CN115314840B - Mobile positioning method and system for minute-level cycle assimilation numerical forecasting system - Google Patents
Mobile positioning method and system for minute-level cycle assimilation numerical forecasting system Download PDFInfo
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Abstract
The invention discloses a mobile positioning method and a mobile positioning system for a minute-level cycle assimilation numerical forecasting system, wherein the method comprises the following steps: determining longitude and latitude areas in a specified range according to the business requirements of disastrous weather, and taking the longitude and latitude areas as positioning areas for minute-level cyclic assimilation numerical forecasting operation, namely, positioning areas for calculating mass centers; selecting a global or regional numerical model to forecast products or derivative products in real time, and determining the mass weight of grid points in a positioning region for calculating the mass center of the region; calculating the longitude and latitude of the centroid in the positioning area by using a non-uniform centroid method; the regional centroid longitude and latitude obtained by calculating the data at different forecasting moments are used as the regional center of the minute-level cycle assimilation numerical forecasting mode, so that the minute-level cycle assimilation forecasting regional center is automatically moved in real time.
Description
Technical Field
The invention relates to a minute-level cycle assimilation numerical forecasting service system, in particular to a mobile positioning method and a mobile positioning system for a minute-level cycle assimilation numerical forecasting system with high space-time precision.
Background
With the demands of refined numerical forecasting, the requirements of spatial resolution and time resolution of the numerical forecasting mode are higher and higher at present, and with the improvement of time resolution, the requirements of calculation consumed time are shorter and shorter to meet the demands of business application.
The current mobile positioning technology of business numerical weather forecast is mainly concentrated in typhoon forecast of coastal areas, is not seen in numerical forecast application of inland areas, and especially the mobile positioning is not seen in rapid cyclic assimilation and update numerical forecast combined with live observation data assimilation technology. This is mainly due to the huge calculation amount of the rapid cycle assimilation update forecast based on the data assimilation technology, especially the high space-time precision operation with the spatial resolution reaching 1km and the time resolution reaching the minute level.
Under the condition that the space-time resolution is not reduced, the problems of large calculation amount and long calculation time consumption of the service numerical forecasting are mainly solved by reducing the calculation area, but a smaller and fixed numerical forecasting area is necessarily unfavorable for weather forecasting outside a deviation area, and meanwhile, when strong weather (such as a strong rainfall area) appears near the boundary of the numerical forecasting area, serious distortion of data estimation is caused.
Therefore, in order to solve the above problem of the fixed prediction area, a method of predicting an area using a moving value is proposed. There are two main types of mobile area techniques used in current weather numerical predictions: specifying the path of movement and vortex self-following technique. However, the specified moving path technology is only suitable for research analysis under the known moving path, and is not suitable for being used in the case of undefined path in service real-time forecast; the vortex self-following technology judges the positioning of a moving area based on a flow field and a low-pressure center calculated in real time in a numerical mode, but because the strong weather center of an inland area is not in one-to-one correspondence with the vortex center, strong weather can occur even in an area without the vortex center, and therefore the vortex self-following technology is not suitable for the strong weather positioning of the inland area.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a mobile positioning method and a mobile positioning system for a minute-scale circulation assimilation numerical forecasting system, which adopt a mobile positioning technology combining a coarser resolution numerical mode real-time forecasting product or derivative product and a non-uniform mass center method to determine a mobile forecasting area positioning technology of the minute-scale circulation assimilation numerical forecasting system suitable for a strong weather occurrence area and establish the mobile positioning system of the real-time minute-scale circulation assimilation forecasting system. By adapting to the prediction area positioning technology of the strong weather occurrence area, the calculation area of the minute-level circulation assimilation numerical prediction system is reduced, the calculation amount is reduced, meanwhile, the mode calculation time is shortened, products are provided for a prediction platform at a higher speed, and the rapid analysis and the timely early warning of the strong weather are realized.
