Methods for obtaining snow depth prediction data

Abstract

This application provides a method for acquiring snow depth prediction data, comprising: obtaining second spatial resolution historical snow depth data for a target location based on first historical snow depth data, first historical snow depth climatological data, and second historical snow depth climatological data; obtaining third spatial resolution historical snow depth data for the target location based on third snow cover data and the second historical snow depth data; obtaining third spatial resolution historical snow depth climatological data based on the third historical snow depth data; and obtaining third spatial resolution predicted snow depth data for the target location based on first predicted snow depth data, first predicted snow depth climatological data, and third historical snow depth climatological data. This application can accurately acquire high spatial resolution predicted snow depth data.

Description

Technical Field

[0001] This application relates to the field of snow cover data prediction technology, specifically to a method for acquiring snow depth prediction data. Background Technology

[0002] Predicting snow depth helps prevent natural disasters such as avalanches and is also beneficial for water resource management and agricultural management in high-latitude and high-altitude regions. Existing technologies use global climate models to simulate future snow depth predictions; however, global climate models can only provide predictions with low spatial resolution, resulting in technical limitations such as low spatial resolution and poor spatial accuracy in the obtained snow depth predictions. Summary of the Invention

[0003] The purpose of this application is to overcome the shortcomings and deficiencies in the prior art and provide a method for acquiring snow depth prediction data, which can obtain snow depth prediction data with high spatial resolution.

[0004] The first aspect of this application provides a method for acquiring snow depth prediction data, including:

[0005] Acquire historical snow depth data, historical snow depth climatological data, predicted snow depth climatological data, and predicted snow depth data at a target location with a first spatial resolution, as well as historical snow depth climatological data at a second spatial resolution and third snow cover data at a third spatial resolution; wherein the resolution of the second spatial resolution is higher than the resolution of the first spatial resolution, and the resolution of the third spatial resolution is higher than the resolution of the second spatial resolution.

[0006] Based on the first historical snow depth data, the first historical snow depth climatological data, and the second historical snow depth climatological data, the second historical snow depth data of the target location with a second spatial resolution is obtained;

[0007] Based on the third snow cover data and the second snow depth history data, obtain the third snow depth history data of the target location at the third spatial resolution;

[0008] Based on the aforementioned third snow depth historical data, third spatial resolution third snow depth historical climatological data are obtained;

[0009] Based on the first snow depth prediction data, the first snow depth prediction climatological data, and the third snow depth historical climatological data, the third snow depth prediction data at the third spatial resolution for the target location is obtained.

[0010] Compared with related technologies, this application obtains third spatial resolution third spatial resolution third snow depth historical climatological data based on first spatial resolution first snow depth historical data, first snow depth historical climatological data, second spatial resolution second snow depth historical climatological data, and third spatial resolution third snow cover data of the target location. Then, it performs spatial downscaling processing on the first spatial resolution first snow depth prediction data by combining the first snow depth prediction climatological data with the first spatial resolution snow depth prediction data to obtain high spatial resolution third snow depth prediction data.

[0011] To provide a clearer understanding of this application, the specific embodiments of this application will be described below in conjunction with the accompanying drawings. Attached Figure Description

[0012] Figure 1 This is a flowchart of a snow depth prediction data acquisition method according to an embodiment of this application.

[0013] Figure 2 This is a flowchart of steps S21-S22 of a snow depth prediction data acquisition method according to an embodiment of this application.

[0014] Figure 3 This is a flowchart of steps S31-S33 of a snow depth prediction data acquisition method according to an embodiment of this application.

[0015] Figure 4 This is a flowchart of steps S51-S52 of a snow depth prediction data acquisition method according to an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0017] It should be understood that the described embodiments are merely some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of the embodiments of this application.

[0018] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances. The singular forms "a," "the," and "the" used in this application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. The word "if" as used herein can be interpreted as "when," "when," or "in response to determination."

[0019] Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0020] Please see Figure 1 This is a snow depth prediction data acquisition method according to an embodiment of the present application, comprising:

[0021] S1: Acquire historical snow depth data, historical snow depth climatological data, predicted snow depth climatological data, and predicted snow depth data at the target location with a first spatial resolution, as well as historical snow depth climatological data at a second spatial resolution and third snow cover data at a third spatial resolution; wherein the resolution of the second spatial resolution is higher than the resolution of the first spatial resolution, and the resolution of the third spatial resolution is higher than the resolution of the second spatial resolution.

