Accumulated snow depth quality control method, device and equipment based on multi-source data

Abstract

The embodiment of the invention provides an accumulated snow depth quality control method, device and equipment based on multi-source data, and is applied to the technical field of meteorological observation. The method comprises the following steps: judging whether a current accumulated snow observation station is a station where snowfall possibly occurs or not; and if the current accumulated snow observation station is the station where snowfall possibly occurs, judging the accumulated snow depth data of the current accumulated snow observation station according to an extreme value judgment rule. In this way, a set of ground accumulated snow depth quality control method based on multi-source data can be established by combining various observation elements such as air temperature, ground temperature and solid rainfall weather phenomena and researching and utilizing the correlation among the meteorological elements when complex weather occurs based on the threshold values of different observation elements. Therefore, the timeliness requirement of business application is met, and a comprehensive quality control method for constructing snow depth time consistency and multi-element collaborative consistency through logic check is obtained.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing, in particular to the technical field of meteorological observation, and specifically relates to a method, device, and equipment for snow depth quality control based on multi-source data. Background Art

[0002] In recent years, with the continuous acceleration of meteorological modernization construction, currently, the world's largest and most comprehensive ground-based integrated meteorological observation system has been built, providing application services for weather, climate and climate change, space meteorology, and key fields. Thousands of ground meteorological observation stations have been built, which can provide daily snow depth data, air temperature, ground temperature, and current weather phenomenon data. Among them, the snow depth refers to the vertical depth of the snow from the snow surface to the ground. It is the depth (thickness) of the snow measured in the observation field when the fallen snow has not melted, in centimeters. The snow depth is an important indicator for measuring the snow cover situation and has an important impact on fields such as meteorology, climate, hydrology, and environmental research.

[0003] However, the observation and data acquisition of snow depth face a series of challenges, including limitations of observation equipment, human errors, and uncertainties in environmental conditions. At the same time, the accuracy of snow depth data is often limited to a certain extent. For example, factors such as the influence of drifting snow, uneven distribution of snow cover, and the environment may all lead to deviations in the observed data. To improve the accuracy of the data, a series of quality control measures need to be taken to determine whether the data is correct.

[0004] The current methods for observing snow depth at surface meteorological observation stations include manual observation and automatic observation. Manual observation mainly uses a measuring scale and is carried out once at 08:00 every day. When there is a need for intensified observation, two additional observation times will be added, which are 14:00 and 20:00. There are at most 3 snow depth data observation times per day. However, the method of manually observing snow depth is relatively accurate, but the observation timeliness is low. It is only observed once a day, and only two additional times are added for intensified observation, which cannot meet the requirement of continuous snow change observation. Automatic snow depth measurement mainly includes two types: laser snow depth measurement and ultrasonic snow depth measurement. Laser snow depth measurement uses the phase method for ranging, and ultrasonic snow depth measurement mainly calculates by measuring the time of ultrasonic pulse emission and return. Since the start time of observation, hourly snow depth data can be obtained. The automatic snow depth observation technology realizes continuous hourly observation through two technical means: laser or ultrasonic. However, due to the influence of observation equipment and observation environment, a large amount of suspect data is generated, which is difficult to be directly used for meteorological forecasting services. Currently, the quality control method for snow depth data in the operation is mainly based on the inspection of the coarse range values in the Moderate-resolution Imaging Spectroradiometer (MODIS) data. All the national observation stations uniformly use the range of 0 to 400 cm for real-time inspection, but it is very difficult to screen out suspect data using this range value.

[0005] Therefore, in the process of business application, there is an urgent need for a snow depth quality control method to perform real-time quality control on the observed data and meet the real-time application requirements of snow depth data in the business. Summary of the Invention

[0006] The present disclosure provides a snow depth quality control method, device, equipment, and storage medium based on multi-source data.

[0007] According to the first aspect of the present disclosure, there is provided a snow depth quality control method based on multi-source data. The method includes:

[0008] Determine whether the current snow observation station is a station where snowfall may occur;

[0009] If the current snow observation station is a station where snowfall may occur, then judge the snow depth data of the current snow observation station according to the extreme value judgment rule;

[0010] The extreme value judgment rule includes:

[0011] When the snow depth at the current snow cover observation site is greater than 0, determine whether the snow depth reaches the corresponding preset extreme value of snow depth; if the snow depth reaches the preset extreme value of snow depth, then determine whether the ground temperature at the current snow cover observation site reaches the corresponding preset extreme value of ground temperature and whether the air temperature reaches the corresponding preset extreme value of air temperature; when there is a preset weather phenomenon at the current snow cover observation site, and the ground temperature does not exceed the preset extreme value of ground temperature and the air temperature does not exceed the preset extreme value of air temperature, then the snow depth data of the current snow cover observation site is correct; if the snow depth does not reach the preset extreme value of snow depth, then determine whether there is a quality control result at 08:00 and whether the quality control result at 08:00 is correct; if there is a quality control result at 08:00 and the quality control result at 08:00 is correct, then determine whether the current snow cover observation site is an automatic observation; if the current snow cover observation site is an automatic observation, then determine whether the snow depth at 08:00 is 0; if the snow depth at 08:00 is 0, then determine whether there is the preset weather phenomenon at the current snow cover observation site from 08:00 to the current moment; when there is the preset weather phenomenon at the current snow cover observation site, then determine whether the change value of the snow depth at the current snow cover observation site increases by more than the preset extreme value of snow depth change, if not, then the snow depth data of the current snow cover observation site is correct.

