Information processing device, information processing method, and computer program

Abstract

To provide an information processing device, information processing method and computer program, which enable accurate estimation of the condition of the clouds.SOLUTION: An information processing device according to an embodiment comprises a first estimation unit configured to estimate height information of the cloud on the basis of data obtained by observing the cloud, a segmentation unit configured to divide the cloud into multiple segments according to the height information, and a second estimation unit configured to estimate the condition of the cloud at a target time for each segment.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present embodiment relates to an information processing device, an information processing method, and a computer program. [Background technology]

[0002] There is a method to estimate the future position of clouds by calculating the motion vector that represents the movement of clouds from satellite images and predicting (extrapolating) the movement of clouds based on the motion vector. However, this method cannot accurately estimate the motion vector of clouds when multiple clouds are stacked three-dimensionally. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Atsushi Hashimoto, Takahisa Kobayashi, Hidetoshi Tamura, Hiromaru Hiraguchi. Development of solar radiation estimation and forecasting model using satellite image data for predicting photovoltaic power output. Technical Report N13003, Central Research Institute of the Electric Power Industry (CRIEPI), 1-6-1 Otemachi, Chiyoda-ku, Tokyo, aug 2013. Central Research Institute of the Electric Power Industry Research Report. Summary of the Invention [Problem to be solved by the invention]

[0004] The present embodiment provides an information processing device, an information processing method, and a computer program for estimating cloud conditions with high accuracy. [Means for solving the problem]

[0005] The information processing device of this embodiment includes a first estimation unit that estimates cloud height information based on cloud sensing data, a decomposition unit that decomposes the cloud into multiple parts based on the height information, and a second estimation unit that estimates the state of the cloud at a target time for each part. [Brief description of the drawings]

[0006] [Figure 1] 1 is a block diagram showing an example of a cloud movement estimation device as an information processing device according to a first embodiment. [Diagram 2] Detailed block diagram of the cloud structure estimation unit. [Diagram 3] FIG. 13 is a diagram showing an example of decomposing a cloud into multiple cloud clusters according to height levels by the cloud decomposition unit. [Figure 4] FIG. 13 shows an example of processing in which cloud clusters whose nearest neighbors are separated by a threshold value or less are considered to be the same cloud cluster. [Diagram 5] FIG. 4 is a block diagram showing an example of a cloud time change estimation unit. [Figure 6] (A) An example of advection vector estimation for multiple cloud clusters in the current time frame, and (B) an example of advection vector estimation for a wider translated region. [Figure 7] A figure showing an example of advection vectors estimated by interpolation for a location where no cloud mass exists. [Figure 8] FIG. 13 is a diagram showing an example in which the estimated value of an advection vector is calculated as 0 for a position where there is no cloud mass in the related art. [Figure 9] FIG. 1 is a diagram illustrating an example of calculation of reflection intensity when multiple cloud masses overlap. [Figure 10] 4 is a flowchart of an example of processing according to the first embodiment. [Figure 11] A diagram showing how cloud advection estimation is performed for each time frame and every time Δτ. [Figure 12] FIG. 11 is a block diagram of an example of a cloud resolution unit according to a second embodiment. [Figure 13] A diagram showing an example of decomposing a cloud (cloud image) that is at the same height level as a whole into multiple cloud clusters. [Figure 14] A diagram showing the concept of hidden cloud estimation. [Figure 15] FIG. 13 is a block diagram of an example of a cloud resolution unit in the third embodiment. [Figure 16] A diagram explaining a Markov random field model. [Figure 17] FIG. 13 is a diagram for explaining an outline of a fourth embodiment. [Figure 18] FIG. 13 is a block diagram of an example of a cloud resolution unit according to a fourth embodiment. [Figure 19] FIG. 13 is a block diagram of an example of a cloud time change estimation unit according to the fifth embodiment. [Figure 20] FIG. 13 is a block diagram of an example of a cloud movement estimation device as an information processing device according to a sixth embodiment. [Figure 21] A diagram showing the difference in cloud decomposition due to different segmentation parameters. [Figure 22] FIG. 13 is a diagram for explaining an outline of a seventh embodiment. [Figure 23] FIG. 13 is a block diagram of an example of a cloud movement estimation device as an information processing device according to the seventh embodiment. [Figure 24] FIG. 2 is a diagram showing an example of a satellite image database. [Diagram 25] FIG. 4 is a diagram showing an example of a numerical meteorological calculation history database. [Figure 26] FIG. 23 is a block diagram of an example of a cloud movement estimation system as an information processing device according to the eighth embodiment. [Figure 27] FIG. 23 is a block diagram of an example of a solar radiation estimation device as an information processing device according to a ninth embodiment. [Figure 28] 1 is a hardware block diagram of an information processing apparatus according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0008] (First embodiment) FIG. 1 is a block diagram showing an example of a cloud movement estimation device 100 (hereinafter, referred to as the device 100) as an information processing device according to the first embodiment. The device 100 relates to a technology for predicting future cloud conditions (e.g., positions) with high accuracy using meteorological satellite images (hereinafter, referred to as satellite images) detected by meteorological satellites. Satellite images are an example of cloud sensing data. Below, satellite images are described to the extent necessary for explaining this embodiment. Details of satellite images are described in Reference 1 in the reference list provided at the end of this specification.

[0009] Satellite images are data obtained by sensing clouds using a sensing device equipped on a meteorological satellite. Satellite images can be broadly divided into visible images and infrared images. Visible images are obtained by imaging devices such as RGB cameras and monochrome cameras. Infrared images are obtained by infrared cameras. Visible images are images that capture reflected light from sunlight, such as visible light or near-infrared light. Therefore, the reflection intensity of clouds (or the earth's surface if there are no clouds) can be known from visible images. The reflection intensity of clouds is affected by the number of water droplets (cloud particles) contained in the clouds, the size of the water droplets, and the properties of the water droplets (water, ice, etc.). In this embodiment, it is considered that the size and properties of cloud particles do not change significantly within the range of cloud advection, and the reflection intensity of clouds is considered to be a quantity that can be replaced with the density of cloud particles.

[0010] An infrared image captures the radiant light generated by the heat contained within the cloud itself. In other words, an infrared image can be said to capture the temperature of the cloud, and contains information on the temperature distribution of the cloud. Cloud temperature is sometimes called brightness temperature. The temperature of a cloud coincides with the temperature of the atmosphere surrounding the cloud. Furthermore, the vertical temperature profile of the atmosphere can be calculated using a numerical meteorological model that treats the movement of the atmosphere hydrodynamically and thermodynamically, so the brightness temperature can be converted into height (altitude). In other words, an infrared image can be said to represent the altitude of the cloud.

[0011] Meteorological satellites are in geostationary orbits, and at regular time intervals, they take images of a hemisphere (the side of the Earth that can be seen from the satellite), or a portion of the hemisphere, in multiple bands (frequencies). The time span (frame) in which one image is taken is hereinafter referred to as a time frame. In this embodiment, only visible images taken in one band and infrared images taken in another band are used. For example, in the case of the Japan Meteorological Agency's Himawari-8, hemispheric images are taken in a total of 16 bands (frequencies) within one time frame of 10 minutes. In this case, the image bands to be used in this embodiment are determined in advance for both visible and infrared images.

[0012] In the following explanation, pixels in satellite images are represented by a pair of indexes (i, j). Satellite images are assumed to have pixels arranged along the longitude and latitude directions, and i and j are considered to correspond to longitude and latitude, respectively. A position based on a pair of latitude and longitude may be expressed simply as x. x has the same meaning as (i, j), and they are considered to be interchangeable.

[0013] In this embodiment, visible and infrared images are used as data (sensing information) detected from the area (first area) including clouds, but as long as the position and height of the clouds can be detected, the data is not limited to visible and infrared images. For example, as long as the position and height of the clouds can be detected by a laser, data detected by a laser or a depth camera may be used.

[0014] The input unit 110 receives satellite images from a satellite via wireless communication. The satellite images include visible images and infrared images. The input unit 110 receives the satellite images for each time frame. The input unit 110 provides the satellite images to the cloud structure estimation unit 10 (first estimation unit). The input unit 110 may store the received satellite images in the storage unit 130.

[0015] The cloud structure estimation unit 10 estimates the horizontal position and vertical position (height) of clouds based on satellite images. The cloud structure estimation unit 10 corresponds to a first estimation unit that estimates at least one of horizontal position information of clouds (e.g., information on the range where clouds exist) and height information of clouds (e.g., height for each position).

[0016] 2 is a detailed block diagram of the cloud structure estimation unit 10. The cloud structure estimation unit 10 includes a cloud horizontal position estimation unit 11 that estimates the horizontal positions of clouds, and a cloud three-dimensional structure estimation unit 12 that estimates the three-dimensional structure of clouds, i.e., the vertical positions (heights) of clouds for each horizontal position, as a distribution of cloud heights.