[0022] In this embodiment, the first historical snow depth data, the first historical climatological data, the second historical climatological data, and the third snow cover data are all historical data of the target location within a preset historical time period. The preset historical time period includes multiple years.

[0023] Among them, the first historical snow depth data includes snow depth data at the first spatial resolution for each month of each year within a preset historical time period.

[0024] The first historical climatological data for snow depth includes the average snow depth data for each month within a preset historical time period at the first spatial resolution.

[0025] The second historical climatological data for snow depth includes average snow depth data at second spatial resolution for each month within a preset historical time period.

[0026] The third snow cover data includes snow cover data at third spatial resolution for each year and month within a preset historical time period, which may specifically include snow cover score and clear skies index.

[0027] In this embodiment, the first spatial resolution can be a 2.5° grid spatial resolution, the second spatial resolution can be a 1° grid spatial resolution, and the third spatial resolution can be a 0.05° grid spatial resolution.

[0028] Both the first snow depth prediction climatological data and the first snow depth prediction data are prediction data obtained by scenario simulation of the target location based on a preset future time period. For example, they can be obtained through simulation using a global climate model.

[0029] Among them, the first snow depth prediction climate state data includes the average snow depth data with first spatial resolution for each month in the preset future time period, obtained by the global climate model through scenario simulation of the target location based on the preset future time period.

[0030] The first snow depth prediction data includes snow depth data at first spatial resolution for each year and month within the preset future time period, obtained by global climate models simulating scenarios for the target location based on a preset future time period.

[0031] S2: Based on the first historical snow depth data, the first historical snow depth climatological data, and the second historical snow depth climatological data, obtain the second historical snow depth data of the target location with a second spatial resolution.

[0032] Please see Figure 2 Step S2 includes:

[0033] S21: Based on the first historical snow depth data and the first historical snow depth climatological data, obtain the first historical snow depth anomaly data with a first spatial resolution.

[0034] In a feasible embodiment, the first historical snow depth anomaly data can be obtained using the following formula:

[0035]

[0036] in, This is the historical anomaly data for the first snow depth in the m month of year y; This is the historical data for the first snow depth in the m month of year y; This is the historical climatological data for the first snow depth in month m.

[0037] S22: Obtain the second historical snow depth data based on the first historical snow depth anomaly data and the second historical snow depth climatological data.

[0038] In one feasible embodiment, the second historical snow depth data is obtained using the following formula:

[0039]

[0040] in, This is the second historical snow depth data for the m-th month of year y; This is the historical anomaly data for the first snow depth in the m month of year y; This is the historical climatological data for the second snow depth in month m.

[0041] S3: Based on the third snow cover data and the second snow depth history data, obtain the third snow depth history data of the target location with a third spatial resolution.

[0042] Please see Figure 3 The third snow cover data includes a snow cover score and a clear skies index; step S3 includes:

[0043] S31: Obtain the snow cover probability based on the snow cover score and the clear sky index.

[0044] In a feasible embodiment, the snow accumulation probability can be obtained using the following formula:

[0045]

[0046] Among them, SCP y,m Let y be the probability of snow cover in month m of year y; FXC y,m CI represents the snow cover score for the m-th month of year y. y,m The sunshine index for month m in year y.

[0047] S32: Obtain the pixel snow accumulation weights at the third spatial resolution based on the number of pixels at the third spatial resolution of the target location and the snow accumulation probability.

[0048] In a feasible embodiment, the cell snow accumulation weight can be obtained using the following formula:

[0049]

[0050] Among them, W i,y,m The pixel snow accumulation weight of the i-th pixel in the m-th month of year y; SCP i,y,m Let ∑ be the probability of snow cover in the i-th pixel of the m-th month of the y-th year; j∈N SCP j,y,mThe sum of the snow cover probabilities of N pixels in the m-th month of year y; N is the number of pixels at the third spatial resolution of the target location.

[0051] S33: The third snow depth historical data is obtained based on the number of pixels, the pixel snow accumulation weight, and the second snow depth historical data.

