is defined as:

[0085]

[0086] Where u is the latitudinal velocity in the east-west direction, v is the longitudinal velocity in the north-south direction, and the other symbols are the same as those in equation (1). The vorticity index is the vertical component of the vector product of the vorticity vector and the equivalent potential temperature gradient, reflecting the atmospheric vertical wind shear ( and ) and atmospheric wet baroclinicity ( and ) coupling effect.

[0087] The low-level jet is one of the main weather systems that drives precipitation, and is characterized by strong vertical wind shear and strong baroclinicity. The vorticity index can characterize the dynamic and thermodynamic properties of the low-level jet, and thus has a certain ability to capture the rainfall caused by the low-level jet.

[0088] 3) Potential vorticity index

[0089] The potential vorticity index is defined as:

[0090]

[0091] Where W is the vertical velocity, θ is the potential temperature, and the other symbols are the same as those in formulas (2) and (9). The potential vorticity index is the three-dimensional vorticity vector In-situ temperature gradient The projection in the direction describes the coupling effect between the atmospheric vorticity vector and the atmospheric moist baroclinicity and potential stability.

[0092] For heavy precipitation systems in mid- and high-latitude regions, there are usually abnormal values ​​of potential vorticity extending from the stratosphere to the troposphere, which represents the intrusion of dry and cold air. On the one hand, it can enhance the rotation of airflow in the middle and lower layers of the troposphere, and on the other hand, it can bring dry and cold air from the stratosphere to the middle and lower layers of the troposphere, resulting in a thermal configuration of cold above and warm below, and enhancing the instability of atmospheric potential. The potential vorticity index can describe the characteristics of this type of dry and cold intrusion of potential vorticity, and has a certain tracking ability for heavy precipitation systems in steep mountainous areas and plains in mid- and high-latitude regions.

[0093] 4) Potential vorticity gradient index

[0094] The potential vorticity gradient index is defined as:

[0095]

[0096] Among them, PV index is the potential vorticity index, and other symbols are the same as those in formula (9).

[0097] The atmosphere has obvious dynamic and thermodynamic discontinuities during precipitation, especially the horizontal distribution of the potential vorticity index is significantly non-uniform, and rainfall often occurs in the potential vorticity index gradient zone. In order to accurately describe the discontinuity of the potential vorticity index, the above-mentioned data processing method of the embodiment of the present invention provides a terrain following coordinate system potential vorticity gradient index, which can characterize the coupling effect of the three-dimensional gradient of the potential vorticity index with the vertical wind shear and vorticity, and has outstanding ability to depict non-uniformly distributed rainfall.

[0098] 5) Helicity index

[0099] The helicity index is defined as:

[0100]

[0101] The helicity index describes the vertical velocity (W) and the vertical vorticity The coupling effect can characterize the vertical flux of vertical vorticity.

[0102] Since precipitation is accompanied by obvious vertical motion and vortex circulation, especially in the middle and lower troposphere, the vertical flux of vertical vorticity is very significant, changing the intensity of local vorticity. In order to characterize the dynamic properties of the rotation and rise of airflow, the above data processing method of the embodiment of the present disclosure provides a terrain-following coordinate system helicity index to describe the characteristics of vortex vertical transport in precipitation.

[0103] 6) Thermal helicity index

[0104] The thermal helicity index is defined as:

[0105]

[0106] The thermal helicity index is the heat flux (uθ c , vθ c ) is the vertical flux of vorticity, representing the vertical velocity (W) versus the equivalent potential temperature flux (uθ e , νθ e )Vertical transport of vorticity.

[0107] The precipitation process is accompanied by the release of latent heat of condensation, and there is obvious vertical transport of heat circulation, which has the characteristics of vorticity up or down. In order to capture the signal of this vertical transport of vorticity in precipitation, the above data processing method of the embodiment of the present disclosure proposes a thermal helicity index to describe the vertical transport of heat vortex circulation in precipitation.

[0108] 7) Divergence Index

[0109] The divergence index is defined as:

[0110]

[0111] The divergence index mainly represents the horizontal divergence and layer stability coupling effect.