[0017] The cloud horizontal position estimation unit 11 identifies pixels that correspond to clouds in the satellite image, and estimates the position corresponding to the identified pixel as the cloud position. For example, it is possible to determine whether a pixel corresponds to a cloud based on the reflection intensity (e.g., at least one of brightness and color) that is the pixel value of the pixel in the visible image. It is also possible to identify pixels that correspond to clouds using an infrared image. For example, if the brightness temperature that is the pixel value of a pixel obtained from an infrared image is lower than the earth's surface temperature, it can be determined that the pixel reflects a cloud. The technique for determining whether a pixel corresponds to a cloud is known as a technology called cloud masking, and various methods have been proposed in addition to the method described above. Any method may be used.

[0018] The cloud three-dimensional structure estimation unit 12 estimates the vertical position (height) of the cloud corresponding to a pixel determined by the cloud horizontal position estimation unit 11 to correspond to the pixel. The brightness temperature of the cloud pixel obtained from the infrared image indirectly indicates the cloud height, and therefore the vertical position (height) of the cloud corresponding to the pixel can be specified based on the brightness temperature of the pixel. The conversion from brightness temperature to height can estimate the height corresponding to the brightness temperature as the cloud height based on information correlating the relationship between temperature and height. For example, the vertical temperature profile obtained as a result of numerical meteorological calculation can be used as the information correlating the relationship between temperature and height.

[0019] As a result of the processing performed by the cloud horizontal position estimation unit 11 and the cloud three-dimensional structure estimation unit 12, the cloud structure estimation unit 10 obtains the following information. H(t, i, j): Cloud height at the position corresponding to pixel (i, j) in time frame t A(t, i, j): Reflection intensity from the cloud at the position corresponding to pixel (i, j) in time frame t A(t, i, j) ≥ 0. If A(t, i, j) is 0 or less than the threshold, it indicates that there is no cloud at the position corresponding to that pixel. If A(t, i, j) is greater than 0 or equal to or greater than the threshold, it indicates that there is a cloud at the position corresponding to that pixel.

[0020] The cloud resolution unit 20 resolves clouds in a satellite image into multiple height levels according to the height of the position corresponding to each pixel of the cloud. Even at the same latitude and longitude, the air flow (wind direction and speed) differs greatly at different altitudes. Therefore, clouds at different levels of height can be considered to be in different movement modes. A collection of cloud pixels at the same height level, or a collection of cloud pixels at the same height level and adjacent to each other, is called a cloud mass image, and the clouds shown in a cloud mass image are called cloud masses.

[0021] As a specific example of processing, multiple thresholds T1, T2, ..., T L (T1 <T2<…<T L ) is set. In other words, multiple temperature ranges are set. Depending on the brightness temperature T of each pixel obtained from the infrared image, pixels included in clouds are classified as follows: T < T1 → Level 1 T1≦T <T2→ レベル2 … T L-1 ≦T < T L → Level L

[0022] For ease of processing, the number of levels to be decomposed, L, may be set to 2. In this case, T1 may be set to 273K and T2 to the average temperature on the Earth's surface. By setting T1 to the freezing point of ice, it is expected that the particles will be separated into water clouds and ice clouds. These two types of clouds are often located at different altitudes because of their different formation methods and properties.

[0023] 3 shows an example of decomposing a cloud into a plurality of cloud masses according to height levels by the cloud decomposition unit 20. In this example, a target cloud is decomposed into two cloud masses (parts): level 1, whose brightness temperature is equal to or lower than T1, and level 2, whose brightness temperature is higher than T1. That is, the target cloud is decomposed into two set temperature ranges, and two cloud masses (parts) are obtained by decomposing the cloud. The level 2 cloud mass is identified by assuming that the level 2 cloud mass is hidden directly below the level 1 cloud mass (the operation of identifying the hidden cloud will be described in the explanation of other embodiments below).

[0024] Depending on the type of cloud, decomposition may generate a large number of cloud clusters (cloud cluster images), which may complicate processing. To reduce the number of cloud clusters, cloud clusters that are at the same height level and spatially close may be treated as the same cloud cluster. A distance threshold (d) may be introduced, and cloud clusters whose nearest neighbors are less than or equal to the threshold d may be treated as the same cloud cluster.

[0025] Figure 4 shows an example of processing in which cloud masses whose nearest neighbors are separated by a distance less than a threshold d are considered to be the same cloud mass. The left side of the figure shows an example in which cloud masses whose nearest neighbors are separated by a distance less than a threshold d are grouped together as a single cloud mass A, while the right side of the figure shows an example in which cloud masses whose nearest neighbors are separated by a distance less than a threshold d are grouped together as a single cloud mass B. Note that both cloud masses A and B are cloud masses contained in the same single satellite image.

[0026] As a result of the processing, the cloud decomposition unit 20 obtains the following information: N: Number of cloud clusters Let n ∈{1, 2, ,,, N} be an index indicating the cloud mass. H n (t, i, j): The height of cloud mass n at the position corresponding to pixel (i,j) in time frame t A n (t, i, j): Reflectance intensity of cloud mass n at the position corresponding to pixel (i, j) in time frame t A n (t, i, j) ≥ 0. n If (t,i,j)=0 or is less than the threshold, it means that there is no cloud mass n at the position corresponding to pixel (i,j). n If (t,i,j) is greater than 0 or equal to or greater than a threshold, it indicates that cloud mass n is present at the position corresponding to pixel (i,j).

[0027] Note that each cloud mass exists only in a part of the image, so A n , H n does not need to hold values ​​for all (i, j). n , H n may be appropriately trimmed depending on where the cloud mass is located.

[0028] The estimation processing unit 30 (second estimation unit) estimates the time change in the state of each cloud (cloud mass) acquired by the cloud resolution unit 20. The estimation processing unit 30 corresponds to the second estimation unit that estimates the state of each cloud mass at a target time. The estimation processing unit 30 includes a plurality of cloud time change estimation units 30_1 to 30_N corresponding to each cloud (cloud mass). When there is no need to particularly distinguish between the cloud time change estimation units 30_1 to 30_N, the cloud time change estimation unit 30 will be described. Each cloud time change estimation unit 30 estimates the time change in the state (position in this embodiment) of the corresponding cloud (cloud mass) independently of each other. The time change in the position of the cloud corresponds to the cloud being carried away by the wind (called advection). In addition to advection, the time change in the cloud includes the development and attenuation of the cloud, which will be described in the fifth embodiment.

[0029] 5 is a block diagram showing an example of the cloud time change estimation unit 30. The cloud time change estimation unit 30 includes an advection vector estimation unit 31 and an advection calculation unit 32.

[0030] The advection vector estimation unit 31 estimates a vector representing the speed (speed and direction) of movement of a cloud (cloud mass) from images of multiple consecutive time frames. This vector is called an advection vector or a movement vector. The time interval (lead time) of the estimation may be the same as the time frame, may be a time interval shorter than the time frame, or may be a time interval longer than the time frame. Furthermore, the estimation is performed for the target (current) time frame for each time (target time) for the above-mentioned lead time after the time of the time frame. The estimation continues after the next time frame (satellite image of the next time frame has not yet been received) independently of the estimation for the next time frame. In other words, even after the satellite image of the next time frame is received, the estimation of the advection vector for the current time frame continues to be performed for each lead time. The estimation for the next time frame is performed in the same manner, independently of the current time frame.

[0031] As an example of estimating the advection vector, the vector between the positions of the cloud (cloud mass) in two time frames, i.e., the vector between the position of the cloud (cloud mass) in the previous time frame and the position of the cloud (cloud mass) in the current time frame, may be used as the advection vector. For example, the position of the cloud (cloud mass) may be the center of gravity of the cloud.

[0032] Figure 6(A) shows an example of advection vector estimation for multiple cloud clusters in the current time frame.

[0033] As another method for estimating the advection vector, a technique (translation optimization) can be used in which images that are moved in a translational manner in time are translated to find the position where the overlapping area is maximized to estimate the advection vector (see Non-Patent Document 1). For details about translation optimization, see the explanation of FIG. 6(B) described later.

[0034] There is also a method of applying a technique called optical flow, which uses image processing to capture the movement of an object from temporally consecutive images (see Reference 2). In this case, for example, the optical flow of a cloud (cloud cluster) is calculated for two or more time frames, and an advection vector is calculated based on the change in the position of the cloud (cloud cluster) along the optical flow. Optical flow is a highly sophisticated technique, with ingenuity in computational efficiency and the ability to handle a wide range of changes in the scale of the target object. The underlying concept is the same as optimization of translational movement.

[0035] The advection vectors of positions where no clouds (cloud clusters) exist may be calculated by spatial interpolation based on the positions or advection vectors of visible clouds (cloud clusters). The kernel spline method may be used as an interpolation method. This method is a method for finding values ​​at positions other than a plurality of positions that are not spatially arranged in a grid by interpolating the values ​​given for the positions (Reference 3). When the advection vectors are given for the positions of each cloud cluster, the advection vectors of positions where no clouds (cloud clusters) exist can be supplemented by the kernel spline method for the latitude and longitude components independently. Calculating the advection vectors of positions where no clouds (cloud clusters) exist in this way enables more accurate advection estimation of clouds (cloud clusters) (estimation of the change in position over time).

