Methods, devices, equipment, and media for volumetric cloud visualization based on meteorological data

Abstract

This invention proposes a method, device, equipment, and medium for visualizing volumetric clouds based on meteorological data. It transforms cloud cover data into texture images and simultaneously constructs anisotropic 3D noise textures using wind meteorological data combined with cloud type, thereby setting different noise parameters for different cloud types. The specific steps are as follows: First, meteorological data is acquired and preprocessed, primarily selecting low, medium, and high cloud cover rates and horizontal winds. Spatial resolution is improved by interpolating low, medium, and high cloud cover rates. A density-modified height correlation function is designed based on cloud type, and the basic 3D outline of the volumetric cloud is constructed by combining it with cloud cover rate. Differentiated noise parameters are used to construct volumetric textures for different cloud types to enhance the details of the volumetric cloud. Finally, volumetric cloud rendering is performed. This invention effectively improves the rendering realism and efficiency of different types of volumetric clouds in large-scale scenes.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of volumetric cloud visualization, and in particular to a volumetric cloud visualization method, device, equipment and medium based on meteorological data. BACKGROUND

[0002] Clouds, as an important part of the atmospheric environment, their corresponding modeling and visualization technology is an important part of natural environment simulation. In order to build a volumetric cloud that can reflect real weather data and enhance the realism of atmospheric cloud simulation, how to model and visualize the volumetric cloud based on real meteorological data becomes very important. Current volumetric cloud simulation based on real meteorological data mostly uses physical modeling methods, such as solving NS equations and particle system methods. Such methods need to solve heavy differential equations and have large computational overhead, so they cannot realize real-time visualization of volumetric clouds in large-scale scenarios. The existing volumetric cloud simulation using real meteorological data and combining noise functions has not further considered setting different noise parameters for different cloud types. SUMMARY

[0003] In view of the defects of the prior art, the purpose of the present application is to provide a volumetric cloud visualization method, device, equipment and medium based on meteorological data.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present application is as follows:

[0005] On the one hand, the present application provides a volumetric cloud visualization method based on meteorological data, comprising:

[0006] According to the altitude, the cloud types are divided into low clouds, middle clouds and high clouds, the cloud coverage rate data and the horizontal wind data of the low clouds, the middle clouds and the high clouds in a specified three-dimensional space are obtained from the meteorological data, the cloud coverage rate data of the low clouds, the middle clouds and the high clouds are respectively subjected to spline interpolation, and the interpolated data is mapped into a two-dimensional gray cloud texture map;

[0007] Different density change height correlation functions are set for the low clouds, the middle clouds and the high clouds respectively to construct the basic three-dimensional contour of the volumetric cloud;

[0008] Different noise parameters are set for the low clouds, the middle clouds and the high clouds, anisotropic three-dimensional noise is calculated according to different cloud types and horizontal wind meteorological data, and a variety of noise functions are superimposed through fractal Brownian motion to generate a three-dimensional noise volume texture;

[0009] The two-dimensional gray cloud texture map is used as a 2D texture mask, combined with the basic three-dimensional contour of the volumetric cloud and the three-dimensional volume texture, the rendering of the volumetric cloud is completed through a ray marching rendering pipeline.

[0010] In another aspect, a volumetric cloud visualization device based on meteorological data is provided, comprising:

[0011] a two-dimensional gray-scale cloud texture map generation module configured to divide cloud types into low clouds, middle clouds and high clouds according to altitudes, obtain cloud coverage data of the low clouds, the middle clouds and the high clouds and horizontal wind data of a specified three-dimensional space from meteorological data, respectively perform spline interpolation on the cloud coverage data of the low clouds, the middle clouds and the high clouds, and map the interpolated data into a two-dimensional gray-scale cloud texture map;

[0012] a base profile construction module configured to set different density change height correlation functions for the low clouds, the middle clouds and the high clouds respectively to construct a base three-dimensional profile of the volumetric cloud;