[0112] The precipitation process is usually accompanied by convergence of lower troposphere airflow and divergence of upper troposphere airflow, and there is also obvious unstable stratification, which accumulates energy for the development of the precipitation system. In view of the characteristics of the above precipitation process, the above data processing method of the embodiment of the present disclosure provides a divergence index to characterize the environmental conditions of lower atmospheric convergence, upper atmospheric divergence and potential instability.

[0113] 8) Shear index

[0114] The shear index is defined as:

[0115]

[0116] The shear index represents the vertical wind shear ( and ), shear deformation Coupling effect with equivalent potential temperature gradient.

[0117] Since there are usually shear lines near the ground during precipitation, which cause water vapor convergence and trigger precipitation, low-altitude shear lines are one of the main mechanisms that cause precipitation to occur and develop. In order to effectively characterize the dynamic and thermal properties of low-altitude shear lines, the above-mentioned data processing method of the embodiment of the present disclosure provides a terrain-following coordinate system shear index to express the comprehensive effects of vertical wind shear, shear deformation, thermal baroclinicity, and conditional instability caused by shear lines during precipitation.

[0118] 9) Deformation index

[0119] The deformation index is defined as:

[0120]

[0121] This deformation index mainly represents the extension deformation of the atmospheric flow field. Coupling effect with stratification instability.

[0122] There is usually a deformed flow field in rainfall weather, in which the stretching deformation of the flow field is closely related to the convergence and divergence of the airflow. The stretching deformation is a very unstable flow field state, which can be easily transformed into horizontal divergence, enhancing the convergence and divergence of the airflow. In order to effectively describe the stretching deformation characteristics of the atmospheric flow field in precipitation, the above-mentioned data processing method of the embodiment of the present disclosure provides a deformation index to characterize the intensity of the stretching deformation of the flow field in precipitation weather.

[0123] 10) Vertical speed index

[0124] The vertical speed index is defined as:

[0125]

[0126] Among them, q x and q y are the x and y components of the Q vector.

[0127] This index is the Q vector divergence in the terrain-following coordinate system. The Q vector is the only forcing term in the vertical motion equation under the static equilibrium dynamic framework, reflecting the forcing effect of the vertical gradient of vorticity advection and potential temperature advection on vertical motion, and vertical motion is the most basic factor in triggering precipitation. Therefore, the Q vector divergence plays an important indicative role in the development and evolution of precipitation, and is a key physical quantity for precipitation analysis and forecasting. For this reason, the above-mentioned data processing method of the embodiment of the present disclosure provides the Q vector divergence in the terrain-following coordinate system, taking into account the contribution of the terrain to the vertical motion, so as to effectively describe the vertical motion triggering mechanism in precipitation in steep mountainous areas and plain areas.

[0128] Based on the above analysis of various precipitation indices for the embodiment of the present disclosure, the above data processing method of the embodiment of the present disclosure provides at least 10 kinds of precipitation indices of the terrain following coordinate system, so that the precipitation index has a clear physical meaning. These precipitation indices can characterize and describe the dynamic and thermodynamic environmental conditions that are conducive to the occurrence and development of precipitation from different aspects, and can indicate the development and movement of the precipitation system. At the same time, based on the quality terrain following coordinate system, the terrain effect can be considered, which is suitable for precipitation forecasting in mountainous areas and plains with steep terrain, and solves the problem of intersection of the coordinate plane with the terrain and inaccurate calculation of near-ground spatial differentials. Moreover, based on these precipitation indices, the precipitation forecasting process of the embodiment of the present disclosure can be further developed.

[0129] In general, the precipitation forecast in mountainous areas with steep terrain has always been a difficult problem in the field of precipitation forecast technology, mainly because the numerical model does not handle the pressure gradient force in steep mountainous areas perfectly and the calculation is inaccurate. At the same time, the precipitation forecast in the numerical model is derived from cloud microphysical processes and cumulus convection parameterization schemes, which have problems of artificial uncertainty and local applicability. Overall, the accuracy of the numerical model in predicting macro elements such as temperature, humidity, pressure, and wind is higher than that of precipitation forecast.

