Air cloud water resource evaluation method and system based on water mass conservation model

Abstract

The application discloses an air cloud water resource evaluation method and system based on a water substance conservation model, relates to the technical field of atmospheric water cycle analysis, and is used for solving the problems that the existing cloud water resource evaluation method depends on a single data source, is difficult to reflect the atmospheric water substance balance relationship and lacks dynamic evaluation capability; the method comprises the following steps: multi-source cloud water observation fusion, cloud water mass conservation modeling, cloud structure inversion reconstruction, cloud water resource dynamic estimation and cloud water development potential evaluation; by constructing the water substance conservation model and combining multi-source observation data, three-dimensional inversion of the cloud structure and dynamic estimation of the cloud water resource are realized, the physical consistency and reliability of the cloud water resource evaluation result are improved, and decision support can be provided for artificial weather modification operation and regional water resource management.

Description

Technical Field

[0001] This invention relates to the field of atmospheric water cycle analysis technology, specifically to a method and system for assessing atmospheric cloud water resources based on a water conservation model. Background Technology

[0002] The method for assessing atmospheric cloud water resources based on the water conservation model refers to a technical approach that utilizes the conservation relationship of atmospheric water to model the transformation process between water vapor, condensate, and precipitation in the regional atmosphere, and combines multi-source meteorological observation data to quantitatively calculate and assess atmospheric cloud water resources. By reasonably assessing cloud water resources, important decision-making basis can be provided for artificial rain (snow) enhancement operations, regional water resource regulation, and climate and environmental research.

[0003] Existing cloud water resource assessment methods typically rely primarily on observational data from a single source, such as satellite remote sensing cloud water path data or numerical weather model simulation results, and estimate regional cloud water resources through empirical relationships or statistical methods. However, because the atmospheric water cycle involves multiple complex processes such as water vapor transport, cloud physical transformation, and precipitation formation, relying solely on a single data source or static estimation methods often fails to accurately reflect the true balance of atmospheric water resources within a region.

[0004] Furthermore, existing technologies for cloud water resource assessment typically focus on static estimation of cloud water content, lacking comprehensive analysis of the three-dimensional distribution and temporal evolution of cloud water structure. For example, when assessing the potential for artificial rain enhancement in a certain area, relying solely on cloud water path data at a single moment may fail to identify the continuous replenishment of cloud water resources or water vapor transport conditions, thus affecting the reliability of the assessment results. Simultaneously, since the formation and changes of cloud water resources are influenced by multiple factors such as water vapor transport, condensation processes, and precipitation consumption, failure to consider the conservation of atmospheric water matter during estimation may lead to discrepancies between the calculated cloud water resource quantity and the actual atmospheric water cycle, thereby reducing the accuracy of the assessment.

[0005] Therefore, how to establish a cloud water resource assessment method that satisfies the water conservation relationship based on multi-source observation data, and realize cloud structure reconstruction and dynamic estimation of cloud water resources, has become an important technical problem that urgently needs to be solved in the field of cloud water resource assessment. Summary of the Invention

[0006] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a method and system for assessing atmospheric cloud water resources based on a water conservation model. The method includes the following steps:

[0007] Step S1: Multi-source cloud and water observation fusion;

[0008] Step S2: Cloud-water mass conservation modeling;

[0009] Step S3: Cloud structure inversion and reconstruction;

[0010] Step S4: Dynamic estimation of cloud water resources;

[0011] Step S5: Assessment of Yunshui's development potential.

[0012] Further, in step S1, the multi-source cloud and water observation fusion is used to acquire multi-source meteorological observation data related to cloud and water resources within the study area, and to uniformly process the observation data to form a basic cloud and water data set for subsequent calculations. Specifically, it acquires satellite remote sensing cloud product data, ground-based meteorological observation data, atmospheric reanalysis data, and surface precipitation observation data, and performs spatial grid unification, time series alignment, and data quality control processing on the data to obtain a multi-source cloud and water observation data set.

[0013] The multi-source cloud and water observation data set specifically includes cloud and water path data, water vapor content distribution data, horizontal wind field data, precipitation observation data, and surface evaporation data.

[0014] Furthermore, in step S2, the cloud water mass conservation modeling is used to establish a mass budget relationship model of atmospheric water substances within the study area and study period. Specifically, based on the transformation relationship between water vapor, condensate and precipitation in the atmospheric water cycle, a cloud water mass conservation relationship model is established for atmospheric water substances in the study area to describe the input, output and phase change process of water substances within the region.

[0015] In the cloud water mass conservation model, the change in atmospheric water condensate mass is composed of a state term, an advection term, and a source-sink term. The state term is used to characterize the atmospheric water condensate state quantity at the beginning and end of the study period; the advection term is used to characterize the transport changes of atmospheric water condensate at the boundary of the study area; and the source-sink term is used to characterize the cloud physics processes of water vapor condensation, water condensate evaporation or sublimation, and precipitation.

[0016] Further, in step S3, the cloud structure inversion and reconstruction is used to reconstruct the atmospheric cloud structure distribution in the study area based on multi-source cloud and water observation data under the constraint of cloud and water mass conservation. Specifically, based on the multi-source cloud and water observation data set obtained in step S1 and the cloud and water mass conservation relationship model established in step S2, the cloud and water state variables in the study area are spatially reconstructed to obtain three-dimensional cloud and water structure data.

[0017] The cloud structure inversion and reconstruction is achieved through a multi-source cloud structure inversion method based on mass conservation constraints, including the following steps:

[0018] Step S31: Initial cloud and water field construction. Based on the water vapor content distribution data and relative humidity data in the multi-source cloud and water observation data obtained in Step S1, cloud areas are determined for each spatial grid in the study area, the areas where clouds exist are identified, and the initial cloud water content of each spatial grid is calculated based on the cloud area determination results to construct the initial three-dimensional cloud and water distribution field of the study area.

[0019] Step S32: Vertical cloud structure layer inversion. Based on the cloud water path data obtained in step S1 and the initial cloud water three-dimensional distribution field obtained in step S31, vertical structure inversion calculation is performed on each cloud area grid in the study area. By establishing the cloud water content distribution function in the vertical direction, the cloud water content of each spatial grid at different height layers is calculated, and the cloud height distribution data of the study area is obtained.

[0020] Step S33: Estimation of cloud particle structure parameters. Based on the spatial distribution data of cloud water content obtained in step S32, the cloud particle size distribution model is established, and the cloud particle concentration parameters and particle size distribution parameters are inverted and calculated to obtain the cloud particle structure parameter data of the study area.

[0021] Step S34: Water substance conservation residual correction. Based on the cloud water mass conservation relationship model established in step S2, the cloud water content spatial distribution data obtained in steps S32 and S33 are subjected to conservation consistency test, and the cloud water mass conservation residual is calculated according to the water substance mass balance relationship. The cloud water content distribution is corrected by residual correction calculation to obtain cloud water content spatial distribution data that meets the water substance conservation conditions.

[0022] Step S35: 3D cloud structure fusion and reconstruction. Based on the spatial distribution data of cloud water content corrected in step S34, the cloud particle structure parameter data obtained in step S33, and the cloud height distribution data obtained in step S32, the 3D cloud structure of the study area is fused and calculated. The cloud water path data is obtained by calculating the integral of cloud water content in the vertical direction, thus forming the 3D cloud water structure data.

[0023] The cloud and water three-dimensional structure data specifically includes cloud water content spatial distribution data, cloud particle structure parameter data, cloud layer height distribution data, and cloud and water path data.

[0024] Furthermore, in step S4, the dynamic estimation of cloud water resources is used to quantitatively estimate the atmospheric cloud water resources in the study area based on cloud structure data and water conservation relationship. Specifically, based on the three-dimensional cloud water structure data obtained in step S3 and the cloud water mass conservation relationship model established in step S2, the total amount of atmospheric water condensate in the study area during the study period is calculated, and the amount of atmospheric cloud water resources is further calculated. In the calculation process, the cloud water resource assessment result data is obtained by calculating the composition of cloud water resources.

[0025] The cloud water resource assessment results include data on total atmospheric water condensate, data on atmospheric cloud water resources, precipitation efficiency, water vapor condensation efficiency, cloud water renewal cycle, and cloud water resource change trends.