[0013] a volumetric texture generation module configured to set differential noise parameters for the low clouds, the middle clouds and the high clouds, calculate anisotropic three-dimensional noise according to different cloud types and the horizontal wind meteorological data, and generate a three-dimensional noise volumetric texture by superimposing a plurality of noise functions through fractal Brownian motion;

[0014] a rendering module configured to use the two-dimensional gray-scale cloud texture map as a 2D texture mask, combine the base three-dimensional profile of the volumetric cloud and the three-dimensional volumetric texture, and complete rendering of the volumetric cloud through a ray marching rendering pipeline.

[0015] In another aspect, the present application provides an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the volumetric cloud visualization method based on meteorological data when executing the computer program.

[0016] In another aspect, the present application provides a computer readable storage medium having a computer program stored thereon, the computer program implementing the steps of the volumetric cloud visualization method based on meteorological data when executed by a processor.

[0017] In another aspect, the present application provides a computer program product stored on a computer readable storage medium and comprising computer instructions which, when executed by a processor, cause a computer device to implement the steps of the volumetric cloud visualization method based on meteorological data.

[0018] Compared with the prior art, the present application has the following advantages:

[0019] The application provides a volume cloud visualization method based on meteorological data, converts cloud amount in meteorological data into a texture picture, simultaneously uses wind meteorological data to combine cloud types to construct an anisotropic three-dimensional noise texture, so that different noise parameters are set for different cloud types. The specific steps are as follows: firstly, meteorological data is acquired and data preprocessing is performed, main selected meteorological factors include low, medium and high cloud coverage and horizontal wind, the spatial resolution is improved by data interpolation on the low, medium and high cloud coverage; the density change height correlation function is designed according to the cloud type, and the basic three-dimensional profile of the volume cloud is constructed in combination with the cloud coverage; the volume texture is constructed by using different noise parameters for different cloud types to enhance the details of the volume cloud; finally, volume cloud rendering is performed. The application effectively improves the rendering authenticity and efficiency of different types of volume clouds in large-scale scenes. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained according to the structures shown in the drawings without creative labor.

[0021] Figure 1 The flowchart of the volume cloud visualization method based on meteorological data of the present application;

[0022] Figure 2 The visualization effect diagram of low layer cloud coverage in an embodiment;

[0023] Figure 3 Different frequency Perlin noise and Worly noise are shown, wherein Figure 3 (a) is low frequency Perlin noise, Figure 3 (b) is medium frequency Perlin noise, Figure 3 (c) is high frequency Perlin noise, Figure 3 (d) is low frequency Worly noise, Figure 3 (e) is medium frequency Worly noise, Figure 3 (f) is high frequency Worly noise;

[0024] Figure 4 The visualization example diagram of horizontal direction anisotropic Perlin, Worly noise is shown, wherein Figure 4 (a) is anisotropic low frequency Perlin noise of (b) is anisotropic low frequency Perlin noise of Figure 4 (c) is anisotropic low frequency Perlin noise of (d) is anisotropic low frequency Worly noise of Figure 4 (e) is anisotropic low frequency Worly noise of anisotropic low-frequency Perlin noise, Figure 4 (d) is anisotropic low-frequency Worly noise, Figure 4 (e) is anisotropic low-frequency Worly noise, Figure 4 (f) is anisotropic low-frequency Worly noise;

[0025] Figure 5 An example of a visual volumetric cloud generated by an embodiment. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be apparently and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0027] An embodiment, as shown in FIG. 1, provides a volumetric cloud visualization method based on meteorological data, comprising: Figure 1

[0028] According to the altitude, the cloud types are divided into low clouds, middle clouds and high clouds, the cloud coverage rate data of the low clouds, the middle clouds and the high clouds in a specified three-dimensional space and the horizontal wind data are obtained from the meteorological data, the cloud coverage rate data of the low clouds, the middle clouds and the high clouds are respectively subjected to spline interpolation, and the interpolated data is mapped into a two-dimensional gray-scale cloud texture map;

