A method, system, and equipment for determining artificial rain enhancement schemes based on ensemble forecasting.

Abstract

This invention belongs to the field of artificial rain enhancement technology and discloses a method, system, and equipment for determining artificial rain enhancement schemes based on ensemble forecasting. The method includes: an ensemble forecasting subsystem assessing precipitation areas and determining potential rain enhancement regions; and a two-parameter cloud seeding subsystem conducting single / multi-grid point AgI catalysis numerical experiments to determine the optimal seeding scheme and regional rain enhancement effect. This invention applies ensemble forecasting technology to the field of weather modification. Within the rain enhancement experimental area, it extracts the thermal profiles, initial specific mass and specific concentration of 10 ensemble members at grid points to drive a two-parameter cloud model. Extensive AgI catalysis numerical experiments are repeatedly conducted to obtain the probability distribution of rain enhancement effects. Simultaneously, the optimal seeding scheme (seeding time, seeding height, seeding dose) is determined, revealing the impact of uncertainties in the macroscopic external variable field on cloud development and seeding effects, thereby improving the operational efficiency and service level of artificial rain enhancement.

Description

Technical Field

[0001] This invention belongs to the field of artificial rain enhancement technology, and in particular relates to a method, system and equipment for determining artificial rain enhancement schemes based on ensemble forecasting. Background Technology

[0002] Currently, the lack of water resources has been a major constraint on economic and social development. Given the urgent need for agriculture, forestry, animal husbandry, and water conservancy to reduce drought, the development and utilization of atmospheric water resources under reasonable natural conditions is particularly important.

[0003] Since Schaefer discovered that dry ice can be used as a refrigerant and Vonnegut discovered the nucleation properties of AgI, dry ice and AgI have often been used as catalysts for artificial rainmaking. Rainmaking methods mainly include smoke generator combustion, aircraft seeding through clouds, anti-aircraft guns, and rockets. In aircraft seeding operations, areas with strong radar echoes or high ground precipitation are primarily selected. However, there is a lack of in-depth research on which part of the cloud area yields the highest precipitation efficiency, and the seeding height is often chosen subjectively and blindly.

[0004] Furthermore, the dosage and timing of AgI seeding should vary depending on the weather system, terrain, and underlying surface conditions. However, currently, a generally consistent, empirically applied amount of catalyst is often used for all practical situations. To evaluate the catalytic effect of AgI and study the changes in the dynamic and microphysical fields of the cloud system after seeding, it is crucial to select the optimal seeding height, dosage, and timing for this process. Preliminary numerical simulations of clouds and precipitation are therefore essential. Existing technology 1 uses a one-dimensional cumulonimbus cloud model to seed artificial ice crystals and large-droplet particles, comparing and analyzing various catalytic methods to derive the dominant microphysical processes that artificial catalysis alters precipitation element formation. Existing technology 2 uses a two-dimensional cloud model to seed AgI and dry ice in stratiform and cumulonimbus clouds, comparing their seeding effects and finding that AgI seeding produces a strong dynamic feedback effect even with low concentrations of supercooled water and ice crystals in the cloud. Existing technology 3 uses a three-dimensional elastic hail cloud catalytic numerical model to study hail cloud catalytic techniques. Existing technology 4 uses a mesoscale numerical model and incorporates the process of AgI-cloud interaction to study the changes in aqueous particles and precipitation after AgI catalysis.

[0005] However, previous numerical simulation studies related to artificial rain enhancement have all been based on deterministic forecasts from single models, failing to consider the uncertainties in forecast results caused by errors in the initial field or the model itself. Constrained by the inherent uncertainties of atmospheric processes, cloud seedability and the determination of optimal seeding locations are highly uncertain. If ensemble forecasting methods can be applied to weather modification, fully considering these uncertainties in artificial rain enhancement operations, it will help overcome blind operations and obtain optimized rain enhancement probabilities.

