A weather disaster high-resolution regional model prediction system and method

Abstract

The application provides a meteorological disaster high-resolution regional mode prediction system and method, relates to the technical field of meteorological disaster prediction, and comprises the following steps: performing scale reduction processing on target data of a region to be processed to obtain scale-reduced target data, and performing fusion on multi-source observation data to obtain grid fusion data; according to a preset period, different target observation data are subjected to physical quantity correction, error estimation, quality control and format conversion processing by using specific technical methods, and the processed target observation data are subjected to hourly rapid cycle assimilation processing to obtain target assimilation analysis data; and the scale-reduced target data, the target assimilation analysis data and localized data are used to simulate the operation of a localized numerical prediction mode, meteorological disaster numerical prediction results of different scale requirements are generated, and the meteorological disaster numerical prediction results are corrected by using corresponding scale grid fusion data, so that the technical problem of low prediction accuracy of an existing meteorological disaster system is solved.

Description

Technical Field

[0001] This invention relates to the technical field of meteorological disaster forecasting, and in particular to a high-resolution regional model forecasting system and method for meteorological disasters. Background Technology

[0002] Because numerical weather prediction models have limited ability to describe the real atmosphere in their initial fields, data assimilation techniques are needed to provide more accurate atmospheric initialization information to the models in order to enable numerical weather prediction systems to obtain more near-real-time information about the real atmosphere. Traditional operational systems typically perform data assimilation twice or four times a day (once every six hours), mainly to address short- and medium-term forecasting issues. However, severe convective systems possess highly fluctuating meteorological elements and smaller temporal and spatial scales, requiring numerical weather prediction models to provide more precise temporal and spatial resolutions to improve forecast accuracy. However, the forecast accuracy of existing meteorological disaster systems remains low.

[0003] No effective solutions have yet been proposed to address the above problems. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a high-resolution regional model forecasting system and method for meteorological disasters, so as to alleviate the technical problem of low forecast accuracy of existing meteorological disaster systems.

[0005] In a first aspect, embodiments of the present invention provide a high-resolution regional model forecasting system for meteorological disasters, comprising: a multi-source model data downscaling subsystem, a multi-source data assimilation processing subsystem, and a regional model localization construction subsystem. The multi-source model data downscaling subsystem is used to downscale target data of the region to be processed, obtaining downscaled target data, and to fuse the multi-source observation data to obtain gridded fused data. The target data is the numerical forecasting result of a global model for meteorological disasters. The multi-source data assimilation processing subsystem, according to a preset period, employs specific technical methods to perform physical quantity correction, error estimation, and quality control for different target observation data. The system performs format conversion processing and then rapidly cyclically assimilates the processed target observation data hourly to obtain target assimilation analysis data. The regional model localization construction subsystem is used to simulate and run a localized numerical weather prediction model using the downscaled target data, the target assimilation analysis data, and the localized data. This generates meteorological disaster numerical weather prediction results at different scales and corrects the prediction results using gridded fusion data at different scales. The downscaled target data serves as the driving field for the localized numerical weather prediction model, and the target assimilation analysis data provides the initial field for the localized numerical weather prediction model. The localized data includes localized geographic data and localized physical parameters.

[0006] Furthermore, the multi-source model data downscaling subsystem is used to downscale the target data of the area to be processed to obtain target data at a first preset scale, and to fuse multi-source observation data to obtain gridded fused data at the first preset scale; the regional model localization construction subsystem is used to localize the geographic data and physical parameters of the area to be processed based on the first preset scale to obtain first preset model localization data; the regional model localization construction subsystem is also used to run a regional model with a spatial resolution of the first preset scale based on the downscaled data at the first preset scale and the first preset model localization data, generate meteorological disaster numerical forecast results at the first preset scale, and correct the forecast results using gridded fused data at the first preset scale.

[0007] Furthermore, the multi-source model data downscaling subsystem is used to downscale the meteorological disaster numerical forecast results at the first preset scale and to fuse the multi-source observation data to obtain the target data downscaled at the second preset scale and the gridded fused data, respectively. The regional model localization construction subsystem is used to obtain the model localization data at the second preset scale based on the second preset scale and the physical parameters corresponding to the second preset scale. The regional model localization construction subsystem is also used to run a regional model with a spatial resolution of the second preset scale using the target data downscaled at the second preset scale and the model localization data at the second preset scale to generate the meteorological disaster numerical forecast results at the second preset scale, and to correct the forecast results using the gridded fused data at the second preset scale.

[0008] Furthermore, the multi-source model data downscaling subsystem is used to downscale the meteorological disaster numerical forecast results at the second preset scale and to fuse the multi-source observation data to obtain the target data and gridded fused data at the third preset scale, respectively. The regional model localization construction subsystem is used to obtain the model localization data at the third preset scale based on the third preset scale and the physical parameters corresponding to the third preset scale. The regional model localization construction subsystem is also used to run a regional model with a spatial resolution of the third preset scale using the target data downscaled at the third preset scale and the model localization data at the third preset scale, to generate meteorological disaster numerical forecast results at the third preset scale, and to correct the forecast results using the gridded fused data at the third preset scale.

[0009] Furthermore, the observation data includes: ground observation data, radiosonde data, radar data, and microwave radiometer data.

[0010] Furthermore, the multi-source model data downscaling subsystem includes: a global model data preprocessing module and a mesoscale regional model preprocessing module. The global model data preprocessing module is used to downscale the global model meteorological disaster numerical forecast results to obtain the driving field of the meteorological disaster numerical forecast model at a first preset scale, and to obtain the meteorological disaster numerical forecast results at the first preset scale through forecast model simulation. It also performs first preset scale fusion on the multi-source observation data to obtain gridded fused data at the first preset scale, thereby correcting the meteorological disaster numerical forecast results. The mesoscale regional model preprocessing module is used to perform stepwise downscaling on the meteorological disaster numerical forecast results at the first preset scale to obtain target data downscaled at second and third preset scales, and to obtain meteorological disaster numerical forecast results at corresponding preset scales through forecast model simulation. It also performs first and second preset scale fusion on the multi-source observation data respectively to obtain gridded fused data at the corresponding preset scales, thereby correcting the corresponding meteorological disaster numerical forecast results.

[0011] Furthermore, the multi-source data assimilation processing subsystem includes: a ground observation data processing module, a radar data processing module, a radiosonde data processing module, a microwave radiometer data processing module, and a fast cyclic assimilation module. The ground observation data processing module is used to sequentially perform spatiotemporal calibration processing, error estimation processing, and quality control verification processing on the ground observation data to obtain target ground observation data, and convert the target ground observation data into BUFR format ground observation data. The radar data processing module is used to perform quality control verification processing on the radar data to obtain target radar data, and convert the target radar data into BUFR format radar data. The radiosonde data processing module is used to process the ground observation data... The radiosonde data undergoes spatiotemporal calibration, error estimation, and quality control verification sequentially to obtain target radiosonde data processing, which is then converted into BUFR format radiosonde data. The microwave radiometer data processing module performs quality control verification on the microwave radiometer data to obtain target microwave radiometer data, which is then converted into BUFR format microwave radiometer data. The rapid cyclic assimilation module performs cyclic assimilation processing on the BUFR format ground observation data, BUFR format radar data, BUFR format radiosonde data, and BUFR format microwave radiometer data according to a preset cycle to obtain the target assimilation analysis data.