[0029] Different density change height correlation functions are respectively set for the low clouds, the middle clouds and the high clouds, so as to construct a basic three-dimensional contour of the volumetric cloud;

[0030] Differential noise parameters are set for the low clouds, the middle clouds and the high clouds, anisotropic three-dimensional noise is calculated according to different cloud types and horizontal wind meteorological data, and a three-dimensional noise volumetric texture is generated by superimposing a plurality of noise functions through fractal Brownian motion;

[0031] The two-dimensional gray-scale cloud texture map is taken as a 2D texture mask, the basic three-dimensional contour of the volumetric cloud and the three-dimensional volumetric texture are combined, and the rendering of the volumetric cloud is completed through a ray marching rendering pipeline.

[0032] The meteorological data, such as the Global Forecast System (GFS), can obtain a multidimensional array data containing a plurality of meteorological factors, from which the required cloud amount data, i.e., the low-level cloud coverage rate, the middle-level cloud coverage rate and the high-level cloud coverage rate, are extracted, and the data unit is percentage; the horizontal wind meteorological data corresponding to the middle clouds and the high clouds are extracted, including the east-west horizontal wind component speed and the south-north horizontal wind component speed.​

[0033] Due to insufficient spatial resolution of the original data, interpolation is required for cloud cover data at low, medium, and high levels. To enhance data granularity to meet simulation requirements while ensuring data continuity and accuracy, spline interpolation is employed to process the cloud cover data at these levels. Spline interpolation is a technique that approximates a set of discrete data points using a smooth curve. This method constructs one or more polynomials of different orders to form a continuous curve that runs through all key data points, offering advantages such as simple construction, ease of use, and accurate fitting. Here, a curve obtained based on a cubic spline interpolation function is chosen for data fitting, improving the spatial resolution to [specific value missing]. Then, the interpolated data is visualized as a two-dimensional grayscale cloud texture map using the OpenCV library, that is, the cloud coverage in the data range of 0~1 is mapped to a single-channel pixel value of 0~255. An example of the low-level cloud coverage visualization effect is shown below. Figure 2 As shown, the grayscale image reflects the low-level cloud coverage. Similarly, the mid-level and high-level clouds are processed by spline interpolation using the same steps to reflect the cloud coverage at different altitudes.

[0034] Different types of clouds have different morphological characteristics. In nature, clouds can be classified into low clouds, middle clouds, and high clouds based on altitude, and each category contains subspecies such as stratus, stratocumulus, cumulus, altocumulus, cirrus, cirrostratus, and cirrocumulus. In GFS meteorological data, the classification of low, middle, and high clouds is primarily based on the pressure layer rather than a fixed altitude. This is because pressure more directly reflects the physical state of the atmosphere and is not significantly affected by topography and latitude. Specific classification criteria are shown in Table 1.

[0035] Table 1

[0036]

[0037] Although the two-dimensional grayscale cloud texture map obtained from meteorological data defines the latitude and longitude range of the cloud layer, it lacks the variation of volumetric cloud density at altitude. Therefore, it is necessary to introduce a density-altitude correlation function according to different cloud types to make their density fields change at altitude, so that the basic shape of the volumetric cloud is more consistent with the data distribution of different cloud morphologies.

[0038] For low clouds such as stratus, the cloud layer is relatively thick and has a wide vertical range, reaching one to two kilometers, exhibiting a uniform layered structure with dense density and low transparency. For high clouds such as cirrus, the cloud layer is thin and has a narrow vertical range, typically several hundred meters, exhibiting a filamentous, feathery, or fibrous structure with dispersed density and high transparency. Accordingly, in one embodiment, different density-change-height correlation functions are set for low clouds, mid-level clouds, and high clouds, as follows:

[0039] ;

[0040] in, Indicates altitude, , , The density variation coefficients correspond to different altitude ranges for low clouds, middle clouds, and high clouds, respectively. The vertical distribution range of low clouds is set to 1000m~3000m, with a relatively slow density change and a peak at 1500m; the vertical distribution range of middle clouds is set to 4000m~5000m, with a moderate density change and a peak at 4500m; and the vertical distribution range of high clouds is set to 7500m~8500m, with a rapid density change and a peak at 8000m.