In order to achieve the above purpose, the embodiment of the invention provides a mobile positioning method for a minute-level cycle assimilation numerical forecasting system, which comprises the following steps:
Determining longitude and latitude areas in a specified range according to the business requirements of the disastrous weather, and taking the longitude and latitude areas as positioning areas for operation of a minute-level cycle assimilation numerical forecasting system, namely, calculating the positioning area range of the mass center;
selecting a global or regional numerical model real-time forecasting product or a derivative product thereof with thicker horizontal resolution, and determining the mass weight of the grid points of the positioning region range for calculating the regional mass center;
calculating the centroid longitude and latitude of the positioning area by utilizing a non-uniform centroid method according to the determined lattice point mass weight;
And taking the longitude and latitude of the centroid of the region obtained by calculating the data of different forecasting moments as the region center of the minute-level cycle assimilation numerical forecasting mode, and realizing real-time automatic movement of the minute-level cycle assimilation numerical forecasting region center.
Optionally, the range of the forecast data of the selected global or regional numerical model real-time forecast product with thicker horizontal resolution needs to cover the range of the selected positioning region, and the forecast data is required to be equal-theodolite point data.
Optionally, determining the quality weight of the grid points in the positioning area range for calculating the centroid, so as to predict the grid point forecast value directly output by the product or the linear function or the nonlinear function of the derived grid point forecast product value through secondary calculation in real time as the grid point quality weight in the positioning area range.
Optionally, one of the following functions is selected as the lattice point quality weight of the location area range for calculating the centroid in the lattice point quality weight of the location area range
weight(i,j)＝C*p(i,j)
weight(i,j)＝p(i,j) C
Wherein weight (i,j) is the lattice point quality weight, p i(i,j) is the lattice point forecast value directly output by the selected numerical forecast product or the derived lattice point forecast product value obtained by secondary calculation, C is the coefficient, and i and j respectively represent the warp and weft lattice point values of the positioning area.
Optionally, in the step of calculating the regional centroid of the positioning regional scope by using the lattice point mass weight, the regional centroid position of the positioning regional scope is obtained by using the following centroid calculation formula:
wherein weight (i,j) represents the lattice quality weight, N and m are the numbers of grid points in the weft direction and the warp direction in the area respectively, and (x (i,j),y(i,j)) is the coordinates of grid points in the area.
In order to achieve the above purpose, the present invention further provides a mobile positioning system for a minute-level cycle assimilation numerical forecasting system, comprising:
The positioning area range determining module is used for determining longitude and latitude areas in a specified range according to the business requirements of the disastrous weather and is used as a positioning area for the operation of the minute-level cycle assimilation numerical forecasting system, namely, the positioning area range for calculating the centroid of the area;
the grid point quality weight determining module is used for selecting a global or regional numerical mode real-time forecast product or a derivative product thereof and determining the quality weight of the grid points in the locating region range for calculating the regional centroid;
the centroid calculating module is used for calculating the centroid longitude and latitude of the positioning area by utilizing a non-uniform centroid method according to the determined lattice point mass weight;
and the mobile positioning module is used for taking the longitude and latitude of the centroid of the region obtained by calculating the data of different forecasting moments as the region center of the minute-level cycle assimilation numerical forecasting mode, so that the minute-level cycle assimilation numerical forecasting region center is automatically moved in real time.
Compared with the prior art, the mobile positioning method and system for the minute-scale circulation assimilation numerical forecasting system are established by combining the global or regional numerical mode real-time forecasting product with a coarser resolution numerical mode or the derivative product thereof with the non-uniform centroid method. By adopting the prediction area positioning technology suitable for the strong weather generation area, the calculation area of minute-level cycle assimilation numerical prediction is reduced, the calculated amount is reduced, meanwhile, the mode calculation time is shortened, products are provided for a prediction platform at a higher speed, and the rapid analysis and the timely early warning of the strong weather are realized.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing embodiments of the present invention in more detail with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, and not constitute a limitation to the invention. In the drawings, like reference numerals generally refer to like parts or steps.