[0012] As described above in the aspect and any possible implementation manner, a further implementation manner is provided, and the method further includes:

[0013] If the current snow cover observation site is a manual observation, then determine whether the change value of the snow depth at the current snow cover observation site increases by more than the preset extreme value of snow depth change, if not, then the snow depth data of the current snow cover observation site is correct.

[0014] As described above in the aspect and any possible implementation manner, a further implementation manner is provided, and the method further includes:

[0015] If the current snow cover observation site is a site where snowfall is almost impossible to occur, then determine whether the current moment at the current snow cover observation site belongs to a preset time range;

[0016] If it belongs to the preset time range, then judge the snow depth data of the current snow cover observation site according to the preset extreme value of snow depth;

[0017] If it does not belong to the preset time range, then determine whether the average ground temperature at the current snow cover observation site reaches a preset ground temperature threshold and whether the average air temperature reaches a preset air temperature threshold;

[0018] If the average ground temperature reaches the preset ground temperature threshold and the average air temperature reaches the preset air temperature threshold, the snow depth data of the current snow observation station is incorrect; otherwise, the snow depth data of the current snow observation station is suspicious.

[0019] In the above aspects and any possible implementation manners, a further implementation manner is provided. The obtaining manner of the preset extreme snow depth values includes:

[0020] Obtain the historical extreme snow depth values of the current snow observation station;

[0021] According to the historical extreme snow depth values, calculate the corresponding three - times standard deviation value or five - times standard deviation value as the preset extreme snow depth values.

[0022] In the above aspects and any possible implementation manners, a further implementation manner is provided. The determining manner of the stations where snowfall is likely to occur and the stations where snowfall is almost impossible to occur includes:

[0023] Obtain the longitude, latitude, altitude, and average snowfall of each snow observation station in the observation area;

[0024] Based on the fuzzy c - means clustering algorithm, cluster the longitude, latitude, altitude, and average snowfall of each snow observation station to determine whether each snow observation station is a station where snowfall is likely to occur or a station where snowfall is almost impossible to occur.

[0025] In the above aspects and any possible implementation manners, a further implementation manner is provided. The obtaining manner of the preset extreme ground temperature values and preset extreme air temperature values includes:

[0026] Obtain the historical snow depth, historical air temperature, and historical ground temperature of the current snow observation station;

[0027] Based on the Meta - Gaussian model, calculate the preset extreme ground temperature values and preset extreme air temperature values according to the historical snow depth, historical air temperature, and historical ground temperature.

[0028] In the above aspects and any possible implementation manners, a further implementation manner is provided. The preset weather phenomena include snow, sleet or ice pellets, snow showers or sleet showers, intermittent snowfall, continuous snowfall, snow grains, isolated stellar snow crystals, wet snow or freezing on contact with the ground, and rain and snow.

[0029] According to the second aspect of the present disclosure, a snow depth quality control device based on multi - source data is provided. The device includes:

[0030] A judgment module, configured to judge whether the current snow observation station is a station where snowfall is likely to occur;

[0031] The determination module is further configured to, if the current snow accumulation observation site is a site where snowfall is likely to occur, determine the snow depth data of the current snow accumulation observation site according to the extreme value determination rule;

[0032] The extreme value determination rule includes:

[0033] When the snow depth of the current snow accumulation observation site is greater than 0, determine whether the snow depth reaches the corresponding preset snow depth extreme value; if the snow depth reaches the preset snow depth extreme value, determine whether the ground temperature of the current snow accumulation observation site reaches the corresponding preset ground temperature extreme value and whether the air temperature reaches the corresponding preset air temperature extreme value; when there is a preset weather phenomenon at the current snow accumulation observation site, and the ground temperature does not exceed the preset ground temperature extreme value and the air temperature does not exceed the preset air temperature extreme value, the snow depth data of the current snow accumulation observation site is correct; if the snow depth does not reach the preset snow depth extreme value, determine whether there is a quality control result at 08:00 and whether the quality control result at 08:00 is correct; if there is a quality control result at 08:00 and the quality control result at 08:00 is correct, determine whether the current snow accumulation observation site is an automatic observation; if the current snow accumulation observation site is an automatic observation, determine whether the snow depth at 08:00 is 0; if the snow depth at 08:00 is 0, determine whether the preset weather phenomenon exists at the current snow accumulation observation site from 08:00 to the current moment; when the preset weather phenomenon exists at the current snow accumulation observation site, determine whether the change value of the snow depth at the current snow accumulation observation site increases by more than the preset snow depth change extreme value, and if not, the snow depth data of the current snow accumulation observation site is correct.

[0034] According to a third aspect of the present disclosure, there is provided an electronic device. The electronic device includes: a memory and a processor, and a computer program is stored on the memory, and when the processor executes the program, the method as described above is implemented.

[0035] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method as described above is implemented.

