Virtual rainfall station setting simulation method based on satellite rainfall data

Abstract

The invention discloses a virtual rainfall station setting simulation method based on satellite rainfall data, and the method comprises the steps: obtaining a potential setting point according to an existing initial observation station, and calculating the value probabilities of continuous rainfall variables of the initial observation station and the potential setting point in different intervals; calculating the weighted cosine similarity of the distribution function of the value probability; calculating information entropy between the initial observation point and the potential setting point; and according to the distribution characteristics and the weighted cosine similarity of the satellite rainfall information entropies of different station positions, screening out potential setting points meeting conditions as virtual rainfall stations. According to the invention, the problems of contradiction between precision and coverage range, insufficient data processing efficiency and the like in the existing rainfall information acquisition technology are solved, and more accurate, comprehensive and timely rainfall information support is provided.

Description

Technical Field

[0001] The present application relates to the field of precipitation measurement, and in particular to a virtual rain gauge setting simulation method based on satellite precipitation data. Background Art

[0002] As the core link of the natural water cycle, precipitation has a direct impact on many aspects of the economy and society due to its intensity changes and distribution characteristics. In the field of agricultural production, timely and appropriate precipitation is an indispensable natural condition for the growth of crops; in water resources management, accurate precipitation information provides a scientific basis for reservoir scheduling and optimal allocation of water resources; in disaster prevention and mitigation, timely grasp of precipitation dynamics is crucial for predicting and responding to natural disasters such as floods and mudslides. Therefore, building an efficient and accurate precipitation monitoring network is of immeasurable value for ensuring agricultural safety, promoting the rational use of water resources, and improving disaster warning capabilities. The rain gauge network is the cornerstone of this monitoring system. By setting up rain gauges in widely distributed geographical locations, continuous observation and recording of precipitation can be achieved, providing basic data support for meteorological services, hydrological research, and environmental management.

[0003] Although the rain gauge network plays an irreplaceable role in precipitation monitoring, its development and application also face many challenges. First, due to the high cost of construction and maintenance, especially in remote areas with poor economic conditions, the layout of rain gauges is often difficult to achieve the ideal density, resulting in scarce or even blank precipitation data in some areas. Secondly, factors such as complex terrain and inconvenient transportation also limit the coverage and data quality of rain gauges, especially in difficult-to-reach areas such as mountains and forests. The effective operation and maintenance of rain gauges face many difficulties. In addition, existing rain gauges often rely on manual maintenance, with low data collection and processing efficiency, and are easily affected by human factors, affecting the accuracy and timeliness of the data. The existence of these problems makes the existing rain gauge network have certain limitations in comprehensively and accurately reflecting regional precipitation conditions.

[0004] Traditional statistical methods often have difficulty accurately capturing the spatial distribution characteristics of precipitation when dealing with such complex terrain, resulting in large errors in the estimation results. Existing precipitation information acquisition technologies often face the trade-off between accuracy and coverage. Although statistical methods based on ground station networks can provide relatively high-precision precipitation data, their coverage is limited and data is seriously missing. Although satellite remote sensing technology can provide large-scale, continuous precipitation observation data, its data accuracy is relatively low, which is difficult to meet the needs of high-precision precipitation research or engineering planning. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a virtual rain gauge setting simulation method based on satellite precipitation data to solve the problems existing in the existing precipitation information acquisition technology, such as the contradiction between accuracy and coverage and insufficient data processing efficiency.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a virtual rainfall station setting simulation method based on satellite precipitation data, comprising: S1. According to the existing initial observation sites, potential setting points are obtained, and the probability of the continuous precipitation variables of the initial observation sites and the potential setting points taking values ​​in different intervals is calculated; S2. Calculate the weighted cosine similarity of the distribution function of the value probability; S3, calculating the information entropy between the initial observation point and the potential setting point; S4. According to the distribution characteristics and weighted cosine similarity of satellite precipitation information entropy at different station locations, potential setting points that meet the conditions are screened out as virtual rain gauge stations.