FIG. 1 is a flow chart of a mobile positioning method for a minute-scale cycle assimilation numerical forecasting system according to an exemplary embodiment of the present invention;
FIGS. 2a, 2b and 2c are respectively short-time strong precipitation probability distributions of 12, 18 and 24 times of three prediction times of 2021, 8, 24 and 20, and short-time strong precipitation centroids determined by a general centroid method (black triangle) and a power centroid method (black dot);
FIG. 3 is a schematic diagram of the mass versus short-time strong precipitation probability for the general centroid method and the power-level centroid method;
Fig. 4 a-4 c are schematic diagrams showing short-time strong precipitation centroids (black triangles) determined by a general centroid method (black circles) and a power centroid method (black dots) corresponding to different short-time strong precipitation probability distributions (color filling) in the embodiment of the invention (the upper left corner of the diagram is the time of reporting in a numerical mode, and the upper right corner of the diagram is the time of forecasting);
fig. 5 is a schematic structural diagram of a mobile positioning system for a minute-level cycle assimilation numerical forecasting system according to an exemplary embodiment of the present invention.
Detailed Description
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
It will be appreciated by those of skill in the art that the terms "first," "second," etc. in embodiments of the present invention are used merely to distinguish between different steps, devices or modules, etc., and do not represent any particular technical meaning nor necessarily logical order between them.
It should also be understood that in embodiments of the present invention, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.
It should also be appreciated that any component, data, or structure referred to in an embodiment of the invention may be generally understood as one or more without explicit limitation or the contrary in the context.
In addition, the term "and/or" in the present invention is merely an association relationship describing the association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.
It should also be understood that the description of the embodiments of the present invention emphasizes the differences between the embodiments, and that the same or similar features may be referred to each other, and for brevity, will not be described in detail.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and apparatus should be considered part of the specification.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations with electronic devices, such as terminal devices, computer systems, servers, etc. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with the terminal device, computer system, server, or other electronic device include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the foregoing, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc., that perform particular tasks or implement particular abstract data types. The computer system/server may be implemented in a distributed cloud computing environment in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Exemplary method
Fig. 1 is a flow chart of a mobile positioning method for a minute-level circulation assimilation numerical forecasting system according to an exemplary embodiment of the invention. As shown in FIG. 1, the mobile positioning method for the minute-level cycle assimilation numerical forecasting system comprises the following steps:
step 101, determining longitude and latitude areas in a specified range according to the business requirements of disastrous weather, and using the longitude and latitude areas as positioning areas for operation of a minute-level cycle assimilation numerical forecasting system, namely, the positioning area range for calculating the mass center.
The method and the device can be suitable for mobile positioning of various kinds of strong weather, such as hail, thunderstorm, strong wind, storm, short-time strong precipitation and other disastrous weather, in the embodiment of the invention, the area range for calculating the centroid is determined according to the short-time strong precipitation prediction range of service requirements by taking short-time strong precipitation prediction as an example, generally, the short-time strong precipitation refers to precipitation with precipitation capacity reaching more than 50 millimeters in 3 hours, in the embodiment, the positioning area range for calculating the centroid is determined to be 25-35 degrees in latitude, 104-118 degrees in longitude, namely, the initial latitude startlat = 25 degrees, the end latitude endlat = 35 degrees, the initial longitude startlon = 104 degrees, the end longitude endlon = 118 degrees and the lattice spacing is 0.25 degrees according to the service requirements.
Step 102, selecting a global or regional numerical model real-time forecasting product or a derivative product thereof, and determining the mass weight of the grid points of the locating region range for calculating the regional mass center.
In the invention, regional mass center calculation is performed under the condition that different grid point mass weights are assumed, namely, a non-uniform mass plane centroid algorithm is adopted, the determination of the grid point mass weights needs to depend on a selected global or regional numerical model forecast product, and the determination of the grid point mass weights depends on a precipitation forecast product or a short-time strong precipitation probability forecast product by taking short-time strong precipitation as an example.
