Meteorological observation sparsification parameter determination method, apparatus and device, and medium

Abstract

The invention discloses a method, a device, equipment and a medium for determining sparse parameters of meteorological observation, relates to the technical field of meteorological numerical forecasting, and aims to solve the problem that an atmospheric state displayed by high-resolution observation data cannot be accurately described by a mode due to the fact that a set sparse parameter value is selected for sparse processing in the conventional meteorological numerical forecasting. The method comprises the steps of determining an upper limit value of a sparsification parameter based on a background field water vapor correlation scale; performing horizontal grid division on the satellite observation data to obtain a plurality of sparse grid units; calculating a local standard deviation corresponding to each sparse grid unit, and determining local standard deviation probability density distribution corresponding to each sparse parameter value; a first target sparsification parameter value is determined based on the local standard deviation probability density distribution. The meteorological observation sparsification parameter determination method provided by the invention is used for determining the sparsification parameter value which enables the regional numerical forecasting assimilation mode to be matched with the resolution of the sparsified observation data, and the meteorological forecasting precision is improved.

Description

Technical Field

[0001] This invention relates to the field of meteorological numerical forecasting technology, and in particular to a method, apparatus, equipment and medium for determining sparsity parameters of meteorological observations. Background Technology

[0002] Meteorological satellite data needs to undergo sparsification before entering a regional numerical weather prediction (NMR) assimilation system. However, current technologies typically use pre-set parameters in the assimilation system for sparsification preprocessing. As satellite observation information increases, these pre-set sparsification parameters can lead to a resolution mismatch between the regional NMR assimilation model and the sparsified observation data. If the pre-set sparsification parameter values ​​are too low, the resolution of the sparsified observations remains high, failing to alleviate the correlation and redundancy between observations. Conversely, if the pre-set sparsification parameter values ​​are too high, the resolution of the sparsified observations is low, resulting in the loss of much crucial observational information. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, equipment, and medium for determining sparsification parameters in meteorological observations, which ensures that the sparsification parameter values ​​used in the regional numerical weather prediction assimilation system match the resolution of the observation data. This improves computational efficiency while retaining sufficient observation information to obtain the optimal analysis field, thus making weather forecasts more accurate.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] In a first aspect, the present invention provides a method for determining sparsity parameters in meteorological observations, comprising:

[0006] The system acquires satellite observation data of the region to be observed and system information of the regional numerical weather prediction assimilation system; the system information includes background water vapor correlation scale and multiple preset sparsification parameter values;

[0007] The upper limit of the sparsification parameter is determined based on the background field water vapor correlation scale;

[0008] The satellite observation data is divided into horizontal grids using the sparsification parameter values ​​to obtain multiple sparse grid cells;

[0009] Calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation;

[0010] The first target sparsification parameter value is determined based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value.

[0011] Optionally, the step of calculating the local standard deviation corresponding to each of the sparsed grid cells, and determining the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation, includes:

[0012] According to the formula:

[0013]

[0014] Calculate the local local deviation for each sparsed grid cell;

[0015] in, For local variance, N is the total number of observations within the sparse grid cell, and x is the local variance. i Let be the value of the i-th observation, and μ be the average value of the N observations within the sparse grid cell;

[0016] The local standard deviation corresponding to the sparse grid cell is obtained by taking the square root of the local standard deviation, and a local standard deviation probability density distribution is generated based on the local standard deviation.

[0017] Optionally, the step of determining the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value further includes:

[0018] The satellite observation data is sparsified using the first target sparsification parameter value to obtain sparsed data within the mode range;

[0019] According to the formula:

[0020]

[0021] Calculate the variance corresponding to the range of the patterns to obtain the global variance;

[0022] in, Let x be the global variance, N1 be the total number of observations in the sparse data within the model range, and x be the global variance. i1 Let μ1 be the value of the i1th observation, and μ1 be the average value of the N1 observations within the model range;

[0023] The first objective sparsification parameter value corresponding to the global variance that is less than the preset threshold is determined as the second objective sparsification parameter value.

[0024] Optionally, the step of determining the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value further includes:

[0025] Based on the regional numerical weather prediction assimilation system, the probability density distribution of the difference between the observation and the background field corresponding to the sparsification parameter value of each first target is determined;

[0026] The first objective sparsification parameter value corresponding to the probability density distribution of the difference between the observation and the background field that is closest to the normal distribution is determined as the third objective sparsification parameter value.

[0027] Optionally, the determination of the probability density distribution of the difference between the observed and background fields corresponding to each first target sparsification parameter value based on the regional numerical prediction assimilation system includes:

[0028] Formula used:

[0029]

[0030] Determine the observation-background field difference dataset corresponding to each first objective sparsification parameter value;

[0031] Where J(x) is the weighted sum of background field error and observation error, and x is the analysis variable, i.e., the analysis field generated after assimilation. b H is the background field; H is the observation operator, y o Let B be the observation field; B is the background error covariance matrix; O is the observation covariance matrix; and T represents the matrix transpose.

[0032] The probability density distribution of the observation and background field difference corresponding to the first target sparsification parameter value is determined based on the observation and background field difference dataset.

[0033] Optionally, the upper limit value is half of the background field water vapor correlation scale.

[0034] Optionally, the horizontal scale relationship between the model variable corresponding to the observation operator and the observation is 1:1.