[0007] Further: S1 includes: S11. According to the existing initial observation sites, the center point positions of each triangulated network structure are obtained as potential setting points; S12, extracting satellite precipitation data at the initial observation point location and satellite precipitation data at the potential setting point location; S13. Compare the probability density function characteristics of the satellite precipitation data at the initial observation point and the potential setting point, and calculate the probability of the continuous precipitation variables at the initial observation site and the potential setting point taking values ​​in different intervals.

[0008] Further: In S13, the coefficient of variation is used CV , Kurtosis K and skewness S Describe the probability density function, its expression is:

[0009]

[0010]

[0011] in, and are the mean and standard deviation of the random variable, n Indicates the amount of data, represents the data mean, x i Indicates i Satellite precipitation data.

[0012] Further: In S2, the weighted cosine similarity of the distribution function of the value probability WCS The expression is:

[0013]

[0014] in, represents the probability of the continuous precipitation variable at the initial observation station taking values ​​in different intervals, The coefficient of variation in the probability density function of the continuous precipitation variable corresponding to the initial observation station CV , Kurtosis K and skewness S , represents the probability of the continuous precipitation variable at the potential setting point taking values ​​in different intervals, The coefficient of variation in the probability density function of the continuous precipitation variable corresponding to the potential set point CV , Kurtosis K and skewness S , WDP express A and B The weighted dot product of represents the diagonal weight matrix, ‖ AW ‖and‖ BW ‖ respectively represent vectors AW and BW The model, superscript T Indicates transpose.

[0015] Further: In S3, the information entropy between the initial observation point and the potential setting point The expression is:

[0016] in, X and Y denote the precipitation random variables at the initial observation point and the potential setting point, respectively, express X and Y The joint probability distribution of represents mutual information, which is used to measure the information transfer between the initial observation point and the potential setting point. m and r are the total number of initial observation points and potential setting points, j and k Respectively refer to j The initial observation point and k potential setting points.

[0017] Further: S4 includes: S41, based on the distribution characteristics and weighted cosine similarity of satellite precipitation information entropy at different station locations, screening similar initial observation points of each potential setting point and assigning calculation weights; S42, checking the information transmission strength of the potential setting points relative to the initial observation points, and further selecting points that meet the accuracy conditions from the screened potential setting points as the original virtual rainfall stations; S43, estimating the daily precipitation value of the original virtual rain gauge station according to the allocated weight; S44. The accuracy of the daily precipitation values ​​of the original virtual rain gauge is evaluated according to the Taylor score, Nash efficiency coefficient and Klinger-Gupta efficiency coefficient, and the preliminary virtual rain gauge that meets the accuracy evaluation is used as the virtual rain gauge.

[0018] Further: S41 includes: S411, calculating the satellite precipitation information entropy of each station; S412, analyzing the distribution characteristics of satellite precipitation information entropy at different station locations, bringing the information entropy into the weighted cosine similarity formula, and sequentially calculating the similarity between each potential setting point and the initial observation point; S413, screening out the initial observation points of each potential setting point that meet the similarity threshold according to the similarity between the initial observation points corresponding to each potential setting point; S414, calculating the sum of similarities of initial observation points that satisfy the similarity threshold for each potential setting point; S415 , calculating weights according to the ratio of the similarity of each potential setting point to the initial observation point that meets the similarity threshold to the total similarity.

[0019] The beneficial effects of the present invention are: 1. The present invention not only considers the direct comparison of precipitation, but also constructs potential observation points through the spatial triangulation structure and introduces the probability density function characteristics to comprehensively describe the distribution characteristics of precipitation data from three dimensions: coefficient of variation, kurtosis and skewness. It can better reflect the complexity and spatial variability of precipitation data than single-point observation or simple statistics. 2. The weighted cosine similarity calculation is proposed, and the weight distribution is introduced to make the comparison of precipitation data characteristics between different stations more precise and flexible, so as to more accurately capture the subtle differences in precipitation characteristics between stations, and provide a scientific basis for selecting similar stations and calculating weights; 3. Information entropy is used to evaluate the uncertainty of precipitation data. The amount of information transmitted between stations is quantified by calculating mutual information, thereby evaluating the impact of a station on the surrounding area. Based on information entropy, virtual rain gauges that meet the accuracy requirements are selected, which can optimize the layout of the precipitation observation network and improve the simulation accuracy of precipitation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Flowchart of the simulation method for setting up a virtual rain gauge based on satellite precipitation data.