[0006] Current numerical simulation studies on artificial rain enhancement are all based on deterministic simulation results, failing to consider the uncertainties in forecast results caused by errors in the initial field or the model itself. Single deterministic forecasting methods, due to their inherent limitations, cannot fully describe the evolution of the actual atmospheric state, especially during the nonlinear growth phase of their errors. Constrained by the uncertainties of the atmospheric processes themselves, cloud seedability and the determination of optimal seeding locations are subject to significant uncertainty. If ensemble forecasting methods can be applied to the field of weather modification, fully considering these uncertainties in artificial rain enhancement operations, it will help overcome the shortcomings of deterministic forecasts and obtain optimized rain enhancement probabilities.

[0007] While mesoscale ensemble forecast models incorporating AgI can also conduct cloud seeding experiments, the drawback of existing technique 4 is the excessive computational resources required, making it difficult to conduct efficient probabilistic catalytic experiments in a short time to determine high-probability rain enhancement zones. Existing techniques 2 and 3, by integrating the effects of external environmental fields and cloud microphysical processes, cannot independently examine the impact of macroscopic weather factors on clouds. Therefore, using ensemble forecast models to provide environmental profiles to drive a two-parameter cloud model incorporating AgI catalytic schemes could offer another approach to solving this problem.

[0008] Based on the above analysis, the problems and defects of the existing technology are as follows: the existing artificial rain enhancement schemes do not take into account the uncertainty of the forecast results caused by errors in the initial field or the model itself, cannot fully describe the evolution process of the true atmospheric state, and cannot reflect the uncertainty and degree of uncertainty of the artificial catalysis effect forecast, resulting in poor design of artificial rain enhancement schemes and failure to effectively improve the operational efficiency and business level of artificial rain enhancement. Summary of the Invention

[0009] To address the problems existing in the prior art, this invention provides a method, system, and device for determining artificial rain enhancement schemes based on ensemble forecasting.

[0010] This invention is implemented as follows: an artificial rain enhancement system based on ensemble forecasting, the artificial rain enhancement system based on ensemble forecasting comprising:

[0011] The ensemble forecast subsystem, comprising one control member and nine disturbance members, is used to assess precipitation areas and identify potential areas of increased rainfall.

[0012] A dual-parameter cloud seeding subsystem is used to conduct single / multi-grid point AgI catalysis numerical experiments to determine the optimal seeding scheme and regional rainfall enhancement effect.

[0013] The parameter configurations used by the two systems are shown in Table 1.

[0014] Table 1. Parameter configurations for the ensemble forecast subsystem and the two-parameter cloud seeding subsystem.

[0015]

[0016] Furthermore, the ensemble forecast subsystem includes:

[0017] The initial and boundary conditions of the control members are provided by high-resolution forecasts from NCEP’s global forecast system;

[0018] The initial boundary conditions of the nine perturbation members are provided by the first nine ensemble members of the NCEP global ensemble forecast system.

[0019] Another object of the present invention is to provide a method for determining ensemble forecast-based artificial rain enhancement schemes for use in the aforementioned ensemble forecast-based artificial rain enhancement system, the method comprising:

[0020] The thermodynamic profiles, initial specific mass and specific concentration of 10 ensemble members at grid points were extracted using the ensemble forecast subsystem in the rain enhancement experimental area. This data were then used to drive the two-parameter cloud seeding subsystem to repeatedly conduct a large number of AgI catalytic numerical experiments, obtain the probability distribution of the rain enhancement effect, and determine the optimal seeding scheme.

[0021] Furthermore, the application scheme includes: application time, application height, and application dosage.

[0022] Furthermore, the method for determining artificial rain enhancement schemes based on ensemble forecasts includes the following steps:

[0023] Step 1: Use the ensemble forecasting subsystem to assess the precipitation area 6-24 hours in advance and identify areas where rainfall may increase.

[0024] Step two: The ensemble forecast subsystem is based on the mesoscale model WRF V4.0, comprising one control member (C00) and nine perturbation members (C01-09). The mesoscale model uses high-resolution forecast data from the NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS) to provide initial and side conditions for the control member, and the first nine ensemble members from the NCEP Global Ensemble Forecast System (GEFS) to provide initial and side conditions for the perturbation members. The ensemble forecast system is generated using ensemble Kalman filtering to address forecast uncertainties.