[0052] In a feasible embodiment, the third historical snow depth data can be obtained using the following formula:

[0053]

[0054] in, The third historical snow depth data for the i-th pixel in the m-th month of year y; W i,y,m Let be the pixel snow accumulation weight of the i-th pixel in the m-th month of the y-th year; represents the second historical snow depth data for the i-th pixel in the m-th month of year y; N represents the number of pixels at the third spatial resolution of the target location.

[0055] S4: Based on the aforementioned third historical snow depth data, obtain third historical snow depth climatological data with third spatial resolution.

[0056] In a feasible embodiment, the third historical snow depth climatological data can be obtained using the following formula:

[0057]

[0058] in, This is the historical climatological data for the third snow depth in month m; This represents the historical snow depth data for the third month of year y; Y represents the total number of years in the historical data.

[0059] S5: Based on the first snow depth prediction data, the first snow depth prediction climatological data, and the third snow depth historical climatological data, obtain the third snow depth prediction data at the third spatial resolution for the target location.

[0060] Please see Figure 4 Step S5 includes:

[0061] S51: Based on the first snow depth prediction data and the first snow depth prediction climatological data, obtain the first snow depth prediction anomaly data with a first spatial resolution.

[0062] In a feasible embodiment, the first snow depth prediction anomaly data can be obtained using the following formula:

[0063]

[0064] in, For the ytho Year m o Abnormal data in the first snow depth prediction of the month; For the yth o Year m o Predicted snow depth for the first snowfall of the month; For the yth o Year m o The first snow depth of the month is predicted by climatological data.

[0065] S52: Based on the first snow depth prediction anomaly data and the third snow depth historical climatological data, the third snow depth prediction data is obtained.

[0066] In a feasible embodiment, the third snow depth prediction data can be obtained using the following formula:

[0067]

[0068] in, For the yth o Year m o The third snow depth forecast for the month; For the yth o Year m o Abnormal data in the first snow depth prediction of the month; This is the historical climatological data for the third snow depth in month m.

[0069] It should be noted that in this application, the superscript "L" indicates that the data is data with the first spatial resolution, the superscript "M" indicates that the data is data with the second spatial resolution, and the superscript "H" indicates that the data is data with the third spatial resolution. The third spatial resolution is higher than the second spatial resolution, and the second spatial resolution is higher than the first spatial resolution.

[0070] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function selected in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function selected in one or more boxes.

[0073] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0074] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0075] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0076] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0077] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for acquiring snow depth prediction data, characterized in that, include: Acquire historical snow depth data, historical snow depth climatological data, predicted snow depth climatological data, and predicted snow depth data at a target location with a first spatial resolution, as well as historical snow depth climatological data at a second spatial resolution and third snow cover data at a third spatial resolution; wherein the resolution of the second spatial resolution is higher than the resolution of the first spatial resolution, and the resolution of the third spatial resolution is higher than the resolution of the second spatial resolution. Based on the first historical snow depth data, the first historical snow depth climatological data, and the second historical snow depth climatological data, the second historical snow depth data of the target location with a second spatial resolution is obtained; Based on the third snow cover data and the second snow depth history data, obtain the third snow depth history data of the target location at the third spatial resolution; Based on the aforementioned third snow depth historical data, third spatial resolution third snow depth historical climatological data are obtained; Based on the first snow depth prediction data, the first snow depth prediction climatological data, and the third snow depth historical climatological data, the third snow depth prediction data at the third spatial resolution for the target location is obtained.

2. The snow depth prediction data acquisition method according to claim 1, characterized in that, The step of obtaining the second historical snow depth data of the target location based on the first historical snow depth data, the first historical snow depth climatological data, and the second historical snow depth climatological data includes: Based on the first historical snow depth data and the first historical snow depth climatological data, the first historical snow depth anomaly data with a first spatial resolution is obtained; Based on the first historical snow depth anomaly data and the second historical snow depth climatological data, the second historical snow depth data is obtained.

3. The snow depth prediction data acquisition method according to claim 2, characterized in that, The step of obtaining the first historical snow depth anomaly data based on the first historical snow depth data and the first historical snow depth climatological data includes: The first historical snow depth anomaly data is obtained using the following formula: in, This is the historical anomaly data for the first snow depth in the m month of year y; This is the historical data for the first snow depth in the m month of year y; This is the historical climatological data for the first snow depth in month m.