Generally, the range of the predicted data of the selected global or regional numerical model prediction product needs to cover the range of the region selected in step 101 and is required to be equal-theodolite data, and the selected global numerical model prediction product may be a lattice precipitation product predicted by numerical prediction modes such as NCEP (National Centers for Environmental Prediction, national environmental prediction center), ECMWF (European Centre for Medium-RANGE WEATHER Forecasts, middle European weather prediction center), CMA (China Meteorological Administration, china weather agency), or a derivative product developed by using global or regional numerical model data, which is not limited in the present invention. In the embodiment of the invention, a central weather table A is selected to forecast multi-element products by using a global numerical mode, a grid point forecast product of short-time strong precipitation probability of a derivative product developed based on objective typing and continuous probability method is used as a grid point quality weight function, and the mass centers of short-time strong precipitation areas at different forecast times in the area range are calculated.
The centroid identification has close relation with the size of the area range for calculating the centroid and the short-time strong precipitation probability, firstly, the plane centroid algorithm with general non-uniform mass is adopted, and the lattice point mass weight (weight) of the area range for calculating the centroid is determined according to the short-time strong precipitation probability value:
weight(i,j)＝p(i,j) (1)
Wherein weight (i,j) is the lattice point quality weight, p (i,j) is the lattice point forecast value directly output by the selected numerical forecast product or the derived lattice point forecast product value obtained by secondary calculation, i and j respectively represent the warp and weft lattice point values of the positioning area.
However, in actual operation, the centroid position calculated based on the general non-uniform mass plane centroid algorithm (hereinafter referred to as the general centroid method) has the problem that the change is not obvious, as shown in fig. 2a, 2b and 2c, the centroid of the black triangle is indicated by the centroid position of 12, 18 and 24 pre-reporting times based on the 20 th day of the 8 th month of the 2021, and it is seen that the centroids (black triangle) of the three pre-reporting times based on the general centroid method are always stabilized near (110.6 e and 28.6 n) and the change is not obvious.
In contrast, in the embodiment of the invention, a linear function or a nonlinear function of a plurality of short-time strong precipitation probability values p i is designed as the grid point quality weight, and the following formulas (2) to (4) are adopted,
weight(i,j)＝C*p(i,j) (2)
weight(i,j)＝p(i,j) C (3)
Wherein the coefficient C is any value and can be adjusted according to the test result.
The user can select a corresponding formula according to the requirement to determine the grid point quality weight, for example, under the condition that the mass center identification effect is not determined, the formula can be manually checked to determine which grid point quality weight has better effect, and the coefficient C is more suitable, so that the grid point quality weight is determined. Experiments prove that in order to accurately concentrate the mass center to a strong precipitation center, the power-level-based non-uniform mass weight of the formula (4) is used, and the effect of C & gt1 is good; however, if the stability requirement on the center of the area of the minute-scale cycle assimilation system is high, the effect of using the formula (2) is good. In the embodiment of the invention, the non-uniform quality weight based on the power level, namely the formula (4), is adopted, and preferably, the coefficient C takes a value of 1.1, namely the quality weight of the lattice point is determined as follows:
and 103, calculating the centroid longitude and latitude of the positioning area by using a non-uniform centroid method according to the determined lattice point mass weight.
In the embodiment of the invention, after determining the lattice point mass weight (i,j), the area centroid position of the positioning area range is obtained by using the following centroid calculation formula:
Wherein weight (i,j) refers to the lattice point quality weight, The centroid position, n, m are the number of latitudinal and longitudinal lattice points in the region, respectively, and (x (i,j),y(i,j)) are the lattice point coordinates in the region.
In the embodiment of the present invention, taking the starting latitude startlat =25°, the ending latitude endlat =35°, the starting longitude startlon =104°, the ending longitude endlon =118° and the lattice spacing of 0.25 ° as an example, the location center of mass position of the location area range is calculated as follows:
wherein the method comprises the steps of Weight (i,j) refers to the mass weight on the lattice point (i, j), i is the warp lattice point value of the location area, j is the weft lattice point value of the location area,/>, for the centroid position (latitude and longitude in order)For the total number of grid points in warp direction of the positioning area,/>The number of total grid points in the weft direction of the positioning area is startlat, the starting latitude of the positioning area is startlon, the starting longitude of the positioning area is space.