[0036] A method, apparatus, device, and storage medium for snow depth quality control based on multi-source data provided by an embodiment of the present application can determine whether the current snow observation station is a station where snowfall may occur; if the current snow observation station is a station where snowfall may occur, the snow depth data of the current snow observation station is judged according to the extreme value judgment rule; the extreme value judgment rule includes: when the snow depth of the current snow observation station is greater than 0, judge whether the snow depth reaches the corresponding preset snow depth extreme value; if the snow depth reaches the preset snow depth extreme value, judge whether the ground temperature of the current snow observation station reaches the corresponding preset ground temperature extreme value and whether the air temperature reaches the corresponding preset air temperature extreme value; when there is a preset weather phenomenon at the current snow observation station, and the ground temperature does not exceed the preset ground temperature extreme value and the air temperature does not exceed the preset air temperature extreme value, the snow depth data of the current snow observation station is correct; if the snow depth does not reach the preset snow depth extreme value, judge whether there is a quality control result at 08:00 and whether the quality control result at 08:00 is correct; if there is a quality control result at 08:00 and the quality control result at 08:00 is correct, judge whether the current snow observation station is an automatic observation; if the current snow observation station is an automatic observation, judge whether the snow depth at 08:00 is 0; if the snow depth at 08:00 is 0, judge whether there is a preset weather phenomenon at the current snow observation station from 08:00 to the current moment; when there is a preset weather phenomenon at the current snow observation station, judge whether the change value of the snow depth at the current snow observation station increases by more than the preset snow depth change extreme value, and if not, the snow depth data of the current snow observation station is correct; based on this, by combining various observation elements such as air temperature, ground temperature, and solid precipitation weather phenomena, and based on the boundary values of different observation elements, the correlation relationship between various meteorological elements during complex weather is studied and utilized to establish a set of ground snow depth quality control methods based on multi-source data, so as to meet the timeliness requirements of business applications and obtain a comprehensive quality control method that constructs the time consistency and multi-element collaborative consistency of snow depth through logical checks.

[0037] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0039] Figure 1 The flowchart of the snow depth quality control method based on multi-source data according to the embodiment of the present disclosure is shown;

[0040] Figure 2 Shows a flowchart of an automatic observation snow depth quality control method based on multi-source data according to an embodiment of the present disclosure;

[0041] Figure 3 Shows a flowchart of an artificial observation snow depth quality control method based on multi-source data according to an embodiment of the present disclosure;

[0042] Figure 4 Shows a block diagram of a snow depth quality control device based on multi-source data according to an embodiment of the present disclosure;

[0043] Figure 5 Shows a block diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure. Detailed implementation manners

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0045] In addition, the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the front and rear associated objects.

[0046] In the present disclosure, multiple observation elements such as air temperature, ground temperature, and solid precipitation weather phenomena can be combined. Based on the threshold values of different observation elements, the correlation relationships between various meteorological elements during complex weather are studied and utilized to establish a set of ground snow depth quality control methods based on multi-source data, so as to meet the timeliness requirements of business applications and obtain a comprehensive quality control method for constructing the time consistency and multi-element collaborative consistency of snow depth through logical checks.

[0047] Figure 1 Shows a flowchart of a snow depth quality control method 100 based on multi-source data according to an embodiment of the present disclosure.

[0048] In block 110, it is determined whether the current snow observation station is a station where snowfall may occur.

[0049] In some embodiments, due to the vast territory, geographical location, terrain and other environmental factors, the climate conditions vary greatly, and the likelihood of snowfall is different in different regions. Before performing snow depth quality control on the current snow cover observation site, it is necessary to determine the likelihood of snowfall at the current snow cover observation site in order to perform a more targeted snow depth quality control method on the current snow cover observation site.

[0050] In some embodiments, according to the different likelihoods of snowfall in different regions, the snow cover observation sites can be divided into sites where snowfall is likely to occur and sites where snowfall is almost impossible to occur.

[0051] In some embodiments, the determination methods for the above-mentioned sites where snowfall is likely to occur and the above-mentioned sites where snowfall is almost impossible to occur include:

[0052] Obtain the longitude, latitude, altitude and average snowfall of each snow cover observation site in the observation area;

[0053] Based on the fuzzy c-means clustering algorithm, cluster the longitude, latitude, altitude and average snowfall of each snow cover observation site to determine whether each snow cover observation site is a site where snowfall is likely to occur or a site where snowfall is almost impossible to occur.

[0054] In some embodiments, when making a regional judgment, that is, when judging whether the current snow cover observation site is a site where snowfall is likely to occur and the site where snowfall is almost impossible to occur, it can be determined based on the position of the current snow cover observation site and the judgments of each snow cover observation site in the observation area.

[0055] In some embodiments, the determination of whether each snow cover observation site in the observation area belongs to a site where snowfall is likely to occur or a site where snowfall is almost impossible to occur can be performed by clustering using the fuzzy c-means clustering algorithm and determined according to the clustering results.