[0035] Compared with existing technologies, this invention provides a method for determining sparsity parameters in meteorological observations. The method involves determining an upper limit for the sparsity parameters based on the background water vapor correlation scale; dividing satellite observation data into horizontal grids using the sparsity parameter values ​​to obtain multiple sparsity grid cells; calculating the local standard deviation corresponding to each sparsity grid cell; and determining the probability density distribution of the local standard deviation corresponding to each sparsity parameter value based on the local standard deviation; and determining a first target sparsity parameter value based on the upper limit and the probability density distribution of the local standard deviation corresponding to each sparsity parameter value. This application can quickly and accurately determine sparsification parameter values ​​according to requirements. Sparsification using the sparsification parameter values ​​determined by this method can reduce redundant information processing, improve computational efficiency, and retain key observational information while matching the background field. Furthermore, the upper limit of the sparsification parameters is determined based on the water vapor correlation scale of the background field of the regional numerical weather prediction assimilation system, which reduces the computational workload of solving the objective function value during assimilation. This also ensures that the observation resolution after sparsification matches the resolution of the assimilation system, allowing the regional numerical weather prediction assimilation system to better absorb observational information and reduce representativeness errors. The background field absorbs observational information and generates an initial field that is more consistent with reality, thereby improving the forecasting effect of the regional numerical weather prediction assimilation system.

[0036] Secondly, the present invention provides a device for determining sparsity parameters in meteorological observations, comprising:

[0037] The data acquisition module is used to acquire satellite observation data of the area to be observed and system information of the regional numerical weather prediction assimilation system; the system information includes background water vapor correlation scale and multiple preset sparsification parameter values;

[0038] The sparsity parameter upper limit determination module is used to determine the upper limit value of the sparsity parameter based on the background field water vapor correlation scale;

[0039] A horizontal sparse grid partitioning module is used to partition the satellite observation data into horizontal grids using the sparsification parameter values ​​to obtain multiple sparse grid cells.

[0040] The local standard deviation probability density distribution result determination module is used to calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation.

[0041] The first target sparsification parameter value determination module is used to determine the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value.

[0042] Thirdly, the present invention provides a device for determining sparsity parameters in meteorological observations, comprising:

[0043] A communication unit / interface is used to acquire satellite observation data of the area to be observed and system information of the regional numerical weather prediction assimilation system; the system information includes background water vapor correlation scale and multiple preset sparsification parameter values;

[0044] A processing unit / processor is used to determine the upper limit of the sparsification parameter based on the background field water vapor correlation scale;

[0045] The satellite observation data is divided into horizontal grids using the sparsification parameter values ​​to obtain multiple sparse grid cells;

[0046] Calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation;

[0047] The first target sparsification parameter value is determined based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value.

[0048] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed, implement the method for determining sparsity parameters of meteorological observations.

[0049] Compared with the prior art, the beneficial effects of the second aspect device-type solution, the third aspect equipment-type solution, and the fourth aspect computer-readable storage medium-type solution provided by the present invention are the same as the beneficial effects of the method for determining the sparsity parameters of meteorological observation described in the above technical solutions, and will not be repeated here. Attached Figure Description

[0050] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0051] Figure 1 A flowchart of a method for determining sparsity parameters in meteorological observations provided by the present invention;

[0052] Figure 2 This is a schematic diagram of the sparse grid cells divided using sparse parameters provided by the present invention;

[0053] Figure 3 The raw L1 level data with a mode resolution of 4km provided for this invention;

[0054] Figure 4 The original CSR data with a mode resolution of 12km provided for this invention;

[0055] Figure 5The observation distribution map provided by this invention has a sparsification parameter value of 20km.

[0056] Figure 6 The observation distribution map provided by this invention has a sparsification parameter value of 30km.

[0057] Figure 7 The observation distribution map with a sparsification parameter value of 40km provided by this invention;

[0058] Figure 8 The observation distribution map provided by this invention has a sparsification parameter value of 50km.

[0059] Figure 9 The observation distribution map with a sparsification parameter value of 60km provided by the present invention;

[0060] Figure 10 The observation distribution map with a sparsification parameter value of 70km provided by the present invention;

[0061] Figure 11 The observation distribution map with a sparsification parameter value of 80km provided by the present invention;

[0062] Figure 12 The observation distribution map with a sparsification parameter value of 200km provided by the present invention;

[0063] Figure 13 This is a schematic diagram of local difference calculation provided by the present invention;

[0064] Figure 14 A schematic diagram of the probability density distribution of local variance provided by the present invention;

[0065] Figure 15 The local standard deviation probability density distribution map corresponding to different sparsification parameter values ​​provided by the present invention;

[0066] Figure 16 A schematic diagram illustrating the variation of the global variance of the mode range with resolution, provided by the present invention.

[0067] Figure 17 This is a schematic diagram of the probability density distribution of the difference between the observation and the background field calculated after the CSR observations obtained by different degrees of sparsification are entered into the assimilation system, as provided by the present invention.

[0068] Figure 18 A schematic diagram of the structure of a sparse parameter determination device for meteorological observation provided by the present invention;

[0069] Figure 19 This is a schematic diagram of a device for determining sparsity parameters in meteorological observations, provided by the present invention. Detailed Implementation

[0070] To facilitate a clear description of the technical solutions in the embodiments of the present invention, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. For example, the first threshold and the second threshold are merely used to distinguish different thresholds and do not limit their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" are not necessarily different.

[0071] It should be noted that in this invention, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0072] In this invention, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, a combination of a and b, a combination of a and c, a combination of b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0073] Before introducing the embodiments of the present invention, the relevant terms involved in the embodiments of the present invention are first defined as follows:

[0074] The horizontal correlation characteristic scale of background error covariance is an indicator that measures the strength of the horizontal correlation of background errors and their spatial distribution. In numerical weather prediction, background error represents the difference between the initial estimate of atmospheric conditions by the numerical prediction model and the actual atmospheric conditions. The horizontal distribution and correlation of this difference have a significant impact on the performance of the assimilation system and the accuracy of numerical forecasts.