In the invention, the calculation of the mass center solves the problem that the vortex center is inconsistent with the precipitation center, improves the appointed moving path technology of the existing numerical prediction moving area technology, and changes the artificial appointed moving path into the moving path of the area short-time strong precipitation mass center calculated in real time based on a numerical mode.
The centroid calculation does not involve forecast aging, so that the centroids of the prediction areas of the short-time strong rainfall at different forecast times are calculated respectively.
104, Taking the longitude and latitude of the regional centroid obtained by calculating the data at different forecasting moments as the regional center of the minute-level cyclic assimilation numerical forecasting mode, and realizing real-time automatic movement of the cyclic assimilation numerical forecasting regional center, namely writing the longitude and latitude of the regional centroid obtained by calculating the data at different forecasting moments into a starting program of the minute-level cyclic assimilation numerical forecasting system corresponding to time respectively, and carrying out system operation of real-time automatic movement positioning, thereby realizing real-time automatic movement of the cyclic assimilation numerical forecasting regional positioning.
In the embodiment of the invention, the starting time frequency of the assimilation mode is updated according to the rapid circulation (the starting time interval can be manually adjusted), the mass center of the short-time strong precipitation prediction area with corresponding time is selected, and the starting program of the area numerical mode is automatically written, so that the mobile positioning is realized. The rapid updating and circulating assimilation system is a prediction technology developed for short-time prediction application, and a more accurate initial field is formed by assimilating high-frequency observation data and continuously updating a mode background field to perform short-time prediction. The mobile positioning refers to a calculation center and an area of a numerical forecasting mode according to the probability of occurrence of the short-time and strong rainfall in the future 24 hours. The mobile positioning data and the numerical mode prediction data update time remain synchronized. The grid point weather forecast products (including short-time strong rainfall probability forecast products) used by the method are updated every 12 hours, so that the calculation of the mobile positioning center is updated every 12 hours, and the positioning center with the interval of 5 hours at 3 hours between 12 and 24 hours in the forecast field can be obtained every update.
Experiments prove that the centroid calculated based on the power centroid method can be more effectively positioned in a short-time strong precipitation center area, as shown in the figure 2, the centroid positions of black dots are indicated by the centroids of three predicted times 12, 18 and 24 based on the power centroid method 2021, 8 months and 24 days, and as can be seen from the figure 2b, the probability of short-time strong precipitation at the 18 th predicted time of the 2021, 8 months and 24 days, which is predicted, is obviously increased, the centroid (black origin) calculated based on the power centroid method is very accurately positioned in the strong precipitation center, and the centroids of other times are also closer to the center of the short-time strong precipitation high probability area than the common centroid method.
Experiments prove that the mass point mass weight of the common mass point method and the power mass point method and the short-time strong precipitation probability are related to each other, as shown in figure 3, after the short-time strong precipitation probability reaches more than 45%, the mass point mass of the power mass point method is rapidly increased along with the increase of the probability. The mass increase of the particles corresponding to the high short-time strong precipitation probability value can enable the mass center to move to a strong precipitation area more accurately and rapidly, the mass decrease of the particles corresponding to the low short-time strong precipitation probability value can enable the mass center to be more stable in the absence of strong precipitation weather, as shown in fig. 4, the mass centers of the short-time strong precipitation determined by a general mass center method (black triangle) and a power-level mass center method (black dot) corresponding to different short-time strong precipitation probability distributions are corresponding, and therefore the mass centers are basically stabilized in the center of the area when the strong precipitation probability is smaller (fig. 4 b), the mass centers of the two mass center calculation methods almost overlap together, and the mass center rapidly moves to the area close to the strong precipitation when the strong precipitation probability is larger, as shown in fig. 4a, the center of the short-time strong precipitation probability is on the southwest side, and the mass center of the power-level mass center method is obviously biased towards the southwest strong precipitation center; in fig. 4c, the high probability center of the short-time strong precipitation is on the southeast side, and the centroid of the power centroid method is obviously biased to the southeast strong precipitation center.