[0036] Figure 7 shows an example of estimating advection vectors for positions where no cloud mass exists by interpolation. For position E between cloud masses C and D, the average of the advection vectors of cloud mass C and D is used.

[0037] In the related art, the height of clouds (cloud masses) is not taken into consideration in estimating cloud advection, and two-dimensional image processing is performed. In other words, the fact that clouds are arranged three-dimensionally in the vertical direction and may overlap in some cases is not taken into consideration. Therefore, when clouds of different heights exist so as to overlap and each has a different advection vector, the advection of clouds (change in position over time) cannot be estimated properly. In this embodiment, the advection vector is calculated for each cloud (cloud mass) of different height levels, so that the advection of clouds can be estimated properly by the advection calculation unit 32 described later.

[0038] In addition, the related technology does not take into account the advection vectors of positions where no clouds exist. By making the area to be translated finer in the translation optimization (see Figure 6(B)), the advection vectors of cloud masses can be estimated with high accuracy, but the estimated value of the advection vector for positions outside the area will be 0.

[0039] FIG. 8 shows an example in which the estimated value of the advection vector is calculated as 0 for a position C where there is no cloud mass in the related art.

[0040] Considering that advection (movement of clouds) is caused by wind, even if there is no cloud mass in a location, if there is wind, the advection vector at that location must be estimated correctly and not 0. Otherwise, when another cloud moves into that location, the cloud will not move from there.

[0041] In the above-mentioned translation optimization, if the region to be translated is widened, the advection vector will have the same value throughout the region, and it is possible to reduce the problem of the advection vector becoming zero.

[0042] Figure 6(B) shows an example of estimating the advection vector by widening the translational region. Each cloud mass belonging to the set region is considered to have the same advection vector. As a result, the advection vector of a position (space) where there is no cloud mass is also estimated.

[0043] However, if the translational region is taken to be wide, the difference in advection vectors of individual cloud masses within the region (see Figure 6(A)) will be overlooked, lowering the accuracy of estimating the movement of cloud masses. This problem occurs because the spatial positions of clouds and the gaps between clouds cannot be handled separately.

[0044] In this embodiment, even when the advection vector is calculated using translation optimization, the advection vector at a position where no clouds exist is also calculated by interpolation (see FIG. 7), so that the advection of clouds can be estimated with high accuracy.

[0045] The advection calculation unit 32 calculates the time change (advection) of the cloud position as the time change of the state of the cloud (cloud mass) using the advection vector estimated by the advection vector estimation unit 31. As a simple method, the time change of the cloud (cloud mass) is calculated by assuming that the cloud (cloud mass) continues to move according to the advection vector, that is, that the cloud (cloud mass) continues to move in the same direction. A method of calculating how the density distribution of particles in a fluid changes due to the flow of the fluid (a method of advection calculation that has traditionally been used in the field of fluid mechanics) may be applied to the satellite image to calculate the time evolution of the density of the cloud particles to calculate the time change of the cloud (cloud mass) position (see Reference 6). In this case, highly accurate calculation is possible by also using the advection vector of the above-mentioned position where no cloud exists. In addition, since the reflection intensity changes due to the change in the density of the cloud particles in the cloud (cloud mass) after advection, the reflection intensity after the change can also be estimated.

[0046] As a result of the processing, the cloud time change estimation unit 30 (advection vector estimation unit 31, advection calculation unit 32) obtains the following information for cloud mass n, with τ being the estimation lead time:

[0047] H' n (t, τ i, j): The height of cloud mass n at the position corresponding to pixel (i, j) in the estimation of lead time τ (time t + τ) in time frame t (in estimating the advection vector, it is possible to calculate only the horizontal component, assuming that the height does not change. In this case, the height value can be considered constant). A' n (t, τ,i, j): Reflection intensity of cloud mass n at the position corresponding to pixel (i,j) in the estimation of lead time τ (time t+τ) in time frame t (You can assume that the reflectance intensity does not change before and after advection. In this case, the value of the reflectance intensity is constant) A' n (t, τ, i, j) ≥ 0. A' n If (t, τ, i, j) = 0 or is less than the threshold, it means that there is no cloud of cloud mass n at the position corresponding to pixel (i, j). A' n If (t, τ, i, j) is greater than 0 or greater than a threshold, it indicates that there is a cloud of cloud mass n at the position corresponding to pixel (i, j).

[0048] The cloud synthesis unit 40 acquires a set of information on the position and height of each cloud (cloud mass) estimated by the cloud time change estimation unit 30 as cloud information of the cloud after movement. For example, the cloud information of the cloud after movement is the information (H' n (t, τ i, j), A' n (t, τ,i, j)). Cloud information is obtained for each lead time in the current time frame (target time). The cloud information represents the three-dimensional structure of the clouds at the target time.

[0049] The cloud synthesis unit 40 may reconstruct a visible image from the calculated three-dimensional structure of the cloud. In other words, if it is assumed that a calculated three-dimensional structure of the cloud exists, a visible image that would be acquired by a satellite for that structure may be acquired by estimation or calculation. For this purpose, an image including information on the reflection intensity at each position may be generated as a reconstructed image. Specifically, if there is no cloud mass at a target position, the cloud reflection intensity at that position is set to 0, and if there is only one cloud mass, the reflection intensity of that cloud mass is set to the reflection intensity at that position (A' n (t, τ, i, j) When multiple cloud masses overlap at the target location, the reflection intensity can be calculated as follows:

[0050] Figure 9 is a diagram explaining an example of calculating reflection intensity when multiple cloud masses overlap. Suppose there are clouds (cloud masses) at the upper and lower levels with reflection intensities A1 and A2, respectively. In this case, upward light reflected by a cloud at the lower level may be reflected again by a cloud at the upper level. This makes the reflection process somewhat complicated. The reflection intensity of the two clouds is given by the following formula: Equivalent to TIFF0007655844000001.tif11170.

number

[0051] When three or more cloud clusters overlap, the reflectance intensity can be calculated by repeatedly applying equation (1).

[0052] As a result of the processing, the following information is obtained in the reconstructed image: A'(t, τ, i, j): Reflection intensity at the position corresponding to pixel (i, j) in the estimated lead time τ (time t + τ) in time frame t

[0053] FIG. 10 is a flowchart of an example of processing according to the first embodiment.

[0054] First, the initial time frame t init is set as the time frame t (S101). At this point, it is assumed that the image in the previous time frame has already been acquired.

[0055] The input unit 110 receives a satellite image (sensing information of an area including clouds) for a time frame t (S102).

[0056] The cloud structure estimation unit 10 estimates the positions where clouds exist and the heights of each position (three-dimensional positions of the clouds) based on the satellite image (S103).

[0057] The cloud decomposition unit 20 decomposes the cloud into a plurality of cloud clusters (portions) according to the cloud height for each position (distribution of cloud height) (S104).

[0058] The lead time τ is set to 0 (S105).

[0059] The cloud time change estimation unit 30 calculates the time change in state of each cloud mass (time change in position in this example), that is, the position after the lead time τ (S106).

[0060] The cloud synthesis unit 40 obtains a set of information on the cloud mass at the estimated position (three-dimensional position) as output information (S107). The cloud synthesis unit 40 also generates an image (visualized image) by recomposing the cloud mass at the estimated position (same S107).

[0061] Δτ is added to τ (S108), and τ reaches the maximum value τ max Steps S106 to S108 are repeated until (S109) is reached, where Δτ represents the time interval for performing advection estimation.

[0062] Δt is added to t (S111), and steps S101 to S109 are repeated until t reaches the end time (referred to as "tend") (S110). Δt is the time frame interval of satellite images. For example, in the case of hemispheric images from Himawari-8, it is 10 minutes.

[0063] The time interval Δτ for advection estimation may be determined independently of the time frame interval Δt. However, if Δτ=Δt (or Δτ=Δt / m: m integers), satellite images for the same time as the advection estimation time are obtained, which has the advantage of making it easier to improve the accuracy of the advection estimation.

[0064] Figure 11 shows how satellite images (visualized images and infrared images) are input for each time frame, and cloud advection estimation is performed for each time frame and every time Δτ. Here, Δτ = Δt. The vertical dotted line indicates that the advection estimation result after lead time Δτ performed in time frame t-Δt corresponds to the cloud state at lead time 0 in time frame t.

[0065] As described above, according to this embodiment, the clouds are decomposed into multiple parts with different height levels based on satellite images, and the time change in the cloud position for each part is estimated, thereby making it possible to improve the accuracy of calculating the cloud position.