[0012] Furthermore, the regional pattern localization construction subsystem includes: a geographic data localization module and a physical parameter localization module, wherein the geographic data localization module is used to perform localization processing on the geographic data; and the physical parameter localization module is used to perform localization processing on the physical parameters.

[0013] Secondly, this invention also provides a high-resolution regional model forecasting method for meteorological disasters, comprising: downscaling target data of the region to be processed to obtain downscaled target data, and fusing multi-source observation data to obtain gridded fused data, wherein the target data is the numerical forecasting result of a global model for meteorological disasters; according to a preset cycle, for different target observation data, using specific technical methods to perform physical quantity correction, error estimation, quality control, and format conversion processing, and performing hourly rapid cyclic assimilation processing on the processed target observation data to obtain target assimilation analysis data; using the downscaled target data, the target assimilation analysis data, and localized data, simulating and running a localized numerical forecasting model to generate meteorological disaster numerical forecasting results with different scale requirements, and using gridded fused data of the corresponding scale to correct the forecasting results. The downscaled target data serves as the driving field of the localized numerical forecasting model, and the target assimilation analysis data provides the initial field for the localized numerical forecasting model; the localized data includes localized geographic data and localized physical parameters.

[0014] Thirdly, embodiments of the present invention also provide a computer-readable storage medium on which a computer program is stored.

[0015] In this embodiment of the invention, a high-resolution regional model forecasting system for meteorological disasters is provided, comprising: a multi-source model data downscaling subsystem, a multi-source data assimilation processing subsystem, and a regional model localization construction subsystem. The multi-source model data downscaling subsystem is used to downscale target data of the region to be processed, obtaining downscaled target data, and simultaneously to fuse multi-source observation data to obtain gridded fused data. The target data is the numerical forecasting result of meteorological disasters from a global model. The multi-source data assimilation processing subsystem is used to preprocess different observation data according to a preset cycle to obtain processed observation data, and to perform hourly rapid cyclic assimilation of the processed target observation data. The data is processed to obtain target assimilation analysis data. The regional model localization construction subsystem is used to simulate and run a localized numerical weather prediction model using the downscaled target data, the target assimilation analysis data, and the localized data to generate meteorological disaster numerical weather prediction results at different scales. The downscaled target data serves as the driving field for the localized numerical weather prediction model, and the target assimilation analysis data provides the initial field for the localized numerical weather prediction model. The localized data includes localized geographic data and localized physical parameters, achieving the goal of accurate meteorological disaster forecasting. This solves the technical problem of low forecast accuracy in existing meteorological disaster systems and improves the technical effect of meteorological disaster forecast accuracy.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A schematic diagram of a high-resolution regional model forecasting system for meteorological disasters provided in an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of rapid cyclic assimilation provided in an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of distributed cyclic assimilation based on WRF and GSI provided for embodiments of the present invention;

[0022] Figure 4 A flowchart of a high-resolution regional model forecasting method for meteorological disasters provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1:

[0025] According to an embodiment of the present invention, a high-resolution regional model forecasting system for meteorological disasters is provided. Figure 1 This is a schematic diagram of a high-resolution regional model forecasting system for meteorological disasters according to an embodiment of the present invention, such as... Figure 1 As shown, the high-resolution regional model forecasting system for meteorological disasters includes: a multi-source model data downscaling subsystem 10, a multi-source data assimilation and processing subsystem 20, and a regional model localization construction subsystem 30.

[0026] The multi-source model data downscaling subsystem 10 is used to downscale the target data of the area to be processed to obtain downscaled target data, and is also used to fuse multi-source observation data to obtain gridded fused data. The target data is the numerical forecast result of global model meteorological disasters.

[0027] The multi-source data assimilation processing subsystem 20 is used to perform corresponding preprocessing on different observation data according to a preset cycle to obtain processed observation data, and to perform hourly rapid cyclic assimilation processing on the processed target observation data to obtain target assimilation analysis data.

[0028] The regional model localization construction subsystem 30 is used to simulate and run a localized numerical forecasting model using the downscaled target data, the target assimilation analysis data, and localized data, and generate meteorological disaster numerical forecasting results with different scale requirements. The downscaled target data serves as the driving field of the localized numerical forecasting model, and the target assimilation analysis data provides the initial field for the localized numerical forecasting model. The localized data includes localized geographic data and localized physical parameters.

[0029] In this embodiment of the invention, a high-resolution regional model forecasting system for meteorological disasters is provided, comprising: a multi-source model data downscaling subsystem, a multi-source data assimilation processing subsystem, and a regional model localization construction subsystem. The multi-source model data downscaling subsystem is used to downscale target data of the region to be processed, obtaining downscaled target data, and simultaneously to fuse multi-source observation data to obtain gridded fused data. The target data is the numerical forecasting result of meteorological disasters from a global model. The multi-source data assimilation processing subsystem is used to preprocess different observation data according to a preset cycle to obtain processed observation data, and to perform hourly rapid cyclic assimilation of the processed target observation data. The data is processed to obtain target assimilation analysis data. The regional model localization construction subsystem is used to simulate and run a localized numerical weather prediction model using the downscaled target data, the target assimilation analysis data, and the localized data to generate meteorological disaster numerical weather prediction results at different scales. The downscaled target data serves as the driving field for the localized numerical weather prediction model, and the target assimilation analysis data provides the initial field for the localized numerical weather prediction model. The localized data includes localized geographic data and localized physical parameters, achieving the goal of accurate meteorological disaster forecasting. This solves the technical problem of low forecast accuracy in existing meteorological disaster systems and improves the technical effect of meteorological disaster forecast accuracy.

[0030] In this embodiment of the invention, the multi-source model data downscaling subsystem is used to downscale the target data of the region to be processed to obtain target data at a first preset scale, and to fuse the multi-source observation data to obtain grid fused data at the first preset scale.

[0031] The regional pattern localization construction subsystem is used to perform localization processing on the geographic data and physical parameters of the region to be processed based on the first preset scale, so as to obtain the first preset pattern localization data.

[0032] The regional model localization construction subsystem is also used to run a regional model with a spatial resolution of the first preset scale based on the downscaled data of the first preset scale and the localized data of the first preset model, and generate meteorological disaster numerical forecast results of the first preset scale.

[0033] The multi-source model data downscaling subsystem is used to downscale the meteorological disaster numerical forecast results at the first preset scale and to fuse the multi-source observation data to obtain the target data and gridded fused data after downscaling at the second preset scale, respectively.

[0034] The regional pattern localization construction subsystem is used to obtain pattern localization data at the second preset scale based on the second preset scale and the physical parameters corresponding to the second preset scale.

[0035] The regional model localization construction subsystem is also used to run a regional model with a spatial resolution of the second preset scale using downscaled data at the second preset scale and model localization data at the second preset scale, and generate meteorological disaster numerical forecast results at the second preset scale.

