Snow depth prediction data acquisition method

Abstract

The invention provides a snow depth prediction data acquisition method, and the method comprises the steps: obtaining second snow depth historical data of a second spatial resolution of a target location according to first snow depth historical data, first snow depth historical climate state data and second snow depth historical climate state data; obtaining third snow depth historical data of a third spatial resolution of the target location according to the third snow coverage data and the second snow depth historical data; obtaining third snow depth historical climate state data of a third spatial resolution according to the third snow depth historical data; and obtaining third snow depth prediction data of a third spatial resolution of the target location according to the first snow depth prediction data, the first snow depth prediction climate state data and the third snow depth historical climate state data. According to the invention, the third snow depth prediction data with high spatial resolution can be accurately obtained.

Description

Technical Field

[0001] The present application relates to the technical field of snow accumulation data prediction, and in particular to a method for obtaining snow depth prediction data. Background Art

[0002] The prediction of snow depth helps prevent natural disasters such as avalanches, and is also beneficial to water resource management and agricultural management in high-latitude and high-altitude areas. Existing technology uses global climate models to simulate and obtain future snow depth prediction data. However, global climate models can only provide prediction data with low spatial resolution, resulting in the technical defects of low spatial resolution and poor spatial accuracy of the obtained snow depth prediction data. Summary of the invention

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

[0004] A first aspect of an embodiment of the present application provides a method for obtaining snow depth prediction data, comprising:

[0005] Acquire first snow depth historical data, first snow depth historical climatic data, first snow depth predicted climatic data, and first snow depth predicted data of a first spatial resolution at a target location, as well as second snow depth historical climatic data of a second spatial resolution and third snow cover data of 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] Obtaining second snow depth historical data of a second spatial resolution for the target location based on the first snow depth historical data, the first snow depth historical climatic data, and the second snow depth historical climatic data;

[0007] Obtaining third snow depth historical data of a third spatial resolution of the target location according to the third snow cover data and the second snow depth historical data;

[0008] Obtaining third snow depth historical climatological data of a third spatial resolution according to the third snow depth historical data;

[0009] Third snow depth prediction data of a third spatial resolution for the target location is obtained based on the first snow depth prediction data, the first snow depth prediction climatic data, and the third snow depth historical climatic data.

[0010] Compared with the related art, the present application obtains third snow depth historical climatic data of a third spatial resolution based on the first snow depth historical data of a first spatial resolution at the target location, the first snow depth historical climatic data, the second snow depth historical climatic data of a second spatial resolution, and the third snow cover data of a third spatial resolution, and then spatially downscales the first snow depth prediction data of the first spatial resolution in combination with the first snow depth prediction climatic data to obtain third snow depth prediction data with high spatial resolution.

[0011] In order to provide a clearer understanding of the present application, the specific implementation of the present application will be described below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a flowchart of a method for acquiring snow depth prediction data according to an embodiment of the present application.

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

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

[0015] Figure 4 This is a flowchart of steps S51-S52 of a method for acquiring snow depth prediction data according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0017] It should be clear that the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the embodiments of the present application.

[0018] When the following description relates to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. The singular forms of "a", "said" and "the" used in the present application and the appended claims are also intended to include the majority form, unless the context clearly indicates other meanings. The words "if" / "if" used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0019] In addition, in the description of this application, unless otherwise specified, "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0020] See also Figure 1 , which is a method for obtaining snow depth prediction data according to an embodiment of the present application, comprising:

[0021] S1: Acquire first snow depth historical data of a first spatial resolution, first snow depth historical climatic data, first snow depth predicted climatic data, first snow depth predicted data, second snow depth historical climatic data of a second spatial resolution, and third snow cover data of a third spatial resolution at a target location; 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] The first snow depth historical data, the first snow depth historical climatic data, the second snow depth historical climatic data and the third snow cover data of this embodiment are all historical data of the target location within a preset historical time period. The preset historical time period includes multiple years.

[0023] The first snow depth historical data includes snow depth data of a first spatial resolution for each year and each month in a preset historical time period.

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

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

[0026] The third snow cover data includes snow cover data of a third spatial resolution for each year and each month in a preset historical time period, and may specifically include a snow cover score and a clearness index.

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

[0028] The first snow depth prediction climatological data and the first snow depth prediction data are both prediction data obtained by performing scenario simulation on the target location according to a preset future time period, for example, they can be obtained through simulation of a global climate model.