[0066] Second Embodiment In the case of a single cloud that shares the same properties, even if the individual parts of the cloud have different height levels (altitudes), it is preferable to treat these parts as the same cloud (cloud mass) without decomposing them. Conversely, in the case of a cloud that includes multiple parts with different properties even if they have the same brightness temperature, it is preferable to treat these parts as separate clouds (cloud masses). In the second embodiment, when a cloud includes multiple parts with different properties, it is possible to decompose the cloud into multiple parts even if these parts are at the same height level. Clouds with different properties may appear as different textures (patterns) in the image, so in this embodiment, the cloud is decomposed using the differences in texture.

[0067] 12 is a block diagram of an example of a cloud resolution unit 20 according to the second embodiment. Other elements are similar to those of the first embodiment. The cloud resolution unit 20 includes an image feature amount calculation unit 21 and an image segmentation unit 22.

[0068] The image feature amount calculation unit 21 calculates one or more feature amounts for each pixel of a satellite image (infrared image) that is an image of an area including clouds, based on the brightness temperature of the pixel and the brightness temperatures of the surrounding pixels. GLCM (see Reference 4) and the like are widely known as feature amounts, and GLCM can also be used in this embodiment.

[0069] The image segmentation unit 22 maps the feature of each pixel in the feature space, judges the similarity of pixels based on the distance between the coordinates (points) that represent the feature, and performs pixel clustering. The set of pixels corresponding to each generated cluster corresponds to the image of the cloud mass. As an example of clustering, there is a method of integrating pixels that are close to each other into the same cluster in order (see Reference 5).

[0070] FIG. 13 shows an example of decomposing a cloud (cloud image) that is at the same height level as a whole into multiple cloud clusters. Clustering divides it into region (cluster) F and region (cluster) G. The light and dark change pattern (texture) of cluster F is homogeneous, and the light and dark change pattern (texture) of cluster G is also homogeneous, but the texture content differs between region F and region G. Therefore, cluster F represents one cloud cluster, and cluster G represents another cloud cluster. In this way, cluster F and cluster G are adjacent or overlapping, and have the same height level, but they can be treated as separate cloud clusters by utilizing the difference in texture.

[0071] As described above, according to this embodiment, more accurate advection estimation is possible by decomposing cloud clusters at the same height level into multiple clusters.

[0072] (Third embodiment) A new function is added to the cloud resolution unit 20 to estimate lower clouds that are not visible in the image because they are hidden by higher clouds.

[0073] FIG. 14 shows the concept of estimating hidden clouds in this embodiment. FIG. 14(A) shows a cloud J1 at a high level (high altitude) and clouds K1 and K2 at a low level (low altitude). Cloud J1 is separated from clouds K1 and K2. That is, there are no other clouds adjacent to cloud J1. In this case, it is plausible to estimate that there are no other clouds below the high-level cloud J1. On the other hand, FIG. 14(B) shows a cloud J2 at a high level (high altitude) and cloud K3 at a low level (low altitude). Cloud K3 at a low level is adjacent to cloud J2 at a high altitude. In this case, it is plausible to estimate that cloud K3 at a low level exists below cloud J2 at a high altitude. That is, the basic concept of estimating clouds in a hidden part is to determine whether it is plausible to think that there is a cloud in the hidden part or not, based on a statistical model of how clouds spread.

[0074] 15 is a block diagram of an example of the cloud resolution unit 20 in this embodiment. The cloud resolution unit 20 includes a cloud spread statistical model unit 23 and a cloud spread estimation unit 24.

[0075] The cloud spread statistical model unit 23 sets a statistical model of cloud spread. The statistical model is stored in the storage unit 130, and the cloud spread statistical model unit 23 reads out the statistical model from the storage unit 130. Here, as the statistical model, a Markov random field model, which is a probabilistic model, is set (see Reference 7). The Markov random field model is a model that is often used in image analysis.

[0076] FIG. 16 is a diagram for explaining the Markov random field model. A pixel with clouds is called pixel PR, a pixel without clouds is called pixel PG, and a pixel (target pixel) for which the presence or absence of clouds is to be determined is called pixel PT. In the Markov random field model, the probability that a cloud exists at a pixel is determined by the presence or absence of clouds at adjacent pixels. For example, as shown in FIG. 16(A), if there are many pixels PR with clouds around the target pixel PT, the probability that the target pixel PT also has clouds is considered to be high. Conversely, as shown in FIG. 16(B), if there are few pixels PR with clouds around the target pixel PT, the probability that the target pixel PT has clouds is low. In this way, the Markov random field model is a model that sets the probability of cloud existence at the target pixel PT to be determined depending on the presence or absence of clouds at adjacent pixels.

[0077] The area hidden under high clouds (cloud clusters) is set as the target area for estimating whether there are low-level clouds (other clouds or other cloud clusters) in that area. Each pixel included in the target area is designated as a target pixel PT and represented by a pair of integers (i, j). The variable that indicates whether there is a cloud at pixel (i, j) included in the target area is represented as follows. The variable is a variable of a probabilistic model, and is the subject of probabilistic estimation. TIFF0007655844000003.tif23153

[0078] It is known whether there are low clouds in the boundary region of the target region. The pixels included in this boundary region are represented by an integer k. Whether there are clouds in the pixels included in the boundary region is expressed as follows. TIFF0007655844000004.tif23153

[0079] bk is uniquely determined from the satellite image. As an example, Fig. 16(C) shows a target pixel PT for estimating the presence or absence of low clouds in the area under the high clouds in Fig. 14(B). In the boundary region, pixels without low clouds PG(b k =0), pixels with low clouds PR(b k =1) is shown.

[0080] According to the Markov Random Field model, the presence or absence of clouds in a target region is given by the set of variables shown in equation (2) below.

number

[0081] Based on the set of variables, a probability model (statistical model) is given, for example, by the following formula:

number

[0082] The cloud spread estimation unit 24 estimates whether or not other clouds exist below a first cloud mass (first part) or the range of other clouds that exist, based on the range adjacent to other cloud masses (other parts) adjacent to a first cloud mass (first part) at the boundary of the first cloud mass (first part) among multiple cloud masses (parts).

[0083] More specifically, the cloud spread estimation unit 24 estimates whether or not there is a cloud at each pixel PT in the target area based on the probability model (see formula (3)) set by the cloud spread statistical model unit 23. Any method may be used to estimate the values ​​of the variables included in the probability model. A simple method is MAP (Maximum a posteriori) estimation. The formula for MAP estimation is shown below.

number

[0084] When performing MAP estimation, the values ​​of the variables included in the probabilistic model are estimated so as to maximize or quasi-maximize equation (3). The cloud spread estimation unit 24 integrates the low clouds estimated to be present in the target region (region hidden by the upper clouds) into the low clouds (cloud K3) in FIG. 14(B).

[0085] As described above, according to this embodiment, clouds hidden behind high-altitude clouds can be detected.

[0086] (Fourth embodiment) Whether or not low-level clouds exist in an area hidden by high-level clouds is estimated by a method different from that of the third embodiment. The estimation function is added to the cloud resolution unit 20.

[0087] FIG. 17 is a diagram for explaining an outline of the fourth embodiment. When a lower cloud moves and is partially or entirely hidden by an upper cloud, or when an upper cloud moves and hides a lower cloud or entirely, the presence or absence of the hidden cloud can be estimated from an image at a previous time. A part OA of a low cloud M that existed at a distant position in a time frame (t-Δt) is hidden under a high cloud N in a current time frame t. In the first to third embodiments, the position of a cloud in a current time frame t is estimated based on an advection calculation from a satellite image in the immediately preceding time frame (t-Δt), but in the fourth embodiment, the presence or absence of a cloud hidden by a higher cloud is estimated using this advection calculation.

[0088] 18 is a block diagram of an example of the cloud resolving unit 20 according to this embodiment. The cloud resolving unit 20 includes an uncertain part identifying unit 26 and an advection estimation synthesis unit 27.

[0089] The uncertain part identification unit 26 identifies pixels (target pixels) that are hidden by upper clouds and the presence or absence of lower clouds is unknown. Since the pixels in the area hidden by upper clouds correspond to the target pixels, the area where clouds exist in the satellite image is designated as an uncertain area. The pixels included in the uncertain area are designated as target pixels, and the altitude (brightness temperature) of the cloud at the target pixel is obtained.

[0090] The advection estimation synthesis unit 27 estimates whether or not other clouds exist (or the extent to which other clouds exist) in the lower layer of multiple cloud masses obtained based on the satellite image of the current time frame t, based on the result of advection calculation for each cloud mass (cloud portion) estimated based on the satellite image of the previous time frame t-Δt. t-Δt corresponds to a first time, for example, and t corresponds to a second time after the first time, for example.