[0041] By altering the density correlation function, the basic 3D outline of volumetric clouds can be constructed. However, the details of volumetric clouds are not realistic enough and lack randomness, requiring enhancement in detail modeling. To compensate for the lack of detail in volumetric clouds, 3D noise is used to further perturb the density field of the cloud, thereby enriching the internal details and giving the cloud a more refined texture. Most existing methods use Perlin and Worly noise for fine cloud modeling, but these methods do not set differentiated noise parameters according to different cloud types, resulting in their applicability only to low clouds such as stratus clouds.

[0042] In one embodiment of the present invention, differentiated noise parameters are set for low clouds, mid clouds, and high clouds. These noise parameters include at least noise frequency, anisotropy properties, and noise mixing ratio. The differentiated noise parameters for low clouds, mid clouds, and high clouds include:

[0043] For low clouds, low-frequency, isotropic Perlin and Worly noise are used. The scaling factor for both Perlin and Worly noise is set to 30, with larger scaling factors indicating lower frequencies. Simultaneously, the noise mixture is predominantly Perlin noise and secondarily Worly noise, meaning the proportion of Perlin noise is higher than that of Worly noise. For example, one embodiment proposes that the proportion of Perlin noise in the low cloud noise mixture is 0.75, and the proportion of Worly noise is 0.25.

[0044] For the medium cloud, mid-frequency, anisotropic Perlin noise and Worly noise are used. The base scaling factor for Perlin noise and Worly noise is set to 10, and the actual scaling factor in each direction is related to the wind speed. At the same time, the proportions of Perlin noise and Worly noise are balanced, with Perlin noise accounting for 0.5 and Worly noise accounting for 0.5.

[0045] For high clouds, high-frequency, anisotropic Perlin and Worly noise are used. The base scaling factor for both Perlin and Worly noise is set to 5, and the actual scaling factor in each direction is related to the wind speed. Simultaneously, the noise mixture is dominated by Worly noise, with Perlin noise playing a secondary role; that is, the proportion of Perlin noise is lower than that of Worly noise. For example, in one embodiment, the proportion of Perlin noise in the noise mixture for high clouds is proposed to be 0.35, and the proportion of Worly noise is 0.65.

[0046] Regarding noise frequency, the frequency magnitude determines the coarseness of the noise texture, which can be used to control the structural scale of volumetric clouds. For example... Figure 3 As shown, Figure 3 Perlin noise and Worly noise at different frequencies were shown, among which Figure 3 (a) represents low-frequency Perlin noise. Figure 3 (b) represents mid-frequency Perlin noise. Figure 3 (c) represents high-frequency Perlin noise. Figure 3 (d) represents low-frequency Worly noise. Figure 3 (e) represents mid-frequency Worly noise. Figure 3 (f) represents high-frequency Worly noise; the higher the frequency, the denser the noise texture.

[0047] For low clouds such as stratus, low-frequency noise is needed to simulate large-scale layered structures, and the smooth variation of low-frequency noise can reflect the continuous uniformity of stratus. Therefore, low-frequency Perlin noise and low-frequency Worly noise are used to represent low clouds such as stratus. For high clouds such as cirrus, high-frequency noise is needed to simulate small-scale filamentary structures, and high-frequency noise will produce more small-scale fluctuations. Therefore, high-frequency Perlin noise and high-frequency Worly noise are used to represent high clouds such as cirrus. For mid-level clouds such as altocumulus and altocumulus, medium-frequency Perlin noise and medium-frequency Worly noise are used.