[0026] Among them, the total amount of atmospheric water condensate is used to characterize the total amount of cloud water, cloud ice and other water condensate in the atmosphere of the study area at the current time; the amount of airborne cloud water resources is used to characterize the amount of effective cloud water resources that still have the potential for subsequent precipitation conversion or artificial weather modification after deducting the amount of low-effective cloud water from the total amount of atmospheric water condensate.

[0027] The airborne cloud water resource quantity is used to characterize the total amount of effective atmospheric water condensate in the study area that remains in the atmosphere during the study period and has the potential for subsequent precipitation conversion or weather modification.

[0028] In step S4, the dynamic estimation of cloud water resources is achieved through a dynamic estimation method for cloud water resources based on conservation calibration and time-series recursion, including the following steps:

[0029] Step S41: Calculate cloud water composition by time period. Based on the time step set for the study period, the study period is divided into multiple continuous time sub-periods. Within each time sub-period, based on the cloud water three-dimensional structure data, water vapor content distribution data, horizontal wind field data, precipitation observation data, and surface evaporation data obtained in Step S3, the atmospheric water condensate state quantity, boundary transport quantity, and phase transformation quantity of the study area are calculated to obtain the atmospheric water condensate composition data for each time sub-period.

[0030] Step S42: Calculate the total amount of atmospheric water condensate. Based on the atmospheric water condensate composition data obtained in Step S41 and combined with the cloud water mass conservation relationship established in Step S2, calculate the total amount of atmospheric water condensate in the study area in each time period to obtain the total amount of atmospheric water condensate data for each time period, and further calculate the airborne cloud water resource data corresponding to each time period.

[0031] Step S43: Conservation deviation calibration processing. Based on the cloud water mass conservation relationship established in step S2, the atmospheric water condensate composition calculated in step S42 is checked for conservation consistency. The conservation residual is calculated based on the mass conservation relationship. The condensation amount and evaporation amount are dynamically corrected to obtain the total atmospheric water condensate data and airborne cloud water resource data that meet the mass conservation conditions.

[0032] Step S44: Calculation of cloud water resource characteristic indicators. Based on the total atmospheric water condensate data and airborne cloud water resource data obtained in step S43, and combined with precipitation observation data and water vapor change data, the relevant characteristic indicators of cloud water resources are calculated and processed to obtain precipitation efficiency data, water vapor condensation efficiency data and cloud water renewal cycle data.

[0033] Step S45: Time series trend analysis. Based on the aerial cloud water resource data obtained in step S43, construct a cloud water resource time series, and perform continuous time recursive calculation on the time series to obtain cloud water resource change trend data, and combine them to obtain cloud water resource assessment results data at different time scales.

[0034] Furthermore, in step S5, the cloud water development potential assessment is used to analyze the cloud water resource development potential of the study area based on the cloud water resource assessment results. Specifically, based on the cloud water resource assessment result data obtained in step S4, a comprehensive analysis is conducted on the distribution characteristics of cloud water resources and the conversion efficiency of cloud water resources in the area to obtain the cloud water resource development potential assessment results.

[0035] The data from the assessment of the development potential of cloud water resources specifically include: cloud water resource potential level data, cloud water resource spatial distribution data, and cloud water resource suitable development area data.

[0036] The aerial cloud water resource assessment system based on the water mass conservation model provided by this invention includes a multi-source cloud water observation and fusion module, a cloud water mass conservation modeling module, a cloud structure inversion and reconstruction module, a cloud water resource dynamic estimation module, and a cloud water development potential assessment module.

[0037] The multi-source cloud and water observation fusion module is used for multi-source cloud and water observation fusion. Through multi-source cloud and water observation fusion, a multi-source cloud and water observation data set is obtained, and the multi-source cloud and water observation data set is sent to the cloud and water mass conservation modeling module and the cloud structure inversion and reconstruction module.

[0038] The cloud water quality conservation modeling module is used for cloud water quality conservation modeling. Through cloud water quality conservation modeling, a cloud water quality conservation relationship model is obtained, and the cloud water quality conservation relationship model is sent to the cloud structure inversion and reconstruction module and the cloud water resource dynamic estimation module.

[0039] The cloud structure inversion and reconstruction module is used for cloud structure inversion and reconstruction. Through cloud structure inversion and reconstruction, cloud and water three-dimensional structure data are obtained, and the cloud and water three-dimensional structure data are sent to the cloud and water resource dynamic estimation module.

[0040] The cloud water resource dynamic estimation module is used for dynamic estimation of cloud water resources. Through dynamic estimation of cloud water resources, cloud water resource assessment result data is obtained, and the cloud water resource assessment result data is sent to the cloud water development potential assessment module.

[0041] The cloud water development potential assessment module is used to assess the development potential of cloud water resources and obtain the assessment result data of cloud water resource development potential through the assessment.

[0042] The beneficial effects achieved by the present invention using the above solution are as follows:

[0043] (1) In view of the technical problem that the existing cloud water resource assessment methods often rely solely on satellite cloud water path data or single numerical model results for estimation, which makes it difficult to simultaneously reflect the material conservation relationship between atmospheric water vapor transport, cloud water phase transformation and precipitation process, resulting in large deviations in the estimation of airborne cloud water resources and insufficient stability of assessment results, this scheme creatively adopts an overall assessment method that combines multi-source cloud water observation fusion with cloud water mass conservation modeling. By introducing satellite remote sensing cloud products, ground-based meteorological observations, atmospheric reanalysis data and ground precipitation observation data, a unified cloud water mass conservation relationship model is established for the regional water material input, output and phase transformation process, and cloud water resources are calculated under a unified spatiotemporal reference, so that the calculated cloud water resources can simultaneously reflect the internal cloud physical processes and the influence of external water vapor transport, thereby improving the physical consistency and reliability of airborne cloud water resource estimation.

[0044] (2) In view of the technical problem that existing cloud structure acquisition methods cannot obtain the three-dimensional cloud and water distribution structure that meets the mass conservation constraint by relying solely on single remote sensing inversion or empirical relationship to infer the cloud structure, thus affecting the accuracy of cloud and water resource calculation, this scheme creatively adopts a multi-source cloud structure inversion and reconstruction method based on mass conservation constraint. Through cloud area identification, vertical cloud structure layer inversion, cloud particle structure parameter estimation and water mass conservation residual correction, the three-dimensional cloud and water structure of the study area is fused and reconstructed, so that the cloud structure information is more consistent with the actual atmospheric cloud physical process, providing a reliable basis for subsequent cloud and water resource calculation;

[0045] (3) In view of the technical problem that existing cloud water resource estimation methods usually adopt a single-moment static estimation method, which is difficult to reflect the dynamic changes of cloud water resources in the time series and is also difficult to further assess the development potential of cloud water resources, this scheme creatively adopts a dynamic estimation method of cloud water resources based on conservation calibration and time series recursion. Through time-segmented cloud water composition calculation, atmospheric water condensate total solution, conservation deviation calibration processing and time series trend analysis, the amount of cloud water resources is continuously and dynamically estimated, and further cloud water resource potential level assessment results are formed, thereby improving the scientific nature and application value of cloud water resource development assessment. Attached Figure Description

[0046] Figure 1 A flowchart illustrating the aerial cloud water resource assessment method based on a water conservation model provided by this invention.

[0047] Figure 2 A schematic diagram of the aerial cloud water resource assessment system based on the water conservation model provided by the present invention;

[0048] Figure 3 This is a schematic diagram of the cloud structure inversion and reconstruction process in step S3;

[0049] Figure 4 This is a flowchart illustrating the process of dynamic estimation of cloud water resources in step S4.

[0050] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0051] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0052] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0053] Example 1, see Figure 1 The present invention provides a method for assessing atmospheric cloud water resources based on a water conservation model, the method comprising the following steps:

[0054] Step S1: Multi-source cloud and water observation fusion;

[0055] Step S2: Cloud-water mass conservation modeling;

[0056] Step S3: Cloud structure inversion and reconstruction;

[0057] Step S4: Dynamic estimation of cloud water resources;

[0058] Step S5: Assessment of Yunshui's development potential.