[0091] More specifically, the advection estimation synthesis unit 27 acquires cloud information (three-dimensional position or three-dimensional structure) corresponding to the current time frame t from the result of the advection calculation obtained in the previous time frame t-Δt. Based on the acquired information, the advection estimation synthesis unit 27 judges whether or not a cloud with a lower altitude (lower height level) than the cloud visible in the satellite image of the current time frame exists at the position of each pixel (target pixel) included in the uncertain region. When the advection estimation synthesis unit 27 judges that a cloud with a low altitude exists at the position of the target pixel in the current time frame t, the advection estimation synthesis unit 27 considers that a cloud with a low altitude level exists at the position of the target pixel in the current time frame t. The advection estimation synthesis unit 27 integrates the cloud considered to exist with a low altitude cloud that is visible adjacent to a cloud with a high altitude level in the satellite image of the time frame t.

[0092] As described above, according to this embodiment, clouds hidden behind high-altitude clouds can be detected.

[0093] Fifth embodiment Clouds such as cumulonimbus clouds may occur due to updrafts and other reasons, and may develop rapidly. On the other hand, clouds formed in an area where water vapor is supersaturated will decay when they move to an area where the water vapor saturation is low. Such a change in clouds can be seen as the cloud disappearing on the image. In the first embodiment, the time change in position was estimated as the time change in the state of the cloud, but in the fifth embodiment, in addition to the time change in position, the development and decay of clouds are estimated.

[0094] FIG. 19 is a block diagram of an example of the cloud time change estimation unit 30 according to this embodiment. The cloud time change estimation unit 30 in FIG. 19 is obtained by adding a cloud matching unit 33, a development and attenuation coefficient estimation unit 34, and a cloud development and attenuation calculation unit 35 to the cloud time change estimation unit 30 in the first embodiment. The cloud time change estimation unit 30 estimates the time change of the cloud development state of each cloud mass based on the satellite image of the current time frame t based on the difference between the development state of each cloud mass (cloud part) based on the satellite image of the previous time frame t-Δt and the development state of each cloud mass based on the satellite image of the current time frame t. In other words, it estimates the development state at the target time. The time change of the cloud development state means an increase or decrease in the density of cloud particles. More details are as follows.

[0095] The cloud matching unit 33 associates a cloud mass obtained in the current time frame t with a cloud mass obtained in the previous time frame t-Δt. The association is performed based on the position of the cloud mass. The central position (center of gravity position) of the area occupied by the cloud mass and the height corresponding to the central position can be used as the position of the cloud mass. The cloud matching unit 33 calculates the distance between the positions of the cloud masses and associates those with the closest distance with each other. In the following description, the cloud mass in the current time frame is represented by index n, and the cloud mass in the previous time frame corresponding to the cloud mass is also represented by the same index n.

[0096] The development / attenuation coefficient estimation unit 34 calculates the development / attenuation coefficient α based on the relationship between the sum of the reflectance intensity for each pixel of cloud mass n in the current time frame and the sum of the reflectance intensity for each pixel of cloud mass n in the previous time frame. As an example, the development / attenuation coefficient α is calculated based on the ratio of these two totals. The following formula shows an example of the formula for calculating the development / attenuation coefficient α using the ratio of two reflectance intensities. Since reflectance intensity represents the density of cloud particles, α represents the rate at which the density of cloud particles increases or decreases during time Δt.

number

[0097] The cloud growth and attenuation calculation unit 35 corrects the advection-estimated cloud reflection coefficient based on the growth and attenuation coefficient α using the following equation (6). The corrected reflection coefficient is TIFF0007655844000009.tif9128. By correcting the cloud reflection coefficient estimated by advection using equation (6), it is possible to estimate the time change in the cloud development state (increase or decrease in the density of cloud particles).

number

[0098] As described above, according to this embodiment, in addition to the time change in position as a time change in cloud conditions, the development and decay of clouds can be estimated.

[0099] Sixth embodiment In contrast to the first embodiment, this embodiment uses ensemble prediction to handle the uncertainty in cloud decomposition and the associated estimation of advection vectors. An example will be described in which there is uncertainty in the setting of a threshold in cloud decomposition using brightness temperature, which was taken up in the first embodiment.

[0100] That is, in the first embodiment, it was shown that clouds (cloud images) are decomposed into high clouds having a brightness temperature lower than the threshold T1 and low clouds having a brightness temperature equal to or higher than the threshold T1 based on a given brightness temperature threshold T1. By setting T1 to 0 degrees Celsius (273K), clouds can be separated into water and ice. However, in reality, clouds are not necessarily ice if they are below 0 degrees Celsius. In addition, the brightness temperature observed on the satellite image may not exactly match the actual value. Therefore, in this embodiment, candidates for the brightness temperature threshold T1 are generated, and the cloud decomposition unit 20, cloud time change estimation unit 30, and cloud synthesis unit 40 process each candidate. The results of the processing performed for each candidate are synthesized. This complements the uncertainty and improves the accuracy of advection estimation. Hereinafter, this embodiment will be described in detail.

[0101] FIG. 20 is a block diagram of an example of a cloud movement estimation device 100A as an information processing device according to this embodiment.

[0102] The cloud segmentation parameter estimation unit 50 estimates the possible brightness temperature threshold T1 values. T1 (1) , T1 (2) , …, T1 (m) , For example, consider the possibility that the brightness temperature threshold can take multiple values ​​near 273K. T1 (1) = 273-(m / 2)ΔT, T1 (2) = 273-(m / 2-1)ΔT, …, T1 (m) = 273+(m / 2-1)ΔT Here, ΔT is an arbitrary value, for example, ΔT=1.

[0103] Figure 21 shows the difference in cloud decomposition due to the difference in the division parameter T1. The lower the T1, the higher the proportion of low-level clouds. This may also change the advection vector of the cloud.

[0104] For each candidate of T1, the cloud decomposition unit 20, the cloud time change estimation unit 30, and the cloud synthesis unit 40 perform m processes independently.

[0105] The estimation synthesis unit 60 synthesizes the results of independently performed estimation of cloud positions. Two examples of the synthesis method are shown below, but other methods are also possible.

[0106] (Derivation of the average value) Let the quantity to be estimated be X. X could be, for example, the center position of a cloud or the amount of solar radiation at a certain point. In this case, the average value is Calculated using TIFF0007655844000011.tif13128. However, X (i) is the value of X estimated from the i-th cloud synthesis unit 40.

[0107] (Probabilistic Estimation) For example, the probability distribution P(X) of X is calculated using kernel density estimation as follows:

number

number

[0108] When kernel density estimation is used, for example, the kernel function may be converted into a cumulative probability distribution, and X corresponding to a predetermined probability (e.g., 0.8) may be the final estimation result. Alternatively, a random number may be generated in the range of 0 to 1, and X corresponding to the value of the random number may be the final estimation result. Random numbers may be generated multiple times to calculate multiple Xs, and the average of the multiple Xs may be the final estimation result. The final estimation result may be calculated based on the Kernel function by other methods.

[0109] As described above, according to this embodiment, by performing advection estimation for each of multiple brightness candidates that serve as a threshold value and integrating the advection estimation for each temperature candidate, it is possible to avoid large errors that tend to occur in a single estimation and improve the accuracy of the advection estimation.

[0110] Seventh embodiment This embodiment is obtained by adding a function of using the results of numerical meteorological calculations to the first embodiment.

[0111] FIG. 22 is a diagram for explaining the outline of this embodiment. It is considered that the advection vector V1 estimated by the cloud time change estimation unit 30 represents the direction of cloud movement with high accuracy at the estimation time (τ=0). In the first embodiment, an example was described in which the future position of the cloud (τ=1, 2,...) was calculated on the assumption that the advection vector will basically not change thereafter. However, if the wind changes over time, it is expected that the direction of cloud movement will also deviate from the advection vector initially estimated as the lead time τ increases. In this embodiment, the result of the numerical meteorological calculation is used to correct the error in the advection vector.

[0112] Numerical weather simulations calculate weather changes based on atmospheric dynamics. Therefore, as the forecast lead time increases, the advection vector derived from the wind obtained by numerical weather simulations is more likely to be a more accurate estimate than the advection vector V1. Advection vectors V2_0, V2_1, ..., V2_6 are advection vectors derived from the wind obtained by numerical weather simulations at τ = 0, 2, ..., 6, and become more reliable than the advection vector V1 over time.

[0113] Therefore, weights according to the lead time are set for the advection vectors V1 and V2 (V2_0 to V2_6), and the two vectors are combined to calculate the advection vectors V3_0 to V3_6 used in the advection calculation at τ = 0, 2, ..., 6. When setting the weights, the weight of the advection vector V1 is made larger at the start of the advection calculation (τ = 0). For example, the weight of the advection vector V1 is set to 1, and the weight of the advection vector V2_0 is set to 0. As the lead time increases, the weight of the advection vector V1 is gradually decreased, and the weight of the advection vector V2 is increased.

[0114] FIG. 23 is a block diagram of an example of a cloud movement estimation device 100B as an information processing device according to this embodiment.