[0075] The resolution of a regional numerical weather prediction system (NMR) is influenced not only by the resolution of the regional NMR model but also by other components and elements within the system. Besides the numerical model, the data assimilation process, the accuracy and distribution of observational data, and the output format of forecast products all affect the system's resolution. System resolution reflects the overall level of detail achievable by the entire regional assimilation forecast system, from data collection and processing to model operation and the final forecast product presentation. The horizontal resolution of the NMR model and the data assimilation model is consistent, and the resolution of the analysis field output by the assimilation model is also consistent with the horizontal resolution of the NMR model, allowing it to be directly used as the initial field (initial value) for the forecast model. Therefore, as long as the horizontal resolution of the observations matches the horizontal resolution of the model (including the forecast model and the assimilation model) at the time of assimilation analysis, the forecast is considered successful.

[0076] Initial values ​​are a crucial factor affecting the accuracy of meteorological numerical weather prediction models. The initial values ​​for regional numerical weather prediction models are typically obtained by fusing various real-time observational information from the analytical field generated by global numerical weather prediction models. With the rapid development of remote sensing technology, meteorological satellite data has become an important source of observational data for numerical weather prediction, especially in areas where conventional observations (such as ground stations and radiosondes) are sparsely distributed. Before entering the numerical weather prediction assimilation system, satellite data must undergo necessary preprocessing, including data sparsification.

[0077] Currently, when performing sparsification preprocessing on satellite data before assimilation, pre-set parameters from the assimilation system are typically used. However, as the amount of satellite observation information increases, these pre-set parameters cannot meet the actual application requirements. For example, for high-resolution observation information, using pre-set, large sparsification parameters will result in excessively low observation resolution, potentially leading to the loss of important information and inaccurate final prediction results. Conversely, if the sparsification parameter value is set too small, the post-sparsed observation resolution will still be high, failing to effectively reduce redundant information processing and improve computational efficiency.

[0078] To address the aforementioned problems, this invention provides a method, apparatus, equipment, and medium for determining sparsity parameters in meteorological observations. Combining regional numerical weather prediction assimilation systems and satellite observation data, it presents a strategy for determining sparsity parameters, facilitating the rapid and accurate determination of sparsity parameter values ​​that meet practical needs during assimilation applications. The following description, in conjunction with the accompanying drawings, further clarifies the process.

[0079] join Figure 1 The present invention provides a method for determining sparsity parameters in meteorological observations, comprising the following steps:

[0080] Step 101: Obtain satellite observation data and system information of the regional numerical weather prediction assimilation system for the area to be observed;

[0081] The system information includes background field water vapor correlation scales and multiple preset sparsification parameter values; the background field water vapor correlation scale is the level correlation characteristic scale of the water vapor background error covariance corresponding to the background field water vapor field.

[0082] The sparsity parameter represents the resolution after sparsifying the observation data, i.e., the observation resolution; the preset sparsity parameter values ​​can be randomly generated by the system or set as needed. For example, multiple preset sparsity parameter values ​​can be 10km, 20km, 30km, 40km, 50km, 60km, 70km, 80km, and 200km.

[0083] Among them, satellite observation data refers to satellite observation information, which can be FY4B-AGRI clear sky radiation products (CSR products), longwave radiation products, or atmospheric sounding products, etc.

[0084] Step 102: Determine the upper limit of the sparsification parameter based on the background field water vapor correlation scale;

[0085] The upper limit is greater than or equal to one-third and less than or equal to one-half of the background field water vapor correlation scale. The background error of the water vapor field exhibits significant spatial correlation within a 100km range. To effectively assimilate the observation data and reduce errors, the horizontal resolution of water vapor observations must match the correlation scale of the background error. Setting approximately one-third to one-half of the horizontal correlation characteristic scale of the background error covariance as the upper limit of the sparsity parameter ensures that the observation data provides sufficient detail to capture water vapor variations in the background field. For example, if the horizontal correlation characteristic scale of the water vapor background error covariance is 100km, then the upper limit of the water vapor observation resolution can be set to approximately 30 to 50km, meaning the final determined upper limit of the sparsity parameter can be set to approximately 30 to 50km.

[0086] As an optional approach, the obtained multiple preset sparsification parameter values ​​are less than or equal to the upper limit value.

[0087] Step 103: Divide the satellite observation data into horizontal grids using the sparsification parameter values ​​to obtain multiple sparse grid cells;

[0088] The sparsed grid cells are nearly rhomboid in shape, and the sparsification parameter is the side length of the sparsed grid cell. Because the grid is divided on the Earth's surface, the lengths of adjacent latitude circles are inconsistent; however, when dividing on longitude circles, the lengths are consistent, resulting in sparsed grid cells that are close to rhomboid in shape. It is important to understand that the sparsed grid cell includes the observation data within that cell.

[0089] Specifically, each sparsity parameter value is used to perform a horizontal grid division of the satellite observation data, with each sparsity parameter value corresponding to multiple sparsely sampled sparse grid cells. The specific steps for grid division of satellite observation data using sparsity parameters are as follows: Figure 2 As shown, the region to be observed is first divided equally along the meridian using the sparsity parameter value. Then, at each division point, it is further divided equally along the latitude using the sparsity parameter value. The area enclosed by four adjacent division points forms a sparsity grid cell, marked as a sampling box. Only one observation is selected within this box as the observation to be included in the assimilation system after sparsification. The side length of the box is the sparsity parameter value. A larger sparsity parameter value results in a greater degree of sparsification and fewer data points after sparsification; conversely, a smaller sparsity parameter value results in a smaller degree of sparsification and more data points after sparsification.

[0090] Step 104: Calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation;

[0091] Specifically, according to formula (1):

[0092]

[0093] Calculate the local local deviation for each sparsed grid cell;

[0094] in, For local variance, N is the total number of observations within the sparse grid cell, and x is the local variance. i Let be the value of the i-th observation point, and μ be the average value of N observations within the sparse grid cell.

[0095] The local standard deviation is obtained by taking the square root of the local deviation, and the probability density distribution of the local standard deviation is generated based on the local standard deviation, thus obtaining the probability density distribution of the local standard deviation for each sparse grid cell.