[0059] By performing the above operations, this solution addresses the technical problem in existing cloud water resource assessment methods. These methods often rely solely on satellite cloud water path data or single numerical model results, making it difficult to simultaneously reflect the material conservation relationships between atmospheric water vapor transport, cloud water phase transformation, and precipitation processes. This leads to significant estimation errors and insufficient stability of assessment results. The solution creatively adopts a holistic assessment method combining multi-source cloud water observation fusion with cloud water mass conservation modeling. By introducing satellite remote sensing cloud products, ground-based meteorological observations, atmospheric reanalysis data, and surface precipitation observation data, it assesses regional water material transport... A unified cloud water mass conservation model is established for the input, output, and phase transformation processes, and cloud water resources are calculated under a unified spatiotemporal reference. For example, in the assessment of cloud water resources in a mountainous watershed, satellite cloud water path data alone can only reflect the overall water content of the cloud layer, and it is difficult to identify the increase in cloud water caused by boundary water vapor transport. This invention introduces reanalysis wind field and water vapor flux data, and incorporates the regional boundary advection transport term into the mass conservation model, so that the calculated cloud water resources can simultaneously reflect the internal cloud physical processes and the influence of external water vapor transport, thereby improving the physical consistency and reliability of aerial cloud water resource estimation.

[0060] Example 2, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S1, the multi-source cloud and water observation fusion is used to acquire multi-source meteorological observation data related to cloud and water resources in the study area, and to uniformly process the observation data to form a basic cloud and water data set for subsequent calculations. Specifically, it acquires satellite remote sensing cloud product data, ground-based meteorological observation data, atmospheric reanalysis data and ground precipitation observation data, and performs spatial grid unification, time series alignment and data quality control processing on the data to obtain a multi-source cloud and water observation data set.

[0061] In this embodiment, the multi-source meteorological observation data sources include satellite remote sensing cloud product data, ground-based meteorological observation data, atmospheric reanalysis data, and surface precipitation observation data; wherein, the satellite remote sensing cloud product data is used to obtain information on cloud water path, cloud top height, and cloud coverage, and may use MODIS, FY series meteorological satellites, or other satellite cloud product data;

[0062] The ground-based meteorological observation data is used to obtain information on surface meteorological elements, including data such as temperature, humidity, air pressure, and wind speed, which can be obtained through automatic weather stations or ground meteorological observation networks.

[0063] The atmospheric reanalysis data is used to obtain three-dimensional water vapor field and wind field information, and can be NCEP / NCAR reanalysis data, ERA reanalysis data or other global atmospheric reanalysis data;

[0064] The ground precipitation observation data is used to obtain precipitation information in the study area, which can be obtained through ground rain gauges or precipitation remote sensing products.

[0065] After acquiring the aforementioned multi-source observation data, the data undergoes unified processing, specifically including the following processing steps:

[0066] First, various types of data are processed using a unified spatial grid. Based on a preset spatial resolution, various types of observation data are transformed into a unified spatial grid structure to form a data set with a unified spatial coordinate system.

[0067] The horizontal resolution of the unified spatial grid can be set to 1km to 10km, and the vertical direction can be divided according to the pressure layer or the altitude layer. The time step can be set to 10min to 1h. When the time resolution of different data sources is inconsistent, time synchronization is achieved by using nearest-time matching, linear interpolation or moving average.

[0068] Secondly, time series alignment processing is performed on various types of data. Data from different sources are synchronized according to a preset time step to ensure that various types of observation data have consistent data records at the same time node.

[0069] Secondly, data quality control processing is carried out on the observation data, and outliers, missing values, and data that deviate significantly from the physical reasonable range are screened or corrected to ensure stable data quality.

[0070] The multi-source cloud and water observation data set specifically includes cloud and water path data, water vapor content distribution data, horizontal wind field data, precipitation observation data, and surface evaporation data;

[0071] In this embodiment, the cloud water path data can be obtained through satellite cloud water path products or numerical model output data; the water vapor content distribution data can be calculated from the specific humidity data in the reanalysis data; the horizontal wind field data is calculated from the wind speed component data in the reanalysis data; the precipitation observation data is obtained from ground rainfall observation records; and the surface evaporation data can be calculated from the land surface process model or the evaporation flux data in the reanalysis data.

[0072] Example 3, see Figure 1 , Figure 2 This embodiment is based on the above embodiment. In step S2, the cloud water mass conservation model is used to establish a mass budget relationship model of atmospheric water substances in the study area and study period. Specifically, based on the transformation relationship between water vapor, water condensate and precipitation in the atmospheric water cycle, a cloud water mass conservation relationship model is established for atmospheric water substances in the study area to describe the input, output and phase change process of water substances in the region.

[0073] In the cloud water mass conservation model, the change in atmospheric condensate mass is composed of a state term, an advection term, and a source-sink term. The state term characterizes the atmospheric condensate state quantities at the beginning and end of the study period; the advection term characterizes the transport changes of atmospheric condensate at the boundary of the study area; and the source-sink term characterizes the cloud physics processes of water vapor condensation, condensate evaporation or sublimation, and precipitation.

[0074] Preferably, in the source and sink terms, water vapor condensation amount is used to represent the process of water vapor converting into condensate; condensate evaporation or sublimation amount is used to represent the process of condensate converting into water vapor; and precipitation amount is used to represent the process of atmospheric condensate converting into ground precipitation.

[0075] In a preferred embodiment, to improve the computability and physical consistency of the cloud water mass conservation model, the study area is divided into several spatial grid units, and the study period is discretized according to a preset time step. The preset time step can be set to 10 min to 1 h based on the time resolution of the observation data, preferably 30 min. The horizontal resolution of the spatial grid units can be set to 1 km to 10 km, and the vertical direction can be divided into 10 to 50 layers according to altitude or pressure layers. For the t-th time sub-period, the atmospheric water condensate mass conservation relationship within the study area can be expressed by the following formula:

[0076] ;

[0077] In the formula, It represents the total amount of atmospheric condensate in the study area at the end of the t-th time period. This represents the total atmospheric water condensate content within the study area at the start of the t-th time period, which can be calculated by summing the cloud water content, cloud ice content, and grid volume within each spatial grid cell. It is the mass of atmospheric water substances input through the boundary of the study area. It is the atmospheric water mass output through the boundary of the study area, which can be calculated by flux integration based on the water vapor content, condensate content, horizontal wind field, and boundary area at the boundary of the study area. It refers to the mass of water vapor condensing to form condensate. It refers to the mass of water vapor that evaporates or sublimates from condensate. It can be estimated based on changes in water vapor content, relative humidity, temperature profile, and cloud area determination results. It refers to the mass of surface precipitation formed by the transformation of atmospheric water condensate within a specific time period. It can be calculated by area integration based on surface precipitation observation data, precipitation remote sensing products, or a fusion of both. It is a conserved residual formed by the combined errors of multi-source observation, spatiotemporal interpolation, and approximation of cloud microphysical processes;

[0078] In a preferred embodiment, to avoid the source-sink term estimation results being affected by a single abnormal observation, constraints can be applied to the water vapor condensation and water condensate evaporation or sublimation amounts. When the relative humidity of a spatial grid cell is not lower than a preset condensation humidity threshold, that grid cell is designated as a priority condensation region; when the relative humidity of a spatial grid cell is lower than a preset evaporation humidity threshold and water condensate is present, that grid cell is designated as a priority evaporation region. The preset condensation humidity threshold can be set to 85% to 95%, preferably 90%; the preset evaporation humidity threshold can be set to 70% to 85%, preferably 80%. Through these threshold constraints, the source-sink term estimation results can be matched with the actual physical conditions of cloud formation, maintenance, and dissipation.

[0079] To ensure that the cloud-water mass conservation model can adapt to the error differences between multi-source observation data, confidence weights can be further set for data from different sources. Specifically, the confidence weight for satellite remote sensing cloud product data can be set to 0.25 to 0.40, the confidence weight for ground-based meteorological observation data can be set to 0.20 to 0.35, the confidence weight for atmospheric reanalysis data can be set to 0.25 to 0.40, and the confidence weight for surface precipitation observation data can be set to 0.15 to 0.30. In specific calculations, the above confidence weights can be normalized and adjusted based on the data missing rate, temporal resolution, spatial resolution, and historical error statistics, so that the sum of the weights for each type of data is 1.

[0080] Through the above modeling process, a cloud water quality conservation model describing the regional water condensate change process can be obtained, and a computational basis for subsequent cloud water resource assessment can be established accordingly.