[0021] Figure 2 Construct a triangulated network structure for the initial observation points and a schematic diagram of the spatial distribution of potential setting points.

[0022] Figure 3 Calculation diagram of the probability density distribution and similarity of satellite precipitation corresponding to the initial observation point and the potential setting point.

[0023] Figure 4 Schematic diagram of Taylor scoring results for potential set point screening and validation evaluation based on information entropy. DETAILED DESCRIPTION

[0024] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0025] like Figure 1 As shown, in one embodiment of the present invention, a virtual rain gauge station setting simulation method based on satellite precipitation data is provided, comprising: S1. According to the existing initial observation sites, potential setting points are obtained, and the probability of the continuous precipitation variables of the initial observation sites and the potential setting points taking values ​​in different intervals is calculated; S2. Calculate the weighted cosine similarity of the distribution function of the value probability; S3, calculating the information entropy between the initial observation point and the potential setting point; S4. According to the distribution characteristics and weighted cosine similarity of satellite precipitation information entropy at different station locations, potential setting points that meet the conditions are screened out as virtual rain gauge stations.

[0026] Furthermore, S1 includes: S11. According to the existing initial observation sites, the center point positions of each triangulated network structure are obtained as potential setting points; Schematic diagram of the spatial distribution of the triangulated network structure and potential setting points constructed from the initial observation points, such as Figure 2 As shown; S12, extracting satellite precipitation data at the initial observation point location and satellite precipitation data at the potential setting point location; S13. Compare the probability density function characteristics of the satellite precipitation data at the initial observation point and the potential setting point, and calculate the probability of the continuous precipitation variables at the initial observation site and the potential setting point taking values ​​in different intervals.

[0027] Specifically, in S13, the coefficient of variation is used CV , Kurtosis K and skewness S Describe the probability density function, its expression is:

[0028]

[0029]

[0030] in, and are the mean and standard deviation of the random variable, n Indicates the amount of data, represents the data mean, x i Indicates i Satellite precipitation data.

[0031] The probability density distribution results calculated using the above three key parameters are as follows Figure 3 shown.

[0032] In this embodiment, an improved calculation scheme of cosine similarity is proposed for the three key parameters of the probability distribution function, that is, the original cosine similarity is extended from two-dimensional space to three-dimensional space. At the same time, considering the weight change distribution of the parameters, a weighted cosine similarity calculation method is proposed.

[0033] Specifically, in S2, the weighted cosine similarity of the distribution function of the value probability is WCS The expression is:

[0034]

[0035] in, represents the probability of the continuous precipitation variable at the initial observation station taking values ​​in different intervals, The coefficient of variation in the probability density function of the continuous precipitation variable corresponding to the initial observation station CV , Kurtosis K and skewness S , represents the probability of the continuous precipitation variable at the potential setting point taking values ​​in different intervals, The coefficient of variation in the probability density function of the continuous precipitation variable corresponding to the potential set point CV , KurtosisK and skewness S , WDP express A and B The weighted dot product of represents the diagonal weight matrix, ‖ AW ‖and‖ BW ‖ respectively represent vectors AW and BW The model, superscript T Indicates transpose.

[0036] Information entropy is used to measure the uncertainty of information and can be used to describe the complexity and amount of information in data. By comparing the information entropy between the initial observation point and the potential setting point, the loss or gain of information in the transmission process can be evaluated. Specifically, in S3, the information entropy between the initial observation point and the potential setting point is The expression is:

[0037] in, X and Y denote the precipitation random variables at the initial observation point and the potential setting point, respectively, express X and Y The joint probability distribution of Represents mutual information, which is used to measure the information transmission between the initial observation point and the potential setting point. The value of information transmission is between 0 and 1. The larger the value, the stronger the correlation between the variables. If the two sites are independent of each other, then is 0, no information is transmitted; if the two sites are completely related, then 1 means that all information is transmitted between the two. m and r are the total number of initial observation points and potential setting points, j and k Respectively refer to j The initial observation point and k potential setting points.