[0096] Step 105: Determine the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value;

[0097] The first objective is to reduce the sparsity parameter value to a value less than or equal to the upper limit.

[0098] Specifically, the sparsification parameter value that is less than the upper limit value and whose corresponding local standard deviation probability density distribution meets the preset conditions is determined as the first target sparsification parameter value.

[0099] The preset conditions can be: the skewness of the local standard deviation probability density distribution is less than or equal to the first preset value and the peak value of the local standard deviation probability density distribution is less than or equal to the second preset value; where the skewness is the distance between the peak value and the average value of the local standard deviation probability density; the first preset value and the second preset value can be set according to the observation requirements, for example, the first preset value can be 0.4, 0.36, etc., and the second preset value can be 1.

[0100] As demonstrated by the above method, this application can quickly and accurately determine the sparsification parameter values ​​according to requirements. Moreover, the sparsification parameters are determined based on the water vapor correlation scale of the background field of the regional numerical weather prediction assimilation system. Specifically, the upper limit of the sparsification parameters is set to be greater than or equal to 1 / 3 of the correlation characteristic scale of the vapor background error covariance level and less than or equal to 1 / 2 of the correlation characteristic scale of the vapor background error covariance level. This reduces the computational workload of solving the objective function value during the assimilation process and also makes the observation resolution after sparsification match the resolution of the assimilation system. The regional numerical weather prediction assimilation model can better absorb observation information and reduce representativeness error. The background field absorbs theoretical observation information and generates an initial field that is more consistent with reality, thereby improving the forecast effect of the regional numerical weather prediction assimilation system.

[0101] As an optional approach, the determination of the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value may further include:

[0102] Step 106: Use the first target sparsification parameter value to perform sparsification processing on the satellite observation data to obtain sparsed data within the mode range;

[0103] Specifically, within the observation range, the data is divided into several small grids—sparse grid units (boxes)—using the first target sparsification parameter value. According to the assimilation mode requirements, one observation is selected from each box as the observation information of that grid after sparsification. The observation data is output according to the format requirements of the assimilation system, thereby obtaining the sparsified data within the mode range.

[0104] Step 107: According to formula (2):

[0105]

[0106] The variance corresponding to the pattern range is calculated to obtain the global variance; in order to distinguish it from the local variance, the variance corresponding to the pattern range is called the global variance.

[0107] in, Let x be the global variance, N1 be the total number of observations in the sparse data within the model range, and x be the global variance. i1 Let be the value of the i1th observation, and μ1 be the average value of the N1 observations within the model range.

[0108] Step 108: Determine the first target sparsification parameter value corresponding to the global variance that is less than the preset threshold as the second target sparsification parameter value.

[0109] Specifically, the preset threshold can be set according to the observation requirements. For example, the preset threshold can be 1.09, 1.1 or 1.11, etc.

[0110] Accuracy can be improved by further filtering the sparsification parameter values ​​of the first objective by calculating the global variance.

[0111] As an alternative approach, the first target sparsification parameter value is determined based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value. Further sparsification parameter values ​​can then be filtered through the following steps:

[0112] Step 109: Use the regional numerical prediction assimilation system to determine the probability density distribution of the difference between the observation and background fields corresponding to each first target sparsification parameter value;

[0113] Specifically, the satellite observation data of the area to be observed is first sparsified using the first target sparsification parameter to obtain sparsified data within the model range. Then, the sparsified data within the model range is input into the regional numerical prediction assimilation system. During operation, the regional numerical prediction assimilation system outputs the observation-background field difference dataset corresponding to the first target sparsification parameter. Finally, the probability density distribution of the observation-background field difference corresponding to the first target sparsification parameter value is determined based on the observation-background field difference dataset.

[0114] For example, the regional numerical weather prediction assimilation system GRAPES can be used to determine the difference dataset between the observation and background fields. The horizontal resolution of this system is 3 km. The assimilation method of this system is three-dimensional variational assimilation, which is essentially a problem of minimizing the objective function as shown in Equation (3). Equation (3) is as follows:

[0115]

[0116] Where J(x) is the weighted sum of background field error and observation error, and x is the analysis variable, i.e., the analysis field generated after assimilation. b The background field is typically generated by a global model; H is the observation operator, which is the radiative transfer model RTTOV that links model variables and observations; y o For observations, H(x) represents the observed radiation brightness temperature, while for satellite observations, H(x) represents the background field, i.e., the simulated brightness temperature of the background field. o-H(x) is the difference between the observed and background fields. Since the brightness temperature is observed, the difference between the observed and background fields is the difference between the observed brightness temperature and the brightness temperature simulated in the background field. This value will be output during system operation. B is the background error covariance matrix, O is the observation covariance matrix, and T represents matrix transpose.

[0117] Formula (3) can be used to determine the observation and background field difference dataset corresponding to each first target sparsification parameter value.

[0118] As can be seen from formula (3), the core of variational assimilation in regional numerical weather prediction assimilation systems is to compare the data from the observation points entering the assimilation system with the background field. The second term (H(x)-y) in formula (3) is calculated. o ) T O -1 (H(x)-y o When the observation points are not usually located on the grid points, i.e., the observed values ​​and the background values ​​are not in the same spatial location, spatial interpolation is required to first interpolate the grid space of the background values ​​to the observation space, and then convert the model background values ​​into the observed brightness temperature values ​​through physical transformation, i.e., converting the background values ​​into simulated brightness temperature values ​​through the radiative transfer model RTTOV, and then calculating the difference with the brightness temperature data of the observation points. If the number of observations is large, the computational load of the second term of formula (3) will increase, and the computational efficiency will decrease. At the same time, when the observations are dense, when using model grid points for horizontal interpolation, the same model grid points will be used for observations at different observation points that are close to each other. Different distance weights will be used to obtain the interpolation, which can increase the correlation and introduce representativeness error. The representativeness error is the difference between the observed and the background field. The GRAPES forecast system uses equidistant latitude and longitude grid points and Arakawa-C format in the horizontal direction. The main physical schemes of the model include the SAS (Simplified Arakawa Schubert) cumulus convection parameterization scheme, WSM6 microphysics processes, SLAB land surface processes, RRTM longwave radiation, and ECMWF shortwave radiation.