[0048] Regarding noise anisotropy, since high clouds are affected by strong high-altitude winds, they are often stretched along the wind direction in the horizontal direction. Therefore, a noise function with horizontal anisotropy is introduced.

[0049] For low clouds such as stratus, which are characterized by thick, dense cloud layers and minimal influence from wind direction, an isotropic noise function can be used. However, for mid-level clouds, high-level clouds, and especially cirrus clouds, which are characterized by thinner cloud layers and greater influence from wind, anisotropic noise functions are used. Only for mid-level and high-level clouds do we need to calculate the anisotropic properties of the anisotropic noise, including the stretching direction and degree. The wind meteorological data for mid-level and high-level clouds are the horizontal wind components at the 600mb and 400mb pressure layers, respectively, from the GFS dataset.

[0050] In one embodiment, the calculation of the anisotropy property in the mid-cloud and high-cloud noise parameters includes:

[0051] Obtain horizontal wind meteorological data corresponding to mid-clouds and high-clouds from meteorological data, including the east-west horizontal wind component velocity and the north-south horizontal wind component velocity corresponding to mid-clouds, and the east-west horizontal wind component velocity and the north-south horizontal wind component velocity corresponding to high-clouds.

[0052] Based on the east-west horizontal wind component velocity corresponding to Zhongyun and the speed of the north-south horizontal wind component The stretching direction of anisotropic noise used in the calculation of clouds Positive values ​​are generated from west to east or from south to north, while negative values ​​are generated from east to west or from north to south; based on the east-west horizontal wind component velocity corresponding to high clouds. and the speed of the north-south horizontal wind component The stretching direction of the anisotropic noise used in the calculation of Gao Yun Positive values ​​are generated from west to east or from south to north, while negative values ​​are generated from east to west or from north to south.

[0053] The degree of anisotropic noise stretching in mid- and high-level clouds is determined by wind speed, where the wind speeds corresponding to mid- and high-level clouds are... , The calculation formula is as follows:

[0054]

[0055] The stretching coefficients of anisotropic noise are used for the middle and high clouds. , The calculation formula is as follows:

[0056] ;

[0057] in Used to control the rate of decrease in tensile strength, set to 2.0; This represents the maximum reference wind speed, taken as 30 m / s.

[0058] Finally, a noise coordinate transformation along the wind direction is performed to obtain anisotropic noise textures, converting the original horizontal coordinates... Converted to wind-stretched coordinates:

[0059] ;

[0060] The positive x-axis runs from west to east, and the positive y-axis runs from south to north.

[0061] right Axis scaling factor , By maintaining the isotropic axis and then calculating the noise function, the structure stretched along the actual wind direction can be obtained, such as... Figure 4 The image shown is a visualization example of Perlin and Worly noise with horizontal anisotropy. Figure 4 (a) is Anisotropic low-frequency Perlin noise, Figure 4 (b) is Anisotropic low-frequency Perlin noise, Figure 4 (c) is Anisotropic low-frequency Perlin noise, Figure 4 (d) is Anisotropic low-frequency Worly noise, Figure 4 (e) is Anisotropic low-frequency Worly noise, Figure 4 (f) is Anisotropic low-frequency Worly noise.

[0062] Regarding the noise mixing ratio, the mixing ratio of the two noises can be used to control the structural dominance of the volumetric cloud. Perlin noise is biased towards continuous smoothness, while Worly noise is biased towards discrete clumps. The ratio of the two needs to be adapted to the morphology of the volumetric cloud.

[0063] For low clouds such as stratus, Perlin noise should be the primary noise source to simulate the layered basement using its continuous and smooth characteristics, while Worly noise should be used as a secondary noise source to add details of the cloud clusters. For high clouds such as cirrus, Worly noise should be the primary noise source to simulate the fibrous structure of cirrus using its "filamentous dispersion" of cell boundaries, while Perlin noise should be used as a secondary noise source to fill in continuous transitions and avoid excessive fragmentation. For mid-level clouds such as altocumulus and altocumulus, the average mixing ratio of low and high clouds should be selected.