[0025] The experiment employed a cold cloud columnar model of a two-parameter cloud seeding system. This model utilizes a two-parameter scheme that incorporates detailed microphysical processes, enabling explicit prediction of the specific concentrations and contents of cloud droplets, raindrops, ice crystals, snowflakes, and graupel. To ensure the full development of all microphysical processes within the model, the cloud model's top height was set at 11 km, with a vertical resolution of 200 m, and a time-step separation method was used to calculate these microphysical processes. The cloud model's catalytic scheme employed an AgI nucleation model, considering three nucleation mechanisms: contact freezing nucleation between AgI and cloud / raindrop droplets due to Brownian motion and inertial collisions, and sublimation nucleation of water vapor on AgI.

[0026] The ensemble forecast subsystem uses one control member (C00) and nine perturbation members (C01-C09). The control member C00 uses high-resolution forecast data from the NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS) to provide initial and boundary conditions. The perturbation members C01-C09 use the first nine ensemble members (out of a total of 30 ensemble members) from the NCEP Global Ensemble Forecast System (GEFS) to provide initial and boundary conditions. The control member and the perturbation members use the same microphysics scheme, long / shortwave radiation scheme, and cumulus convection parameterization scheme, as detailed in Table 1. Temperature, humidity, vertical velocity, and specific abundance and specific concentration profiles of initial aqueous particles are extracted every 3 minutes from the 10 members. Multiple grid points drive a one-dimensional two-parameter cloud seeding subsystem.

[0027] Step 3: Using the first catalytic scheme, AgI catalytic experiments were conducted to obtain the rainfall enhancement rate and rainfall enhancement rate threshold of 10 members at multiple grid points, and a probability distribution map of the rainfall enhancement effect under the rainfall enhancement rate threshold was obtained.

[0028] Step 4: Repeatedly conduct AgI catalysis experiments using different catalytic schemes to obtain the rainfall enhancement rate and rainfall enhancement rate threshold of 10 members at multiple grid points, and obtain the probability distribution map of rainfall enhancement effect under the rainfall enhancement rate threshold.

[0029] Step 5: Combine the probability distribution diagrams of the rain enhancement effect under the rain enhancement rate threshold obtained in Step 3 and Step 4 to determine the optimal seeding scheme.

[0030] Furthermore, in step one, the ensemble forecasting subsystem is used to assess the precipitation area 6-24 hours in advance, identifying areas where rainfall may increase, including:

[0031] The ensemble forecasting subsystem employs a double-layer nested grid with 10 members. The outer layer uses the Kain-Fritsch cumulus convection parameterization scheme, while the inner layer uses the Morrison 2-moment explicit cloud microphysics scheme, the RRTM longwave radiation calculation scheme, the Dudhia shortwave radiation calculation scheme, and the YSU boundary layer scheme to assess precipitation areas 6-24 hours in advance and identify areas where rainfall may increase.

[0032] Furthermore, in step four, the different catalytic schemes are catalytic schemes composed of different catalytic heights, different catalytic times, and different amounts of catalyst.

[0033] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for determining artificial rain enhancement schemes based on ensemble forecasts.

[0034] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for determining artificial rain enhancement schemes based on ensemble forecasts.

[0035] Another objective of this invention is to provide an information data processing terminal for implementing the artificial rain enhancement scheme determination system based on ensemble forecasting.

[0036] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0037] Ensemble forecasting techniques are still relatively rare in the field of weather modification, as single deterministic forecasting methods cannot fully describe the evolution of the true atmospheric state. Constrained by the inherent uncertainties of atmospheric processes, cloud seedability and the determination of optimal seeding locations also involve significant uncertainties. Applying ensemble forecasting methods to weather modification, and fully considering these uncertainties in artificial rain enhancement operations, would help overcome the shortcomings of deterministic forecasts and obtain optimized rain enhancement probabilities. Mesoscale models are developing rapidly, but mesoscale ensemble forecasting models incorporating AgI require excessive computational resources, making it difficult to conduct efficient probabilistic catalytic experiments in a short period to identify high-probability rain enhancement areas. Existing two-dimensional or three-dimensional convective cloud models, which integrate the effects of external environmental fields and intra-cloud microphysical processes, cannot independently examine the impact of macroscopic weather elements on clouds. Therefore, using mesoscale ensemble forecasting models to provide environmental profiles to drive two-parameter cloud models incorporating AgI catalytic schemes could offer another approach to solving this problem. This invention applies ensemble forecasting technology to the field of weather modification. It extracts the thermal profiles, initial specific mass and specific concentration of 10 ensemble members from grid points within a rain enhancement experimental area. These are used to drive a two-parameter cloud model. Extensive numerical experiments with AgI catalysis are repeatedly conducted to obtain the probability distribution of rain enhancement effects. Simultaneously, the optimal seeding scheme (seeding time, seeding height, seeding dose) is determined, revealing the impact of uncertainties in the macroscopic external variable field on cloud development and seeding effects, thereby improving the operational efficiency and service level of artificial rain enhancement.