[0119] It should be noted that the regional numerical weather prediction assimilation system GRAPES selected above is only an example and is not a specific limitation. In practical applications, other types of regional numerical weather prediction assimilation systems can be used as needed to determine the difference dataset between observation and background fields.

[0120] In the variational assimilation method of the regional numerical weather prediction assimilation system, the distance at which observation information propagates in the grid space is determined by the background error covariance matrix. Therefore, the horizontal correlation characteristic scale of the background error covariance matrix is ​​one of the key factors determining the variational assimilation effect. Thus, the difference dataset between the observation and background fields is determined through the regional numerical weather prediction assimilation system, the probability density distribution of the difference between the observation and background fields is determined based on the dataset, and finally the final sparsification parameter value is determined based on the probability density distribution of the difference between the observation and background fields. This step fully considers the resolution of the background error covariance matrix. Therefore, the final sparsification parameter can enable sparsification to reduce redundant information processing, improve computational efficiency, and match the background field while retaining key observation information.

[0121] As an optional approach, determining the probability density distribution of the observation-background field difference corresponding to the first target sparsification parameter value based on the observation-background field difference dataset can include: grouping the observation-background field difference dataset according to appropriate intervals, calculating the probability of each group, and these probabilities forming the probability density distribution. Appropriate intervals can be selected as needed, or the probability of each observation-background field difference in the dataset can be calculated, and the probability density distribution can be formed based on these probabilities.

[0122] Step 110: Determine the first target sparsity parameter value corresponding to the probability density distribution of the difference between the observation and the background field that is closest to a normal distribution as the third target sparsity parameter value. Specifically, select the first target sparsity parameter value corresponding to the probability density distribution of the measurement error that is closest to zero as the third target sparsity parameter value.

[0123] Using the aforementioned regional numerical weather prediction assimilation system, the y value corresponding to each first target sparsification parameter value is calculated. o Before calculating the probability density distribution of H(x), it is necessary to confirm the horizontal scale relationship between the observations of the observation operator and the model variables, that is, the scale relationship between CSR observations and model water vapor quantity (specific humidity). Since the CSR observations of the water vapor channel are to be assimilated, model water vapor quantity is selected as the model variable.

[0124] The basic principle of the radiative transfer mode RTTOV is to solve formula (4):

[0125]

[0126] Among them, L Clr (v,θ) represents the radiation energy in the clear-sky region detected by the satellite, B(v,T) is the Planck function of channel i at an ambient temperature of T, and τ s (v,θ) represents the transmittance from the Earth's surface to the top of the atmosphere in the direction observed by the satellite, ε s (v,θ) represents the surface emissivity, and T is the average temperature of the model layer. sθ is the surface temperature, θ is the satellite observation angle, and v is the channel center wavenumber.

[0127] The key to solving formula (4) is to calculate the transmittance and solve the second term. The main feature of the RTTOV model is that it simultaneously considers the absorption of gases with uniform mixing ratios (such as carbon dioxide) and gases with variable mixing ratios (such as water vapor). For the absorption of water vapor, the corresponding forecast factors were selected, and the optical thickness related to water vapor was calculated by statistical regression, as shown in formula (5):

[0128]

[0129] Where, σ v,j For the optical thickness related to water vapor, a ν,j,k denoted as regression coefficient, X as forecast factor, j as model layer, v as channel center wavenumber, and M as the number of forecast factors.

[0130] This is further converted into the transmittance of the water vapor channel, as shown in formula (6):

[0131]

[0132] Where, τ v,j This represents the transmittance of the water vapor channel.

[0133] The change in the water vapor transmission rate is then calculated using formula (7), as shown in formula (7):

[0134]

[0135] Where, dτ ν，j This represents the change in the permeability of the water vapor channel.

[0136] The forecast factors for continuous water vapor absorption selected in formula (4) are as follows: and W represents the model's water vapor content, and T represents the average temperature of the model layer.

[0137] For formula (5), the regression coefficient a ν,j,k The magnitude of the saturation is relatively small, and the magnitude of the wetness is also relatively small, therefore the maximum optical thickness value is on the order of 10. -5Substituting this into formula (6), the transmittance value of the water vapor channel is close to 1. The change in transmittance calculated by formula (7) is also close to 1. As can be seen from formula (4), the horizontal scale of CSR observation and model water vapor quantity is in a 1:1 relationship, and the brightness temperature change caused by the change in water vapor is also in a 1:1 relationship. For example, if the resolution of brightness temperature is 40 km, the corresponding resolution of water vapor in the model is also 40 km. In the CMA-MESO assimilation system, the correlation scale of the background field error of the given water vapor is 100 km, which means that the background error of the water vapor field has significant spatial correlation within a range of 100 km. In order to effectively assimilate the observation data and reduce the error, the horizontal resolution of water vapor observation should match the correlation scale of the background error. According to relevant studies, the observation resolution should be smaller than the correlation scale of the background error to ensure that the observation data can provide enough detail to capture the water vapor changes in the background field. Specifically, the observation resolution can be set to about 1 / 3 to 1 / 2 of the correlation scale of the background error. With a water vapor background error correlation scale of 100km, the resolution of water vapor observation can be set to about 30 to 50km. Therefore, the resolution of FY-4B-AGRI-CSR observation is about 30 to 50km, which can achieve more effective variational assimilation.