[0064] Different noise parameters are set according to different cloud types, anisotropic three-dimensional noise is calculated, and multiple noise functions are superimposed through fractal Brownian motion to generate a three-dimensional noise volume texture, as shown below:

[0065] ;

[0066] in, Indicates the first A type of noise function, This represents a point within the volumetric texture. Indicates the first The stretching direction of the noise function. Indicates the first The degree of stretching of the noise function, Indicates the first The noise frequency of a noise function. It is the first The amplitude of the noise function of a certain noise function.

[0067] Using a 2D grayscale cloud texture map as a 2D texture mask, combined with the basic 3D outline of the volumetric cloud and the 3D volumetric texture, the volumetric cloud is rendered through the ray stepping rendering pipeline.

[0068] In one embodiment, the volumetric cloud rendering operation is completed using the ray stepping rendering pipeline integrated in Unreal Engine 5. First, the volumetric cloud material and its instances are created, and the material's domain and blending mode are configured. Reflectivity and emissivity are adjusted, and the acquired 2D grayscale cloud texture map is used as a 2D texture mask. The absolute world position is obtained, and its... x, y By sampling the imported 2D grayscale cloud texture using coordinates, the outline of the volumetric cloud can be set to a specified shape within the 2D grayscale cloud texture. Then, for different cloud types, the cloud base height and cloud height are set to construct the basic 3D outline of the volumetric cloud. Combining the basic 3D outline of the volumetric cloud with the 3D volumetric texture, each sampling point in the ray stepping rendering pipeline is calculated. The volumetric cloud density is calculated using the following formula:

[0069] ;

[0070] in, Indicates sampling point Cloud coverage at the location Indicates sampling point The base density at that location, Sampling points The 3D noise function value at that location.

[0071] The specific steps for rendering volumetric clouds in Unreal Engine 5 include: 1) Setting up scene lights, selecting sunlight and ambient light, and initializing light source positions, light intensities, and background colors; 2) Constructing a bounding box for the Earth's clouds to calculate the start and end positions of ray stepping; 3) Adding pre-generated volumetric textures based on meteorological data and noise functions to the material system; 4) Initializing the camera position and orientation, starting ray stepping, and using... The function calculates the intersection of the ray and the bounding box, and the sampling steps and step size can be adjusted during this process; 5) Execute The function calculates the optical thickness at the sampling point based on the volumetric cloud density formula, and then uses this to calculate the transparency and color at the sampling point. Finally, the output color is the sum of the color contributions of all sampling points up to the endpoint plus the background color corresponding to the remaining transparency, such as... Figure 5 The image shown is a visualization of the volumetric cloud generated in the final embodiment.

[0072] On the other hand, a volumetric cloud visualization device based on meteorological data is provided, including:

[0073] The 2D grayscale cloud texture map generation module is used to classify cloud types into low clouds, middle clouds and high clouds according to altitude. It obtains cloud coverage data and horizontal wind data of low clouds, middle clouds and high clouds in a specified three-dimensional space from meteorological data, performs spline interpolation on the cloud coverage data of low clouds, middle clouds and high clouds respectively, and maps the interpolated data into a 2D grayscale cloud texture map.

[0074] The basic contour construction module is used to set different density-changing height correlation functions for low clouds, middle clouds, and high clouds to construct the basic three-dimensional contour of volumetric clouds.

[0075] The volumetric texture generation module is used to set differentiated noise parameters for low clouds, middle clouds and high clouds. It calculates anisotropic three-dimensional noise based on different cloud types and horizontal wind meteorological data, and generates three-dimensional noise volumetric texture by superimposing multiple noise functions through fractal Brownian motion.

[0076] The rendering module is used to use a 2D grayscale cloud texture map as a 2D texture mask, combined with the basic 3D outline of the volumetric cloud and the 3D volumetric texture, to complete the rendering of the volumetric cloud through the ray stepping rendering pipeline.