[0038] Furthermore, S4 includes: S41, based on the distribution characteristics and weighted cosine similarity of satellite precipitation information entropy at different station locations, screening similar initial observation points of each potential setting point and assigning calculation weights; S42, checking the information transmission strength of the potential setting points relative to the initial observation points, and further selecting points that meet the accuracy conditions from the screened potential setting points as the original virtual rainfall stations; S43, estimating the daily precipitation value of the original virtual rain gauge station according to the allocated weight; S44. The accuracy of the daily precipitation values ​​of the original virtual rain gauge is evaluated according to the Taylor score, Nash efficiency coefficient and Klinger-Gupta efficiency coefficient, and the preliminary virtual rain gauge that meets the accuracy evaluation is used as the virtual rain gauge.

[0039] Specifically, S41 includes: S411, calculating the satellite precipitation information entropy of each station; S412, analyzing the distribution characteristics of satellite precipitation information entropy at different station locations, bringing the information entropy into the weighted cosine similarity formula, and sequentially calculating the similarity between each potential setting point and the initial observation point; S413, screening out the initial observation points of each potential setting point that meet the similarity threshold according to the similarity between the initial observation points corresponding to each potential setting point; S414, calculating the sum of similarities of initial observation points that satisfy the similarity threshold for each potential setting point; S415 , calculating weights according to the ratio of the similarity of each potential setting point to the initial observation point that meets the similarity threshold to the total similarity.

[0040] Specifically, in S42, the method for verifying the information transmission strength of the potential setting point relative to the initial observation point includes: S421, calculating the information entropy of the potential setting points after screening and the initial observation point at the nearest spatial position; S422. Compare the relative errors of the information entropy of the potential setting points after screening and the information entropy of the initial observation point at the nearest position to obtain the information transfer intensity value.

[0041] Specifically, in S43, Taylor score TSS The spatial correlation coefficient, root mean square error and standard deviation are combined to evaluate the accuracy of the precipitation expression ability of the potential setting point. The calculation formula is:

[0042] in, R is the correlation coefficient between the simulated value and the observed value, σ is the ratio of the simulated and observed standard deviations, R 0 represents the ideal optimal correlation, and the value is generally 1.

[0043] Nash efficiency coefficient NSE Used to effectively calculate the relative error between data, and the Klinger-Gupta efficiency coefficient KGE Taking into account variability and bias, the following is published:

[0044]

[0045] in, O t is the observed value, P t is the model prediction value, is the mean of the observed values, B is the ratio of the simulated and observed means; In order to verify the information transmission strength of the potential setting point relative to the initial observation point, gridded precipitation data can be used to verify the precipitation performance of the station. The gridded precipitation data is spatial gridded data formed by interpolation of dense rain gauge stations. The storage format is consistent with the satellite precipitation data, and it records the actual observed precipitation information. It can be used to verify whether the accuracy of the virtual rain gauge station set by the present invention meets the requirements.

[0046] According to the number and location of rain gauge stations used in gridded precipitation data, it can be divided into verification data 1, verification data 2 and verification data 3 in this application. The Taylor score results of potential setting point screening and verification evaluation based on information entropy are as follows: Figure 4 shown.

[0047] The technology of this application has been applied and verified in plain areas, and virtual rainfall station encryption and precipitation simulation are realized based on satellite precipitation data. More effective precipitation information can be obtained through the processing of the technology of this invention, promoting the wide coverage and precise monitoring of regional precipitation information.

[0048] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A virtual rain gauge setting simulation method based on satellite precipitation data, characterized in that: include: S1. According to the existing initial observation sites, potential setting points are obtained, and the probability of the continuous precipitation variables of the initial observation sites and the potential setting points taking values ​​in different intervals is calculated; S2. Calculate the weighted cosine similarity of the distribution function of the value probability; S3, calculating the information entropy between the initial observation point and the potential setting point; S4. According to the distribution characteristics and weighted cosine similarity of satellite precipitation information entropy at different station locations, potential setting points that meet the conditions are screened out as virtual rain gauge stations.