[0130] In order to solve the technical problem of inaccurate precipitation forecast in the steep mountainous area, the data processing method of the embodiment of the present disclosure provides a multi-precipitation index average precipitation forecast method. In order to solve the terrain characterization problem, overcome the problem that the coordinate plane is below the terrain height, intersects with the terrain, and the near-ground calculation is inaccurate, the data processing method of the embodiment of the present disclosure designs a quality terrain tracking coordinate system and coordinate transformation relationship, which can accurately calculate the spatial difference of physical quantities near the ground.

[0131] Furthermore, during precipitation, the convergence of low-level airflow, the divergence of high-level airflow, stratification instability, vertical movement, vertical wind shear, cyclonic circulation and wet baroclinicity are all very significant, which are typical dynamic and thermodynamic characteristics of precipitation. In order to accurately describe these characteristics of the precipitation atmosphere, the above-mentioned data processing method of the embodiment of the present disclosure further provides at least 10 precipitation indices such as frontogenesis index, vorticity index, potential vorticity index, potential vorticity gradient index, helicity index, thermal helicity index, divergence index, shear index, deformation index and vertical velocity index of the mass terrain tracking coordinate system. These precipitation indices are established in the mass terrain tracking coordinate system, which solves the problem of accurate calculation near the ground. They are suitable for mountainous areas with steep terrain and can also be used in plain areas. Since these indices can accurately describe the dynamic and thermodynamic structural characteristics of precipitation, they are closely related to the precipitation system and can indicate the development and movement of precipitation, which provides an applied theoretical basis for achieving accurate precipitation forecasting.

[0132] like Figure 1 and Figure 2 As shown, according to an embodiment of the present disclosure, in step S102, based on the inversion precipitation rule, obtaining at least one current inversion precipitation data corresponding to at least one precipitation index includes:

[0133] At least one current inverted precipitation data is determined according to at least one precipitation index and at least one index coefficient corresponding to the at least one precipitation index.

[0134] As shown in the above formula (1), the inverted precipitation rule actually reflects the relationship between a certain precipitation index and the 6-hour cumulative precipitation inverted by the precipitation index. Among them, when the precipitation index is determined based on the current precipitation data, the 6-hour cumulative precipitation inverted by the precipitation index is actually the current inverted precipitation data corresponding to the precipitation index. It can be understood that the correlation coefficient a of the precipitation index determined based on the current precipitation data and the inverted precipitation rule corresponding to the precipitation index determined based on the historical precipitation data is i and b i Substitute into the above formula (1) and calculate the inverted precipitation R corresponding to the precipitation index: i , that is, the current inverted precipitation data. When there are multiple different types of precipitation indices, they correspond to multiple different current inverted precipitation data.

[0135] like Figure 1 and Figure 2 As shown, according to an embodiment of the present disclosure, before the current inverted precipitation data is processed in step S103 to determine the average precipitation forecast data corresponding to the current precipitation data, the following further includes:

[0136] Determine the total number of correlation coefficients between at least one inverted precipitation and the observed precipitation respectively corresponding to at least one precipitation index;

[0137] Determine the order of the correlation coefficient of the inverted precipitation corresponding to at least one precipitation index in the total number of correlation coefficients;

[0138] The inverted precipitation weight of each precipitation index corresponding to at least one precipitation index is determined by the total number of correlation coefficients and the sequence number.

[0139] like Figure 2 As shown, in step S204, the correlation between the precipitation inverted by various precipitation indices and the observed precipitation is first analyzed, and the correlation coefficients are sorted in descending order to determine the inverted precipitation weight. Specifically, the inverted precipitation weight can be reflected by the following weight function:

[0140]

[0141] Where M is the total correlation coefficient between the inverted precipitation of the precipitation index and the observed precipitation, m i is the sequence number of the correlation coefficient of the inverted precipitation of the i-th precipitation index among the M correlation coefficients, W i is the weight of the inverted precipitation of the i-th precipitation index.

[0142] For example, if the correlation coefficients between the inverted precipitation of 10 precipitation indices and the observed precipitation are calculated, then M = 10; if the correlation coefficient of the i-th precipitation index is the highest, then its weight W i for: If the jth index has the lowest correlation coefficient, its weight W j For: W j =exp(-1).

[0143] like Figure 1 and Figure 2 As shown, according to an embodiment of the present disclosure, in step S103, the current inverted precipitation data is processed to determine the average precipitation forecast data corresponding to the current precipitation data, including:

[0144] According to the inverted precipitation weight of at least one index, the current inverted precipitation data is linearly superimposed to determine the corresponding average precipitation forecast data.