[0029] The first snow depth prediction climate data includes average snow depth data of the first spatial resolution for each month in a preset future time period, obtained by a global climate model performing scenario simulation on a target location according to a preset future time period.

[0030] The first snow depth prediction data includes snow depth data of a first spatial resolution for each year and each month in a preset future time period, obtained by a global climate model performing scenario simulation on a target location according to a preset future time period.

[0031] S2: Obtain second snow depth historical data of a second spatial resolution for the target location based on the first snow depth historical data, the first snow depth historical climatic data, and the second snow depth historical climatic data.

[0032] See also Figure 2 , step S2 comprises:

[0033] S21: Obtaining first snow depth historical anomaly data of a first spatial resolution based on the first snow depth historical data and the first snow depth historical climatological data.

[0034] In a feasible embodiment, the first snow depth historical abnormal data may be obtained by the following formula:

[0035]

[0036] in, The first snow depth historical abnormal data of the mth month of the yth year; The historical data of the first snow depth in the mth month of the yth year; It is the historical climatological data of the first snow depth in month m.

[0037] S22: Obtaining the second snow depth historical data according to the first snow depth historical abnormal data and the second snow depth historical climatological data.

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

[0039]

[0040] in, The second snow depth historical data of the mth month of the yth year; The first snow depth historical abnormal data of the mth month of the yth year; It is the historical climatological data of the second snow depth in the mth month.

[0041] S3: Obtaining third snow depth historical data of a third spatial resolution of the target location according to the third snow cover data and the second snow depth historical data.

[0042] See also Figure 3 , the third snow cover data includes a snow cover score and a clear index; step S3 includes:

[0043] S31: Obtain snow accumulation probability according to the snow cover score and the clearness index.

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

[0045]

[0046] Among them, SCP y,m is the snow accumulation probability in the mth month of the yth year; FXC y,m is the snow cover fraction in month m of year y; CI y,m is the clearness index of the mth month of the yth year.

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

[0048] In a feasible embodiment, the pixel snow weight can be obtained by the following formula:

[0049]

[0050] Among them, W i,y,m is the pixel snow weight of the ith pixel in the mth month of the yth year; SCP i,y,m is the snow accumulation probability of the ith pixel in the mth month of the yth year; ∑ j∈N SCP j,y,mis the sum of the snow accumulation probabilities of N pixels in the mth month of the yth year; N is the number of pixels of the third spatial resolution of the target location.

[0051] S33: Obtain the third snow depth historical data according to the number of pixels, the pixel snow weight and the second snow depth historical data.

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

[0053]

[0054] in, is the third snow depth historical data of the ith pixel in the mth month of the yth year; W i,y,m is the pixel snow weight of the ith pixel in the mth month of the yth year; is the historical data of the second snow depth of the ith pixel in the mth month of the yth year; N is the number of pixels of the third spatial resolution of the target location.

[0055] S4: Obtain third snow depth historical climatological data of a third spatial resolution based on the third snow depth historical data.

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

[0057]

[0058] in, The third snow depth historical climatological data of the mth month; is the historical data of the third snow depth in the mth month of the yth year; Y is the total number of years of historical data.

[0059] S5: Obtain third snow depth prediction data of a third spatial resolution for the target location based on the first snow depth prediction data, the first snow depth prediction climatic data, and the third snow depth historical climatic data.

[0060] See also Figure 4 , step S5 comprises:

[0061] S51: Obtaining first snow depth prediction anomaly data of a first spatial resolution based on the first snow depth prediction data and the first snow depth prediction climatological data.

[0062] In a feasible embodiment, the first snow depth prediction abnormal data may be obtained by the following formula:

[0063]

[0064] in, For the ytho Year m o The first snow depth forecast anomaly data of the month; For the yth o Year m o First snow depth forecast data of the month; For the yth o Year m o First snow depth forecast climatological data of the month.

[0065] S52: Obtaining the third snow depth prediction data according to the first snow depth prediction abnormal data and the third snow depth historical climatological data.

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

[0067]

[0068] in, For the yth o Year m o The third snow depth forecast data of the month; For the yth o Year m o The first snow depth forecast anomaly data of the month; It is the historical climatological data of the third snow depth in the mth month.

[0069] It should be noted that, in the present application, the superscript "L" indicates that the data is data of the first spatial resolution, the superscript "M" indicates that the data is data of the second spatial resolution, and the superscript "H" indicates that the data is data of the third spatial resolution, wherein 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 appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0071] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the function selected in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 function selected in a box or multiple boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 steps for the function selected in a box or multiple boxes.