[0056] For example, according to historical observation data, it is known that in the continental region north of 25°N, at least one snowfall weather phenomenon generally occurred during the climate reference period, and it can be judged as a region where snowfall is likely to occur, while the region south of 25°N can be judged as a region where snowfall is almost impossible to occur. Further, in order to better judge the snow cover results, according to the site conditions, the fuzzy c-means clustering method can also be used. According to the longitude, latitude, altitude and average snowfall over the years of the sites in the region south of 25°N, they are divided into two categories, namely, sites where snowfall is likely to occur and sites where snowfall is almost impossible to occur for clustering. According to the clustering results, the sites where snowfall is likely to occur are screened and judged according to the discrimination method of the regional sites north of 25°N, and the sites where snowfall is almost impossible to occur are judged according to the discrimination method of the region south of 25°N.

[0057] Specifically, the Fuzzy-c means (FCM) clustering algorithm is an unsupervised learning classification method that can determine the relationship between data based on membership degrees. Based on this, it can be assumed that X = {x 1 , …, x n} is the sample data set with a total of n sample sizes, which is divided into Y = {y 1 , …, y k} classes. The objective function and constraint conditions of FCM can be expressed as Equation (1) and Equation (2) respectively,

[0058]

[0059] where u ij represents the membership degree of the sample point x i to the cluster y j , m is the fuzzy exponent (m > 1), d ij is the distance between the sample point x i and the cluster center y j . Set the number of clusters to 2, input the sample data set, and cluster the sample points.

[0060] According to the embodiments of the present disclosure, through the above method, each snow cover observation site in the observation area can be further subdivided, reducing the deviation of the observation data caused by different probabilities of snowfall in different regions, thereby improving the accuracy of the data and further improving the quality of the snow depth data.

[0061] In block 120, if the current snow cover observation site is a site where snowfall is likely to occur, then the snow depth data of the current snow cover observation site is judged according to the extreme value judgment rule;

[0062] The extreme value judgment rule includes:

[0063] When the snow depth at the current snow observation station is greater than 0, determine whether the snow depth reaches the corresponding preset extreme value of snow depth; if the snow depth reaches the preset extreme value of snow depth, then determine whether the ground temperature at the current snow observation station reaches the corresponding preset extreme value of ground temperature and whether the air temperature reaches the corresponding preset extreme value of air temperature; when there is a preset weather phenomenon at the current snow observation station, and the ground temperature does not exceed the preset extreme value of ground temperature and the air temperature does not exceed the preset extreme value of air temperature, then the snow depth data of the current snow observation station is correct; if the snow depth does not reach the preset extreme value of snow depth, then determine whether there is a quality control result at 08:00 and whether the quality control result at 08:00 is correct; if there is a quality control result at 08:00 and the quality control result at 08:00 is correct, then determine whether the current snow observation station is an automatic observation; if the current snow observation station is an automatic observation, then determine whether the snow depth at 08:00 is 0; if the snow depth at 08:00 is 0, then determine whether there is a preset weather phenomenon at the current snow observation station from 08:00 to the current moment; when there is a preset weather phenomenon at the current snow observation station, then determine whether the change value of the snow depth at the current snow observation station increases by more than the preset extreme value of snow depth change, if not, then the snow depth data of the current snow observation station is correct.

[0064] In some embodiments, the boundary values of different observation elements can be formed by using the probability density function method, such as the preset extreme value of snow depth, the preset extreme value of ground temperature, the preset extreme value of air temperature, and the preset extreme value of snow depth change.

[0065] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0066] It is possible to determine whether the current snow accumulation observation station is a station where snowfall is likely to occur; if the current snow accumulation observation station is a station where snowfall is likely to occur, then the snow depth data of the current snow accumulation observation station is judged according to the extreme value judgment rule; the extreme value judgment rule includes: when the snow depth of the current snow accumulation observation station is greater than 0, it is judged whether the snow depth reaches the corresponding preset snow depth extreme value; if the snow depth reaches the preset snow depth extreme value, it is judged whether the ground temperature of the current snow accumulation observation station reaches the corresponding preset ground temperature extreme value and whether the air temperature reaches the corresponding preset air temperature extreme value; when there is a preset weather phenomenon at the current snow accumulation observation station, and the ground temperature does not exceed the preset ground temperature extreme value and the air temperature does not exceed the preset air temperature extreme value, the snow depth data of the current snow accumulation observation station is correct; if the snow depth does not reach the preset snow depth extreme value, it is judged whether there is a quality control result at 08:00 and whether the quality control result at 08:00 is correct; if there is a quality control result at 08:00 and the quality control result at 08:00 is correct, it is judged whether the current snow accumulation observation station is an automatic observation; if the current snow accumulation observation station is an automatic observation, it is judged whether the snow depth at 08:00 is 0; if the snow depth at 08:00 is 0, it is judged whether there is a preset weather phenomenon at the current snow accumulation observation station from 08:00 to the current moment; when there is a preset weather phenomenon at the current snow accumulation observation station, it is judged whether the change value of the snow depth at the current snow accumulation observation station increases by more than the preset snow depth change extreme value, and if not, the snow depth data of the current snow accumulation observation station is correct; based on this, it is possible to combine multiple observation elements such as air temperature, ground temperature, and solid precipitation weather phenomena, and based on the boundary values of different observation elements, study and utilize the correlation between various meteorological elements when complex weather occurs, and establish a set of ground snow depth quality control methods based on multi-source data, so as to meet the timeliness requirements of business applications and obtain a comprehensive quality control method that constructs the time consistency and multi-element collaborative consistency of snow depth through logical checks.