Exemplary System
Fig. 5 is a schematic structural diagram of a mobile positioning system for a minute-level cycle assimilation numerical forecasting system according to an exemplary embodiment of the present invention. As shown in fig. 5, the mobile positioning system for a minute-level cycle assimilation numerical forecasting system of the invention comprises:
the positioning area range determining module 501 is configured to determine, according to a requirement of a disaster weather service, a latitude and longitude area in a specified range, as a positioning area for operation of the minute-level cycle assimilation numerical forecasting system, that is, a positioning area range for calculating a centroid.
The invention can be suitable for mobile positioning of numerical predictions of various types of strong weather, such as hail, thunderstorm, strong wind, storm, short-time strong precipitation and the like, in the embodiment of the invention, the area range for calculating the centroid is determined according to the short-time strong precipitation prediction range of service requirements by taking short-time strong precipitation numerical prediction as an example, generally speaking, the short-time strong precipitation refers to precipitation with precipitation capacity of more than 50 millimeters in 3 hours, in the embodiment, the positioning area range for calculating the centroid is determined to be 25-35 degrees in latitude, 104-118 degrees in longitude, namely, the initial latitude startlat = 25 degrees, the end latitude endlat = 35 degrees, the initial longitude startlon = 104 degrees, the end longitude endlon = 118 degrees and the lattice spacing is 0.25 degrees according to the service requirements.
The lattice point quality weight determining module 502 is configured to select a global or regional numerical model real-time forecast product or a derivative product thereof, and determine the quality weight of the lattice point of the location region range for calculating the regional centroid.
In the invention, regional mass center calculation is performed under the condition that different grid point mass weights are assumed, namely, a plane centroid algorithm with non-uniform mass is adopted, the determination of the grid point mass weights needs to depend on a selected weather forecast product, and the determination of the grid point mass weights depends on a precipitation forecast product or a short-time strong precipitation probability forecast product by taking short-time strong precipitation as an example. Generally, the range of the prediction data of the selected numerical prediction product needs to cover the range of the selected area in the positioning area range determining module 501, and the selected weather prediction product is equal-theodolite data, and the selected weather prediction product may be a grid precipitation product predicted by numerical prediction modes such as NCEP (National Centers for Environmental Prediction, national environmental prediction center), ECMWF (European Centre for Medium-RANGE WEATHER Forecasts, european middle weather prediction center), CMA (China Meteorological Administration, china weather office), or a short-time strong precipitation probability grid prediction product developed by using the numerical mode data, which is not limited in the invention. In the embodiment of the invention, a short-time strong precipitation probability grid point forecasting product developed by the A-center weather table is selected, namely, the barycenter of the short-time strong precipitation area at different times in the area range is calculated by utilizing the short-time strong precipitation probability forecasting result developed by the A-center weather table.
In the embodiment of the invention, a linear function or a nonlinear function of a plurality of short-time strong precipitation probability values p i is designed as the grid point quality weight:
weight(i,j)＝C*p(i,j)
weight(i,j)＝p(i,j) C
Wherein weight (i,j) is the lattice point quality weight, p (i,j) is the lattice point forecast value directly output by the selected numerical forecast product or the derived lattice point forecast product value obtained by secondary calculation, i and j respectively represent the warp and weft lattice point values of the positioning area, and the coefficient C is any value and can be adjusted according to the test result.
The user can select a corresponding formula according to the requirement to determine the grid point quality weight, for example, under the condition that the mass center identification effect is not determined, the formula can be manually checked to determine which grid point quality weight has better effect, and the coefficient C is more suitable, so that the grid point quality weight is determined. Experiments prove that in order to accurately concentrate the mass center to a strong precipitation point, the non-uniform mass weight based on the power level is used, and the effect of C & gt1 is better; however, if the stability requirement for the centroid of the region is high, the effect of using weight i＝C*pi is good. In the embodiment of the invention, the non-uniform quality weight based on the power level, namely the formula (4), is adopted, and preferably, the coefficient C takes a value of 1.1, namely the quality weight of the lattice point is determined as follows:
And the centroid calculation module 503 is configured to calculate, according to the determined lattice point mass weight, the longitude and latitude of the regional centroid of the positioning regional range by using a non-uniform centroid method.