[0036] The multi-source model data downscaling subsystem is used to downscale the meteorological disaster numerical forecast results at the second preset scale and to fuse the multi-source observation data to obtain the target data and gridded fused data after downscaling at the third preset scale, respectively.

[0037] The regional pattern localization construction subsystem is used to obtain pattern localization data at the third preset scale based on the third preset scale and the physical parameters corresponding to the third preset scale.

[0038] The regional model localization construction subsystem is also used to run a regional model with a spatial resolution of the third preset scale using the downscaled data at the third preset scale and the model localization data at the third preset scale, and generate meteorological disaster numerical forecast results at the third preset scale.

[0039] In this embodiment of the invention, the regional pattern localization construction subsystem includes: a geographic data localization module and a physical parameter localization module.

[0040] The geographic data localization module is used to perform localization processing on the geographic data;

[0041] The physical parameter localization module is used to perform localization processing on the physical parameters.

[0042] This module selects various digital elevation models and uses multiple data sources such as vector and raster, including GTOPO30, the more real-time SRTM, and ASTER global digital elevation models, to simulate the real terrain distribution.

[0043] This module was constructed by importing the aforementioned high-precision topographic data into the WRF model for comparative studies. The results show that the GTOPO30 topographic data has significant errors in describing the topographic undulations within the region, but the SRTM3 and ASTER data have certain correction capabilities and can improve the model's near-surface simulation performance to some extent. This improves the understanding of the impact of complex undulating terrain on near-surface atmospheric and pressure fields, thereby enhancing the forecast accuracy of weather and climate models for the target region.

[0044] By coupling novel land use data with the WRF model and referencing previous research results and land cover characteristics of the target area, a unified classification mapping between other data and the WRF built-in data was established according to the IGBP 20 classification criteria, i.e., the WRF built-in land use data classification criteria. The mapping process mainly consists of two scenarios: ① Land use types in MODIS2012, GLC2009, and GLC2000 data directly correspond to land use types in MODIS2001 data, such as evergreen coniferous forest, evergreen broad-leaved forest, and deciduous coniferous forest; ② Land use types in MODIS2012, GLC2009, and GLC2000 data do not directly correspond to land use types in MODIS2001 data. For data types without direct correspondence (mainly existing in the correspondence between IGBP and GLC data), all data are first uniformly resampled for 30 seconds, and then classified according to the category with the largest area proportion in the mixed pixels using the area dominance method.

[0045] The model has a richer internal parameterization scheme than other mesoscale models, and considers physical processes in greater detail. The research on numerical models mainly revolves around the selection of different physical parameterization schemes. Which parameterization scheme can achieve the best simulation effect? ​​The selection of parameterization scheme under different weather conditions affects the simulation capability of the model. The physical parameterization schemes introduced in numerical models mainly include: (1) microphysical process scheme; (2) cumulus convection scheme; (3) boundary layer scheme;

[0046] The WRF model is a mesoscale numerical weather prediction model for a fully compressible atmosphere and is a non-hydrostatic model. The model uses an Arakawa-C grid in the horizontal direction and a topographic-following hydrostatic vertical coordinate system in the vertical direction. The WRF-ARW dynamic solver integrates the fully compressible, non-hydrostatic Euler primitive equations. After transformation, the variables in the equations are calculated in flux form to ensure their conservation. The equations are in Cartesian coordinate space and include the influence of atmospheric water vapor. The equations employ a time-split integration scheme. Slow waves or low-frequency waves (meteorologically defined low-frequency waves) are integrated using a third-order Runge-Kutta time integration scheme, while high-frequency waves are calculated using small time steps to ensure numerical stability. Horizontally propagating acoustic modes and gravity waves use a forward-backward time integration scheme, while vertically propagating acoustic modes and elastic oscillations use a vertical implicit integration scheme.

[0047] Since high-resolution global models are not yet operational, it is still necessary to nest numerical weather prediction models over finite regions to achieve higher-resolution numerical forecasts and simulations "locally," thereby improving the forecasting and simulation capabilities of the models. This involves the issue of nesting techniques for finite region side boundaries. To reduce computational load, the grid spacing within the finite region is reduced, which constitutes the nested grid forecasting method.

[0048] Nested grids refer to using a relatively coarse grid for the large computational area, while using a finer grid with a relatively coarse resolution for the smallest area of ​​interest. This improves forecast quality while keeping the computational load relatively low. The finer grids in a nested grid can be fixed or move with the weather system. Multiple nested grids can also be used, where a further region with an even finer resolution is selected within a larger region, creating multiple nested grids.

[0049] In this module, a three-layer mesh bidirectional nested scheme is adopted, with mesh spacing of 9km (i.e., the first preset scale), 3km (i.e., the second preset scale) and 1km (i.e., the third preset scale) respectively. This achieves high resolution in the critical 1km*1km region, while the parent region above it uses coarse resolution to provide boundary conditions, thus better balancing the dual requirements of computational efficiency and simulation accuracy.

[0050] In this embodiment of the invention, the multi-source data assimilation processing subsystem includes: a ground observation data processing module, a radar data processing module, a radiosonde data processing module, a microwave radiometer data processing module, and a rapid cyclic assimilation module.

[0051] The ground observation data processing module is used to sequentially perform spatiotemporal calibration processing, error estimation processing, and quality control verification processing on the ground observation data to obtain target ground observation data, and convert the target ground observation data into ground observation data in BUFR format.

[0052] The radar data processing module is used to perform quality control and inspection on the radar data to obtain target radar data processing, and to convert the target radar data processing into radar data in BUFR format.

[0053] The radiosonde data processing module is used to sequentially perform spatiotemporal calibration processing, error estimation processing, and quality control inspection processing on the radiosonde data to obtain target radiosonde data processing, and convert the target radiosonde data processing into radiosonde data in BUFR format.

[0054] The microwave radiometer data processing module is used to perform quality control and inspection on the microwave radiometer data to obtain target microwave radiometer data, and convert the target microwave radiometer data into microwave radiometer data in BUFR format.

[0055] The rapid cyclic assimilation module is used to perform cyclic assimilation processing on the BUFR format ground observation data, the BUFR format radar data, the BUFR format radiosonde data, and the BUFR format microwave radiometer data according to a preset cycle to obtain the target assimilation analysis data.

[0056] In this embodiment of the invention, in order to assimilate multi-source meteorological observation data and ground-based densified observation data of the target area and provide detailed model forecast initial values ​​and diagnostic products of atmospheric physical quantities, a three-dimensional variational assimilation method is adopted to realize the assimilation analysis of conventional, unconventional and ground-based densified data, and to provide a high-resolution mesoscale meteorological analysis field.

[0057] The preprocessing and assimilation data selection strategies and assimilation processes for multi-source observation data are implemented as follows:

[0058] (I) Ground Data Assimilation

[0059] Ground observation data must meet certain format requirements to be accepted and assimilated by the assimilation system. In order for ground observation data to be effectively entered into the assimilation system, the observation data of the assimilation system needs to be preprocessed, so as to provide the assimilation system with ground observation data that meets the format requirements.