[0145] like Figure 2 As shown, in step S209, the precipitation forecast rule of the preset precipitation index is used to feedback the corresponding relationship between the inverted precipitation weight of each precipitation index and the average precipitation forecast data corresponding to the current inverted precipitation data, wherein the inverted precipitation weight can refer to the aforementioned Figure 2 The content of step S204 is shown in FIG. 1 . The current inversion precipitation data can refer to the aforementioned Figure 2 The contents of steps S203-S208 are shown.

[0146] Specifically, the precipitation forecast rule of the preset precipitation index can be embodied as follows:

[0147]

[0148] Among them, R f is the final average precipitation forecast data of multiple precipitation indices; R i is the current inverted precipitation data of the i-th precipitation index, as shown in formula (1); W i is the inverted precipitation weight of the i-th precipitation index, as shown in formula (18).

[0149] It can be seen that by linearly superimposing the current inverted precipitation data of each different precipitation index according to the corresponding inverted precipitation weight, the average precipitation forecast data corresponding to multiple precipitation indices can be obtained according to the precipitation forecast equation of the above formula (19). Therefore, the precipitation index can be calculated using the forecast data of the current numerical model, and the current precipitation forecast can be realized through the above formulas (1), (18) and (19).

[0150] Therefore, the above data processing method of the embodiment of the present disclosure can use the forecast output data of the numerical model to calculate multiple precipitation indices, establish the precipitation inversion rules of the precipitation index through long-term series statistical analysis, and obtain the inverted precipitation corresponding to each precipitation index. Since each precipitation index can only grasp certain dynamic thermodynamic characteristics of the precipitation process and cannot comprehensively characterize all dynamic thermodynamic characteristics of the precipitation process, the precipitation inverted by each precipitation index is further superimposed with different weights, and finally the average precipitation of multiple precipitation indices is obtained to achieve the forecast of precipitation area and intensity.

[0151] It can be seen that the above method can be used as an objective precipitation forecasting method based on the terrain following coordinate system of at least one precipitation index numerical forecast, which can give full play to the forecasting potential of numerical models, improve the accuracy of numerical model precipitation forecasts, introduce terrain effects, and improve the precipitation forecast level or ability in mountainous areas and river basins with steep terrain, effectively avoid the intersection of isobaric surfaces or contour surfaces in the lower troposphere with the ground, and overcome the problem of not being able to accurately analyze and forecast the intensity and movement of mountain precipitation.

[0152] It should be noted that the core idea of ​​the data processing method applied to the precipitation forecast process can be understood as using the historical data of observations and numerical model forecasts to establish the various coefficients of the precipitation forecast equation, and then substituting the current forecast precipitation index into the precipitation forecast equation to obtain the precipitation forecast based on the precipitation index. In essence, this data processing method belongs to the extended interpretation and application of numerical forecast products. It uses the basic elements of temperature, humidity, pressure, and wind predicted by numerical models to make precipitation forecasts from the perspective of macroscopic dynamics and thermodynamic characteristics, which is different from the model precipitation forecast of numerical model cloud microphysical processes and cumulus convection parameterization.

[0153] In order to further demonstrate the beneficial effects of the above data processing method for precipitation forecasting and enable those skilled in the art to have a clearer understanding of the method, a specific application example is provided to make the following description:

[0154] The forecasting technology was applied to the rainfall process in North China from July 19 to July 20, 2016 to test the forecasting effect. Among them, the 6-hour cumulative observed rainfall from 06:00 to 12:00 (UTC) on July 20, 2016 was selected. From 06:00 to 12:00 on July 20, 2016, there were mainly two rainfall areas in North China and Central China. The rainfall area in North China was mainly located in Hebei, Beijing, Tianjin and the western part of Liaoning, with an east-west belt distribution. The heavy rainfall area extended from Beijing to the southeast of Hebei, and the center of heavy rainfall reached 75mm. The rainfall area in Central China occurred in southern Shandong, northwestern Jiangsu and northern Henan, with a northeast-southwest belt distribution. The center of heavy rainfall was located in central and southern Shandong, with an intensity of about 15mm. The rainfall in North China was much stronger than that in Central China, and this rainfall caused serious flood disasters in Beijing and Hebei.