In the embodiment of the invention, after determining the lattice point mass weight (i,j), the area centroid position of the positioning area range is obtained by using the following centroid calculation formula:
Wherein weight (i,j) refers to the lattice point quality weight, The centroid position, n, m are the number of latitudinal and longitudinal lattice points in the region, respectively, and (x (i,j),y(i,j)) are the lattice point coordinates in the region.
The mobile positioning module 504 is configured to take the longitude and latitude of the center of mass of the area calculated from the data at different forecasting moments as the center of the minute-level cycle assimilation numerical value forecasting mode, and implement real-time automatic movement of the center of the cycle assimilation numerical value forecasting area, that is, the mobile positioning module 504 writes the longitude and latitude of the center of mass of the area calculated from the data at different forecasting moments into the starting program of the minute-level cycle assimilation numerical value forecasting system at corresponding time, and performs real-time automatic movement positioning of the system, thereby implementing real-time automatic movement of the center of the cycle assimilation numerical value forecasting area.
Examples
Example 1, 12, 18, 24 pre-reporting centroid (black triangle) as reported in figures 2 a-2 c,2021, 8, 24, 20, always stabilized around (110.6 e,28.6 n). The probability of short-time strong precipitation at the 18 th forecast of the forecast at the 24 th month 2021 is obviously increased, the centroid (black origin) calculated based on the power centroid method is accurately positioned to the center of strong precipitation, and the centroids of other times are closer to the center of a high-probability area of short-time strong precipitation than the common centroid method.
In example 2, as shown in fig. 4a, in the case of 20 days of 4 months and 23 months of 2022, the high probability center of short-time strong precipitation at 24 days 08 is located on the southwest side of the region, the centroid of the power centroid method is obviously biased to the southwest strong precipitation center, the general centroid method is outside about 200km on the northeast side of the short-time strong precipitation probability center, and for a small region rapid updating circulation numerical forecasting system with a radius of about 200km, the positioning center of the general centroid method can easily enable the strong precipitation center to be located near the mode boundary, so that false forecasting is generated.
In example 3, as shown in fig. 4c, the center of high probability of short-time strong precipitation at 25 days 08 is predicted at 24 days 08 of 4 months of 2022, the centroid of the power centroid method is obviously biased to the center of strong precipitation in southeast, and the common centroid rule is more approximate to the centroid of the current area beyond about 200km in northwest of the center of probability of short-time strong precipitation.
The basic principles of the present disclosure have been described above in connection with specific embodiments, but it should be noted that the advantages, benefits, effects, etc. mentioned in the present disclosure are merely examples and not limiting, and these advantages, benefits, effects, etc. are not to be considered as necessarily possessed by the various embodiments of the present disclosure. Furthermore, the specific details disclosed herein are for purposes of illustration and understanding only, and are not intended to be limiting, since the disclosure is not necessarily limited to practice with the specific details described.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different manner from other embodiments, so that the same or similar parts between the embodiments are mutually referred to. For system embodiments, the description is relatively simple as it essentially corresponds to method embodiments, and reference should be made to the description of method embodiments for relevant points.
The block diagrams of the devices, apparatuses, devices, systems referred to in this disclosure are merely illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. As will be appreciated by one of skill in the art, the devices, apparatuses, devices, systems may be connected, arranged, configured in any manner. Words such as "including," "comprising," "having," and the like are words of openness and mean "including but not limited to," and are used interchangeably therewith. The terms "or" and "as used herein refer to and are used interchangeably with the term" and/or "unless the context clearly indicates otherwise. The term "such as" as used herein refers to, and is used interchangeably with, the phrase "such as, but not limited to.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, firmware. The above-described sequence of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the sequence specifically described above unless specifically stated otherwise. Furthermore, in some embodiments, the present disclosure may also be implemented as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
It is also noted that in the apparatus, devices and methods of the present disclosure, components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered equivalent to the present disclosure. The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the disclosure to the form disclosed herein. Although a number of example aspects and embodiments have been discussed above, a person of ordinary skill in the art will recognize certain variations, modifications, alterations, additions, and subcombinations thereof.