[0067] In some embodiments, the above method further includes:

[0068] If the current snow accumulation observation station is an artificial observation, it is judged whether the change value of the snow depth at the current snow accumulation observation station increases by more than the preset snow depth change extreme value, and if not, the snow depth data of the current snow accumulation observation station is correct.

[0069] In some embodiments, based on the differences in observation timeliness and observation technical means of the current ground meteorological observation station for observing snow depth, sites are screened according to the snow accumulation observation method, which is divided into two categories: artificial observation and automatic observation. For the problem that the artificial observation data is relatively accurate but the observation timeliness is low, it is possible to directly judge whether the change value of the snow depth at the current snow accumulation observation station increases by more than the preset snow depth change extreme value to determine whether the snow depth data of the current snow accumulation observation station is correct.

[0070] In some embodiments, the above method further includes:

[0071] If the current snow accumulation observation site is a site where snowfall is almost impossible to occur, determine whether the current moment of the current snow accumulation observation site belongs to a preset time range;

[0072] If it belongs to the preset time range, judge the snow depth data of the current snow accumulation observation site according to the preset extreme value of snow depth;

[0073] If it does not belong to the preset time range, judge whether the average ground temperature of the current snow accumulation observation site reaches the preset ground temperature threshold and whether the average air temperature reaches the preset air temperature threshold;

[0074] If the average ground temperature reaches the preset ground temperature threshold and the average air temperature reaches the preset air temperature threshold, the snow depth data of the current snow accumulation observation site is incorrect; otherwise, the snow depth data of the current snow accumulation observation site is suspicious.

[0075] In some embodiments, based on the influence of seasonal changes, the observation period can be further subdivided to reduce the deviation of observation data caused by different snowfall probabilities in different observation periods, thereby improving the accuracy of the data and further improving the quality of the snow depth data. For example, the preset time range can be set as November - February, that is, the period with a relatively high probability of snowfall.

[0076] In some embodiments, the preset ground temperature threshold, the preset air temperature threshold, and the preset extreme value of snow depth change can be set according to user requirements. For example, the preset ground temperature threshold can be set to 30°C, the preset air temperature threshold can be set to 25°C, and the preset extreme value of snow depth change can be 15 cm.

[0077] In some embodiments, the method for obtaining the above preset extreme value of snow depth includes:

[0078] Obtain the historical extreme value of snow depth of the current snow accumulation observation site;

[0079] According to the historical extreme value of snow depth, calculate the corresponding three - times standard deviation or five - times standard deviation as the preset extreme value of snow depth.

[0080] In some embodiments, the extreme value of snow depth of the current snow accumulation observation site can be obtained by establishing the extreme values of snow depth of each snow accumulation observation site.

[0081] For example, based on the extreme value data of snow depth recorded each year since the establishment of 2474 ground snow accumulation observation sites in the current observation area, assuming X = {x 1 ,…,x n} is the sample data set of the maximum snow depth at a certain site over n years, with a total of n sample sizes. The three-sigma law formula (3) can be used to establish the three times standard deviation (i.e., μ + 3σ) of the snow depth extreme value data at each snow observation site, forming the variation range of the snow depth extreme value at the site.

[0082] P snowmax (μ - 3σ, μ + 3σ)(3)

[0083] Where μ is the standard deviation of the sample set and σ is the variance.

[0084] In some embodiments, since snowfall is greatly affected by extreme weather, the snow depth value may exceed the historical extreme value under harsh winter conditions. Therefore, the five times standard deviation (i.e., μ + 5σ) of the site can be further calculated as the basis for judging the snow depth extreme value of the site.

[0085] In some embodiments, when the current snow observation site is a site where snowfall is almost impossible to occur and the current moment of the current snow observation site belongs to the preset time range, the snow depth data of the current snow observation site is judged according to the preset snow depth extreme value, which specifically includes:

[0086] Compare the snow depth at this time with the sizes of μ + 3σ and μ + 5σ, and judge whether the snow depth at this time is greater than μ + 3σ. If not, the snow depth data of the current snow observation site is correct. If so, then judge whether the snow depth at this time is greater than μ + 5σ. If not, the snow depth data of the current snow observation site is suspicious. If so, the snow depth data of the current snow observation site is incorrect.

[0087] In some embodiments, the acquisition methods of the above-mentioned preset ground temperature extreme value and preset air temperature extreme value include:

[0088] Obtain the historical snow depth, historical air temperature, and historical ground temperature of the current snow observation site;

[0089] Based on the Meta-Gaussian model, calculate the preset ground temperature extreme value and preset air temperature extreme value according to the historical snow depth, historical air temperature, and historical ground temperature.

[0090] In some embodiments, due to the generation and accumulation of snow, that is, the process of the phase conversion between rain and snow is often closely related to the air temperature and ground temperature in the actual weather. Snowfall needs to reach certain temperature conditions during the formation process. When it reaches the ground, if it needs to retain the solid form, the ground temperature also needs to meet certain conditions to ensure the formation of snow and prevent it from sublimating or melting. Therefore, the deviation of the observation data can be reduced by establishing the extreme values of the air temperature and surface temperature, that is, the ground temperature, at each snow observation site, thereby improving the accuracy of the data and further improving the quality of the snow depth data.