[0155] For the above example, in the embodiments of the present disclosure, the forecast data of the Global Forecast System (GFS) of the National Center for Environmental Prediction (NCEP) of the United States and the GRAPES model of the China Meteorological Administration can be selected according to the embodiments of the present disclosure. Figure 1 and Figure 2 The data processing flow shown realizes the average rainfall forecast of multiple rainfall indices.

[0156] Among them, according to the average 6-hour cumulative rainfall forecast data of the multi-rainfall index based on the GFS model and the GRAPES model at 12:00 (UTC) on July 19, 2016, the terrain-following coordinate system multi-rainfall index average rainfall forecast based on the GFS model and the GRAPES model both well predicted the heavy rainfall areas in North China and Central China and the heavy rainfall center near Qinhuangdao, and the rainfall intensity and range were slightly larger than the observation. Relatively speaking, for the heavy rainfall area in North China, the multi-index rainfall forecast based on the GFS model is more reasonable and closer to the actual situation; the multi-index rainfall forecast based on the GRAPES model is weaker than the actual situation and has a smaller range. For the rainfall area in Central China, the range of the two forecasts is close to the actual situation, but the intensity is stronger. In summary, the multi-index rainfall forecast based on the GFS model can more reasonably predict and reproduce the heavy rainfall areas in North China and Central China, showing a certain forecast effect.

[0157] In order to quantitatively evaluate the forecast effect of this rainfall case, the ETS score and Bias deviation were calculated respectively. The ETS and Bias scores are defined as:

[0158]

[0159]

[0160] Among them, NA is the number of stations predicted correctly, NB is the number of stations with false reports, NC is the number of stations with missed reports, and R is a random number.

[0161] Specifically, as shown in Table 1 below, the ETS scores and Bias scores of the GFS model, the GRAPES model itself, and the terrain-following coordinate system multi-rainfall index rainfall forecast from 06:00 to 12:00 (UTC) on July 19, 2016 are shown. Among them, for rainfall greater than 10 mm, the ETS score of the multi-index rainfall forecast based on the GFS model is significantly higher than the rainfall forecast of the GFS model itself (from the cumulus convection parameterization and cloud microphysical process of the model); for the ETS score of rainfall greater than 20 mm, the multi-index rainfall forecast is slightly lower than the rainfall forecast of the model itself. The corresponding Bias terrain coordinate rainfall index forecast rainfall is also smaller. In general, the data processing results of the terrain-following coordinate system multi-rainfall index rainfall forecast provided by the embodiment of the present disclosure performed better in this process. The GRAPES model also performs better in multi-index rainfall forecast. From the comparison of the two models, the ETS score of the GFS model multi-index rainfall forecast is slightly higher, and the accuracy is higher than the GRAPES forecast; the Bias score of the GFS model multi-index forecast is smaller than that of GRAPES. In general, the multi-index rainfall forecast of the GFS model is better than that of the GRAPES model.

[0162] Table 1

[0163]

[0164] Obviously, the above-mentioned data processing method of the embodiment of the present disclosure can make full use of the advantage of numerical models in predicting basic meteorological elements such as temperature, humidity, pressure, and wind more accurately, use historical numerical model forecast data to calculate the precipitation index, and then fit it with historical precipitation observation data to establish a precipitation index inversion precipitation equation; at the same time, comprehensively consider the various possibilities of dynamic and thermal conditions for precipitation, perform non-equal weight linear superposition of the inverted precipitation of each precipitation index, and establish a multi-precipitation index average precipitation forecast equation; finally, input the temperature, humidity, pressure and wind data predicted by the current numerical model to generate the current precipitation forecast.

[0165] It can be seen that the above-mentioned data processing method of the embodiment of the present disclosure is not a simple mathematical statistical forecast. It is based on a precipitation index with a clear physical meaning and is an organic combination of atmospheric dynamics and statistics. The data processing method uses historical data of numerical model forecasts and observations, contains certain historical forecast error information, and reflects the correction effect of historical forecasts on current forecasts. The input data of the data processing method can come from various different models, for example: the output data of WRF, GRAPES, GFS models and ECMWF models can be easily accessed. Therefore, the above-mentioned data processing method of the embodiment of the present disclosure has strong flexibility and adaptability.