[0081] Example 4, see Figure 1 , Figure 2 and Figure 3 This embodiment is based on the above embodiment. In step S3, the cloud structure inversion and reconstruction is used to reconstruct the atmospheric cloud structure distribution in the study area under the constraint of cloud and water mass conservation based on multi-source cloud and water observation data. Specifically, based on the multi-source cloud and water observation data set obtained in step S1 and the cloud and water mass conservation relationship model established in step S2, the cloud and water state variables in the study area are spatially reconstructed to obtain three-dimensional cloud and water structure data.

[0082] The cloud structure inversion and reconstruction is achieved through a multi-source cloud structure inversion method based on mass conservation constraints, including the following steps:

[0083] Step S31: Initial cloud and water field construction. Based on the water vapor content distribution data and relative humidity data in the multi-source cloud and water observation data obtained in Step S1, cloud areas are determined for each spatial grid in the study area, the areas where clouds exist are identified, and the initial cloud water content of each spatial grid is calculated based on the cloud area determination results to construct the initial three-dimensional cloud and water distribution field of the study area.

[0084] In a preferred embodiment, to improve the reproducibility of the initial cloud-water field construction, cloud areas are first determined for each spatial grid cell within the study area. For the i-th spatial grid cell, if its relative humidity is not lower than a preset cloud area humidity threshold, and the cloud-water path or cloud cover information at the corresponding location meets a preset cloud presence condition, the spatial grid cell is determined to belong to a cloud presence area. The preset cloud area humidity threshold can be set to 85% to 95%, preferably 90%. The preset cloud presence condition may include a cloud-water path greater than 0.01 kg / m². 2 Or the cloud coverage rate is greater than at least one of the preset coverage thresholds;

[0085] For spatial grid cells identified as areas with clouds, the initial cloud moisture content can be calculated based on their water vapor content and relative humidity exceeding the threshold. In a preferred embodiment, the formula for calculating the initial cloud moisture content of the i-th spatial grid cell is:

[0086] ;

[0087] In the formula, It is the initial cloud water content of the i-th spatial grid cell. This is the condensation conversion coefficient, which can be set from 0.10 to 0.35, preferably 0.20. It is the relative humidity of the i-th spatial grid cell. It is the humidity threshold for the cloud area; It is the water vapor content within the i-th spatial grid cell;

[0088] It should be noted that, and All of these are dimensionless relative humidity parameters, with values ​​ranging from 0 to 1. When relative humidity data is obtained as a percentage, it should be converted to a value between 0 and 1 before being used in the calculation.

[0089] In this way, the initial cloud water content can simultaneously reflect the local water vapor reserves and condensation formation conditions, thus providing a reasonable initial field basis for subsequent vertical stratification inversion.

[0090] In one embodiment of constructing the initial three-dimensional cloud water distribution field of the study area, in order to avoid discrete jumps in the initial cloud water field caused by local anomalous observations, the initial cloud water content can be smoothed in a neighborhood. The neighborhood smoothing can be implemented by a weighted average of three-dimensional adjacent grids, wherein the weight of the current grid can be set to 0.50 to 0.70, and the total weight of adjacent grids can be set to 0.30 to 0.50, which can improve the spatial continuity of the initial three-dimensional cloud water distribution field.

[0091] Step S32: Vertical cloud structure layer inversion. Based on the cloud water path data obtained in step S1 and the initial cloud water three-dimensional distribution field obtained in step S31, vertical structure inversion calculation is performed on each cloud area grid in the study area. By establishing the cloud water content distribution function in the vertical direction, the cloud water content of each spatial grid at different height layers is calculated, and the cloud height distribution data of the study area is obtained.

[0092] In a preferred embodiment, to ensure that the vertical inversion results are consistent with the cloud water path observation data, the cloud water content at the k-th altitude layer within the i-th horizontal grid is calculated using a normalized vertical allocation method. The calculation formula can be expressed as:

[0093] ;

[0094] In the formula, It represents the cloud water content at the k-th altitude level for the i-th horizontal grid. It is the cloud and water path corresponding to the i-th horizontal grid. It is the vertical weight assignment of the i-th horizontal grid at the k-th height level. It is the thickness of the k-th vertical layer, where K is the number of vertical layers, calculated using this formula. satisfy ;

[0095] The vertical allocation weight The height can be determined by combining relative humidity, temperature profile, and cloud boundary position. In a preferred embodiment, the height layer located between the cloud base height and cloud top height and with higher relative humidity is given a larger vertical allocation weight, while the height layer located at the cloud edge or with lower relative humidity is given a smaller vertical allocation weight. The cloud base height and cloud top height can be jointly determined based on satellite cloud top height products, ground-based observation data, or reanalysis profile data.

[0096] In one specific implementation, the vertical layer can be divided into 10 to 40 layers, preferably 20 layers; different weight allocation strategies can be adopted for convective clouds and stratiform clouds, wherein convective clouds can be assigned a higher weight in the upper and middle altitude layers, and stratiform clouds can be assigned a higher weight in the middle altitude layer.

[0097] Step S33: Estimation of cloud particle structure parameters. Based on the spatial distribution data of cloud water content obtained in step S32, the cloud particle size distribution model is established, and the cloud particle concentration parameters and particle size distribution parameters are inverted and calculated to obtain the cloud particle structure parameter data of the study area.

[0098] In a preferred embodiment, the cloud particle size distribution model can be described using a Gamma distribution model, and the calculation formula is expressed as follows:

[0099] ;

[0100] In the formula, This is the number concentration distribution of cloud particles with a diameter of r. It is a particle concentration scale parameter. It is a distribution shape parameter. is the spectral width attenuation parameter, and r is the particle size parameter; through this model, the order of magnitude of cloud particles, particle size distribution morphology, and particle size attenuation trend can be uniformly characterized.

[0101] In practical implementation, the spatial distribution data of cloud water content, cloud temperature conditions, and cloud type information obtained in step S32 can be used to... , and Inversion estimation is performed; wherein, the effective radius of liquid cloud droplets can be constrained to the range of 4μm to 25μm, preferably 6μm to 18μm; the characteristic scale of ice phase particles can be set differently according to the height layer with temperature below 0℃. By constraining the above particle size distribution parameters, the particle size inversion results can be avoided from exceeding the physically reasonable range.

[0102] When direct particle size observation data is lacking, it can be determined first based on cloud type and temperature conditions. The initial range is then determined by inverse calculation based on cloud water content constraints. and This ensures that the particle size distribution integral result is consistent with the cloud moisture content at the corresponding altitude layer;

[0103] In another optional implementation, to improve the estimation stability of cloud particle concentration parameters, a lightweight particle parameter auxiliary estimation model can be constructed to correct the particle concentration parameters obtained from the initial inversion. This lightweight particle parameter auxiliary estimation model can be implemented using a three-layer multilayer perceptron (MLP). Its inputs may include cloud water content, temperature, relative humidity, and cloud height, and its output is the corrected particle concentration parameters. The number of hidden layer nodes can be set to 32 or 16. It should be noted that this lightweight particle parameter auxiliary estimation model is only used to assist in correcting the physical model inversion results and is not intended to replace the main inversion process based on the particle size distribution model. In specific implementations, it should be ensured that the estimation of cloud particle structure parameters still prioritizes physical interpretability.

[0104] Step S34: Water substance conservation residual correction. Based on the cloud water mass conservation relationship model established in step S2, the cloud water content spatial distribution data obtained in steps S32 and S33 are subjected to conservation consistency test, and the cloud water mass conservation residual is calculated according to the water substance mass balance relationship. The cloud water content distribution is corrected by residual correction calculation to obtain cloud water content spatial distribution data that meets the water substance conservation conditions.

[0105] In a preferred embodiment, the cloud water mass conservation residual within the current time period is first calculated based on the cloud water mass conservation relationship model established in step S2. The cloud water mass conservation residual is used to characterize the degree of deviation between the cloud structure inversion results obtained based on steps S32 and S33 and the regional water mass conservation relationship.