[0038] This invention can effectively determine the optimal seeding scheme, obtain a probability distribution map of the rain enhancement effect, and guide actual artificial rain enhancement field operations.

[0039] This invention fills a technological gap in the industry both domestically and internationally: it is the first to apply the concept of ensemble forecasting to the field of weather modification, thus filling a technological void in the industry both at home and abroad. It develops ensemble forecasting techniques for mesoscale cloud structure and cloud seeding models, forming a probabilistic forecasting method for cloud structure and rain enhancement operation effects. Based on probabilistic forecasting, it comprehensively reflects the rain enhancement effects of various seeding schemes, facilitating operators to more objectively formulate and select the optimal seeding operation plan.

[0040] Does the technical solution of this invention solve a long-standing technical problem that people have long desired to solve but have yet to succeed in? This method can significantly improve the efficiency and operational level of current artificial rain enhancement operations. It allows for the selection of different catalytic schemes (catalytic height, catalyst quantity, catalytic timing) for different operational cloud systems, scientifically guiding actual aircraft, anti-aircraft artillery, and rocket seeding operations. Based on cloud structure probability forecasts, it formulates alternative rain enhancement operation schemes, achieving probabilistic forecasting of rain enhancement effects. This represents a technological breakthrough for disaster prevention and mitigation, alleviating water shortages, and improving the ecological environment. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the method for determining artificial rain enhancement schemes based on ensemble forecasting provided in an embodiment of the present invention.

[0042] Figure 2 This is a flowchart of the method for determining artificial rain enhancement schemes based on ensemble forecasting provided in an embodiment of the present invention;

[0043] Figure 3 This is a probability distribution diagram of the rain enhancement effect with a rain enhancement rate greater than 3%, 5%, 8%, and 10%, provided by an embodiment of the present invention.

[0044] Figure 3 (a) is a probability distribution diagram of the rain enhancement effect with a rain enhancement rate greater than 3% provided in the embodiments of the present invention;

[0045] Figure 3 (b) is a probability distribution diagram of the rain enhancement effect with a rain enhancement rate greater than 5% provided in the embodiments of the present invention;

[0046] Figure 3 (c) is a probability distribution diagram of rain enhancement effect with a rain enhancement rate greater than 8% provided in the embodiments of the present invention;

[0047] Figure 3 (d) is a probability distribution diagram of the rain enhancement effect when the rain enhancement rate is greater than 10% provided in the embodiment of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] To enable those skilled in the art to fully understand how the present invention is specifically implemented, this section provides an explanatory description of the embodiments that expand upon the technical solutions of the claims.

[0050] like Figures 1-2 As shown, the method for determining artificial rain enhancement schemes based on ensemble forecasting provided in this embodiment of the invention includes the following steps:

[0051] S101 uses the ensemble forecasting subsystem to assess precipitation areas 6-24 hours in advance, identify areas that may experience increased rainfall, and select areas of interest within those areas.

[0052] S102 utilizes the ensemble forecast subsystem's 10 members to acquire temperature, humidity, vertical velocity, and initial specific content and concentration profiles of aqueous particles every 3 minutes, driving a one-dimensional dual-parameter cloud seeding subsystem at multiple grid points within the S101 rain enhancement area.

[0053] S103, using the first catalytic scheme, an AgI catalytic experiment was conducted to obtain the rainfall enhancement rate and rainfall enhancement rate threshold of 10 members at multiple grid points, and a probability distribution map of the rainfall enhancement effect under the rainfall enhancement rate threshold was obtained.