[0091] In some embodiments, during the process of snow depth generation, the air temperature and ground temperature need to meet certain conditions. Since the geographical locations and environments of ground stations vary, the conditions to be met are also different. Therefore, for accurate judgment, the Meta-Guassian model can be used to establish corresponding extreme value judgments for each snow observation station in turn.

[0092] In some embodiments, the hourly air temperature, surface temperature, and snow depth values in winter can be extracted as the sample size when the quality control codes of 2474 stations in the past four years are correct. Assume that T = {t 1 , …, t n} is the air temperature data set that meets the conditions of a certain station, G = {g 1 , …, g n} is the ground temperature data set that meets the conditions of this station, and Y = {y 1 , …, y n} is the snow depth data set that meets the conditions of this station. The probability density distribution functions of snow depth and air temperature, and snow depth and ground temperature are established respectively using Equation (4).

[0093]

[0094] Among them, μ represents the mean value between variables Y and T, G, and σ represents the variance between variables Y and T, G.

[0095] In some embodiments, the above preset weather phenomena include snow, sleet or ice pellets, snow showers or sleet showers, intermittent snowfall, continuous snowfall, snow grains, isolated stellar snow crystals, wet snow or freezing on contact with the ground, and rain and snow.

[0096] In some embodiments, since the generation of snow depth is necessarily accompanied by the occurrence of relevant weather phenomena, the precipitation phenomenon instrument of the meteorological observation station can establish a model between the particle diameter, velocity and precipitation type according to the precipitation type characteristics, and match the diameter and falling velocity of the precipitation particles detected by the sensor in the model, so as to identify the precipitation type in the observation area, such as weather phenomena such as rain, sleet, hail, snow, etc. Therefore, by judging the corresponding values of the weather phenomena and combining with the weather phenomenon data, the observation data deviation can be reduced, thereby improving the accuracy of the data and further improving the quality of the snow depth data.

[0097] In some embodiments, the current weather phenomena of the ground station are recorded in numerical form. Referring to Table 1, the snow-related snow weather phenomena can be screened out as the judgment basis, and the set of weather phenomenon values is marked as W.

[0098] Table 1: Description and corresponding values of current weather phenomena related to snow

[0099]

[0100]

[0101] As Figure 2 shown, for the convenience of overall understanding, the automatic observation snow depth quality control method based on multi-source data will be elaborated in detail as follows:

[0102] Divide the regions. For the regions south of 25°N, for the stations where snowfall is almost impossible to occur, from November to February, the snow depth quality control can be carried out according to the preset extreme values of snow depth; from March to October, judge whether the average ground temperature in the previous three hours of this time reaches above 30°C and the air temperature reaches above 25°C. If so, the snow depth data of the current snow observation station is incorrect. If not, the snow depth data of the current snow observation station is suspicious.

[0103] For the stations where snowfall may occur south of 25°N and the stations where snowfall may occur in the regions north of 25°N, if the snow depth of the current snow observation station reaches μ + 3σ, judge whether the limit value reaches the error flag, that is, judge whether the limit value reaches μ + 5σ. If so, the snow depth data of the current snow observation station is incorrect. If not, calculate the average ground temperature and air temperature within the previous three hours of the record point, and judge whether the average ground temperature reaches above 30°C and the air temperature reaches above 25°C. If so, the snow depth data of the current snow observation station is incorrect. If not, judge whether one of the obtained ground temperature or air temperature values reaches (is lower than) the condition. If so, the snow depth data of the current snow observation station is correct. If not, the snow depth data of the current snow observation station is suspicious. If the snow depth of the current snow observation station does not reach μ + 3σ, judge whether it is 08:00. If so, calculate the average ground temperature and air temperature within the previous three hours of the record point for judgment. If not, judge whether there is a quality control result at 08:00 within 24 hours. If so, judge whether the quality control result is correct. If not, calculate the average ground temperature and air temperature within the previous three hours of the record point for judgment. If so, judge whether the snow depth at 08:00 is 0. If not, directly judge whether the change value of the snow depth increases by 15 cm. If so, judge whether there is a snow weather phenomenon from 08:00 to this moment. If not, the snow depth data of the current snow observation station is incorrect; if so, judge whether the change value of the snow depth increases by 15 cm. If not, the snow depth data of the current snow observation station is correct. If so, calculate the average ground temperature and air temperature within the previous three hours of the record point for judgment.

[0104] As Figure 3 shown, for the convenience of overall understanding, the manual observation snow depth quality control method based on multi-source data will be elaborated in detail as follows:

[0105] Sub-region: For stations where snowfall is almost impossible to occur in the area south of 25°N, from November to February, the snow depth quality control can be carried out according to the preset extreme value of snow depth; from March to October, it is judged whether the average ground temperature in the first three hours before this time reaches above 30°C and the air temperature reaches above 25°C. If so, the snow depth data of the current snow observation station is incorrect. If not, the snow depth data of the current snow observation station is suspicious.