[0106] To avoid local cloud structure distortion caused by evenly distributing the conserved residuals across the entire spatial grid, a weighted residual distribution method is used to correct the cloud water content at the k-th altitude layer of the i-th horizontal grid. The calculation formula can be expressed as:

[0107] ;

[0108] In the formula, This is the corrected cloud water content. This is the cloud moisture content before correction. It is the residual assignment weight of the i-th horizontal grid at the k-th height level. This corresponds to the spatial grid volume, N is the total number of horizontal grids, and K represents the total number of vertical layers. The residual allocation weights are... The residual correction amount can be determined comprehensively based on cloud water content, relative humidity level, and boundary water vapor transport contribution, so that areas with higher cloud water content, more sufficient condensation conditions, and stronger water vapor supply bear a higher proportion of the residual correction amount.

[0109] It should be noted that, It is the conserved residual of the mass dimension, and It is the spatial grid volume, therefore the residual assignment term has the dimension of cloud water content concentration, when When, it indicates that the current cloud structure inversion result has insufficient condensate compared to the conservation relationship, and the residual correction term is used to increase the cloud water content of the corresponding cloud region grid; when When this occurs, it indicates that there is an excess of condensate in the current cloud structure inversion result, and the residual correction term is used to reduce the cloud water content of the corresponding cloud area grid.

[0110] In a preferred embodiment, to ensure the stability of the conservation residual correction process, an iterative correction method can be used to repeatedly perform residual calculation and cloud water content update; when the relative change rate of the conservation residual between two consecutive iterations is not higher than a preset convergence threshold, the iteration is stopped and the correction result is output; wherein, the preset convergence threshold can be set to 1% to 5%, preferably 2%. By setting the convergence threshold, while ensuring conservation consistency, overcorrection can be avoided to prevent abnormal distortion of local cloud structure.

[0111] In another implementation, if a spatial grid cell has a negative cloud water content after residual correction, it is truncated to zero, and the corresponding overshoot correction is redistributed to adjacent cloud grid cells. Through non-negativity constraint processing, it can be ensured that the correction result meets the physical rationality of cloud water content.

[0112] Step S35: 3D cloud structure fusion and reconstruction. Based on the spatial distribution data of cloud water content corrected in step S34, the cloud particle structure parameter data obtained in step S33, and the cloud height distribution data obtained in step S32, the 3D cloud structure of the study area is fused and calculated. The cloud water path data is obtained by calculating the integral of cloud water content in the vertical direction, thus forming the 3D cloud water structure data.

[0113] In a preferred embodiment, during the three-dimensional cloud structure fusion and reconstruction process, the corrected spatial distribution data of cloud water content is used as the main variable of the mass field, the cloud particle structure parameter data is used as the microstructure characterization variable, the cloud height distribution data is used as the geometric boundary constraint variable, and the three types of data are uniformly spatially mapped and fused to obtain the three-dimensional cloud structure expression result of the study area.

[0114] In this embodiment, after fusion is completed, the cloud water path is reconstructed based on the cloud water content integral results of each horizontal grid in the vertical direction, and its consistency is compared with the original cloud water path observation data in step S1. When the relative deviation between the reconstructed cloud water path and the original cloud water path is not higher than a preset path deviation threshold, the 3D cloud structure fusion result is determined to meet the path consistency requirement. The preset path deviation threshold can be set to 5% to 10%, preferably 8%. By introducing a path consistency check, it can be ensured that the reconstructed 3D cloud structure satisfies both local spatial distribution characteristics and is consistent with macroscopic observation results.

[0115] In another implementation, cloud water spatial distribution slice data, typical vertical profile data and cloud volume statistics can be formed based on the corrected cloud water content spatial distribution data, cloud particle structure parameter data and cloud height distribution data, so as to serve as the input basic data for solving the total amount of atmospheric water condensate and dynamically estimating cloud water resources in the subsequent step S4.

[0116] The cloud and water three-dimensional structure data specifically includes cloud water content spatial distribution data, cloud particle structure parameter data, cloud height distribution data, and cloud and water path data;

[0117] In one implementation, a cloud particle structure model can be further constructed to estimate the cloud particle size distribution characteristics, thereby obtaining more refined cloud structure information. The cloud particle structure model preferably employs a parameterized particle size distribution model with clear physical meaning, and combines information on cloud water content, temperature, relative humidity, and cloud height to jointly estimate particle concentration parameters and particle size distribution parameters. If it is necessary to improve the stability of local parameter estimation, a lightweight auxiliary estimation model can be introduced to correct the particle concentration parameters. However, this auxiliary estimation model serves only as a supplementary step in the particle structure parameter inversion process to ensure that the cloud structure inversion and reconstruction are still dominated by mass conservation constraints and the physical distribution laws of the cloud.

[0118] By inverting and reconstructing cloud structures, basic cloud water structure data can be obtained for cloud water resource calculations, providing a reliable data foundation for subsequent cloud water resource estimation.

[0119] By performing the above operations, this solution addresses the technical problem in existing cloud structure acquisition methods where relying solely on single remote sensing inversion or empirical relationships to extrapolate cloud structure makes it difficult to obtain a three-dimensional cloud-water distribution structure that satisfies the mass conservation constraint, thus affecting the accuracy of cloud-water resource calculations. This solution creatively employs a multi-source cloud structure inversion and reconstruction method based on mass conservation constraints. Through processes such as cloud region identification, hierarchical inversion of vertical cloud structure, estimation of cloud particle structure parameters, and correction of water mass conservation residuals, the three-dimensional cloud-water structure of the study area is fused and reconstructed. For example, when analyzing a convective cloud system, traditional methods often directly estimate the average cloud water content based on cloud-water paths. However, this method fails to reflect the vertical distribution structure within the cloud layer. This invention establishes a vertical distribution function of cloud water content and combines it with cloud-water path data for hierarchical inversion. Simultaneously, it uses water mass conservation residuals to correct the cloud water content, obtaining spatial distribution data of cloud water content that satisfies the mass conservation condition. This makes the cloud structure information more consistent with actual atmospheric cloud physics processes, providing a reliable foundation for subsequent cloud-water resource calculations.

[0120] Example 5, see Figure 1 , Figure 2 and Figure 4This embodiment is based on the above embodiment. In step S4, the dynamic estimation of cloud water resources is used to quantitatively estimate the atmospheric cloud water resources in the study area based on cloud structure data and water conservation relationship. Specifically, based on the three-dimensional cloud water structure data obtained in step S3 and the cloud water mass conservation relationship model established in step S2, the total amount of atmospheric water condensate in the study area during the study period is calculated, and the amount of atmospheric cloud water resources is further calculated. In the calculation process, the cloud water resource assessment result data is obtained by calculating the composition of cloud water resources.

[0121] The cloud water resource assessment results include data on total atmospheric water condensate, data on atmospheric cloud water resources, precipitation efficiency, water vapor condensation efficiency, cloud water renewal cycle, and cloud water resource change trends.

[0122] The cloud water resource quantity is used to characterize the total amount of atmospheric water condensate in the study area that participated in the atmospheric water cycle but did not form ground precipitation during the study period;

[0123] In a preferred embodiment, to avoid confusion between the concepts of atmospheric cloud water resources and total atmospheric water condensate, the total atmospheric water condensate is used as the basic stock, and the cloud water portion with low development effectiveness is deducted as a deduction item to calculate the atmospheric cloud water resources. The total atmospheric water condensate is used to characterize the total mass of cloud water, cloud ice, and other water condensates that can participate in cloud physical transformation currently still in the atmosphere within the study area, while the atmospheric cloud water resources are used to characterize the effective cloud water resources stock that are still in the atmosphere within the current time sub-period and have the potential for subsequent precipitation transformation or artificial weather modification.

[0124] In a preferred embodiment, the amount of aerial cloud water resources in the t-th time sub-period can be expressed as:

[0125] ;

[0126] In the formula, This represents the amount of cloud water resources in the air during the t-th time period. It represents the total amount of atmospheric condensate in the study area during the t-th time period. This refers to low-effective cloud water volume that does not meet the conditions for effective development due to excessively low cloud water content, insufficient cloud thickness, or short cloud duration. Specifically, it can be obtained by summing the cloud water volume of spatial grids that are below the effective cloud water threshold, below the effective cloud thickness threshold, or below the effective duration threshold.

[0127] In step S4, the dynamic estimation of cloud water resources is achieved through a dynamic estimation method for cloud water resources based on conservation calibration and time-series recursion, including the following steps:

[0128] Step S41: Calculate cloud water composition by time period. Based on the time step set for the study period, the study period is divided into multiple continuous time sub-periods. Within each time sub-period, based on the cloud water three-dimensional structure data, water vapor content distribution data, horizontal wind field data, precipitation observation data, and surface evaporation data obtained in Step S3, the atmospheric water condensate state quantity, boundary transport quantity, and phase transformation quantity of the study area are calculated to obtain the atmospheric water condensate composition data for each time sub-period.