[0166] In addition, the above data processing method of the embodiment of the present disclosure realizes precipitation forecasting in a completely different way from numerical model forecasting. It forecasts precipitation from the macroscopic dynamic and thermodynamic perspectives such as temperature, humidity, pressure, wind and precipitation index, while the numerical model forecasts precipitation through cloud microphysical processes and cumulus convection parameterization. In fact, the two can be combined and integrated to form complementary advantages in precipitation forecasting and jointly improve the accuracy of precipitation forecasting.

[0167] Furthermore, the above-mentioned data processing method of the embodiment of the present disclosure is scalable. For the typical precipitation process in a certain area, it is possible to explore new precipitation formation mechanisms, propose new precipitation indexes, incorporate them into historical data sets, expand the capacity of the precipitation index system, and enhance the ability of objective precipitation forecasting.

[0168] It can be seen that the above data processing method of the embodiment of the present disclosure has moderate computational complexity, high computational efficiency, and no special requirements on hardware. Usually, a desktop computer equipped with a LINUX operating system can meet the computational requirements.

[0169] The formation mechanisms of precipitation weather in different regions are different, but they all have some common macro-weather situations, such as the convergence of cold and warm air masses, low-level convergence and high-level divergence, vertical upward movement, water vapor concentration, vertical wind shear, isentropic surface tilt, atmospheric baroclinicity, condensation latent heat release and potential instability. The accuracy of numerical model forecasts for these weather situations is much higher than that for precipitation. Therefore, in view of the uncertainty of numerical model precipitation forecasts, the above-mentioned data processing method of the embodiment of the present disclosure utilizes the macro-weather situation field with high forecast accuracy of numerical models, gives full play to the advantages and potential of numerical models, introduces terrain following coordinates, and provides a data processing scheme based on the terrain following coordinate system of numerical forecasts, which can be applied to objective precipitation forecasts, thereby effectively solving the problem of rainfall forecasts in areas with steep terrain and river basins, complementing the numerical model precipitation forecasts, correcting the model forecast results, and improving the accuracy of rainfall forecasts.

[0170] Among them, the above-mentioned data processing method of the embodiment of the present disclosure mainly includes two parts: the precipitation index processing process of the terrain following coordinate system and the precipitation forecast data processing process. The precipitation index processing process of the terrain following coordinate system is a series of dynamic thermodynamic indices that are indicative of precipitation developed in the terrain following coordinate system. These precipitation indices can mainly include frontogenesis index, vorticity index, potential vorticity index, potential vorticity gradient index, helicity index, thermal helicity index, divergence index, shear index, deformation index, vertical velocity index, etc. The precipitation forecast processing process refers to using the above-mentioned precipitation index theoretical system to statistically analyze the relationship between the precipitation index and the observed precipitation, establish a model equation for precipitation forecast based on the precipitation index, and on this basis, construct an automated precipitation forecast system that can be used in practice.

[0171] It can be seen that the above-mentioned data processing method of the embodiment of the present disclosure, as a data processing means based on computer technology, can be applied to the field of precipitation forecast in weather forecasting, and is suitable for precipitation forecasting in meteorological business departments, meteorological institutions of scientific research institutes and meteorological industries, providing an objective correction forecast method for numerical precipitation forecasting.

[0172] In summary, the above data processing method of the embodiment of the present disclosure can significantly improve the precipitation forecast capability, and the forecast accuracy is significantly higher than the precipitation forecast of the numerical model itself (such as from the cumulus convection parameterization scheme and cloud microphysical process). In fact, the above data processing method of the embodiment of the present disclosure can not only provide rainfall forecasts, but also provide winter snowfall forecasts. It should be noted that the input data used by the above data processing method of the embodiment of the present disclosure are all obtained in accordance with relevant laws and regulations and with legal authorization.

[0173] Therefore, the above-mentioned data processing method of the embodiment of the present disclosure has a small amount of data calculation during the data processing process, and the storage space occupied by the data is small. The requirements for the computer software and hardware environment are not high (such as the software using the LINUX system and ifort compiler, and the hardware using an ordinary desktop computer can be achieved). It is easy to modularize and transplant, has high computing efficiency, and can be easily promoted and applied in meteorological services and various industry sectors. It has extremely high scientific research value and commercial utilization value.