[0106] South of 25°N, for stations where snowfall may occur, and stations where snowfall may occur in the area north of 25°N, if the snow depth of the current snow observation station reaches μ + 3σ, it is judged whether the limit value reaches the error mark, that is, whether the limit value reaches μ + 5σ. If so, the snow depth data of the current snow observation station is incorrect. If not, calculate the average ground temperature and air temperature within the first three hours before the record point, and judge whether the average ground temperature reaches above 30°C and the air temperature reaches above 25°C. If so, the snow depth data of the current snow observation station is incorrect. If not, judge whether one of the obtained ground temperature or air temperature values reaches (is lower than) the condition. If so, the snow depth data of the current snow observation station is correct. If not, the snow depth data of the current snow observation station is suspicious. If the snow depth of the current snow observation station does not reach μ + 3σ, it is judged whether it is 08:00. If so, calculate the average ground temperature and air temperature within the first three hours before the record point for judgment. If not, judge whether there is a quality control result at 08:00 within 24 hours. If so, judge whether the quality control result is correct. If not, calculate the average ground temperature and air temperature within the first three hours before the record point for judgment. If so, directly judge whether the change value of the snow depth increases by 15 cm. If not, the snow depth data of the current snow observation station is correct. If so, calculate the average ground temperature and air temperature within the first three hours before the record point for judgment.

[0107] To further test and evaluate the above automatic observation snow depth quality control method based on multi-source data, the self-evaluation of the detection correct rate of the above automatic observation snow depth quality control method based on multi-source data can be carried out by using the hourly snow depth data of the ground station from November 28, 2022 to April 10, 2023. During this period, a total of 1126 replies were obtained for the algorithm to quality control abnormal snow depth data. Among the 1126 replies, it was confirmed that there were 1019 snowmelt precipitation, 484 snow depth suspicious error data were caused by equipment failures, and 535 were data outliers caused by environmental or human reasons. It can be concluded that the detection correct rate of snow depth suspicious errors of the above automatic observation snow depth quality control method based on multi-source data is 90.49%.

[0108] It should be noted that for the snow depth anomaly data that fails to be detected, the above-mentioned automatic observation snow depth quality control method based on multi-source data can also perform algorithm customization according to different regions and environmental conditions, so as to improve the accuracy of the data and further improve the quality of the snow depth data.

[0109] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0110] The above is the introduction of the method embodiments. The following further describes the solution of the present disclosure through device embodiments.

[0111] Figure 4 A block diagram of a snow depth quality control device 400 based on multi-source data according to an embodiment of the present disclosure is shown. As Figure 4 shown, the device 400 includes:

[0112] A judgment module 410, configured to judge whether the current snow observation site is a site where snowfall may occur;

[0113] The judgment module 410 is further configured to, if the current snow observation site is a site where snowfall may occur, judge the snow depth data of the current snow observation site according to the extreme value judgment rule;

[0114] The extreme value judgment rule includes:

[0115] When the snow depth at the current snow observation station is greater than 0, determine whether the snow depth reaches the corresponding preset extreme value of snow depth; if the snow depth reaches the preset extreme value of snow depth, then determine whether the ground temperature at the current snow observation station reaches the corresponding preset extreme value of ground temperature and whether the air temperature reaches the corresponding preset extreme value of air temperature; when there is a preset weather phenomenon at the current snow observation station and the ground temperature does not exceed the preset extreme value of ground temperature and the air temperature does not exceed the preset extreme value of air temperature, then the snow depth data of the current snow observation station is correct; if the snow depth does not reach the preset extreme value of snow depth, then determine whether there is a quality control result at 08:00 and whether the quality control result at 08:00 is correct; if there is a quality control result at 08:00 and the quality control result at 08:00 is correct, then determine whether the current snow observation station is an automatic observation; if the current snow observation station is an automatic observation, then determine whether the snow depth at 08:00 is 0; if the snow depth at 08:00 is 0, then determine whether there is a preset weather phenomenon at the current snow observation station from 08:00 to the current moment; when there is the preset weather phenomenon at the current snow observation station, then determine whether the change value of the snow depth at the current snow observation station increases by more than the preset extreme value of snow depth change, and if not, the snow depth data of the current snow observation station is correct.

[0116] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the foregoing method embodiment and will not be elaborated herein.

[0117] In the technical solution of the present disclosure, the acquisition, storage, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0118] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0119] Figure 5 The block diagram of an exemplary electronic device 500 capable of implementing the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0120] The electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to computer programs stored in the ROM 502 or computer programs loaded from the storage unit 508 into the RAM 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. The I / O interface 505 is also connected to the bus 504.

[0121] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, an optical disc, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0122] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 508.

[0123] In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the method 100 described above can be executed. Alternatively, in other embodiments, the computing unit 501 can be configured to execute method 100 in any other appropriate manner (e.g., by means of firmware).

[0124] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, executed partially on the machine as an independent software package and partially on a remote machine, or executed entirely on a remote machine or server.

[0126] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0128] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), and the Internet.

[0129] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, can also be a server of a distributed system, or a server incorporating a blockchain.

[0130] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this is not limited herein.