[0129] In a preferred embodiment, the time-segmented cloud water composition data includes state inventory, boundary input, boundary output, condensation generation, evaporation consumption, and precipitation consumption. The state inventory is calculated by summing the cloud water content spatial distribution data, cloud ice content estimation data, and spatial grid volume obtained in Example 4. The boundary input and boundary output are calculated by flux integration based on the water vapor content, condensate content, horizontal wind field, and boundary area at the boundary of the study area. The condensation generation and evaporation consumption are estimated based on relative humidity, temperature profile, water vapor variation, and cloud area determination results. The precipitation consumption is calculated by fusing ground precipitation observation data and precipitation remote sensing data.

[0130] Further preferably, to ensure the continuity of calculation results between different time sub-periods, the time step size can be set to 10 min to 1 h, preferably 30 min, based on the temporal resolution of the multi-source observation data. When the temporal resolutions of satellite remote sensing cloud products, atmospheric reanalysis data, and ground precipitation observation data are inconsistent, time synchronization can be achieved using nearest-neighbor time matching, linear interpolation, or sliding window averaging. The sliding window length can be set to 2 to 4 time steps, preferably 3 time steps, to reduce the impact of instantaneous observation errors on the calculation results of the component quantities.

[0131] In one implementation, for missing or abnormal component data, corrections can be made based on the changing trends of adjacent time sub-periods and the cloud and water conditions of adjacent spatial grids. When the change in a certain component compared to the previous time sub-period exceeds a preset abrupt change threshold, and no corresponding change is observed in adjacent grids, the component is marked as data to be corrected. The preset abrupt change threshold can be set to 30% to 60%, preferably 40%. Through the above processing, the interference of single-point abnormal observations on the dynamic estimation process can be reduced.

[0132] Step S42: Calculate the total amount of atmospheric water condensate. Based on the atmospheric water condensate composition data obtained in Step S41 and combined with the cloud water mass conservation relationship established in Step S2, calculate the total amount of atmospheric water condensate in the study area in each time period to obtain the total amount of atmospheric water condensate data for each time period, and further calculate the airborne cloud water resource data corresponding to each time period.

[0133] In a preferred embodiment, the total amount of atmospheric condensate can be solved by spatial grid integration; for the t-th time sub-period, the formula for calculating the total amount of atmospheric condensate in the study area is:

[0134] ;

[0135] In the formula, It represents the total amount of atmospheric water condensate in the t-th time period. It is the cloud water content at the k-th altitude layer of the i-th horizontal grid. It represents the cloud ice content at the k-th altitude layer of the i-th horizontal grid. This corresponds to the spatial grid volume. If the temperature conditions in the study area mainly correspond to liquid cloud processes, the cloud ice content term can also be set to zero or estimated based on the temperature threshold.

[0136] Furthermore, the amount of atmospheric cloud water resources can be calculated based on the total amount of atmospheric water condensate combined with low-effective cloud water content. When the cloud water content of a spatial grid cell is lower than a preset effective cloud water threshold, or the cloud thickness is lower than a preset effective cloud thickness threshold, the corresponding cloud water content is included in the low-effective cloud water content. The effective cloud water threshold can be set to 0.01 g / m³. 3 Up to 0.05g / m 3 The preferred value is 0.02 g / m 3 The effective cloud thickness threshold can be set from 300m to 800m, preferably 500m.

[0137] Step S43: Conservation deviation calibration processing. Based on the cloud water mass conservation relationship established in step S2, the atmospheric water condensate composition calculated in step S42 is checked for conservation consistency. The conservation residual is calculated based on the mass conservation relationship. The condensation amount and evaporation amount are dynamically corrected to obtain the total atmospheric water condensate data and airborne cloud water resource data that meet the mass conservation conditions.

[0138] In a preferred embodiment, the conservation deviation calibration process does not directly change all components, but prioritizes correcting the condensation and evaporation quantities with higher uncertainty, while making limited corrections or keeping unchanged the precipitation and boundary wind field transport quantities that are directly obtained from the observation equipment and have high reliability; thereby, the water material budget closure can be improved while maintaining the authenticity of the observation data.

[0139] For the t-th time period, first calculate the conservation residual based on the cloud water mass conservation relationship in step S2:

[0140] ;

[0141] In the formula, It is the conserved residual for the t-th time sub-period. and These represent the total amount of atmospheric condensate at two adjacent time points. It is the boundary input quantity. It is the boundary output quantity. It is the amount of condensation generated. It is the amount consumed by evaporation. It is the amount of precipitation consumed;

[0142] Furthermore, the conservation residuals are adjusted based on the uncertainty weights of condensation and evaporation. The condensation adjustment weight can be set to 0.50 to 0.70, and the evaporation adjustment weight can be set to 0.30 to 0.50, with the sum of their weights being 1. When the relative humidity in the study area is generally high and cloud water resources show an increasing trend, the condensation adjustment weight is increased; when the relative humidity in the study area decreases and cloud water resources show a decreasing trend, the evaporation adjustment weight is increased.

[0143] In a preferred embodiment, the conservation deviation calibration can be performed using a step-by-step iterative approach. When the ratio of the absolute value of the calibrated conservation residual to the total amount of atmospheric water condensate is lower than a preset conservation error threshold, calibration is stopped and the calibrated total amount of atmospheric water condensate and airborne cloud water resource data are output. The preset conservation error threshold can be set to 3% to 8%, preferably 5%. If the conservation error threshold is not reached after a preset number of iterations, the last calibration result is retained, and this time period is marked as a low-confidence estimation period to reduce its weight in subsequent development potential assessments.

[0144] Step S44: Calculation of cloud water resource characteristic indicators. Based on the total atmospheric water condensate data and airborne cloud water resource data obtained in step S43, and combined with precipitation observation data and water vapor change data, the relevant characteristic indicators of cloud water resources are calculated and processed to obtain precipitation efficiency data, water vapor condensation efficiency data and cloud water renewal cycle data.

[0145] The precipitation efficiency characterizes the extent to which cloud water resources are converted into surface precipitation; the water vapor condensation efficiency characterizes the effectiveness of water vapor conversion into condensate within the study area; and the cloud water renewal cycle characterizes the replenishment and renewal rate of cloud water resources under the combined effects of boundary transport and phase transformation. To ensure the comparability of each characteristic indicator, the indicators within different time sub-periods can be normalized, and the original calculated values ​​are retained as physical quantity results.

[0146] In a preferred embodiment, the precipitation efficiency, water vapor condensation efficiency, and cloud water renewal cycle can be expressed as follows:

[0147] ;

[0148] In the formula, This represents the precipitation efficiency for the t-th time period. It is the water vapor condensation efficiency in the t-th time sub-period. This is the cloud and water update cycle for the t-th time sub-period. It is the amount of precipitation consumed. It is the total amount of atmospheric water condensate. It is the amount of condensation generated. It is the total amount of water vapor that can participate in condensation within the study area. It is a stability coefficient to prevent the denominator from being zero, and its value is preferably set to 10. −5 ;

[0149] It should be noted that when When the time period is in a state of net consumption or net output of cloud water resources in the study area, this time period is not used as the object of cloud water positive update cycle calculation, but is marked as a period of net cloud water loss.

[0150] Furthermore, when precipitation efficiency is low, airborne cloud water resources are high, and the cloud water renewal cycle is short, it indicates that there are continuously replenished cloud water resources in the study area that have not been fully converted into natural precipitation; when precipitation efficiency is high and airborne cloud water resources are declining rapidly, it indicates that a large amount of cloud water resources have been converted into natural precipitation, and their subsequent development potential is relatively reduced. The above combination of indicators provides a quantitative basis for assessing the cloud water development potential in step S5.

[0151] Step S45: Time series trend analysis. Based on the aerial cloud water resource data obtained in step S43, construct a cloud water resource time series, and perform continuous time recursive calculation on the time series to obtain cloud water resource change trend data, and combine them to obtain cloud water resource assessment results data at different time scales.