[0174] In addition, for meteorological business departments, this data processing method can be seamlessly linked with the numerical model forecasting system of meteorological business stations, and can be integrated with each other to provide accurate and high-resolution precipitation forecasts for the national and social welfare. For water conservancy departments, this data processing method can provide precipitation forecasts for river basins, and provide services for reservoir water storage and hydropower generation; for emergency disaster reduction departments, this data processing method can provide precipitation forecasts for disaster weather such as rainstorms and typhoons, and provide scientific support for emergency measures for disaster prevention and reduction; for agricultural and forestry departments, this data processing method can provide rainfall and snowfall forecasts for agricultural planting and animal husbandry, and provide services for agricultural food safety production; for traffic management departments, this data processing method can provide road water and snow forecasts, and contribute to the safety of people's transportation. Obviously, this data processing method can meet the needs of water conservancy, disaster prevention and reduction, agriculture, transportation and other industry departments for precipitation or snowfall forecasts, and has broad application prospects and significant application value.

[0175] Based on the above data processing method for precipitation forecast, the present disclosure also provides a data processing device for precipitation forecast. Figure 3 The device 300 is described in detail.

[0176] Figure 3 The structural block diagram of the data processing device 300 applied to precipitation forecast according to an embodiment of the present disclosure is schematically shown.

[0177] like Figure 3 As shown, the data processing device 300 applied to precipitation forecast in this embodiment includes a rule index determination module 310 , an inversion data acquisition module 320 and a data processing module 330 .

[0178] The rule index determination module 310 is used to determine the inverted precipitation rule according to the historical precipitation data, and to determine at least one precipitation index according to the current precipitation data. In one embodiment, the rule index determination module 310 can be used to perform the operation S101 described above, which will not be described in detail here.

[0179] The inversion data acquisition module 320 is used to acquire at least one current inversion precipitation data corresponding to at least one precipitation index based on the inversion precipitation rule. In one embodiment, the inversion data acquisition module 320 can be used to perform the operation S102 described above, which will not be repeated here.

[0180] The data processing module 330 is used to process the current inverted precipitation data and determine the average precipitation forecast data corresponding to the current precipitation data. In one embodiment, the data processing module 330 can be used to perform the operation S103 described above, which will not be described in detail here.

[0181] According to an embodiment of the present disclosure, any multiple modules of the rule index determination module 310, the inversion data acquisition module 320 and the data processing module 330 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the rule index determination module 310, the inversion data acquisition module 320 and the data processing module 330 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, at least one of the rule index determination module 310, the inversion data acquisition module 320 and the data processing module 330 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0182] Figure 4 A block diagram of an electronic device 400 suitable for implementing a data processing method for precipitation forecasting according to an embodiment of the present disclosure is schematically shown.

[0183] An embodiment of the present disclosure also provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned data processing method applied to precipitation forecasting.

[0184] like Figure 4 As shown, the electronic device 400 according to an embodiment of the present disclosure includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage part 408 to a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include an onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0185] In RAM 403, various programs and data required for the operation of electronic device 400 are stored. Processor 401, ROM 402 and RAM 403 are connected to each other via bus 404. Processor 401 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 402 and / or RAM 403. It should be noted that the program can also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0186] According to an embodiment of the present disclosure, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to the bus 404. The electronic device 400 may further include one or more of the following components connected to the I / O interface 405: an input portion 406 including a keyboard, a mouse, etc.; an output portion 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 408 including a hard disk, etc.; and a communication portion 409 including a network interface card such as a LAN card, a modem, etc. The communication portion 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage portion 408 as needed.

[0187] An embodiment of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned data processing method for precipitation forecasting.

[0188] The computer-readable storage medium of the embodiments of the present disclosure may be included in the device / apparatus / system described in the above embodiments; or it may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0189] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 402 and / or RAM 403 described above and / or one or more memories other than ROM 402 and RAM 403.

[0190] An embodiment of the present disclosure further provides a computer program product, including a computer program, which implements the above-mentioned data processing method for precipitation forecasting when executed by a processor.