[0138] Next, taking the clear-sky radiation product (CSR data) of FY4B-AGRI satellite observation data as an example, with a system resolution of 3km, a background field water vapor correlation scale of 100km, and an upper limit of the sparsity parameter value of 50km, the content of steps 103-110 will be explained in detail.

[0139] FY4B is the newest operational Fengyun geostationary satellite, which began drifting from 133°E to 105°E on February 1, 2024, to provide operational application products. CSR data is clear-sky optimized data developed by the National Satellite Meteorological Center for data assimilation. See also... Figures 3-12 Taking 12:00 on May 3, 2024 as an example, the observation distribution of FY4B-AGRI-CSR data after different degrees of sparsification is given. The range in the figure represents the model range; the actual area to be observed is larger than the model range. After sparsification, only observations falling within the model range are selected. Figure 3 and Figure 4 As shown, if the model area is mostly clear, the distribution of L1-level data and CSR data will be similar. However, if there are many clouds, the data volume will differ significantly, and the CSR distribution will clearly not include the cloud-covered portion. L1-level data refers to the data product after radiometric and coarse geometric corrections have been applied to the raw satellite observation data. Figures 5-12As shown, the greater the degree of sparsification, the less discernible the brightness temperature distribution details become in the observed distribution after sparsification. When the sparsification parameter value is 200km, the difference between cloud areas and non-cloud areas is basically indistinguishable, and the details of the brightness temperature distribution are also not visible, resulting in the loss of a lot of observation information.

[0140] See Figure 13 , Figure 2 The box in the middle can be seen as Figure 13 The local variance method calculates the variance of the observed data within a square box. If the sparsity parameter value is small, meaning the sparsity is low, the box is small, containing fewer observations. The distance between Obs2 and Obs4 is small, indicating high similarity. The values ​​of the observed data within the box are closer, resulting in a smaller calculated variance and a larger number of boxes within the model range. Conversely, if the sparsity parameter value is large, meaning the sparsity is high, the box is large, containing more observations. The distance between Obs2 and Obs4 is large, indicating lower similarity. The differences between distant observations within the box increase, resulting in a larger calculated variance and a smaller number of boxes within the model range.

[0141] For example, within the range of a regional numerical weather prediction model, the local locality probability density distributions corresponding to sparsity parameter values ​​of 10km, 20km, 30km, 40km, 50km, 60km, 70km, 80km, and 200km are as follows: Figure 14 As shown, when the sparsity parameter value is small, there are more boxes within the model range, resulting in more calculated local variances. The corresponding local variance probability density distribution curve is steeper with a shorter tail, indicating that most variance values ​​are too concentrated around the mean, i.e., concentrated near 0. The correlation between observations is high, and the purpose of sparsity is not achieved. Conversely, when the sparsity parameter value is large, there are fewer boxes within the model range, resulting in fewer calculated local variances. The variance values ​​are larger, and the corresponding local variance probability density distribution curve is too wide with a long tail, indicating that the observations deviate from the mean to a greater extent. Although the correlation is reduced, a lot of observation information is easily lost.

[0142] Because the local variance probability density distribution varies considerably, it can be converted into a local standard deviation probability density distribution. Standard deviation provides a more intuitive description of the data distribution. When the first preset value is 0.36 and the second preset value is 1, such as... Figure 15As shown in the figure, the data in parentheses represent the values ​​corresponding to the peak value, the mean value, the peak value, and the difference between the mean and mean values ​​of the local standard deviation probability density distribution. The absolute value of the difference between the peak value and the mean value is the skewness. The peak value of the local standard deviation probability density distribution corresponding to 40km is 0.85, which is less than the second preset value, and the skewness is 0.36, which is equal to the first preset value. The peak value of the local standard deviation probability density distribution corresponding to 30km is 0.51, which is less than the second preset value, and the skewness is 0.26, which is less than the first preset value. Therefore, 30km and 40km are the first target sparsification parameter values.

[0143] Combination Figures 3-12 Comparing the distributions, a sparsification parameter value of 30-40km is more in line with the requirements and also satisfies that it is less than or equal to half of the background water vapor correlation scale.

[0144] See Figure 16 In the figure, the average local variance is the global variance. Within the regional model range, different values ​​of the sparsity parameter result in different observation resolutions. The variances of observations at different resolutions after sparsification within the model range are calculated separately. For example... Figure 16 As shown, when the sparsity parameter value is ≤40km, the change in variance is not significant; when the sparsity parameter value is ≥50km, the variance increases significantly. When the sparsity parameter value is small, the number of observations retained after sparsification is also large, and a small variance value indicates small differences and high correlation between observations. When the sparsity parameter value is large, the number of observations retained after sparsification is small, the distance between observations is relatively large, the differences are large, the correlation decreases, and the representativeness error also decreases. The global variance corresponding to sparsity parameter values ​​of 10km, 20km, 30km, and 40km is less than 2, while the global variance corresponding to sparsity parameter values ​​of 50km, 60km, 70km, 80km, and 200km is greater than 2. Since the first target sparsity parameter values ​​are 30km and 40km, the selected second target sparsity parameter values ​​are 30km and 40km.

[0145] Through the above steps, it can be preliminarily confirmed that for a regional numerical weather prediction assimilation system with a system resolution of 3 km, a first target sparsity parameter value of 30 or 40 km is more suitable. To further confirm the value of the sparsity parameter, CSR observation data was assimilated using a regional numerical assimilation model. The probability density distribution of the difference between the observed brightness temperature obtained during the assimilation process and the simulated brightness temperature of the background field was compared and analyzed. (See [link to relevant documentation]). Figure 17 Within the regional model range, different values ​​of the sparsity parameter result in different observation resolutions. The variance of observations at different resolutions after sparsification within the model range is calculated. For example... Figure 17As shown, the difference between the observed brightness temperature and the simulated brightness temperature of the background field at a resolution of 40km is closer to a normal distribution, and the difference between the observed brightness temperature and the simulated brightness temperature of the background field corresponding to the peak value is closer to 0.