[0054] S104. Different catalytic schemes were used to repeatedly conduct AgI catalysis experiments to obtain the rainfall increase rate and rainfall increase rate threshold of 10 members of multiple grid points, and to obtain the probability distribution map of rainfall increase effect under the rainfall increase rate threshold.

[0055] S105. The optimal seeding scheme is determined by combining the probability distribution map of the rain enhancement effect under the rain enhancement rate threshold obtained in steps S103 and S104.

[0056] The artificial rain enhancement system based on ensemble forecasting provided in this invention includes two subsystems: an ensemble forecasting subsystem and a two-parameter cloud seeding subsystem. Single / multi-grid point AgI catalysis numerical experiments are conducted to explore the optimal seeding scheme and regional rain enhancement effect from a probabilistic perspective.

[0057] The ensemble forecast subsystem provided in this embodiment of the invention is based on WRF V4.0 and includes one control member (C00) and nine perturbation members (C01-C09). High-resolution forecasts from the NCEP Global Forecast System (GFS) provide initial and boundary conditions for the control member, while the first nine ensemble members from the NCEP Global Ensemble Forecast System (GEFS) provide initial and boundary conditions for the perturbation members. All model members employ the Kain-Fritsch cumulus convection parameterization scheme, the Morrison2-moment cloud microphysics explicit scheme, the RRTM longwave radiation calculation scheme, the Dudhia shortwave radiation calculation scheme, and the YSU boundary layer scheme.

[0058] The one-dimensional dual-parameter cloud seeding system provided in this embodiment of the invention is based on a one-dimensional stratiform cold cloud model. The dual-parameter scheme employed in this model can explicitly predict the specific concentration and specific content of cloud droplets, raindrops, ice crystals, snowflakes, and graupel. The model has a top altitude of 11 km, a vertical resolution of 200 m, and a time step of 5 s. The model's catalytic scheme considers three nucleation mechanisms for AgI particles: contact freezing nucleation between AgI and cloud droplets / raindrops due to Brownian motion and inertial collisions, and sublimation nucleation of water vapor on AgI, including condensation-freezing nucleation.

[0059] To demonstrate the inventiveness and technical value of the technical solution of this invention, this section provides application examples of the technical solution of the claims applied to specific products or related technologies.

[0060] The method for determining artificial rain enhancement schemes based on ensemble forecasts provided in the application embodiments of the present invention is applied to a computer device, the computer device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method for determining artificial rain enhancement schemes based on ensemble forecasts.

[0061] The method for determining artificial rain enhancement schemes based on ensemble forecasts provided in the application embodiments of the present invention is applied to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor performs the steps of the method for determining artificial rain enhancement schemes based on ensemble forecasts.

[0062] The artificial rain enhancement scheme determination system based on ensemble forecasting provided in the application embodiment of the present invention is applied to an information data processing terminal, which is used to implement the artificial rain enhancement scheme determination system based on ensemble forecasting.

[0063] like Figure 3 As shown, Figure 3 To conduct AgI catalytic simulations on 71 national weather stations in Zhejiang Province, probability distribution maps of rainfall enhancement effects with enhancement rates greater than 3%, 5%, 8%, and 10% under the optimal seeding scheme were generated. In ensemble forecasting, probability forecast refers to the relative frequency of the number of members (n) predicting a specific forecast event compared to the total number of ensemble members (N).

[0064]

[0065] Where R is the precipitation after seeding, r is the natural cloud precipitation before seeding, and P... rec_inc This is the threshold for rainfall increase rate.

[0066] The probability distribution map of rainfall enhancement rates greater than 3% shows that seeding in the northwest and northern regions resulted in high rainfall enhancement efficiency, with probabilities generally greater than 60%, and some even greater than 80%. This indicates that in these areas, eight members of the ensemble forecast reported rainfall enhancement rates greater than 3%. In contrast, seeding in the southeast and other areas had the worst rainfall enhancement effect, with only 0-1 members reporting rainfall enhancement rates greater than 3%. Figure 3 a).