Claims (4)
1. A mobile positioning method for a minute-level cycle assimilation numerical forecasting system comprises the following steps:
Determining longitude and latitude areas in a designated range according to the business requirements of disastrous weather, and using the longitude and latitude areas as positioning areas for operation of a minute-level cycle assimilation numerical forecasting system, wherein the positioning areas are used for calculating the positioning area range of the centroid of the area;
Selecting a global or regional numerical model real-time forecasting product or a derivative product thereof, determining the quality weight of grid points in a locating region range for calculating regional centroid, taking a linear function or a nonlinear function of a grid point forecasting product value directly output by the real-time forecasting product or a derivative grid point forecasting product value subjected to secondary calculation as the quality weight of the grid points in the locating region range, wherein one of the following functions is selected as the quality weight of the grid points in the locating region range in the determining region range
weight(i,j)＝C*p(i,j)
weight(i,j)＝p(i,j) C
Wherein weight (i,j) is the lattice point quality weight, p (i,j) is the lattice point forecast value directly output by the selected numerical forecast product or the derived lattice point forecast product value obtained by secondary calculation, C is a coefficient, i and j respectively represent the warp and weft lattice point values of the positioning area;
calculating the centroid longitude and latitude of the positioning area by utilizing a non-uniform centroid method according to the determined lattice point mass weight;
And taking the longitude and latitude of the centroid of the region obtained by calculating the data of different forecasting moments as the region center of the minute-level cycle assimilation numerical forecasting mode, and realizing real-time automatic movement of the minute-level cycle assimilation numerical forecasting region center.
2. The method of claim 1, wherein the selected global or regional numerical model with a thicker horizontal resolution is used to predict the product data range in real time to cover the selected positioning region and to obtain the equal theodolite point data.
3. The mobile positioning method for a minute-scale cycle assimilation numerical prediction system according to claim 1, wherein in the step of calculating the regional centroid longitude and latitude of the positioning regional range by using a non-uniform centroid method, the centroid position of the positioning regional is obtained by using the following centroid calculation formula:
wherein weight (i,j) represents the lattice quality weight, N and m are the numbers of grid points in the weft direction and the warp direction in the area respectively, and (x (i,j),y(i,j)) is the coordinates of grid points in the area.
4. A mobile positioning system for a minute-scale cycle assimilation numerical forecasting system, comprising:
The positioning area range determining module is used for determining longitude and latitude areas in a specified range according to the business requirements of the disastrous weather, and is used as a positioning area for the operation of the minute-level cycle assimilation numerical forecasting system and used for calculating the positioning area range of the centroid of the area;
The lattice point quality weight determining module is used for selecting a global or regional numerical model real-time forecasting product or a derivative product thereof, determining the quality weight of lattice points in a locating region range for calculating regional centroid, taking a linear function or a nonlinear function of a lattice point forecasting product value directly output by the real-time forecasting product or a derivative lattice point forecasting product value subjected to secondary calculation as the lattice point quality weight of the locating region range, wherein one of the following functions is selected as the quality weight of the lattice points in the locating region range in the determining region centroid
weight(i,j)＝C*p(i,j)
weight(i,j)＝P(i,j) C
Wherein weight (i,j) is the lattice point quality weight, p (i,j) is the lattice point forecast value directly output by the selected numerical forecast product or the derived lattice point forecast product value obtained by secondary calculation, C is a coefficient, i and j respectively represent the warp and weft lattice point values of the positioning area;
the centroid calculating module is used for calculating the centroid longitude and latitude of the positioning area by utilizing a non-uniform centroid method according to the determined lattice point mass weight;
And the mobile positioning module is used for taking the longitude and latitude of the centroid of the region obtained by calculating the data of different forecasting moments as the region center of the minute-level cycle assimilation numerical forecasting mode to realize the real-time automatic movement of the region center of cycle assimilation numerical forecasting.
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