[0131] The above specific implementation manners do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A snow depth quality control method based on multi-source data, characterized in that: include: Determine whether the current snow observation site is a site where snowfall may occur; If the current snow observation site is a site where snowfall may occur, the snow depth data of the current snow observation site is judged according to the extreme value judgment rule; The extreme value judgment rules include: When the snow depth at the current snow observation site is greater than 0, determine whether the snow depth has reached the corresponding preset snow depth extreme value; if the snow depth has reached the preset snow depth extreme value, determine whether the ground temperature at the current snow observation site has reached the corresponding preset ground temperature extreme value, and whether the air temperature has reached the corresponding preset air temperature extreme value; when the current snow observation site has a preset weather phenomenon, and the ground temperature does not exceed the preset ground temperature extreme value, and the air temperature does not exceed the preset air temperature extreme value, then the snow depth data of the current snow observation site is correct; if the snow depth has not reached the preset snow depth extreme value, determine whether the current snow observation site has 08:00 quality If the quality control result at 08 o'clock is correct, then determine whether the current snow observation site is automatically observed; if the current snow observation site is automatically observed, then determine whether the snow depth at 08 o'clock is 0; if the snow depth at 08 o'clock is 0, then determine whether the preset weather phenomenon exists at the current snow observation site from 08 o'clock to the present moment; when the preset weather phenomenon exists at the current snow observation site, then determine whether the snow depth change value of the current snow observation site increases by more than the preset snow depth change extreme value, if not, the snow depth data of the current snow observation site is correct.

2. The method according to claim 1, characterized in that The method further comprises: If the current snow observation site is manually observed, it is determined whether the snow depth change value of the current snow observation site increases by more than the preset snow depth change extreme value. If not, the snow depth data of the current snow observation site is correct.

3. The method according to claim 2, characterized in that The method further comprises: If the current snow observation site is a site where snowfall is almost impossible, determining whether the current time of the current snow observation site falls within a preset time range; If it is within the preset time range, the snow depth data of the current snow observation site is judged according to the preset snow depth extreme value; If it does not fall within the preset time range, determining whether the average ground temperature of the current snow observation site reaches the preset ground temperature threshold and whether the average air temperature reaches the preset air temperature threshold; If the average ground temperature reaches the preset ground temperature threshold and the average air temperature reaches the preset air temperature threshold, the snow depth data of the current snow observation site is wrong; otherwise, the snow depth data of the current snow observation site is suspicious.

4. The method according to any one of claims 1 to 3, characterized in that: The method for obtaining the preset extreme value of snow depth includes: Obtain the historical extreme snow depth of the current snow observation site; According to the historical snow depth extreme value, a corresponding three times standard deviation value or five times standard deviation value is calculated as a preset snow depth extreme value.

5. The method according to claim 3, characterized in that: The method for determining the site where snowfall is likely to occur and the site where snowfall is unlikely to occur includes: Obtain the latitude and longitude, altitude and average snowfall value of each snow observation station in the observation area; Based on the fuzzy mean clustering algorithm, the latitude and longitude, altitude and average snowfall value of each snow observation station are clustered to determine whether each snow observation station is a station where snowfall may occur or a station where snowfall is almost impossible.

6. The method according to any one of claims 1 to 3, characterized in that: The method for obtaining the preset ground temperature extreme value and the preset air temperature extreme value includes: Obtain the historical snow depth, historical air temperature and historical ground temperature of the current snow observation site; Based on the Meta-Gaussian model, the preset ground temperature extreme value and the preset air temperature extreme value are calculated according to the historical snow depth, historical air temperature and historical ground temperature.

7. The method according to any one of claims 1 to 3, characterized in that: The preset weather phenomena include snow, sleet or ice pellets, snow showers or sleet showers, intermittent snowfall, continuous snowfall, sleet, isolated star-shaped snow crystals, wet snow or freezing of objects on the ground, and rain plus snow.

8. A snow depth quality control device based on multi-source data, characterized in that: include: A judgment module is used to judge whether the current snow observation site is a site where snowfall may occur; The judgment module is further used to judge the snow depth data of the current snow observation site according to the extreme value judgment rule if the current snow observation site is a site where snowfall may occur; The extreme value judgment rules include: When the snow depth at the current snow observation site is greater than 0, determine whether the snow depth has reached the corresponding preset snow depth extreme value; if the snow depth has reached the preset snow depth extreme value, determine whether the ground temperature at the current snow observation site has reached the corresponding preset ground temperature extreme value, and whether the air temperature has reached the corresponding preset air temperature extreme value; when the current snow observation site has a preset weather phenomenon, and the ground temperature does not exceed the preset ground temperature extreme value, and the air temperature does not exceed the preset air temperature extreme value, then the snow depth data of the current snow observation site is correct; if the snow depth has not reached the preset snow depth extreme value, determine whether the current snow observation site has 08:00 quality If the quality control result at 08 o'clock is correct, then determine whether the current snow observation site is automatically observed; if the current snow observation site is automatically observed, then determine whether the snow depth at 08 o'clock is 0; if the snow depth at 08 o'clock is 0, then determine whether the preset weather phenomenon exists at the current snow observation site from 08 o'clock to the present moment; when the preset weather phenomenon exists at the current snow observation site, then determine whether the snow depth change value of the current snow observation site increases by more than the preset snow depth change extreme value, if not, the snow depth data of the current snow observation site is correct.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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