[0067] The probability distribution maps of rainfall enhancement effects with enhancement rates greater than 8% and 10% reveal that seeding in the northeast and northwest yields the best results, with over 50% of participants forecasting rainfall enhancement rates greater than 8%, and even greater than 10%, in these areas. In summary, from the perspective of optimal rainfall enhancement probability, seeding should be carried out in the northwest and north, especially the northeast. Figure 3(c, 3d). Compared with traditional deterministic rainfall rate forecasts (Geresdi et al. 2020; Liu et al. 2021), ensemble forecasts can better explain the uncertainties of initial conditions and random errors in model simulations, fully reflect the uncertainty and degree of uncertainty in artificial catalysis effect forecasts, and provide more possibilities for rainfall rate forecast results.

[0068] The method for determining artificial rain enhancement schemes based on ensemble forecasting provided in this embodiment of the invention specifically includes:

[0069] Step 1: Using an ensemble forecast subsystem comprising 10 members, precipitation areas are assessed 6-24 hours in advance to identify potential areas of increased rainfall. Then, temperature, humidity, vertical velocity, and initial specific content and concentration profiles of water particles provided every 3 minutes by the ensemble forecast model's 10 members are used to drive a one-dimensional two-parameter cloud model with multiple grid points.

[0070] Step 2: Using catalytic scheme 1, an AgI catalytic experiment was conducted to obtain the rainfall enhancement rate of 10 members at multiple grid points, determine the rainfall enhancement rate threshold 'a', and obtain the probability distribution map of rainfall enhancement effect under rainfall enhancement rate 'a'.

[0071] Step 3: Repeat Step 2, using different catalytic schemes (different catalytic heights / different catalytic times / different catalyst amounts) to repeatedly conduct AgI catalytic experiments, and obtain the probability distribution of the rain enhancement effect under the rain enhancement rate 'a' after catalysis.

[0072] Step four: Combining steps 2 and 3, determine the optimal seeding plan (optimal seeding height, optimal seeding dosage, optimal seeding time) and obtain a probability distribution map of the rain enhancement effect, which will be used to guide actual artificial rain enhancement field operations.

[0073] (Results are as follows) Figure 3 (As shown).

[0074] like Figure 3 As shown in the probability distribution map of rainfall enhancement rates greater than 3%, seeding in the northwest and northern regions yielded high rainfall enhancement efficiency, with probabilities generally greater than 60%, and some even greater than 80%. This indicates that in these areas, ensemble forecasts reported rainfall enhancement rates greater than 3% in 8 members. In contrast, seeding in the southeast and other areas had the worst rainfall enhancement effect, with only 0-1 members reporting rainfall enhancement rates greater than 3%. Figure 3 a). From the probability distribution maps of rainfall enhancement effects with enhancement rates greater than 8% and 10%, it can be seen that seeding in the northeast and northwest regions yields the best rainfall enhancement effects, with over 50% of members forecasting rainfall enhancement rates greater than 8%, and even greater than 10%, in these areas. In general, from the perspective of optimal rainfall enhancement probability, seeding should be carried out in the northwest and north, especially the northeast. Figure 3 (c, 3d). This method is most effective and can be used to guide actual work.

[0075] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0076] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A system for determining artificial rain enhancement schemes based on ensemble forecasting, characterized in that, The artificial rain enhancement system based on ensemble forecasting includes: The ensemble forecast subsystem, comprising one control member and nine disturbance members, is used to assess precipitation areas and identify potential areas of increased rainfall. A dual-parameter cloud seeding subsystem is used to conduct single / multi-grid point AgI catalysis numerical experiments to determine the optimal seeding scheme and regional rainfall enhancement effect; The thermodynamic profiles, initial specific content and specific concentration of 10 ensemble members at grid points were extracted using the ensemble forecast subsystem in the rain enhancement experimental area. This data were then used to drive the two-parameter cloud seeding subsystem to repeatedly conduct a large number of AgI catalytic numerical experiments to obtain the probability distribution of the rain enhancement effect and determine the optimal seeding scheme.

2. The artificial rain enhancement scheme determination system based on ensemble forecasting as described in claim 1, characterized in that, The ensemble forecast subsystem includes: The initial and boundary conditions of the control members are provided by high-resolution forecasts from NCEP’s global forecast system; The initial boundary conditions of the nine perturbation members are provided by the first nine ensemble members of the NCEP global ensemble forecast system.