2. The virtual rain gauge setting simulation method based on satellite precipitation data according to claim 1 is characterized in that: S1 includes: S11. According to the existing initial observation sites, the center point positions of each triangulated network structure are obtained as potential setting points; S12, extracting satellite precipitation data at the initial observation point location and satellite precipitation data at the potential setting point location; S13. Compare the probability density function characteristics of the satellite precipitation data at the initial observation point and the potential setting point, and calculate the probability of the continuous precipitation variables at the initial observation site and the potential setting point taking values ​​in different intervals.

3. The virtual rain gauge setting simulation method based on satellite precipitation data according to claim 2 is characterized in that: In S13, the coefficient of variation is used CV , Kurtosis K and skewness S Describe the probability density function, its expression is: in, and are the mean and standard deviation of the random variable, n Indicates the amount of data, represents the data mean, x i Indicates i Satellite precipitation data.

4. The virtual rain gauge station setting simulation method based on satellite precipitation data according to claim 2 is characterized in that: In S2, the weighted cosine similarity of the distribution function of the value probability WCS The expression is: in, represents the probability of the continuous precipitation variable at the initial observation station taking values ​​in different intervals, The coefficient of variation in the probability density function of the continuous precipitation variable corresponding to the initial observation station CV , Kurtosis K and skewness S , represents the probability of the continuous precipitation variable at the potential setting point taking values ​​in different intervals, The coefficient of variation in the probability density function of the continuous precipitation variable corresponding to the potential set point CV , Kurtosis K and skewness S , WDP express A and B The weighted dot product of represents the diagonal weight matrix, ‖ AW ‖and‖ BW ‖ respectively represent vectors AW and BW The model, superscript T Indicates transpose.

5. The virtual rain gauge setting simulation method based on satellite precipitation data according to claim 1 is characterized in that: In S3, the information entropy between the initial observation point and the potential setting point The expression is: in, X and Y denote the precipitation random variables at the initial observation point and the potential setting point, respectively, express X and Y The joint probability distribution of represents mutual information, which is used to measure the information transfer between the initial observation point and the potential setting point. m and r are the total number of initial observation points and potential setting points, j and k Respectively refer to j The initial observation point and k potential setting points.

6. The virtual rain gauge station setting simulation method based on satellite precipitation data according to claim 1 is characterized in that S4 include: S41, based on the distribution characteristics and weighted cosine similarity of satellite precipitation information entropy at different station locations, screening similar initial observation points of each potential setting point and assigning calculation weights; S42, checking the information transmission strength of the potential setting points relative to the initial observation points, and further selecting points that meet the accuracy conditions from the screened potential setting points as the original virtual rainfall stations; S43, estimating the daily precipitation value of the original virtual rain gauge station according to the allocated weight; S44. The accuracy of the daily precipitation values ​​of the original virtual rain gauge is evaluated according to the Taylor score, Nash efficiency coefficient and Klinger-Gupta efficiency coefficient, and the preliminary virtual rain gauge that meets the accuracy evaluation is used as the virtual rain gauge.

7. The method for simulating the setting of virtual rain gauge stations based on satellite precipitation data according to claim 6, characterized in that: S41 includes: S411, calculating the satellite precipitation information entropy of each station; S412, analyzing the distribution characteristics of satellite precipitation information entropy at different station locations, bringing the information entropy into the weighted cosine similarity formula, and sequentially calculating the similarity between each potential setting point and the initial observation point; S413, screening out the initial observation points of each potential setting point that meet the similarity threshold according to the similarity between the initial observation points corresponding to each potential setting point; S414, calculating the sum of similarities of initial observation points that satisfy the similarity threshold for each potential setting point; S415 , calculating weights according to the ratio of the similarity of each potential setting point to the initial observation point that meets the similarity threshold to the total similarity.

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