[0060] Since the GSI system only recognizes the BUFR format, data format conversion is required before data is input into GSI. Furthermore, quality control is necessary, requiring the setting of background error covariance and observation error covariance; both settings are indispensable and significantly impact the system's simulation performance. The rapid update cyclic assimilation module utilizes four elements from ground observation data—temperature, humidity, wind, and air pressure—to preprocess the ground observation data of the assimilation system, aiming to provide the assimilation system with ground observation data that meets the format requirements.

[0061] This module is compiled using FORTRAN or other suitable compilers, and mainly includes the following steps:

[0062] (1) Read in the original format ground observation file and rearrange and combine the data;

[0063] (2) Read in the operating parameters of the ground observation data preprocessing module;

[0064] (3) Specify the region and time window to determine the unique observation domain, and perform spatial and temporal calibration;

[0065] (4) Add ground observation data and observations within the same time window to each observation station;

[0066] (5) Estimate the observation error based on the pre-given error file;

[0067] (6) Conduct various quality control checks on the ground observation data that will be entered into the assimilation system;

[0068] (7) Write the ground observation data files into the BUFR format that the assimilation system can recognize;

[0069] (8) Numerous diagnostic files can be output after the ground observation data is preprocessed, which details the quality control methods and error estimation.

[0070] Two commonly used ground assimilation schemes in existing WRF mesoscale models are the Ruggiero ground data assimilation scheme and the Guo Yongrun ground data assimilation scheme. This system adopts the Ruggiero ground data assimilation scheme for physical quantity correction, which can ensure the full utilization of observation data and take into account the factors of complex terrain, thereby improving the rationality of ground data utilization.

[0071] The Ruggiero ground data assimilation scheme takes into account the difference between the model topography and the actual observation station topography. It divides the ground observation data into three categories for assimilation: (1) When the station topography is greater than the model's lowest layer height, the ground observation data is entered into the model as upper-air data; (2) When the model's lowest layer height is more than 100 meters higher than the station topography, the data for that station is discarded; (3) When the model's lowest layer height is higher than the station topography, and the model's lowest layer height is less than 100 meters higher than the station topography, the data for that station is inverted to the model's lowest layer using background field information.

[0072] The Ruggiero ground data assimilation scheme data correction mainly includes the following:

[0073] (1) Correcting the wind field based on similarity theory

[0074] The correction factor was calculated based on similarity theory to convert the surface wind field to a 40-meter wind field. The model assumes a ground height of 40 meters, free convection, and that the atmosphere below the lowest level is well-mixed.

[0075] The lower unsteady troposphere is divided into three layers: from the surface to hs, the mixing layer from hs to h1, and the transition layer from h1 to h2. Using near-surface similarity theory, the 10-meter wind field observations are corrected to the height of hs, and the wind speed in the hs layer can represent the wind speed of the entire mixing layer (hs~h1). We approximate the wind field of this layer as the wind field of the lowest layer of the model (40 meters, σ=0.995), using the logarithmic wind profile method assumed in the similarity theory. As shown in the figure below, u40 / u10 is more affected by surface roughness than the Monin-Obukhov length L. Therefore, under free convection conditions, the mean wind estimate can be calculated based on the surface elevation and roughness.

[0076] The calculation process is as follows:

[0077] Richardson number:

[0078] In the above formula, za = hm - ho, (θ se ) m 、(θ se ) o The altitude and pseudo-equivalent potential temperature of the lowest σ layer and the observation surface in the hm and ho models, respectively.

[0079] in,

[0080] Monin-Obukhov length:

[0081] Considering only the case of free convection:

[0082] when hour:

[0083] If z0 < 0.2, then

[0084] If z0 < 0.2, then

[0085] In the above formula, u40 is the 40-meter wind field (the lowest layer of the model), u10 is the 10-meter wind field (the observation surface), and z0 is the ground roughness length.

[0086] (2) Ruggiero's scheme corrects the temperature field

[0087] The temperature of the lowest layer of the model is corrected using the potential temperature of the observed surface and the potential temperature derived recursively from the average profile. First, the potential temperatures of two layers 100 hPa and 200 hPa higher than the lowest layer of the model are calculated based on the background field information. Then, the atmospheric potential temperature lapse rate is calculated. The reason for choosing layers 100 hPa and 200 hPa higher than the model surface is to ensure that the calculated potential temperature lapse rate is within the boundary layer. Next, the temperatures on the observed surface and the lowest layer of the model are recursively calculated from the obtained lapse rate. These temperatures differ slightly from the previously observed temperatures and the lowest layer potential temperature in the background field, which is due to the height difference between the observed and model surfaces. After recursively calculating the potential temperature of the lowest σ layer of the model and at the height of the observed surface, the analytical quantity, the potential temperature deviation at the lowest layer of the model and at the height of the observed surface, is obtained. Finally, the increment is added to the original lowest σ layer of the model in the background field.

[0088] Calculation process:

[0089] First, identify the layers k_100mb and k_200mb (within the boundary layer) above the lowest σ layer of the first mode, at 100 hPa and 200 hPa, respectively, and calculate the potential temperature θ of these two layers. 100mb θ 200mb and the potential temperature θ of the observation surface o .

[0090] The potential temperatures of the lowest layer and the observation surface are extrapolated from the potential temperature lapse rates of k_100mb and k_200mb:

[0091]

[0092]

[0093] Where hm and ho represent the elevations of the lowest level of the model and the observation surface, respectively.

[0094] The observation surface and the lowest-level increment of the model:

[0095] dth_obs=(θ o ) inv -θ o ;

[0096]

[0097] Bring the increment back to the lowest level of the pattern: (θ) m ) inv =θ m -dth_sfc;

[0098] Find the temperature of the lowest layer:

[0099] (3) Correcting the pressure field using static equilibrium equations

[0100] according to get

[0101] Among them, P m It is the lowest atmospheric pressure in the corrected model; h m It is the lowest level σ of the pattern kx The altitude of the layer; R is the ideal gas constant; g is the acceleration due to gravity; P o It is the air pressure at the observation surface; h o It is the altitude of the observation surface, T v Altitude h m and h o The average virtual temperature between them is calculated using the formula T. v =0.5×(T) vm +T vo ).

[0102] The formula for calculating the virtual temperature of the model is: T vm =T m ×(1.0+0.608Q m );

[0103] Among them, T m and Q m These are the lowest level σ of the pattern. kx Temperature and humidity, observed virtual temperature T vo Based on the observed temperature and humidity, there are three scenarios:

[0104] 1) When T o and Q o When both exist, T vo =T o ×(1.0+0.608Q o );

[0105] 2) Only T o At that time, T vo =T o ;

[0106] 3) If T o and Q o When none of them are present, T vo =T vm .

[0107] Based on the basic framework of the Ruggiero ground data assimilation scheme, and making full use of existing observation data, this scheme avoids discarding data due to excessive differences in height between the model topography and the actual topography (observation surface height exceeding 100 meters above the lowest model layer). Furthermore, considering the influence of topography and geomorphology, different temperature and specific humidity correction methods are adopted according to the observed topography. The specific design of the ground data assimilation scheme for this system is as follows:

[0108] (1) If the terrain height of the station is greater than the lowest layer height of the model, the ground observation data will be entered into the model as upper-air data.