[0077] On the other hand, the present invention provides an electronic device including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the volumetric cloud visualization method based on meteorological data provided in any of the above embodiments. The computer device may be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores sample data. The network interface of the computer device is used for communication with external terminals via a network connection.

[0078] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the volumetric cloud visualization method based on meteorological data provided in any of the above embodiments.

[0079] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0080] Matters not covered in this invention are common knowledge.

[0081] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for volumetric cloud visualization based on meteorological data, characterized in that, The method comprises the following steps: According to the altitude, the cloud type is divided into low cloud, middle cloud and high cloud, the cloud coverage rate data and horizontal wind data of the low cloud, middle cloud and high cloud in the specified three-dimensional space are obtained from the meteorological data, the cloud coverage rate data of the low cloud, middle cloud and high cloud are respectively subjected to spline interpolation, and the interpolated data is mapped into a two-dimensional gray cloud texture image; Different density change height correlation functions are respectively set for the low cloud, middle cloud and high cloud to construct the basic three-dimensional profile of the volume cloud; Differentiated noise parameters are set for the low cloud, middle cloud and high cloud, anisotropic three-dimensional noise is calculated according to different cloud types and horizontal wind meteorological data, and a three-dimensional noise volume texture is generated by superimposing multiple noise functions through fractal Brown motion. The noise parameters at least include noise frequency, anisotropic property and noise mixing ratio, differentiated noise parameters are set for the low cloud, middle cloud and high cloud, and the noise parameters include: For the low cloud, low-frequency and isotropic Perlin noise and Worly noise are adopted; the scaling scale of the Perlin noise and the Worly noise is set to 30, and the larger the scaling scale, the lower the frequency; at the same time, the noise mixing is mainly Perlin noise and supplemented by Worly noise, and the proportion of the Perlin noise is higher than that of the Worly noise; For the middle cloud, middle-frequency and anisotropic Perlin noise and Worly noise are adopted, the basic scaling scale of the Perlin noise and the Worly noise is set to 10, and the actual scaling scale in each direction is related to the wind speed; at the same time, the proportions of the Perlin noise and the Worly noise are balanced, and the proportion of the Perlin noise is 0.5 and the proportion of the Worly noise is 0.5; For the high cloud, high-frequency and anisotropic Perlin noise and Worly noise are adopted, the basic scaling scale of the Perlin noise and the Worly noise is set to 5, and the actual scaling scale in each direction is related to the wind speed; at the same time, the noise mixing is mainly Worly noise and supplemented by Perlin noise, and the proportion of the Perlin noise is lower than that of the Worly noise; The calculation of the anisotropic property in the noise parameters of the middle cloud and the high cloud includes: The horizontal wind meteorological data corresponding to the middle cloud and the high cloud are obtained from the meteorological data, including the east-west horizontal wind component speed and the south-north horizontal wind component speed corresponding to the middle cloud, and the east-west horizontal wind speed and the south-north horizontal wind speed corresponding to the high cloud; Based on the east-west horizontal wind component velocity corresponding to the middle cloud and the north-south horizontal wind component velocity The stretching direction of the anisotropic noise calculated for the middle cloud , positive from west to east or from south to north, negative from east to west or from north to south; Based on the east-west horizontal wind component velocity corresponding to the high cloud and the north-south horizontal wind component velocity The stretching direction of the anisotropic noise calculated for the high cloud , positive from west to east or from south to north, negative from east to west or from north to south; The degree of anisotropic noise stretching in mid- and high-level clouds is determined by wind speed, where the wind speeds corresponding to mid- and high-level clouds are... , The calculation formula is as follows: The middle and high clouds employ a stretch coefficient of anisotropic noise , The calculation formula is as follows: wherein for controlling the attenuation speed of the stretch coefficient, take 2.0; represent the maximum reference wind speed, take 30 m / s; The two-dimensional gray cloud texture image is taken as a 2D texture mask, combined with the basic three-dimensional profile of the volume cloud and the three-dimensional volume texture, the rendering of the volume cloud is completed through a ray marching rendering pipeline.