[0115] The numerical meteorological calculation unit 200 numerically models the physical dynamics of the atmosphere (wind, etc.) and calculates its evolution over time. The numerical meteorological calculation unit 200 may perform calculations using simulation software that implements the model. It is possible to calculate estimated values ​​of temperature, wind direction, wind speed, air pressure, and humidity at any point and any time. The wind calculated by the numerical meteorological calculation unit 200 at time t, position x, and altitude (height) z is represented as W(t,x,z). W(t,x,z) includes at least the wind direction, and may also include temperature, wind speed, etc. The device 100B may receive the results of the numerical meteorological calculation from a server of an institution (such as the Japan Meteorological Agency).

[0116] The wind vector calculation unit 36 ​​calculates a wind vector V from the wind field (e.g., at what position and height, in what direction, and at what wind speed at a certain time) in the result of the numerical meteorological calculation. s (t,x) is calculated. The wind advection vector includes information on wind direction and wind speed, for example. Since the wind causes cloud advection, the height H n Using this, the wind advection vector is expressed as follows:

number

[0117] In the following, the advection vector calculated from the results of numerical meteorological calculations in this way is called the wind advection vector.

[0118] The advection vector synthesis unit 37 synthesizes the advection vector V(t, x) in the time frame t estimated by the advection vector estimation unit 31 and the wind advection vector V calculated by the wind advection vector calculation unit 36. s (t+τ,x) is combined to calculate the combined advection vector (advection vector V'(t,τ,x)) at lead time τ. For example, V(t,x) and V s The weights of (t+τ,x) and (t+τ,x) are expressed as the first weights w τ (x), 1-w corresponding to the second weight τ(x), the composite advection vector is calculated using the following formula: τ (x), 1-w τ (x) is calculated by weight calculation unit 280, which will be described later.

number

[0119] The satellite image database 220 is a database that stores past satellite image data for a certain period of time (for example, several months or several years). Fig. 24 shows an example of the satellite image database 220. Visible band data (visible image) and infrared band data (infrared image) are stored for each time frame. The visible band data is a visible image containing a reflection intensity value for each pixel. The infrared band data is an infrared image containing a brightness temperature value for each pixel.

[0120] The database analysis unit 240 includes a cloud structure estimation unit 241, a cloud resolution unit 242, and an advection vector estimation unit 243, which have the same functions as the elements of the same names in the first embodiment. The database analysis unit 240 estimates the advection vector for each time frame of the past satellite image data stored in the satellite image database 220 by the same process as in the first embodiment.

[0121] The advection vector error history calculation unit 250 calculates the error of the advection vector calculated for the past satellite image data based on the following formula.

number

[0122] V(h,x) represents an advection vector calculated from data at past time h in satellite image database 220. V(h,x) corresponds to the advection vector at time h. V(h,x) corresponds to a vector representing the direction and speed of movement at position x, estimated based on a satellite image at a third time. V(h+τ,x) represents an advection vector calculated from data at past time h+τ in the satellite image database 220. It represents the advection vector at time h+τ. V(h+τ,x) corresponds to a vector representing the direction and speed of movement at position x, estimated based on a satellite image at a fourth time after the third time. One example, but not limited to, is a time frame interval Δt=Δτ. e(h, τ, x) is the error (difference) between the two advection vectors mentioned above.

[0123] The numerical meteorological calculation history database 230 is a database that stores the results of numerical meteorological calculations.

[0124] Fig. 25 shows an example of the numerical meteorological calculation history database 230. The results of numerical meteorological calculations for periods and areas common to those covered by the satellite image database 220 are stored. Meteorological variables are stored for each time. In the example shown in the figure, meteorological variables such as reflection intensity, temperature, and wind are stored.

[0125] Each meteorological variable for each time has a table format. When the meteorological variable is reflection intensity, the reflection intensity information is a table that stores values ​​related to reflection intensity for each two-dimensional position (latitude, longitude). When the meteorological variable is temperature, the temperature information is a table that stores values ​​related to temperature for each three-dimensional position (latitude, longitude, altitude). When the meteorological variable is wind, the wind information is a table that stores values ​​related to the wind field for each three-dimensional position (latitude, longitude, altitude).

[0126] The time in the numerical meteorological calculation history database 230 in Fig. 25 does not necessarily have to match the time frame of the satellite image. In this case, the value of the meteorological variable at the time corresponding to the time frame may be obtained by interpolation.

[0127] The wind advection vector history calculation unit 260 calculates a wind advection vector for each cloud mass obtained by the cloud resolution unit 20 of the database analysis unit 240 based on the numerical meteorological calculation history database 230, and calculates the history of the wind advection vector. The method of calculating the wind advection vector is the same as that of the wind advection vector calculation unit 36.

[0128] The wind advection vector error history calculation unit 270 obtains the history of the error between the wind advection vector and the advection vector based on the history of the wind advection vector calculated by the wind advection vector history calculation unit 260 and the history of the advection vector obtained by the database analysis unit 240. The error of the wind advection vector is calculated based on equation (12).

number

[0129] V s (h+τ,x) is the wind advection vector at lead time τ. V s (h+τ,x) corresponds to a vector representing the wind direction and wind speed in the numerical meteorological calculation for the fourth time. e s (h+τ,x) is the wind advection vector V at time h+τ s This is the error (difference) between (h+τ,x) and the advection vector V(h+τ,x) at time h+τ.

[0130] The weight calculation unit 280 calculates the weight w τ (x) and the wind advection vector V s Weight 1-w given to (t+τ,x) τ Calculate (x).

[0131] The weight calculation unit 280 calculates the weight TIFF0007655844000018.tif14152 is calculated using the following formula: Therefore, the weights are calculated based on the past history.

number

[0132] Weights are calculated for each location (point) x. If it is assumed that weights do not vary significantly from point to point, the weights averaged over all points may be used, as in the following formula:

number

[0133] The past history times (the third and fourth times) used to calculate the weights can be any times prior to the current time frame, but more accurate advection estimation can be expected if they are closer to the current time frame.

[0134] As described above, according to this embodiment, by using the results of the numerical meteorological calculation, it is possible to perform highly accurate estimation of cloud advection (especially estimation of cloud positions).

[0135] Eighth embodiment In the eighth embodiment, the result of estimating the cloud position in the first embodiment is combined with the result of a numerical meteorological calculation to estimate the reflection intensity of the estimated cloud (cloud mass). In the seventh embodiment, the advection vector obtained during the estimation of the cloud position is combined with the wind advection vector based on the result of the numerical meteorological calculation. In the eighth embodiment, the reflection intensity of the cloud (cloud mass) obtained as a result of the advection estimation is combined with the reflection intensity based on the result of the numerical meteorological calculation. By combining the estimation of the cloud position in the first embodiment, which excels in short-term prediction, with the result of the numerical meteorological calculation, which excels in long-term prediction, highly accurate prediction is possible regardless of the length of the prediction time.

[0136] FIG. 26 is a block diagram of an example of a cloud movement estimation system as an information processing device according to this embodiment.

[0137] The cloud movement estimation device 100 is the same as the cloud movement estimation device according to the first embodiment (see FIG. 1). The numerical meteorological calculation unit 200, the satellite image database 220, and the numerical meteorological calculation history database 230 are the same as those according to the seventh embodiment (see FIG. 23).

[0138] The cloud movement estimation device for past history 310 calculates an estimated value A'(h, τ, x) of reflection intensity at time h, lead time τ, and position x of a past time frame based on the past history of satellite images in the satellite image database 220. The cloud movement estimation device for past history 310 has the same configuration as the cloud movement estimation device 100. The cloud movement estimation device 100 may also serve as the cloud movement estimation device for past history 310. In this case, the cloud movement estimation device for past history 310 may be omitted.

[0139] The reflection intensity estimation error history calculation unit 320 calculates the error of the reflection intensity A'(h, τ, x) estimated by the past history cloud movement estimation device 310 based on the satellite image database 220 using the following formula.

number

[0140] The reflection intensity A'(h, τ, x) represents the reflection intensity at each position in the area including clouds (the imaging area of ​​the satellite image) at the time (h+τ) to be estimated. A(h+τ, x) is the reflection intensity at position x at time h+τ stored in the satellite image database 220. In other words, A(h+τ, x) represents the reflection intensity for each pixel of the satellite image (captured image) at time h+τ. e(h, τ, x) represents the difference between the reflection intensity for each position x estimated for a fourth time (h+τ) based on the satellite image (captured image) at a third time (h) and the reflection intensity for each pixel based on the satellite image at the fourth time. It is assumed that Δτ is the same as Δt, the time interval (time interval of a time frame) at which satellite images are acquired, but is not limited to this.

[0141] The reflection intensity calculation history calculation unit 330 calculates the reflection intensity A at the time h+τ and the position x as a result of past numerical meteorological calculation from the numerical meteorological calculation past history database. s Obtain (h+τ,x).