[0109] (2) If the model's lowest layer height is higher than the station's topographic height, the station's data is inverted to the model's lowest layer using background field information. The wind field inversion is calculated using the near-surface layer similarity theory in the original Ruggiero scheme. The main changes to the system are inversion methods for temperature and humidity. Based on relevant research results, the difference in topographic height of the observation surface is corrected for temperature and specific humidity using local temperature and specific humidity lapse rate.

[0110] The lowest level of the model is the potential temperature of the observed surface:

[0111]

[0112]

[0113] Pseudo-equivalent potential temperature:

[0114]

[0115]

[0116] Stability:

[0117]

[0118] Here, hm and ho represent the elevations of the model's bottom layer and the observation surface, respectively. Based on ho and stability intervals, the observed temperature (t0) and specific humidity (q0) are corrected to the model's bottom layer.

[0119] (II) Assimilation of Sounding Data

[0120] Radiosonde data is stable, reliable, and has high vertical detection accuracy, providing a complete description of the three-dimensional atmospheric structure. Therefore, radiosonde data has long been one of the most fundamental data sources for numerical model forecasts and a standard for verifying the forecast performance and the reliability of other observational data. Studies have shown that assimilating radiosonde data can improve the forecast accuracy of atmospheric temperature, humidity, and wind profiles, thereby increasing the accuracy of convective weather forecasts.

[0121] Radiosonde data must meet predetermined format requirements to be accepted and assimilated by the assimilation system. In order for radiosonde data to be effectively incorporated into the assimilation system, it is necessary to preprocess the radiosonde data to provide the assimilation system with radiosonde data that meets the format requirements.

[0122] This module is compiled using FORTRAN and mainly includes the following steps:

[0123] (1) Read in the original format radiosonde file and rearrange and combine the data;

[0124] (2) Read in the operating parameters of the observation data preprocessing module;

[0125] (3) Specify the region and time window to determine the unique observation domain, and perform spatial and temporal calibration;

[0126] (4) Add radiosonde data from the same location and observations within the same time window to each observation station;

[0127] (5) Estimate the observation error based on the pre-given error file;

[0128] (6) Conduct various quality control tests on the sounding data that will be entered into the assimilation system, such as using static balance to check the pressure field and altitude field.

[0129] (7) Write the sounding data files into the BUFR format that the assimilation system can recognize;

[0130] (8) Numerous diagnostic files can be output after radiosonde data preprocessing, detailing the quality control methods and error estimation.

[0131] (III) Radar Data Assimilation

[0132] Doppler weather radar is a type of weather radar specifically designed for operational meteorological observations. Its data is characterized by a wide distribution range and higher spatial and temporal resolution compared to conventional ground-based observation data. This effectively compensates for the shortcomings of conventional data in terms of insufficient temporal and spatial observation samples, providing valuable assimilated data for effectively improving the performance of higher-resolution model forecasts. It has become one of the most widely used unconventional observation data sources. Radar data assimilation functions can effectively assimilate Doppler radar data, improve the accuracy of the initial field, and add small-to-medium scale weather information to the initial field.

[0133] The radar data processing function primarily targets the study area, employing next-generation weather radar 3D mosaic technology to obtain 31 standard-layer radar reflectivity data in netCDF format. The GSI system requires 31 vertical layers of radar reflectivity data to enter the assimilation system. Based on the maximum detection height of the radar elevation angle and the upper limit of the model layer height, we set the height resolution of each layer as follows: 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 18.0 km; and the X and Y values ​​of each observation data point must match the model grid point number. The obtained three-dimensional reflectivity data is interpolated onto the pattern grid points. Finally, the execution code for reading radar reflectivity data in the GSI source code is modified so that GSI can recognize and store three-dimensional reflectivity data in text format.

[0134] Gridding of radar reflectivity data involves interpolating radar data with uneven spatial resolution in a spherical coordinate system to a unified Cartesian coordinate system to form grid data with uniform spatial resolution.

[0135] By comprehensively applying radar data and other observation data, or by mosaicking multiple radar data, the first step is to interpolate the radar data with uneven spatial resolution in the polar coordinate system to a unified Cartesian coordinate system to form grid data with uniform spatial resolution. In the interpolation process, the original reflectivity structure characteristics of the original volume scan data are preserved as much as possible.

[0136] (1) Use Fortran to read the radar reflectivity data and latitude and longitude of the initial field of the numerical model from the radar 3D mosaic (the radar range must be greater than the coverage of the initial field). Based on the latitude and longitude of any point in the initial field of the numerical model, find the radar reflectivity latitude and longitude of 4 nearby points, and use weighted interpolation to interpolate the radar reflectivity onto the grid of the initial field of the numerical model.

[0137] (2) Based on the radar reflectivity writing format in the bufr.table table, the radar reflectivity interpolated into the initial field of the numerical model is converted into the bufr format.

[0138] Doppler weather radar, based on the Doppler effect, is a radar system that quantitatively estimates information such as echo intensity and radial velocity through coherent transmission and reception. It can acquire high temporal and spatial resolution unmatched by conventional data, making it one of the main methods for studying small- and medium-scale severe convective weather. The basic observations of Doppler weather radar are radial velocity and reflectivity factor; therefore, radar data assimilation includes radar radial wind assimilation and radar reflectivity assimilation.

[0139] Radar radial wind assimilation methods: Currently, there are two main methods for radar radial wind assimilation in the field of numerical weather prediction: The first is indirect assimilation, which involves obtaining inverted wind field data through various inversion algorithms, processing it into a wind field that matches the model, and assimilating it into the model's initial field in the form of "super observations" at the model grid. The second is direct assimilation, which involves directly interpolating the radar radial wind field data to the model grid and directly assimilating it into the numerical model using three-dimensional variational assimilation techniques.

[0140] Radar reflectivity assimilation methods: Reflectivity echo observations can detect the interior of clouds, containing a wealth of water condensation information not found in other conventional data. However, compared to radial wind or other observation data, this type of data exhibits more pronounced nonlinear characteristics. Direct assimilation of radar reflectivity data has little effect on improving the initial field. Furthermore, radar reflectivity data reflects the overall reflectivity state of the cloud, and there are no clearly corresponding control variables in the model, thus direct assimilation lacks a clear focus. Current reflectivity data assimilation mainly involves updating the distribution of water condensation-related variables such as QC, QR, and QS in the model's initial field through stratiform or convective cloud analysis techniques.

[0141] Wind profiler radar is a remote sensing device that detects atmospheric wind fields by utilizing the scattering effect of radar electromagnetic waves caused by the inhomogeneous structure of the atmospheric refractive index due to atmospheric turbulence. It can provide real-time 3D atmospheric wind field information, vertical airflow, atmospheric refractive index structure constant, and other meteorological elements distributed with altitude. It is a powerful supplement to conventional radiosonde observations, significantly increasing the amount of upper-level wind data provided for numerical models, influencing the results of mesoscale assimilation and forecasting systems, and overall improving short-term forecast accuracy. Wind profiler radar provides three types of data: power spectrum data, radial data (spectral parameters), and product data. The data includes horizontal wind, vertical and radial velocity, echo intensity, and echo power.