[0152] Furthermore, in some implementations, the changes in cloud water resources over time can be dynamically analyzed through continuous time series calculations to obtain cloud water resource change trend data.

[0153] In a preferred embodiment, the cloud water resource change trend data includes cloud water resource change rate, cloud water resource accumulation, cloud water resource continuous replenishment intensity, and cloud water resource stability index; wherein, the cloud water resource change rate is used to characterize the increase or decrease trend of atmospheric cloud water resource quantity within adjacent time sub-periods, the cloud water resource accumulation is used to characterize the cumulative level of exploitable cloud water resources within a continuous time window, the cloud water resource continuous replenishment intensity is used to characterize the continuous replenishment capacity of cloud water resources by boundary input and condensation generation, and the cloud water resource stability index is used to characterize the degree of fluctuation of cloud water resource time series;

[0154] In a preferred embodiment, to avoid outliers at a single time point affecting trend judgment, a sliding time window can be used to recursively analyze the amount of airborne cloud water resources. The length of the sliding time window can be set to 3 to 6 time steps, preferably 4 time steps. Within each sliding window, the average value, slope of change, and fluctuation coefficient of the amount of airborne cloud water resources are calculated. When the slope of change is greater than a preset growth threshold and the fluctuation coefficient is lower than a preset stability threshold, the time window is determined as a stable accumulation window for cloud water resources. The growth threshold can be determined based on historical sample statistics, preferably 1.2 to 1.5 times the average historical change rate of the corresponding region. The stability threshold can be set to 0.15 to 0.30, preferably 0.20.

[0155] In one optional implementation, to improve the stability of short-term trend identification, a lightweight gated cyclic unit (GRU) model can be used to assist in the prediction of airborne cloud water resource time series. The inputs to the GRU model include airborne cloud water resource quantity, precipitation efficiency, water vapor condensation efficiency, cloud water renewal cycle, and boundary input-output difference. The output is the cloud water resource change trend for the next time sub-period. The number of hidden GRU units can be set to 16 to 32, and the time window length can be set to 4 to 8 time steps. It should be noted that the GRU model is only used to assist in identifying short-term trend changes and does not replace the main cloud water resource estimation process based on mass conservation relationships, to ensure that the dynamic estimation results still have a clear physical constraint basis.

[0156] Through the above dynamic estimation process, the cloud water resources assessment results of the study area at different time scales can be obtained.

[0157] By performing the above operations, this solution addresses the technical problem that existing cloud water resource estimation methods typically employ static estimation at a single moment, which fails to reflect the dynamic changes of cloud water resources over time and further assess their development potential. This approach creatively adopts a dynamic cloud water resource estimation method based on conservation calibration and time-series recursion. Through time-segmented calculation of cloud water composition, solution for total atmospheric water condensate, conservation bias calibration, and time-series trend analysis, it continuously and dynamically estimates cloud water resources and further generates a cloud water resource potential level assessment result. For example, in weather modification operation planning, relying solely on cloud water resource levels at a single moment can easily lead to inaccurate timing judgments. This invention, by constructing a cloud water resource time series and analyzing its changing trends, can identify time periods where cloud water resources accumulate continuously but precipitation efficiency is low, determining that these periods have high potential for artificial rain enhancement, thus improving the scientific rigor and application value of cloud water resource development assessment.

[0158] Example 6, see Figure 1 and Figure 2This embodiment is based on the above embodiment. In step S5, the cloud water development potential assessment is used to analyze the cloud water resource development potential of the study area based on the cloud water resource assessment results. Specifically, based on the cloud water resource assessment result data obtained in step S4, a comprehensive analysis is conducted on the cloud water resource distribution characteristics and cloud water resource conversion efficiency in the area to obtain the cloud water resource development potential assessment results.

[0159] The data from the assessment of the development potential of cloud water resources specifically include: cloud water resource potential level data, cloud water resource spatial distribution data, and cloud water resource suitable development area data.

[0160] In this embodiment, the steps for comprehensively analyzing the distribution characteristics and conversion efficiency of cloud water resources within the study area specifically include:

[0161] First, statistical analysis was conducted on the amount of cloud water resources in each grid unit within the study area, and spatial distribution data of cloud water resources were generated based on the amount of cloud water resources.

[0162] Secondly, based on the ratio between cloud water resources and precipitation, the conversion efficiency of cloud water resources is calculated and analyzed to obtain the degree of cloud water resource utilization in each region.

[0163] Secondly, based on the distribution characteristics and conversion efficiency of cloud water resources, the study area is classified into potential levels to obtain cloud water resource potential level data.

[0164] In this embodiment, the development potential of cloud water resources can be divided into high-potential areas, medium-potential areas, and low-potential areas according to a preset classification rule. More preferably, high-potential areas are used to represent areas with large cloud water resources and low precipitation conversion efficiency, and these areas usually have high potential for artificial weather modification operations. Medium-potential areas are used to represent areas with moderate cloud water resources and certain development conditions. Low-potential areas are used to represent areas with small cloud water resources or where cloud water resources have been largely converted into precipitation.

[0165] After completing the potential level classification, the suitable areas for cloud and water resource development can be determined by combining the topographic conditions, meteorological conditions and historical records of artificial weather modification operations in the study area, thus forming data on suitable areas for cloud and water resource development.

[0166] The above assessment can identify suitable areas and timeframes for conducting weather modification operations, providing a decision-making reference for related operations.

[0167] Example 7, see Figure 1 and Figure 2Based on the above embodiments, the aerial cloud water resource assessment system based on the water mass conservation model provided by the present invention includes a multi-source cloud water observation fusion module, a cloud water mass conservation modeling module, a cloud structure inversion and reconstruction module, a cloud water resource dynamic estimation module, and a cloud water development potential assessment module.

[0168] The multi-source cloud and water observation fusion module is used for multi-source cloud and water observation fusion. Through multi-source cloud and water observation fusion, a multi-source cloud and water observation data set is obtained, and the multi-source cloud and water observation data set is sent to the cloud and water mass conservation modeling module and the cloud structure inversion and reconstruction module.

[0169] The cloud water quality conservation modeling module is used for cloud water quality conservation modeling. Through cloud water quality conservation modeling, a cloud water quality conservation relationship model is obtained, and the cloud water quality conservation relationship model is sent to the cloud structure inversion and reconstruction module and the cloud water resource dynamic estimation module.

[0170] The cloud structure inversion and reconstruction module is used for cloud structure inversion and reconstruction. Through cloud structure inversion and reconstruction, cloud and water three-dimensional structure data are obtained, and the cloud and water three-dimensional structure data are sent to the cloud and water resource dynamic estimation module.

[0171] The cloud water resource dynamic estimation module is used for dynamic estimation of cloud water resources. Through dynamic estimation of cloud water resources, cloud water resource assessment result data is obtained, and the cloud water resource assessment result data is sent to the cloud water development potential assessment module.

[0172] The cloud water development potential assessment module is used to assess the development potential of cloud water resources and obtain the assessment result data of cloud water resource development potential through the assessment.