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

[0074] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0075] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0076] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0077] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for obtaining snow depth prediction data, characterized in that: include: Acquire first snow depth historical data, first snow depth historical climatic data, first snow depth predicted climatic data, and first snow depth predicted data of a first spatial resolution at a target location, as well as second snow depth historical climatic data of a second spatial resolution and third snow cover data of 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; Obtaining second snow depth historical data of a second spatial resolution for the target location based on the first snow depth historical data, the first snow depth historical climatic data, and the second snow depth historical climatic data; Obtaining third snow depth historical data of a third spatial resolution of the target location according to the third snow cover data and the second snow depth historical data; Obtaining third snow depth historical climatological data of a third spatial resolution according to the third snow depth historical data; Third snow depth prediction data of a third spatial resolution for the target location is obtained based on the first snow depth prediction data, the first snow depth prediction climatic data, and the third snow depth historical climatic data.

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

3. The snow depth prediction data acquisition method according to claim 2, characterized in that: The step of obtaining first snow depth historical abnormal data according to the first snow depth historical data and the first snow depth historical climatological data comprises: The first snow depth historical abnormal data is obtained by the following formula: in, The first snow depth historical abnormal data of the mth month of the yth year; The first snow depth historical data of the mth month of the yth year; It is the historical climatological data of 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 snow depth historical data according to the first snow depth historical abnormal data and the second snow depth historical climatological data comprises: The second snow depth historical data is obtained by the following formula: in, The second snow depth historical data of the mth month of the yth year; The first snow depth historical abnormal data of the mth month of the yth year; It is the historical climate data of the second snow depth in the mth month.

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 snow depth historical data of the target location according to the third snow cover data and the second snow depth historical data comprises: Obtaining snow accumulation probability according to the snow cover fraction and the clearness index; Obtaining pixel snow weights of the third spatial resolution according to the number of pixels of the third spatial resolution of the target location and the snow probability; The third snow depth historical data is obtained according to the number of pixels, the pixel snow 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 accumulation probability according to the snow cover fraction and the clearness index comprises: The snow accumulation probability is obtained by the following formula: Among them, SCP y,m is the snow accumulation probability in the mth month of the yth year; FSC y,m is the snow cover fraction in month m of year y; CI y,m is the clearness index of month m in year y; The step of obtaining the pixel snow weight of the third spatial resolution according to the number of pixels of the third spatial resolution of the target location and the snow probability comprises: The pixel snow weight is obtained by the following formula: Among them, W i,y,m is the pixel snow weight of the ith pixel in the mth month of the yth year; SCP i,y,m is the snow accumulation probability of the ith pixel in the mth month of the yth year; ∑ j∈N SCP j,y,m is the sum of the snow accumulation probabilities of N pixels in the mth month of the yth year; N is the number of pixels of the third spatial resolution of the target location; The step of obtaining the third snow depth historical data according to the number of pixels, the pixel snow weight and the second snow depth historical data comprises: The third snow depth historical data is obtained by the following formula: in, is the third snow depth historical data of the ith pixel in the mth month of the yth year; W i,y,m is the pixel snow weight of the ith pixel in the mth month of the yth year; is the historical data of the second snow depth of the ith pixel in the mth month of the yth year; N is the number of pixels of 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 according to the third snow depth historical data comprises: The third snow depth historical climatological data is obtained by the following formula: in, The third snow depth historical climatological data of the mth month; is the historical data of the third snow depth in the mth month of the yth year; Y is the total number of years of 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 of the target location according to the first snow depth prediction data, the first snow depth prediction climatic data and the third snow depth historical climatic data comprises: Obtaining first snow depth prediction anomaly data with a first spatial resolution according to the first snow depth prediction data and the first snow depth prediction climatological data; The third snow depth prediction data is obtained based on the first snow depth prediction abnormal 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 first snow depth prediction abnormal data according to the first snow depth prediction data and the first snow depth prediction climatic data comprises: The first snow depth prediction abnormal data is obtained by the following formula: in, For the yth o Year m o The first snow depth forecast anomaly data of the month; For the yth o Year m o First snow depth forecast data of the month; For the yth o Year m o First snow depth forecast climatological data of the month.

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 according to the first snow depth prediction abnormal data and the third snow depth historical climatological data comprises: The third snow depth prediction data is obtained by the following formula: in, For the yth o Year m o The third snow depth forecast data of the month; For the yth o Year m o The first snow depth forecast anomaly data of the month; It is the historical climatological data of the third snow depth in the mth month.

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