[0142] This module employs a three-dimensional variational assimilation method to assimilate wind profiler radar data within a fast cyclic assimilation model. First, quality control of the wind profiler radar data is essential, primarily including: 1. Correcting and supplementing the data to address distortions caused by detection conditions; 2. Removing data based on its applicability to the model assimilation. Due to the presence of small-scale micro-air masses in the atmosphere, the wind field measured by wind profiler radar exhibits short-period instantaneous fluctuations in wind direction and speed. In meteorological analysis, these high-frequency fluctuations in the wind field are meaningless and severely affect the representativeness of the observational data. Directly applying raw wind profiler radar data containing high-frequency fluctuations to the data assimilation system could negatively impact the analysis results, leading to significant analytical errors. Therefore, average information over a specific time period is extracted to reduce the impact of fluctuations and obtain relatively stable and representative measurement data. Furthermore, research indicates that for numerical weather prediction data assimilation, 1-hour average sampled wind products are superior to real-time sampled wind products. Therefore, the data assimilated in this paper is the product at the 1-hour average sampling height. Quality control must include quality checks on the observation data. First, the raw observation data is checked for extreme climate values ​​to remove erroneous wind field data that exceed meteorological ranges. Second, if there are missing values ​​in the vertical direction in the observation data for a certain time period, the data for that time period will be directly discarded. Third, considering that the wind profiler radar has a high data acquisition rate within the boundary layer, only the vertical layers close to the model layer height are selected. Finally, the wind profiler radar data processed in the above way is converted into the format specified by the assimilation system.

[0143] This module is compiled using Python and is implemented through the following steps:

[0144] (1) Read in the original format wind profiler radar data file;

[0145] (2) Perform quality control based on the data, including checking for extreme climate values ​​and data sparsification;

[0146] (3) Based on the wind profile radar writing format in prepbufr.table, the radar wind profile data file is written into the BUFR format that the assimilation system can recognize.

[0147] (iv) Microwave radiometer data assimilation

[0148] Ground-based microwave radiometers are less affected by clouds, rain, and fog, and have the advantages of being unattended, operating 24 / 7, and possessing a certain penetration capability. They can obtain continuous, objective, and quantitative monitoring results, and are widely used in various fields. At the same time, ground-based microwave radiometers also have the ability to measure the vertical integral quantity and distribution of atmospheric water vapor and cloud liquid water. In addition, ground-based microwave radiometers also have powerful functions such as measuring temperature profiles using multi-frequency microwave radiometers, obtaining boundary layer temperature profiles through scanning observations of microwave radiometers, and studying cloud structure through angular scanning observations of radiometers.

[0149] The microwave radiometer used in this application can provide various meteorological data, including atmospheric temperature, relative humidity, and water vapor density, at 83 vertical layers from the Earth's surface to the 10km atmospheric layer, every 2 minutes. The vertical resolution is 25m for data from the ground to 500m, 50m for data from 500-2000m, and 250m for data from 2000m to 10000m, exhibiting high spatiotemporal resolution. This data is a valuable supplement to sparse radiosonde observations. In particular, the timeliness, accuracy, and distribution of humidity observation data are crucial factors in understanding and forecasting mesoscale heavy rainfall, playing a vital role in quantitative precipitation forecasting. Therefore, the assimilation and application of high spatiotemporal resolution water vapor data is especially important.

[0150] This module uses microwave radiometer data, which is secondary meteorological product data, mainly including temperature profiles, water vapor density profiles, and relative humidity profiles. This data is input into the assimilation system in radiosonde data format. This module is compiled using Python and is implemented through the following steps:

[0151] (1) Read in the original format microwave radiometer data file;

[0152] (2) Perform quality control on the data, including removing outliers and data sparsification;

[0153] (3) Based on the format in the prepbufr.table table, the microwave radiometer data file is written into the BUFR format that the assimilation system recognizes.

[0154] (V) Rapid Cyclic Assimilation System

[0155] The Gridpoint Statistical Interpolation (GSI) analysis system overcomes some shortcomings of the SSI method and is a new generation of assimilation system capable of both regional and global analysis. GSI is widely used in various meteorological models and has good versatility. GSI offers various assimilation methods, such as three-dimensional variational and mixed ensemble variational methods. This paper conducts experiments based on three-dimensional variational assimilation (3DVAR). 3DVAR primarily seeks the minimum value of the objective function, mainly by solving for the analysis field (the solution to the minimum value), achieving the best fit between the analysis field and both the background and observation fields. The minimum solution of the cost function at this point represents the analysis field. The cost function is defined as follows:

[0156]

[0157] Where x represents the analysis variable at the pattern grid point, x b Let y0 represent the background field, y0 represent the observed data, and B represent x.b The background field error covariance, H is the observation operator, E+F represents the error (instrument error, representativeness error), J c Indicates constraint terms (dynamic constraints, etc.).

[0158] First, the background field and observation field are input into the system. The background field can be the result of global or regional model forecasts; in this project, the background field is obtained from the WRF model. The observation field is the assimilated data fed into the assimilation system. It's important to note that the GSI system only recognizes BUFR (Binary Universal Form for the Representation of Meteorological Data) format; data format conversion is required before inputting the data into GSI. Furthermore, statistical control (quality control) is necessary, requiring the setting of background error covariance and observation error covariance. Both settings are essential and significantly impact the system's simulation performance. During the GSI assimilation process, parameter settings are also required, such as the assimilation time window. Different parameter settings will yield different assimilation results. The process of finding the minimum cost function mainly includes two outer loops, each containing multiple inner iterations (using a nonlinear conjugate algorithm). The number of iterations can be set in the relevant GSI modules based on the data type and total amount. After each loop, the forecast results are updated until the optimal solution, i.e., the analysis field, is obtained.

[0159] Cloud initialization technology targets cloud and fog physical quantities (such as water condensates) that are difficult to observe directly in practice. It utilizes multi-source observational data from meteorological satellites, weather radars, radiosondes, and ground-based observations. Through physical and empirical relationships, it constructs the initial field of cloud and fog physical quantities required for the forecast equations at the model's three-dimensional grid points. This acquires crucial information such as the amount, shape, base height, and top height of clouds at the analysis time, as well as important information about cloud condensates. Furthermore, it adjusts the model's relevant dynamic and thermodynamic variables to ensure that the cloud-related information and characteristics at the initial time are as closely matched or balanced as possible with the model's dynamic and physical processes. It is one of the important means to solve the short-term forecasting problems in nowcast numerical weather prediction and improve forecast accuracy.

[0160] The GSI cloud analysis module is used to directly assimilate cloud water particles in cloud microphysical parameterization. A three-dimensional cloud amount analysis algorithm with multi-source / multi-temporal and spatial features is developed to effectively assimilate cloud and fog observation information including radar, ground automatic stations, radiosonde, etc., and to provide analysis products of macro and micro parameters such as cloud ice, cloud water, rainwater, cloud amount field, cloud classification, cloud base height, cloud top height, and cloud top temperature.