[0173] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0174] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0175] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for assessing atmospheric cloud water resources based on a water conservation model, characterized in that: The method includes the following steps: Step S1: Multi-source cloud and water observations are fused to obtain a multi-source cloud and water observation data set; the multi-source cloud and water observation data set includes cloud and water path data, water vapor content distribution data, horizontal wind field data, precipitation observation data, and surface evaporation data; Step S2: Cloud water mass conservation modeling, establishing a cloud water mass conservation relationship model including state terms, advection terms, and source-sink terms; Step S3: Cloud structure inversion and reconstruction. Based on the multi-source cloud-water observation data set obtained in Step S1 and the cloud-water mass conservation relationship model established in Step S2, the cloud-water state variables in the study area are spatially reconstructed to obtain three-dimensional cloud-water structure data. The cloud structure inversion and reconstruction is achieved through a multi-source cloud structure inversion method based on mass conservation constraints, including the following steps: Step S31: Initial cloud and water field construction, constructing the initial three-dimensional cloud and water distribution field of the study area; Step S32: Vertical cloud structure layer inversion. Based on the cloud water path data obtained in step S1 and the initial cloud water three-dimensional distribution field obtained in step S31, vertical structure inversion calculation is performed on each cloud area grid in the study area. By establishing the cloud water content distribution function in the vertical direction, the cloud water content of each spatial grid at different height layers is calculated, and the cloud height distribution data of the study area is obtained. The cloud water content at the k-th altitude layer within the i-th horizontal grid is calculated using a normalized vertical distribution method. The calculation formula can be expressed as: ； In the formula, It represents the cloud water content at the k-th altitude level for the i-th horizontal grid. It is the cloud and water path corresponding to the i-th horizontal grid. It is the vertical weight assignment of the i-th horizontal grid at the k-th height level. It is the thickness of the k-th vertical layer, where K is the number of vertical layers, calculated using this formula. satisfy ; Step S33: Estimation of cloud particle structure parameters. Based on the spatial distribution data of cloud water content obtained in step S32, the cloud particle size distribution model is established, and the cloud particle concentration parameters and particle size distribution parameters are inverted and calculated to obtain the cloud particle structure parameter data of the study area. Step S34: Water substance conservation residual correction. Based on the cloud water mass conservation relationship model established in step S2, the cloud water content spatial distribution data obtained in steps S32 and S33 are subjected to conservation consistency test, and the cloud water mass conservation residual is calculated according to the water substance mass balance relationship. The cloud water content distribution is corrected by residual correction calculation to obtain cloud water content spatial distribution data that meets the water substance conservation conditions. The cloud water content at the k-th altitude layer of the i-th horizontal grid is corrected using a weighted residual allocation method. The calculation formula can be expressed as: ； In the formula, This is the corrected cloud water content. This is the cloud moisture content before correction. It is the residual assignment weight of the i-th horizontal grid at the k-th height level. This corresponds to the spatial grid volume, where N is the total number of horizontal grid cells and K is the number of vertical layers. It is the residual of cloud and water quality conservation; Step S35: 3D cloud structure fusion and reconstruction. Based on the spatial distribution data of cloud water content after correction in step S34, the cloud particle structure parameter data obtained in step S33, and the cloud height distribution data obtained in step S32, the 3D cloud structure of the study area is fused and calculated. The cloud water path data is obtained by calculating the integral of cloud water content in the vertical direction, thus forming the 3D cloud water structure data. Step S4: Dynamic estimation of cloud water resources. Based on the three-dimensional cloud water structure data obtained in Step S3 and the cloud water mass conservation relationship model established in Step S2, the total amount of atmospheric water condensate in the study area during the study period is calculated, and the amount of atmospheric cloud water resources is also calculated. During the calculation process, the composition of cloud water resources is calculated to obtain the cloud water resource assessment results. The dynamic estimation of cloud water resources is achieved through a dynamic estimation method based on conservation calibration and time series recursion, including the following steps: calculation of cloud water composition in different time periods, solution of total atmospheric water condensate, conservation deviation calibration processing, calculation of cloud water resource characteristic indicators, and time series trend analysis. The formula for calculating the amount of atmospheric cloud water resources is as follows: ； In the formula, This represents the amount of cloud water resources in the air during the t-th time period. It represents the total amount of atmospheric condensate in the study area during the t-th time period. It is low effective cloud water volume; the low effective cloud water volume is the spatial grid cloud water volume that does not meet the conditions for effective development because the cloud water content is lower than the effective cloud water threshold, the cloud layer thickness is lower than the effective cloud thickness threshold, or the cloud area duration is lower than the effective duration threshold. The conservation deviation calibration process involves verifying the conservation consistency of atmospheric water condensate composition based on the cloud water mass conservation relationship established in step S2, calculating the conservation residual based on the mass conservation relationship, and dynamically correcting the condensation and evaporation amounts to obtain the total atmospheric water condensate data and airborne cloud water resource data that meet the mass conservation conditions. The formula for calculating the conserved residual is as follows: ； In the formula, It is the conserved residual for the t-th time sub-period. and These represent the total amount of atmospheric condensate at two adjacent time points. It is the boundary input quantity. It is the boundary output quantity. It is the amount of condensation generated. It is the amount consumed by evaporation. It is the amount of precipitation consumed; Step S5: Cloud water development potential assessment. Based on the cloud water resource assessment results obtained in Step S4, a comprehensive analysis is conducted on the distribution characteristics and conversion efficiency of cloud water resources in the region to obtain the cloud water resource development potential assessment results.

2. The method for assessing atmospheric cloud water resources based on a water conservation model according to claim 1, characterized in that: In step S2, the state term is used to characterize the atmospheric condensate state quantity at the beginning and end of the study period; the advection term is used to characterize the transport changes of atmospheric condensate at the boundary of the study area; and the source-sink term is used to characterize the cloud physics processes of water vapor condensation, condensate evaporation or sublimation, and precipitation.

3. The method for assessing atmospheric cloud water resources based on a water conservation model according to claim 2, characterized in that: In step S3, the cloud and water three-dimensional structure data specifically includes cloud water content spatial distribution data, cloud particle structure parameter data, cloud height distribution data, and cloud and water path data.

4. The method for assessing atmospheric cloud water resources based on a water conservation model according to claim 3, characterized in that: In step S4, the calculation of cloud water composition in different time periods involves setting a time step according to the study period, dividing the study period into multiple continuous time sub-periods, and calculating the atmospheric water condensate state quantity, boundary transport quantity, and phase transformation quantity of the study area based on the cloud water three-dimensional structure data, water vapor content distribution data, horizontal wind field data, precipitation observation data, and surface evaporation data obtained in step S3, thereby obtaining the atmospheric water condensate composition data for each time sub-period. The calculation of the total amount of atmospheric water condensate is based on the composition data of atmospheric water condensate and the cloud water mass conservation relationship established in step S2. The total amount of atmospheric water condensate in the study area in each time sub-period is calculated and processed to obtain the total amount of atmospheric water condensate data for each time sub-period. The data of airborne cloud water resources corresponding to each time sub-period is further calculated. The cloud water resource characteristic index calculation is based on the total atmospheric water condensate data and the airborne cloud water resource data, combined with precipitation observation data and water vapor change data, to calculate and process the relevant characteristic indexes of cloud water resources, and obtain precipitation efficiency data, water vapor condensation efficiency data and cloud water renewal cycle data. The time series trend analysis involves constructing a cloud water resource time series based on aerial cloud water resource volume data, performing continuous time recursive calculations on the time series to obtain cloud water resource change trend data, and combining the data to obtain cloud water resource assessment results at different time scales.

5. The method for assessing atmospheric cloud water resources based on a water conservation model according to claim 4, characterized in that: In step S4, the cloud water resource assessment results data include total atmospheric water condensate data, aerial cloud water resource data, precipitation efficiency data, water vapor condensation efficiency data, cloud water renewal cycle data, and cloud water resource change trend data.

6. A system for assessing airborne cloud water resources based on a water conservation model, used to implement the airborne cloud water resource assessment method based on a water conservation model as described in any one of claims 1-5, characterized in that: It includes a multi-source cloud and water observation fusion module, a cloud and water quality conservation modeling module, a cloud structure inversion and reconstruction module, a cloud and water resource dynamic estimation module, and a cloud and water development potential assessment module.

7. The aerial cloud water resource assessment system based on the water conservation model according to claim 6, characterized in that: The multi-source cloud and water observation fusion module is used for multi-source cloud and water observation fusion. Through multi-source cloud and water observation fusion, a multi-source cloud and water observation data set is obtained, and the multi-source cloud and water observation data set is sent to the cloud and water mass conservation modeling module and the cloud structure inversion and reconstruction module. The cloud water quality conservation modeling module is used for cloud water quality conservation modeling. Through cloud water quality conservation modeling, a cloud water quality conservation relationship model is obtained, and the cloud water quality conservation relationship model is sent to the cloud structure inversion and reconstruction module and the cloud water resource dynamic estimation module. The cloud structure inversion and reconstruction module is used for cloud structure inversion and reconstruction. Through cloud structure inversion and reconstruction, cloud and water three-dimensional structure data are obtained, and the cloud and water three-dimensional structure data are sent to the cloud and water resource dynamic estimation module. The cloud water resource dynamic estimation module is used for dynamic estimation of cloud water resources. Through dynamic estimation of cloud water resources, cloud water resource assessment result data is obtained, and the cloud water resource assessment result data is sent to the cloud water development potential assessment module. The cloud water development potential assessment module is used to assess the development potential of cloud water resources and obtain the assessment result data of cloud water resource development potential through the assessment.

Images