4. The snow depth prediction data acquisition method according to claim 2, characterized in that, The step of obtaining the second historical snow depth data based on the first historical snow depth anomaly data and the second historical snow depth climatological data includes: The second historical snow depth data is obtained using the following formula: in, This is the second historical snow depth data for the m-th month of year y; This is the historical anomaly data for the first snow depth in the m month of year y; This is the historical climatological data for the second snow depth in month m.

5. The snow depth prediction data acquisition method according to claim 1, characterized in that, The third snow cover data includes a snow cover score and a clearness index; The step of obtaining the third historical snow depth data of the target location based on the third snow cover data and the second historical snow depth data includes: The probability of snow cover is obtained based on the snow cover score and the clear skies index. The pixel snow accumulation weights at the third spatial resolution are obtained based on the number of pixels at the third spatial resolution of the target location and the snow accumulation probability. The third snow depth historical data is obtained based on the number of pixels, the pixel snow accumulation weight, and the second snow depth historical data.

6. The snow depth prediction data acquisition method according to claim 5, characterized in that, The step of obtaining the snow cover probability based on the snow cover score and the clear skies index includes: The probability of snow accumulation can be obtained using the following formula: Among them, SCP y,m Let FSC be the probability of snow cover in the m-th month of year y; y,m CI represents the snow cover score for the m-th month of year y. y,m The sunshine index for the m-th month of year y; The step of obtaining the pixel snow cover weights at the third spatial resolution based on the number of pixels at the third spatial resolution of the target location and the snow cover probability includes: The cell snow accumulation weight is obtained using the following formula: Among them, W i,y,m The pixel snow accumulation weight of the i-th pixel in the m-th month of year y; SCP i,y,m Let ∑ be the probability of snow cover in the i-th pixel of the m-th month of the y-th year; j∈N SCP j,y,m The sum of the snow cover probabilities of N pixels in the m-th month of year y; N is the number of pixels at the third spatial resolution of the target location; The step of obtaining the third snow depth historical data based on the number of pixels, the pixel snow accumulation weight, and the second snow depth historical data includes: The third historical snow depth data is obtained using the following formula: in, The third historical snow depth data for the i-th pixel in the m-th month of year y; W i,y,m Let be the pixel snow accumulation weight of the i-th pixel in the m-th month of the y-th year; represents the second historical snow depth data for the i-th pixel in the m-th month of year y; N represents the number of pixels at the third spatial resolution of the target location.

7. The snow depth prediction data acquisition method according to claim 1, characterized in that, The step of obtaining the third snow depth historical climatological data based on the third snow depth historical data includes: The third historical climatological data of snow depth is obtained using the following formula: in, This is the historical climatological data for the third snow depth in month m; This represents the historical snow depth data for the third month of year y; Y represents the total number of years in the historical data.

8. The snow depth prediction data acquisition method according to claim 1, characterized in that, The step of obtaining the third snow depth prediction data for the target location based on the first snow depth prediction data, the first snow depth prediction climatological data, and the third snow depth historical climatological data includes: Based on the first snow depth prediction data and the first snow depth prediction climatological data, first snow depth prediction anomaly data with a first spatial resolution is obtained; The third snow depth prediction data is obtained based on the first snow depth prediction anomaly data and the third snow depth historical climatological data.

9. The snow depth prediction data acquisition method according to claim 8, characterized in that, The step of obtaining the first snow depth prediction anomaly data based on the first snow depth prediction data and the first snow depth prediction climatological data includes: The first snow depth prediction anomaly data is obtained using the following formula: in, For the yth o Year m o Abnormal data in the first snow depth prediction of the month; For the yth o Year m o Predicted snow depth for the first snowfall of the month; For the yth o Year m o The first snow depth of the month is predicted by climatological data.

10. The snow depth prediction data acquisition method according to claim 8, characterized in that, The step of obtaining the third snow depth prediction data based on the first snow depth prediction anomaly data and the third snow depth historical climatological data includes: The third snow depth prediction data is obtained using the following formula: in, For the yth o Year m o The third snow depth forecast for the month; For the yth o Year m o Abnormal data in the first snow depth prediction of the month; This is the historical climatological data for the third snow depth in month m.

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