3. A method for determining an ensemble forecast-based artificial rain enhancement scheme applied to an ensemble forecast-based artificial rain enhancement system as described in any one of claims 1-2, characterized in that, The method for determining artificial rain enhancement schemes based on ensemble forecasting includes: The thermodynamic profiles, initial specific content and specific concentration of 10 ensemble members at grid points were extracted using the ensemble forecast subsystem in the rain enhancement experimental area. This data were then used to drive the two-parameter cloud seeding subsystem to repeatedly conduct a large number of AgI catalytic numerical experiments to obtain the probability distribution of the rain enhancement effect and determine the optimal seeding scheme.

4. The method for determining artificial rain enhancement schemes based on ensemble forecasting as described in claim 3, characterized in that, The application scheme includes: application time, application height, and application dosage.

5. The method for determining artificial rain enhancement schemes based on ensemble forecasting as described in claim 3, characterized in that, The method for determining artificial rain enhancement schemes based on ensemble forecasts includes the following steps: Step 1: Use the ensemble forecasting subsystem to assess the precipitation area 6-24 hours in advance and identify areas where rainfall may increase. Step 2: The ensemble forecasting subsystem uses 10 members to acquire temperature, humidity, vertical velocity, and the specific content and specific concentration profiles of initial aqueous particles every 3 minutes. Multiple grid points drive the one-dimensional dual-parameter cloud seeding subsystem. Step 3: Using the first catalytic scheme, AgI catalytic experiments were conducted to obtain the rainfall enhancement rate and rainfall enhancement rate threshold of 10 members at multiple grid points, and a probability distribution map of the rainfall enhancement effect under the rainfall enhancement rate threshold was obtained. Step 4: Repeatedly conduct AgI catalysis experiments using different catalytic schemes to obtain the rainfall enhancement rate and rainfall enhancement rate threshold of 10 members at multiple grid points, and obtain the probability distribution map of rainfall enhancement effect under the rainfall enhancement rate threshold. Step 5: Combine the probability distribution diagrams of the rain enhancement effect under the rain enhancement rate threshold obtained in Step 3 and Step 4 to determine the optimal seeding scheme.

6. The method for determining artificial rain enhancement schemes based on ensemble forecasting as described in claim 5, characterized in that, In step one, the ensemble forecasting subsystem is used to assess the precipitation area 6-24 hours in advance, identifying areas where rainfall may increase, including: Using a double-layer nested grid, the 10 members of the ensemble forecasting subsystem employ an outer Kain-Fritsch cumulus convection parameterization scheme, an inner Morrison 2-moment explicit cloud microphysics scheme, an RRTM longwave radiation calculation scheme, a Dudhia shortwave radiation calculation scheme, and a YSU boundary layer scheme to assess precipitation areas 6-24 hours in advance and identify potential areas for increased rainfall.

7. The method for determining artificial rain enhancement schemes based on ensemble forecasting as described in claim 5, characterized in that, The ensemble forecast subsystem is based on the mesoscale model WRF V4.0 and includes one control member C00 and nine perturbation members C01-09. The mesoscale model uses high-resolution forecast data from the NCEP global forecast system to provide initial and side conditions for the control member, and uses the first nine ensemble members from the NCEP global ensemble forecast system to provide initial and side conditions for the perturbation members. The ensemble forecast system is generated using ensemble Kalman filtering technology to address forecast uncertainties. The ensemble forecast subsystem uses one control member C00 and nine perturbation members C01-09 to acquire temperature, humidity, vertical velocity, and initial specific content and specific concentration profiles of aqueous particles every 3 minutes. Multiple grid points drive a one-dimensional two-parameter cloud seeding subsystem.

8. The method for determining artificial rain enhancement schemes based on ensemble forecasting as described in claim 5, characterized in that, In step four, the different catalytic schemes are catalytic schemes composed of different catalytic heights, different catalytic times, and different amounts of catalyst.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for determining artificial rain enhancement schemes based on ensemble forecasts as described in any one of claims 3-7.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the artificial rain enhancement scheme determination system based on ensemble forecasting as described in any one of claims 1-2.

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