[0191] The computer program product of the embodiment of the present disclosure includes a computer program, which contains program codes for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program codes are used to enable the computer system to implement the above method of the embodiment of the present disclosure.

[0192] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 401. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0193] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 409, and / or installed from the removable medium 411. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0194] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the processor 401, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by a computer program module.

[0195] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0196] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0197] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0198] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

[0199] So far, the embodiments of the present disclosure have been described in detail with reference to the accompanying drawings.

[0200] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data processing method for precipitation forecasting, wherein: include: Determine the inversion precipitation rule based on historical precipitation data, and determine at least one precipitation index based on current precipitation data. The historical precipitation data includes historical precipitation observation data and precipitation index forecast data corresponding to historical numerical models, and the at least one precipitation index includes at least one of: frontogenesis index, vorticity index, potential vorticity index, potential vorticity gradient index, helicity index, divergence index, shear index, deformation index and vertical velocity index; In the determining of the inverted precipitation rule according to the historical precipitation data, the method includes: corresponding to the at least one precipitation index, performing fitting analysis on the precipitation index forecast data and the historical precipitation observation data, and determining the inverted precipitation rule according to the least square method; Determining at least one precipitation index based on current precipitation data includes: Determine the forecast output data of the current numerical model based on the current precipitation data; Extracting basic precipitation element data based on the forecast output data, wherein the basic precipitation element data includes at least one of temperature, air pressure, humidity and wind speed; Determining the at least one precipitation index according to the quality terrain following coordinate system by means of the basic precipitation element data; Based on the inverted precipitation rule, obtaining at least one current inverted precipitation data corresponding to the at least one precipitation index; and The current inverted precipitation data is processed to determine average precipitation forecast data corresponding to the current precipitation data.

2. The method according to claim 1, wherein: In the step of obtaining at least one current inverted precipitation data corresponding to the at least one precipitation index based on the inverted precipitation rule, the step includes: The at least one current inverted precipitation data is determined according to the at least one precipitation index and at least one index coefficient corresponding to the at least one precipitation index.

3. The method according to claim 1, wherein: Before the current inverted precipitation data is processed to determine the average precipitation forecast data corresponding to the current precipitation data, the method further includes: Determine the total number of correlation coefficients between at least one inverted precipitation and the observed precipitation respectively corresponding to the at least one precipitation index; Determine the sequence number of the correlation coefficient of the inverted precipitation corresponding to the at least one precipitation index in the total number of the correlation coefficients; The inverted precipitation weight of each precipitation index corresponding to the at least one precipitation index is determined by the total number of correlation coefficients and the sequence number.

4. The method according to claim 3, wherein: The processing of the current inverted precipitation data to determine the average precipitation forecast data corresponding to the current precipitation data includes: According to the inverted precipitation weight of the at least one index, the current inverted precipitation data is linearly superimposed to determine the corresponding average precipitation forecast data.

5. A data processing device for precipitation forecasting, wherein: include: A rule index determination module is used to determine the inversion precipitation rule according to the historical precipitation data and determine at least one precipitation index according to the current precipitation data. The historical precipitation data includes historical precipitation observation data and precipitation index forecast data corresponding to historical numerical models, and the at least one precipitation index includes at least one of: frontogenesis index, vorticity index, potential vorticity index, potential vorticity gradient index, helicity index, divergence index, shear index, deformation index and vertical velocity index; In determining the inverted precipitation rule according to the historical precipitation data, it includes: corresponding to the at least one precipitation index, fitting analysis is performed on the precipitation index forecast data and the historical precipitation observation data, and the inverted precipitation rule is determined according to the least square method; In determining at least one precipitation index according to the current precipitation data, it includes: determining the forecast output data of the current numerical model according to the current precipitation data; extracting basic precipitation element data based on the forecast output data, the basic precipitation element data including: at least one of temperature, air pressure, humidity and wind speed; through the basic precipitation element data, determining the at least one precipitation index according to the quality terrain tracking coordinate system; an inversion data acquisition module, configured to acquire, based on the inversion precipitation rule, at least one current inversion precipitation data corresponding one-to-one to the at least one precipitation index; and The data processing module is used to process the current inverted precipitation data to determine the average precipitation forecast data corresponding to the current precipitation data.

6. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 4.

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