[0142] The reflection intensity calculation error history calculation unit 340 calculates the reflection intensity error of the numerical weather calculation by the following formula.

number

[0143] e s (h+τ,x) corresponds to the difference between the reflection intensity at the previous position of the numerical meteorological calculation for the fourth time (h+τ) and the reflection intensity for each pixel based on the satellite image at the fourth time.

[0144] The weight calculation unit 350 calculates the lead time τ and the weight w at the position x by the following formula, as in the seventh embodiment. τ Calculate (x).

number

[0145] If it is assumed that the weights do not vary significantly from one point to another, then a weight averaged over all points may be used, as in the following formula:

number

[0146] The synthesis unit 360 synthesizes the reflection intensity A′(t, τ, x) at the time frame t, the lead time τ, and the position x estimated by the cloud movement estimation device 100 using the third weighting w τ The synthesis unit 360 weights the reflection intensity at the time t+τ and the position x calculated by the numerical weather calculation unit 200. TIFF0007655844000025.tif12153, 1-w τ The reflection intensities are weighted by (x). The synthesis unit 360 sums (synthesizes) these weighted reflection intensities to calculate a new estimated value A"(t, τ, x) of the reflection intensity. The formula for calculating A"(t, τ, x) is shown below.

number

[0147] In other words, the synthesis unit 360 sets a third weight to the reflection intensity at each estimated position, sets a fourth weight to the reflection intensity at each position of the numerical weather calculation, and synthesizes the reflection intensities based on the third and fourth weights, thereby estimating the reflection intensity at each position at the time (t+τ) to be estimated.

[0148] The output unit outputs (for example, displays on a screen) information based on the new reflection intensity calculated by the synthesis unit 360.

[0149] As described above, according to this embodiment, by using the results of the numerical meteorological calculation, it is possible to perform highly accurate estimation of cloud advection (especially estimation of the cloud development state).

[0150] Ninth embodiment One of the important applications of cloud movement estimation is the estimation of the amount of solar radiation on the earth's surface. In recent years, solar power generation has become increasingly widespread. In the case of solar power generation, the amount of power generation depends on the amount of solar radiation. In order to utilize power effectively or efficiently, it is necessary to know the amount of power generation in advance, and in order to know the amount of power generation, it is necessary to predict the amount of solar radiation. In this embodiment, a solar radiation estimation unit is added to the cloud movement estimation device 100 according to the first embodiment, thereby making it possible to estimate the amount of solar radiation.

[0151] FIG. 27 is a block diagram of an example of a solar radiation estimation device as an information processing device according to the present embodiment.

[0152] The cloud movement estimating device 100 is the same as the cloud movement estimating device according to the first embodiment (see FIG. 1).

[0153] The solar radiation calculation unit 380 calculates the ground surface solar radiation corresponding to point x (ground surface solar radiation at a point directly below point x) based on the time frame t, lead time τ, and reflection intensity R(t, τ, x) at position (point) x estimated by the cloud movement estimation device 100.

[0154] There are several known methods for calculating the solar radiation on the ground surface S based on the cloud reflectance R. For example, the following formula may be used, although a relatively simple model is used (see Reference 8).

number

[0155] I represents the energy (power) received per unit area and unit time on a plane perpendicular to the radiation from the sun at the top of the Earth's atmosphere. Although it varies with the seasons, the average value is approximately 1.37×10 3 (W / m 2 ).

[0156] θ represents the solar zenith angle. The zenith angle can be calculated from the date and time.

[0157] A G is the surface albedo. Surface albedo can be calculated from remote sensing data.

[0158] As described above, according to this embodiment, the amount of solar radiation on the earth's surface can be estimated with high accuracy.

[0159] (Hardware configuration) 28 shows the hardware configuration of a cloud movement estimation device (information processing device) according to each embodiment. The cloud movement estimation device is configured by a computer device 600. The computer device 600 includes a CPU 601, an input interface 602, a display device 603, a communication device 604, a main storage device 605, and an external storage device 606, which are connected to each other by a bus 607.

[0160] The CPU (Central Processing Unit) 601 executes an information processing program, which is a computer program, on the main storage device 605. The information processing program is a program that realizes each of the above-mentioned functional configurations of the cloud movement estimation device. The information processing program may be realized by a combination of multiple programs or scripts, rather than a single program. Each functional configuration is realized by the CPU 601 executing the information processing program.

[0161] The input interface 602 is a circuit for inputting operation signals from an input device such as a keyboard, a mouse, a touch panel, etc., to the cloud movement estimation device. The input interface 602 corresponds to the input device 120.

[0162] The display device 603 displays data output from the cloud movement estimation device. The display device 603 is, for example, but not limited to, an LCD (liquid crystal display), an organic electroluminescence display, a CRT (cathode ray tube), or a PDP (plasma display). Data output from the computer device 600 can be displayed on the display device 603.

[0163] The communication device 604 is a circuit for the cloud movement estimation device to communicate with an external device wirelessly or by wire. Data can be input from an external device via the communication device 604. The data input from the external device can be stored in the main memory device 605 or the external memory device 606.

[0164] The main memory device 605 stores an information processing program, data required for executing the information processing program, and data generated by executing the information processing program. The information processing program is deployed and executed on the main memory device 605. The main memory device 605 is, for example, a RAM, a DRAM, or an SRAM, but is not limited to these. Each memory unit or database of the cloud movement estimation device may be constructed on the main memory device 605.

[0165] The external storage device 606 stores an information processing program, data required for executing the information processing program, and data generated by executing the information processing program. These information processing programs and data are read into the main storage device 605 when the information processing program is executed. The external storage device 606 is, for example, a hard disk, an optical disk, a flash memory, and a magnetic tape, but is not limited to these. Each storage unit or database of the cloud movement estimation device may be constructed on the external storage device 606.

[0166] The information processing program may be pre-installed in the computer device 600, or may be stored in a storage medium such as a CD-ROM. The information processing program may also be uploaded onto the Internet.

[0167] Furthermore, the cloud movement estimation device may be configured by a single computer device 600, or may be configured as a system made up of a plurality of computer devices 600 connected to each other.

[0168] The present invention is not limited to the above-described embodiments as they are, and the components can be modified and embodied in the implementation stage without departing from the gist of the invention. Various inventions can be formed by appropriately combining multiple components disclosed in the above-described embodiments. For example, a configuration in which some components are deleted from all the components shown in each embodiment can also be considered. Furthermore, the components described in different embodiments can be appropriately combined.

[0169] [References list] ·References 1 Kazuyori Ozeki, Yukio Sasaki. Overview of geostationary meteorological satellites Himawari 8 and 9 (Ground system of Himawari 8 and 9) Meteorological Satellite Center Technical Report, pp. 3-16, 2016. ·Reference 2 Maarten Reyniers. Quantitative precipitation forecasts based on radar observations: Principles, algo- rithms and operational systems. Institut Royal M´et´eorologique de Belgique, 2008. · Reference 3 Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al. The elements of statistical learning, Vol. 1. Springer series in statistics New York, 2001. · Reference 4 Mryka Hall-Beyer. GLCM texture: a tutorial. National Council on Geographic Information and Analysis Remote Sensing Core Curriculum, Vol. 3, p. 75, 2000. · Reference 5 M Lalitha, M Kiruthiga, and C Loganathan. A survey on image segmentation through clustering algorithm. International Journal of Science and Research, Vol. 2, No. 2, pp. 348-358, 2013. · Reference 6 Benoit Cushman-Roisin and Jean-Marie Beckers. Introduction to geophysical fluid dynamics: physical and numerical aspects. Academic press, 2011. · Reference 7 Gerhard Winkler. Image analysis, random fields and Markov chain Monte Carlo methods: a mathe-matical introduction, Vol. 27. Springer Science & Business Media, 2012. ·Reference 8 G Dedieu, PY Deschamps, and YH Kerr. Satellite estimation of solar irradiance at the surface of the earth and of surface albedo using a physical model applied to metcosat data. Journal of Applied Meteorology and Climatology, Vol. 26, No. 1, pp. 79-87, 1987. [Explanation of symbols]

[0170] 1. Weather satellites 10 Cloud structure estimation part 11 Cloud horizontal position estimation section 12 Cloud structure estimation section 20 Cloud Decomposition Section 21 Image feature calculation unit 22 Image Segmentation 23 Statistical Model Department 24 Estimation part 26 Uncertain part identification part 27 Advection estimation synthesis section 30 Estimation processing unit 30_1~30_N Cloud time change estimation section 31 Advection vector estimation section 32 Advection calculation section 33 Cloud Matching Department 34 Development and attenuation coefficient estimation section 35 Cloud Development and Attenuation Calculation Section 36 Wind vector calculation section 37 Advection vector synthesis section 40 Cloud Synthesis 50 Cloud segmentation parameter estimation unit 60 Estimation synthesis section 100 Cloud movement estimation device 100A Cloud movement estimation device 100B Cloud movement estimation device 110 Input section 120 Input Device 130 Storage section 130 Output Device 200 Numerical Meteorological Calculation Department 220 Satellite Image Database 230 Numerical Weather Calculation History Database 240 Database Analysis Department 241 Cloud structure estimation section 242 Cloud Decomposition Department 243 Advection Vector Estimation Unit 250 Advection vector error history calculation section 260 Wind Advection Vector History Calculation Unit 270 Wind Advection Vector Error History Calculation Unit 280 Weight Calculation Unit 310 Cloud movement estimation device for past history 320 Reflection intensity estimation error history calculation unit 330 Reflection intensity calculation history calculation unit 340 Reflection Intensity Calculation Error History Calculation Unit 350 Weight Calculation Unit 360 Synthesis section 380 Solar radiation calculation section 600 Computer equipment 602 Input Interface 603 Display device 604 Communication equipment 605 Main storage 606 External storage device 607 Bus