[0161] The fast-update cyclic assimilation system, based on high-resolution numerical models, employs high-frequency update cycles and assimilation analysis to absorb dense observational data, providing high-quality initial fields for refined numerical forecasts. Using the forecast from the previous time interval as the background field, the data assimilation module continuously incorporates the latest observational data to refine the background field, forming the initial forecast field for short-term predictions. Because convective systems develop rapidly and have short lifespans, it is necessary to use a fast-update cyclic assimilation system to update the model's initial values ​​at short intervals, adding important small- and medium-scale information.

[0162] like Figure 2 As shown, based on the GSI assimilation system, a step-by-step assimilation scheme is used in the 1-hour update cycle system to assimilate observation data such as ground meteorological station, radiosonde, and radar monitoring information at different scales in stages. Different parameter combinations are used in different assimilation steps to achieve better acquisition of observation information at the corresponding scale, and finally obtain more accurate initial model values, thereby improving forecast accuracy.

[0163] like Figure 3 As shown, Figure 3 This is a flowchart of the distributed cyclic assimilation process based on WRF and GSI. The model starts a cold start at 1200 UTC (06:00 local time), with the initial field being 0.25° GFS (Global Forecast System) analysis field data. Observational data from automatic weather stations, radiosondes, and radar are assimilated to obtain the analysis field. The output is used as the initial field for an 18-hour WRF model forecast, with the first hour's (t=1h) WRF forecast serving as the background field for the next hour's assimilation. The second step is repeated at 1300 UTC and 1400 UTC, assimilating only unconventional data such as radar data. At 1500 UTC, the above process is repeated for a two-step assimilation.

[0164] In this embodiment of the invention, downscaling is used to establish the relationship between large-scale and small-scale information variables in meteorological forecasting and meteorological change prediction studies. Large-scale variables change slowly, representing the circulation characteristics of a vast area, such as atmospheric oscillations and circulation patterns; while small-scale variables change more rapidly, representing local temperature, precipitation, etc. Downscaling is proposed to address the problem that the predictive power of models cannot meet the needs of real-world forecasting. Although large-scale variables have higher predictability, in reality, we need more information on local meteorological elements, and the meteorological elements directly output by the model cannot meet the accuracy requirements. Therefore, it is necessary to downscale large-scale variables to obtain small-scale element information.

[0165] The focus of multi-source meteorological data fusion is on ground station observation data and surface observation data obtained by radar and other remote sensing methods, spatiotemporal matching technology between surface observation data of different resolutions, systematic deviation correction technology between different observations, and multi-source observation data fusion analysis technology.

[0166] In this embodiment of the invention, the multi-source model data downscaling subsystem includes: a global model data preprocessing module and a mesoscale regional model preprocessing module.

[0167] The global model data preprocessing module is used to downscale the global model meteorological disaster numerical forecast results to obtain the driving field of the meteorological disaster numerical forecast model at a first preset scale, and to obtain the meteorological disaster numerical forecast results at the first preset scale through the forecast model simulation. It also performs first preset scale fusion on the multi-source observation data to obtain gridded fusion data at the first preset scale, thereby correcting the meteorological disaster numerical forecast results.

[0168] The mesoscale regional model preprocessing module is used to perform step-by-step downscaling of the meteorological disaster numerical forecast results at the first preset scale to obtain target data after downscaling at the second and third preset scales, and to obtain meteorological disaster numerical forecast results at the corresponding preset scales through forecast model simulation, and to fuse the multi-source observation data at the first and second preset scales to obtain gridded fused data at the corresponding preset scales, thereby correcting the corresponding meteorological disaster numerical forecast results.

[0169] In this embodiment of the invention, the global model data preprocessing mainly comprises two parts: model downscaling and data fusion. Its input data sources include EC, GFS, T639, GRAPES-GFS, etc.

[0170] (1) Model downscaling

[0171] Some meteorological data cannot meet the high-resolution requirements of the current smart grid. The global model data preprocessing module has adopted objective analysis and other downscaling techniques to process the numerical forecast results at the original resolution onto the standard smart grid.

[0172] (2) Data Fusion

[0173] Numerical forecast correction requires an accurate gridded observation field as an evaluation field and basic standard. Multi-scale observation data fusion technology is used to fuse the available ground observation data, upper-air data, and radar data in the target area to obtain the corresponding gridded observation field, which is used for gridded operational evaluation and numerical forecast calibration.

[0174] Example 2:

[0175] This invention also provides a high-resolution regional model forecasting method for meteorological disasters. The high-resolution regional model forecasting system for meteorological disasters provided in the above-mentioned embodiments of this invention is used to execute the high-resolution regional model forecasting method for meteorological disasters. The following is a detailed introduction to the high-resolution regional model forecasting method for meteorological disasters provided in this invention.

[0176] like Figure 4 As shown, Figure 4 This is a flowchart of the high-resolution regional model forecast of the above-mentioned meteorological disasters. The high-resolution regional model forecast method for meteorological disasters includes:

[0177] Step S102: Downscale the target data of the area to be processed to obtain downscaled target data, and fuse multi-source observation data to obtain gridded fused data, wherein the target data is the numerical forecast result of global model meteorological disasters;

[0178] Step S104: According to a preset cycle, specific technical methods are used to perform physical quantity correction, error estimation, quality control and format conversion for different target observation data. The processed target observation data is then subjected to hourly rapid cyclic assimilation processing to obtain target assimilation analysis data.

[0179] Step S106: Using the downscaled target data, the target assimilation analysis data, and the localized data, simulate and run a localized numerical weather prediction model to generate meteorological disaster numerical weather prediction results at different scales. The downscaled target data serves as the driving field for the localized numerical weather prediction model, and the target assimilation analysis data provides the initial field for the localized numerical weather prediction model. The localized data includes localized geographic data and localized physical parameters.

[0180] In this embodiment of the invention, a high-resolution regional model forecasting system for meteorological disasters is provided, comprising: a multi-source model data downscaling subsystem, a multi-source data assimilation processing subsystem, and a regional model localization construction subsystem. The multi-source model data downscaling subsystem is used to downscale target data of the region to be processed, obtaining downscaled target data, and simultaneously to fuse multi-source observation data to obtain gridded fused data. The target data is the numerical forecasting result of meteorological disasters from a global model. The multi-source data assimilation processing subsystem is used to preprocess different observation data according to a preset cycle to obtain processed observation data, and to perform hourly rapid cyclic assimilation of the processed target observation data. The data is processed to obtain target assimilation analysis data. The regional model localization construction subsystem is used to simulate and run a localized numerical weather prediction model using the downscaled target data, the target assimilation analysis data, and the localized data to generate meteorological disaster numerical weather prediction results at different scales. The downscaled target data serves as the driving field for the localized numerical weather prediction model, and the target assimilation analysis data provides the initial field for the localized numerical weather prediction model. The localized data includes localized geographic data and localized physical parameters, achieving the goal of accurate meteorological disaster forecasting. This solves the technical problem of low forecast accuracy in existing meteorological disaster systems and improves the technical effect of meteorological disaster forecast accuracy.

[0181] Example 3:

[0182] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the method described in Embodiment 1 above.