[0146] In summary, based on the horizontal distribution of the observations after sparsification, the probability density distribution of the local local variance under the Cronsky conditions of the difference scheme, the change of the global variance with the sparsification resolution, and the distribution of the difference between the observed brightness temperature and the simulated brightness temperature of the background field after assimilation, it can be determined that a sparsification parameter value of 40km is more suitable for a regional numerical weather prediction assimilation model with a resolution of 3km.

[0147] The embodiments of the present invention can divide functional modules according to the above method examples. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the embodiments of the present invention is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0148] When dividing each function into modules according to its corresponding function. Figure 18 A schematic diagram of a sparse parameter determination device for meteorological observation provided by the present invention is shown. Figure 18 As shown, the device includes:

[0149] The data acquisition module 181 is used to acquire satellite observation data of the area to be observed and system information of the regional numerical forecast assimilation system; the system information includes background water vapor correlation scale and multiple preset sparsification parameter values;

[0150] The sparsity parameter upper limit determination module 182 is used to determine the upper limit value of the sparsity parameter based on the background field water vapor correlation scale;

[0151] The horizontal sparse grid partitioning module 183 is used to partition the satellite observation data into a horizontal grid using the sparsification parameter value to obtain multiple sparse grid cells; the sparse grid cell is nearly rhomboid, and the sparsification parameter value is the side length of the sparse grid cell;

[0152] The local standard deviation probability density distribution result determination module 184 is used to calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation.

[0153] The first target sparsification parameter value determination module 185 is used to determine the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value.

[0154] Optionally, the local standard deviation probability density distribution result determination module 184 may specifically include:

[0155] The local variance calculation unit is used to calculate the variance based on the formula:

[0156]

[0157] Calculate the local local deviation for each sparsed grid cell;

[0158] in, For local variance, N is the total number of observations within the sparse grid cell, and x is the local variance. i Let be the value of the i-th observation, and μ be the average value of the N observations within the sparse grid cell;

[0159] The local standard deviation probability density distribution determination unit is used to calculate the local standard deviation corresponding to the sparse grid cell by taking the square root of the local standard deviation, and to generate the local standard deviation probability density distribution based on the local standard deviation.

[0160] Optionally, the apparatus further includes a second target sparsification parameter value determination module, comprising:

[0161] A sparsification processing unit is used to perform sparsification processing on the satellite observation data using the first target sparsification parameter value to obtain sparsified data within the mode range;

[0162] The global variance calculation unit is used to calculate the variance based on the formula:

[0163]

[0164] Calculate the variance corresponding to the range of the patterns to obtain the global variance;

[0165] in, Let x be the global variance, N1 be the total number of observations in the sparse data within the model range, and x be the global variance. i1 Let μ1 be the value of the i1th observation, and μ1 be the average value of the N1 observations within the model range;

[0166] The second target sparsification parameter value determination unit is used to determine the first target sparsification parameter value corresponding to the global variance that is less than a preset threshold as the second target sparsification parameter value.

[0167] Optionally, the apparatus further includes a third target sparsification parameter value determination module, including:

[0168] The regional numerical weather prediction assimilation unit is used to determine the probability density distribution of the difference between the observation and the background field corresponding to each first target sparsification parameter value based on the regional numerical weather prediction assimilation system.

[0169] The third objective sparsification parameter value determination unit is used to determine the first objective sparsification parameter value corresponding to the probability density distribution of the difference between the observation and the background field that is closest to the normal distribution as the third objective sparsification parameter value.

[0170] Optionally, the regional numerical forecast assimilation unit can be specifically used for:

[0171] Formula used:

[0172]

[0173] Determine the observation-background field difference dataset corresponding to each first objective sparsification parameter value;

[0174] Where J(x) is the weighted sum of background field error and observation error, and x is the analysis variable, i.e., the analysis field generated after assimilation. b H is the background field; H is the observation operator, y o Let B be the observation field; B is the background error covariance matrix; O is the observation covariance matrix; and T represents the matrix transpose.

[0175] The probability density distribution of the observation and background field difference corresponding to the first target sparsification parameter value is determined based on the observation and background field difference dataset.

[0176] Optionally, the upper limit value is half of the background field water vapor correlation scale.

[0177] Optionally, the horizontal scale relationship between the model variable corresponding to the observation operator and the observation is 1:1.

[0178] The above mainly describes the solutions provided by the embodiments of the present invention from the perspective of the interaction between various modules. It is understood that, in order to achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0179] When using the corresponding integrated unit Figure 19 This is a schematic diagram of a device for determining sparsity parameters in meteorological observations, provided by the present invention. Figure 19 As shown, the device includes:

[0180] A communication unit / interface is used to acquire satellite observation data of the area to be observed and system information of the regional numerical weather prediction assimilation system; the system information includes background water vapor correlation scale and multiple preset sparsification parameter values;

[0181] A processing unit / processor is used to determine the upper limit of the sparsification parameter based on the background field water vapor correlation scale;

[0182] The satellite observation data is divided into horizontal grids using the sparsification parameter values ​​to obtain multiple sparse grid cells;

[0183] Calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation;

[0184] The first target sparsification parameter value is determined based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value.

[0185] The processing unit can be a processor or controller, such as a Central Processing Unit (CPU), a general-purpose processor, a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication module can be a transceiver, transceiver circuitry, or communication interface, etc. The storage module can be a memory.

[0186] like Figure 19 As shown, the processor described above can be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. The communication interface described above can be one or more. The communication interface can use any transceiver-like device for communicating with other devices or communication networks.

[0187] like Figure 19As shown, the terminal device described above may also include a communication line. The communication line may include a path for transmitting information between the components described above.