Claims

1. Estimating the cloud height information based on the cloud sensing data; Decomposing the cloud into a plurality of parts based on the height information; a processing unit that estimates a direction and speed of movement of the cloud for each of the portions, sets a first weight to a first vector of the direction and speed of the movement estimated for each of the portions of the cloud, sets a second weight to a second vector of a wind direction and wind speed corresponding to a position of the portion obtained by a numerical meteorological calculation, and estimates a position of the cloud for each of the portions by combining the first vector and the second vector based on the first weight and the second weight; An information processing device comprising:

2. Estimating the cloud height information based on cloud sensing data; Decomposing the cloud into a plurality of parts based on the height information; A processing unit is provided for estimating a state of the cloud for each of the portions; The cloud sensing data includes an image of a first area including the cloud, and pixels of the image represent reflection intensities at positions corresponding to the pixels; The processing unit estimates a reflection intensity at each position in the first area, and combines the estimated reflection intensity at each position with the reflection intensity at each position in the first area of ​​a numerical meteorological calculation. Information processing device.

3. the data includes a temperature distribution of the cloud; The processing unit estimates the height information based on a temperature distribution of the cloud.

3. The information processing device according to claim 1 or 2.

4. The temperature distribution is an infrared image of the cloud. The information processing device according to claim 3 .

5. The processing unit acquires the plurality of portions by decomposing the cloud into a plurality of temperature ranges based on the temperature distribution.

5. The information processing device according to claim 3.

6. The processing unit estimates a direction and a speed of movement of the cloud for each of the portions, and estimates at least one of a position and a shape of the cloud based on the direction and the speed of movement. The information processing device according to claim 2 .

7. The processing unit generates an image by synthesizing the clouds estimated for each portion. The information processing device according to any one of claims 1 to 5.

8. the data includes an image of the cloud; The processing unit decomposes the cloud into the plurality of parts by segmenting the captured image based on feature amounts of pixels included in the captured image of the cloud. The information processing device according to any one of claims 1 to 7.

9. The processing unit estimates whether or not another cloud exists below the first portion based on a range adjacent to another portion adjacent to the first portion at a boundary of the first portion among the plurality of portions, or an area where another cloud exists. The information processing device according to any one of claims 1 to 8.

10. The processing unit estimates whether or not other clouds exist or the range in which other clouds exist below the plurality of portions of the cloud obtained based on the data at the second time based on the positions of the clouds for each portion at the second time estimated based on the data at the first time, The information processing device according to any one of claims 1 to 8.

11. The processing unit estimates a development state of the cloud for each of the portions at a target time after the second time based on a difference between a development state of the cloud for each of the portions based on the data at a first time and a development state of the cloud for each of the portions based on the data at a second time after the first time. The information processing device according to any one of claims 1 to 10.

12. The processing unit decomposes the cloud into a plurality of portions based on a plurality of candidate parameters; aggregating the cloud state estimates for each of the candidate parameters; The information processing device according to any one of claims 1 to 11.

13. The height information includes a temperature distribution of the cloud, The processing unit acquires the plurality of portions by decomposing the cloud into a plurality of temperature ranges based on the temperature distribution; the plurality of candidate parameters are parameters defining the plurality of temperature ranges; The information processing device according to claim 12.

14. The processing unit estimates a direction and a speed of movement of the cloud for each of the parts, and estimates a position of the cloud based on the estimated direction and speed of movement and a wind direction and a wind speed of a numerical meteorological calculation. The information processing device according to claim 2 .

15. the processing unit sets a first weight to a first vector of the direction and speed of the movement estimated for each of the portions of the cloud, sets a second weight to a second vector of a wind direction and a wind speed corresponding to the position of the portion by a numerical meteorological calculation, and estimates the position of the cloud for each of the portions by combining the first vector and the second vector based on the first weight and the second weight. The information processing device according to claim 14.

16. The processing unit calculates the first weight and the second weight based on a difference between the direction and speed of the movement estimated based on the data at a third time and the direction and speed of the movement estimated based on the data at a fourth time that is later than the third time, and a difference between the direction and speed of the movement estimated based on the data at the fourth time and the wind direction and wind speed of the numerical meteorological calculation for the fourth time. The information processing device according to claim 1 or 15, comprising:

17. The cloud sensing data includes an image of a first area including the cloud, and pixels of the image represent reflection intensities at positions corresponding to the pixels; The processing unit estimates a reflection intensity at each position in the first area, The estimated reflection intensity at each position is combined with the reflection intensity at each position in the first area of ​​the numerical meteorological calculation. The information processing device according to claim 1 .

18. The processing unit sets a third weight to the estimated reflection intensity at each position, sets a fourth weight to the reflection intensity at each position in the first area of ​​the numerical meteorological calculation, and synthesizes the reflection intensities based on the third weight and the fourth weight.

18. The information processing device according to claim 2 or 17.

19. The processing unit calculates the third weight and the fourth weight based on a difference between the reflection intensity for each position estimated for the fourth time based on the image captured at the third time and the reflection intensity for each pixel based on the image captured at the fourth time, and a difference between the reflection intensity for each pixel based on the image captured at the fourth time and the reflection intensity for each position in the numerical meteorological calculation for the fourth time. The information processing device according to claim 18.

20. The cloud sensing data includes an image of a first area including the cloud, and pixels of the image represent reflection intensities at positions corresponding to the pixels; The processing unit estimates a reflection intensity at each position in the first area, and calculates an amount of solar radiation on the ground surface based on the estimated reflection intensity at each position. The information processing device according to any one of claims 1 to 19, comprising:

21. The target time is a time later than the time when the cloud is sensed. The information processing device according to claim 11.

22. The processing unit further estimates position information of the cloud based on the data obtained by sensing the cloud. The information processing device according to any one of claims 1 to 21.

23. The height information includes a height of each horizontal position of the cloud. The information processing device according to any one of claims 1 to 22.

24. Estimating the cloud height information based on the cloud sensing data; Decomposing the cloud into a plurality of parts based on the height information; Estimating the direction and speed of movement of the cloud for each of the portions; setting a first weight on a first vector of the direction and speed of movement estimated for each of the portions of the cloud; setting a second weight on a second vector of wind direction and wind speed corresponding to the position of the portion obtained by the numerical meteorological calculation; Estimating the position of the cloud for each of the portions by combining the first vector and the second vector based on the first weight and the second weight. A computer-implemented method for processing information.

25. Estimating a distribution of the cloud heights based on the cloud sensing data; Decomposing the cloud into a plurality of parts based on the height information; Estimating the direction and speed of movement of the cloud for each of the portions; setting a first weight on a first vector of the direction and speed of movement estimated for each of the portions of the cloud; setting a second weight on a second vector of wind direction and wind speed corresponding to the position of the portion obtained by the numerical meteorological calculation; Estimating the position of the cloud for each of the portions by combining the first vector and the second vector based on the first weight and the second weight. A computer program for causing a computer to execute the program.

26. Estimating the cloud height information based on cloud sensing data; Decomposing the cloud into a plurality of parts based on the height information; Estimating the cloud height information based on the cloud sensing data; Decomposing the cloud into a plurality of parts based on the height information; Estimating the state of the cloud for each of the portions; The cloud sensing data includes an image of a first area including the cloud, and pixels of the image represent reflection intensities at positions corresponding to the pixels; A reflection intensity at each position in the first area is estimated, and the estimated reflection intensity at each position is combined with a reflection intensity at each position in the first area calculated by a numerical meteorological calculation. A computer-implemented method for processing information.

27. ​​Estimating the cloud height information based on cloud sensing data; Decomposing the cloud into a plurality of parts based on the height information; Estimating the cloud height information based on the cloud sensing data; Decomposing the cloud into a plurality of parts based on the height information; Estimating the state of the cloud for each of the portions; The cloud sensing data includes an image of a first area including the cloud, and pixels of the image represent reflection intensities at positions corresponding to the pixels; A reflection intensity at each position in the first area is estimated, and the estimated reflection intensity at each position is combined with a reflection intensity at each position in the first area calculated by a numerical meteorological calculation. A computer program for causing a computer to execute the program.

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