2. The volumetric cloud visualization method based on meteorological data according to claim 1, characterized in that, Different density change height correlation functions are respectively set for the low cloud, middle cloud and high cloud, as follows: wherein, represents the altitude, , , respectively correspond to the density variation coefficients in different altitude intervals of low clouds, medium clouds and high clouds, the vertical distribution range of the low clouds is set to 1000 meters to 3000 meters, the density variation is slow, and the peak value is at 1500 meters; the vertical distribution range of the medium clouds is set to 4000 meters to 5000 meters, the density variation is moderate, and the peak value is at 4500 meters; the vertical distribution range of the high clouds is set to 7500 meters to 8500 meters, the density variation is rapid, and the peak value is at 8000 meters.

3. The volumetric cloud visualization method based on meteorological data according to claim 1, characterized in that, For the noise mixing of the low cloud, the proportion of the Perlin noise is 0.75 and the proportion of the Worly noise is 0.25; for the noise mixing of the high cloud, the proportion of the Perlin noise is 0.35 and the proportion of the Worly noise is 0.

65.

4. The volumetric cloud visualization method based on meteorological data according to claim 1, characterized in that, Differentiated noise parameters are set according to different cloud types, anisotropic three-dimensional noise is calculated, and a three-dimensional noise volume texture is generated by superimposing multiple noise functions through fractal Brown motion, as follows: wherein, represents a first i noise function, represents a point within a volume texture, represents a first i stretch direction of a noise function, represents a first i stretching degree of a noise function, represents a first i noise frequency of a noise function, is a noise function amplitude of a first i noise function.

5. The volumetric cloud visualization method based on meteorological data according to claim 4, characterized in that, The rendering of the volume cloud by the ray marching rendering pipeline comprises: calculating the volume cloud density of each sampling point on the ray marching rendering pipeline The calculation formula is: wherein, denotes the cloud coverage at a sampling point denotes the base density at a sampling point denotes the cloud coverage at a sampling point denotes the base density at a sampling point is the 3D noise function value at a sampling point is the 3D noise function value at a sampling point 6. Apparatus for volumetric cloud visualization based on meteorological data, characterized in that, A method for implementing the volumetric cloud visualization based on meteorological data according to any one of claims 1 to 5, comprising: a two-dimensional gray-scale cloud texture map generation module, configured to divide cloud types into low clouds, middle clouds and high clouds according to altitudes, obtain cloud coverage data and horizontal wind data of the low clouds, the middle clouds and the high clouds in a specified three-dimensional space from meteorological data, respectively perform spline interpolation on the cloud coverage data of the low clouds, the middle clouds and the high clouds, and map the interpolated data into a two-dimensional gray-scale cloud texture map; a basic profile construction module, configured to set different density change height correlation functions for the low clouds, the middle clouds and the high clouds respectively, so as to construct a basic three-dimensional profile of the volumetric cloud; a volumetric texture generation module, configured to set differential noise parameters for the low clouds, the middle clouds and the high clouds, calculate anisotropic three-dimensional noise according to different cloud types and horizontal wind meteorological data, and generate a three-dimensional noise volumetric texture by superimposing a plurality of noise functions through fractal Brownian motion; a rendering module, configured to use the two-dimensional gray-scale cloud texture map as a 2D texture mask, combine the basic three-dimensional profile of the volumetric cloud and the three-dimensional volumetric texture, and complete rendering of the volumetric cloud through a ray marching rendering pipeline. 7.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein the computer program comprises instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 6. The processor executes the computer program to implement the steps of the volumetric cloud visualization method based on meteorological data according to claim 1.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the volumetric cloud visualization method based on meteorological data according to claim 1.

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