[0188] Optional, such as Figure 19 As shown, the terminal device may further include a memory. The memory stores computer execution instructions for implementing the present invention, and the execution is controlled by a processor. The processor executes the computer execution instructions stored in the memory, thereby implementing the method provided in the embodiments of the present invention.

[0189] like Figure 19 As shown, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed discs, laser discs, optical discs, universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these. The memory can exist independently and be connected to the processor via communication lines. The memory can also be integrated with the processor.

[0190] In a specific implementation, as one example, such as Figure 19 As shown, a processor may include one or more CPUs, such as Figure 19 CPU0 and CPU1 in the CPU.

[0191] In a specific implementation, as one example, such as Figure 19 As shown, the terminal device may include multiple processors, such as Figure 19 The processors in the system. Each of these processors can be a single-core processor or a multi-core processor.

[0192] On the one hand, a computer-readable storage medium is provided, which stores instructions that, when executed, enable a method for determining sparsity parameters for meteorological observations.

[0193] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD).

[0194] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, the disclosure, and the appended claims in carrying out the claimed invention. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0195] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely exemplary descriptions of the invention as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for determining sparsity parameters in meteorological observations, characterized in that, include: Acquire satellite observation data of the area to be observed and system information of the regional numerical weather prediction assimilation system; The system information includes background water vapor correlation scales and multiple preset sparsification parameter values; The upper limit of the sparsification parameter is determined based on the background field water vapor correlation scale; The satellite observation data is divided into horizontal grids using the sparsification parameter values ​​to obtain multiple sparse grid cells; Calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation; The first target sparsification parameter value is determined based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value.

2. The method for determining sparsity parameters in meteorological observations according to claim 1, characterized in that, The calculation of the local standard deviation corresponding to each of the sparsed grid cells, and the determination of the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation, includes: According to the formula: Calculate the local locality of each sparsed grid cell; in, For local variance, N is the total number of observations within the sparse grid cell, and x is the local variance. i Let be the value of the i-th observation, and μ be the average value of the N observations within the sparse grid cell; The local standard deviation corresponding to the sparse grid cell is obtained by taking the square root of the local standard deviation, and a local standard deviation probability density distribution is generated based on the local standard deviation.

3. The method for determining sparsity parameters in meteorological observations according to claim 1, characterized in that, The process of determining the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value further includes: The satellite observation data is sparsified using the first target sparsification parameter value to obtain sparsed data within the mode range; According to the formula: Calculate the variance corresponding to the range of the patterns to obtain the global variance; in, Let x be the global variance, N1 be the total number of observations in the sparse data within the model range, and x be the global variance. i1 Let be the value of the i1th observation, and μ1 be the average value of the N1 observations within the model range; The first objective sparsification parameter value corresponding to the global variance that is less than the preset threshold is determined as the second objective sparsification parameter value.

4. The method for determining sparsity parameters in meteorological observations according to claim 1, characterized in that, The process of determining the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value further includes: Based on the regional numerical weather prediction assimilation system, the probability density distribution of the difference between the observation and the background field corresponding to the sparsification parameter value of each first target is determined; The first objective sparsification parameter value corresponding to the probability density distribution of the difference between the observation and the background field that is closest to the normal distribution is determined as the third objective sparsification parameter value.

5. The method for determining sparsity parameters in meteorological observations according to claim 4, characterized in that, The method for determining the probability density distribution of the observation-background field difference corresponding to each first target sparsification parameter value based on the regional numerical prediction assimilation system includes: Formula used: Determine the observation-background field difference dataset corresponding to each first objective sparsification parameter value; Where J(x) is the weighted sum of background field error and observation error, x is the analysis variable, i.e., the analysis field generated after assimilation, and xb is the background field; H is the observation operator, and y o Let B be the observation field; B is the background error covariance matrix; O is the observation covariance matrix; and T represents the matrix transpose. The probability density distribution of the observation and background field difference corresponding to the first target sparsification parameter value is determined based on the observation and background field difference dataset.

6. The method for determining sparsity parameters in meteorological observations according to claim 1, characterized in that, The upper limit value is half of the background field water vapor correlation scale.

7. The method for determining sparsity parameters in meteorological observations according to claim 5, characterized in that, The horizontal scale relationship between the model variable and the observation corresponding to the observation operator is 1:

1.

8. A device for determining sparsity parameters in meteorological observations, characterized in that, include: The data acquisition module is used to acquire satellite observation data of the area to be observed and system information of the regional numerical weather prediction assimilation system; The system information includes background water vapor correlation scales and multiple preset sparsification parameter values; The sparsity parameter upper limit determination module is used to determine the upper limit value of the sparsity parameter based on the background field water vapor correlation scale; A horizontal sparse grid partitioning module is used to partition the satellite observation data into horizontal grids using the sparsification parameter values ​​to obtain multiple sparse grid cells. The local standard deviation probability density distribution result determination module is used to calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation. The first target sparsification parameter value determination module is used to determine the first target sparsification parameter value based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value.

9. A device for determining sparsity parameters in meteorological observations, characterized in that, include: The communication unit / communication interface is used to acquire satellite observation data of the area to be observed and system information of the regional numerical weather prediction assimilation system. The system information includes background water vapor correlation scales and multiple preset sparsification parameter values; A processing unit / processor is used to determine the upper limit of the sparsification parameter based on the background field water vapor correlation scale; The satellite observation data is divided into horizontal grids using the sparsification parameter values ​​to obtain multiple sparse grid cells; Calculate the local standard deviation corresponding to each of the sparsed grid cells, and determine the local standard deviation probability density distribution corresponding to each sparsed parameter value based on the local standard deviation; The first target sparsification parameter value is determined based on the upper limit value and the local standard deviation probability density distribution corresponding to each sparsification parameter value.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, implement the method for determining sparsity parameters of meteorological observations as described in any one of claims 1-7.

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