[0183] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0184] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for 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 the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0185] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0186] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0187] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0188] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A high-resolution regional model forecasting system for meteorological disasters, characterized in that, include: The system comprises a multi-source model data downscaling subsystem, a multi-source data assimilation processing subsystem, and a regional model localization construction subsystem. The multi-source model data downscaling subsystem is used to perform three-level progressive downscaling of the global model meteorological disaster numerical forecast results for the area to be processed, to obtain downscaled data with resolutions of 9km, 3km, and 1km. At the same time, it fuses multi-source observation data for each scale to generate corresponding gridded fused data. The multi-source data assimilation processing subsystem is used to preprocess ground observation data, radiosonde data, radar data and microwave radiometer data respectively. The preprocessing includes physical quantity correction based on the Ruggiero scheme, error estimation, quality control and BUFR format conversion, and then assimilation based on the GSI algorithm to obtain target assimilation analysis data. The regional model localization construction subsystem is used to simulate and run the WRF model using the downscaling data, target assimilation analysis data, and localized data to generate numerical forecast results of meteorological disasters at different scales, and to correct them using corresponding grid fusion data. The localized data includes localized geographic data based on GTOPO30 / SRTM / ASTER topographic data and IGBP20 land use data, as well as localized physical parameters of microphysics, cumulus convection, and boundary layer schemes.

2. The high-resolution regional model forecasting system for meteorological disasters according to claim 1, characterized in that, The multi-source model data downscaling subsystem is used to downscale the target data of the area to be processed to obtain downscaled target data at a first preset scale, and to fuse multi-source observation data at a first preset scale to obtain grid fused data at a first preset scale. The regional pattern localization construction subsystem is used to perform localization processing on the geographic data and physical parameters of the region to be processed based on the first preset scale to obtain the first preset pattern localization data. The regional model localization construction subsystem is also used to run a regional model with a spatial resolution of a first preset scale based on the downscaled data of the first preset scale and the localized data of the first preset model, and generate meteorological disaster numerical forecast results of the first preset scale.

3. The high-resolution regional model forecasting system for meteorological disasters according to claim 2, characterized in that, The multi-source model data downscaling subsystem is used to downscale the meteorological disaster numerical forecast results at the first preset scale to obtain the downscaled target data at the second preset scale, and to fuse the multi-source observation data at the second preset scale to obtain gridded fused data at the second preset scale. The regional pattern localization construction subsystem is used to obtain pattern localization data at the second preset scale based on the second preset scale and the physical parameters corresponding to the second preset scale. The regional model localization construction subsystem is also used to run a regional model with a spatial resolution of the second preset scale using the target data downscaled at the second preset scale and the model localization data at the second preset scale, and generate meteorological disaster numerical forecast results at the second preset scale.

4. The high-resolution regional model forecasting system for meteorological disasters according to claim 3, characterized in that, The multi-source model data downscaling subsystem is used to downscale the meteorological disaster numerical forecast results at the second preset scale to obtain the downscaled target data at the third preset scale, and to fuse the multi-source observation data at the third preset scale to obtain gridded fused data at the third preset scale. The regional pattern localization construction subsystem is used to obtain pattern localization data at the third preset scale based on the third preset scale and the physical parameters corresponding to the third preset scale. The regional model localization construction subsystem is also used to run a regional model with a spatial resolution of the third preset scale using the downscaled data and the model localization data at the third preset scale, and generate meteorological disaster numerical forecast results at the third preset scale.

5. The high-resolution regional model forecasting system for meteorological disasters according to claim 1, characterized in that, The multi-source model data downscaling subsystem includes: a global model data preprocessing module and a mesoscale regional model preprocessing module, wherein... The global model data preprocessing module is used to downscale the global model meteorological disaster numerical forecast results to obtain the driving field of the meteorological disaster numerical forecast model at a first preset scale, and to obtain the meteorological disaster numerical forecast results at the first preset scale through the forecast model simulation. It also performs first preset scale fusion on the multi-source observation data to obtain gridded fusion data at the first preset scale, thereby correcting the meteorological disaster numerical forecast results. The mesoscale regional model preprocessing module is used to perform step-by-step downscaling of the meteorological disaster numerical forecast results at the first preset scale to obtain target data after downscaling at the second and third preset scales. The forecast models are then used to simulate the meteorological disaster numerical forecast results at the corresponding preset scales. The multi-source observation data are then fused at the first and second preset scales to obtain gridded fused data at the corresponding preset scales, thereby correcting the corresponding meteorological disaster numerical forecast results.

6. The high-resolution regional model forecasting system for meteorological disasters according to claim 1, characterized in that, The multi-source data assimilation processing subsystem includes: a ground observation data processing module, a radar data processing module, a radiosonde data processing module, a microwave radiometer data processing module, and a fast cyclic assimilation module, wherein... The ground observation data processing module is used to sequentially perform spatiotemporal calibration processing, error estimation processing, and quality control verification processing on the ground observation data to obtain target ground observation data, and convert the target ground observation data into ground observation data in BUFR format. The radar data processing module is used to perform quality control and inspection on the radar data to obtain target radar data processing, and to convert the target radar data processing into radar data in BUFR format. The radiosonde data processing module is used to sequentially perform spatiotemporal calibration processing, error estimation processing, and quality control inspection processing on the radiosonde data to obtain target radiosonde data processing, and convert the target radiosonde data processing into radiosonde data in BUFR format. The microwave radiometer data processing module is used to perform quality control and inspection on the microwave radiometer data to obtain target microwave radiometer data, and convert the target microwave radiometer data into microwave radiometer data in BUFR format. The rapid cyclic assimilation module is used to perform cyclic assimilation processing on the BUFR format ground observation data, the BUFR format radar data, the BUFR format radiosonde data, and the BUFR format microwave radiometer data according to a preset cycle to obtain the target assimilation analysis data.

7. The high-resolution regional model forecasting system for meteorological disasters according to claim 1, characterized in that, The regional model localization construction subsystem includes: a geographic data localization module and a physical parameter localization module, wherein, The geographic data localization module is used to perform localization processing on the geographic data; The physical parameter localization module is used to perform localization processing on the physical parameters.

8. A high-resolution regional model forecasting method for meteorological disasters, characterized in that, include: The global model meteorological disaster numerical forecast results for the region to be processed are downscaled in three levels to obtain downscaled data at resolutions of 9km, 3km, and 1km. At the same time, multi-source observation data are fused for each scale to generate corresponding gridded fused data. Ground observation data, radiosonde data, radar data, and microwave radiometer data are preprocessed respectively. The preprocessing includes physical quantity correction based on the Ruggiero scheme, error estimation, quality control, and BUFR format conversion. Then, the data is assimilated based on the GSI algorithm to obtain target assimilation analysis data. The WRF model was simulated using the downscaled data, target assimilation analysis data, and localized data to generate numerical forecasts of meteorological disasters at different scales, and corrected using corresponding grid fusion data. The localized data included localized geographic data based on GTOPO30 / SRTM / ASTER topographic data and IGBP20 land use data, as well as localized physical parameters for microphysics, cumulus convection, and boundary layer schemes.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method described in claim 8.

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