Rime ice accumulation forecasting method and system considering underlying surface and cloud physical parameterization

Abstract

The invention discloses a rime ice accretion forecasting method and system considering underlying surface and cloud physical parameterization, and belongs to the technical field of electric power meteorologies.The rime ice accretion forecasting method comprises the steps that high-temporal-spatial-resolution land coverage data MODIS data are collected, and the MODIS data are spliced and preprocessed; the method comprises the following steps: by virtue of full coupling of an MAKKONEN rime icing model and a cloud micro physical scheme in WRF-ARW, obtaining a small and medium-scale numerical mode WRF-Icing-Fog capable of directly carrying out rime icing calculation; the method comprises the following steps: acquiring a pre-processing system WPS for driving a small and medium-scale mode WRF-ARW through global atmosphere reanalysis data or global forecast system forecast data; and constructing a high-resolution rime ice accumulation numerical prediction model by using WRF-Icing-Fog so as to obtain final ice accumulation results such as rime ice accumulation diameter, rime ice accumulation quality and the like. According to the method, a cloud microphysical parameterization scheme, namely a Thomson-aero-MP scheme, in a WRF-ARW mode is fully coupled with a MAKKONEN rime ice accumulation model, a WRF-Icing-Fog fully-coupled rime ice accumulation numerical prediction mode is constructed, the calculation step of offline ice accumulation calculation is obviously reduced, and rime icing feature data are directly obtained.

Description

Technical Field

[0001] The present invention belongs to the field of electric power meteorology technology, specifically to the field of numerical prediction technology of rime and ice accumulation, and more specifically to an electric power meteorology rime and ice accumulation prediction method and system considering the coupling of complex underlying surface and cloud physics parameterization. Background Art

[0002] The main ice accumulation characteristics that the State Grid and the meteorological department focus on during the cold season and the cold and warm season include the average ice diameter of rime, rime or snow, the temporal and spatial distribution of ice accumulation, and the duration of ice accumulation. At present, there are generally three methods for obtaining ice-related data in domestic and foreign scientific research on ice monitoring and forecasting and power meteorological business services:

[0003] 1. Obtain ice observation data at the observation point through observations by the meteorological department or the State Grid Department. This is the most accurate method to obtain ice diameter or mass data at a certain point, but the spatial representativeness of the site observation is insufficient, and large-scale ice monitoring cannot be carried out. At the same time, this method requires a lot of manpower and material resources, and it is difficult to obtain long-term observation time series; in complex areas of China, especially in areas with complex underlying surfaces, it is very difficult to conduct site observations of ice in uninhabited areas. It is necessary to overcome the problem of spatiotemporal continuity of ice observation data, and the establishment of a site requires a lot of money and manpower maintenance.

[0004] Second, the rime ice accumulation model is driven by the grid elements such as surface precipitation, vertical multi-layer air temperature, vertical multi-layer liquid water content, wind direction, wind speed, etc. in the global atmospheric reanalysis data products released by various world meteorological centers under the WMO (World Meteorological Organization) and meteorological departments at home and abroad (such as European and American countries or China, Germany), for calculation and acquisition, such as but not limited to the MAKKONEN model. The most widely used ones are the latest generation of global atmospheric reanalysis data ERA5-Reanalysis of ECMWF (European Centre for Medium-Range Weather Forecasts), NCEP (National Centers for Environmental Prediction) or the FNL (Final) data set of global weather analysis data of the National Oceanic and Atmospheric Administration of the United States, which provide historical long-term series (longer than 20 years) of spatiotemporally continuous surface precipitation, vertical multi-layer air temperature, vertical multi-layer liquid water content, wind direction, wind speed, etc., which are used to drive the rime ice accumulation model offline to obtain a relatively long-term series of rime ice data sets. However, global data have the following obvious shortcomings: the global atmospheric reanalysis data are relatively coarse in space, generally ranging from 25km to 100km, and are unable to analyze the heterogeneity of meteorological elements caused by complex underlying surfaces, making it difficult to meet the needs of refined monitoring of icing disasters in national power meteorology.

[0005] 3. Use the output data of small and medium-scale numerical weather forecast models to drive the rime and icing model to perform icing post-report or forecast to obtain the icing diameter. At present, both at home and abroad, offline coupling is used for ice monitoring and forecasting. It is common to use the small and medium-scale forecast model WRF-ARW (Advanced Research Weather Research and Forecasting) to forecast regional meteorological conditions, obtain high-resolution precipitation, liquid water content, temperature, wind direction, wind speed and other meteorological element data, and then drive the rime and icing model to run offline to obtain rime and icing detection or forecast data for relatively large areas. Among them, the MAKKONEN rime and icing model is used more. The calculation process needs to run WRF-ARW first, extract the required meteorological elements in the WRF-ARW output results after the end, and drive the rime and icing model to obtain icing products. The process steps are redundant and complicated.

[0006] Prior art document 1 (CN112711919A) discloses a conductor icing forecasting method and system based on small and medium scale model coupling. However, the forecasting method described in prior art document 1 uses the meteorological elements calculated and output by the small and medium scale model as the data input of the conductor icing model, which belongs to the traditional one-way coupling calculation method. The meteorological elements calculated and output by the small and medium scale model are then input into the icing model, which cannot realize the direct output of icing models at different height levels, and the process steps are redundant and complicated.

[0007] Prior art document 2 (CN105184407B) discloses a method for predicting the growth of ice on power transmission lines based on an atmospheric numerical model. However, the prediction method described in prior art document 2 can only output the growth of ice driven by near-ground meteorological elements, and fails to output the ice conditions at other vertical heights. In addition, the invention also uses the meteorological elements calculated and output by small and medium-scale models as the data input of the conductor ice model, which also belongs to the traditional one-way coupling calculation method, and the process steps are redundant and complicated. Summary of the invention

[0008] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0009] In view of the above-mentioned existing problems, the purpose of the present invention is to provide a numerical prediction method and system for rime and icing that takes into account complex underlying surfaces, which can solve the technical problems that traditional rime and icing data products have less observation data and are discontinuous in time and space, and the rime and icing products calculated by the global atmospheric reanalysis products driven by the rime and icing models disclosed at home and abroad have low resolution.

[0010] In order to solve the above technical problems, the present invention provides the following technical solutions.

[0011] A first aspect of the present invention provides a method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization, comprising the following steps:

[0012] Collect high-resolution MODIS land cover data, stitch and preprocess them to obtain a high-precision underlying surface dataset;

[0013] The MAKKONEN rime and ice accumulation model is fully coupled with the cloud microphysical parameterization scheme in the WRF-ARW model to obtain the small and medium-scale numerical model WRF-Icing-Fog, which can directly calculate rime and ice.

[0014] Collect atmospheric reanalysis meteorological data or forecast system data according to the data requirements of the small and medium-scale numerical model WRF-Icing-Fog;

[0015] The original underlying surface static data of the WRF-ARW model is replaced by the high-precision underlying surface data set, and then the atmospheric reanalysis meteorological data or forecast system data is processed by the WRF-ARW pre-processing system WPS to compile and generate a regional high-resolution rime and ice numerical prediction model, thereby obtaining a regional, high-temporal and spatial resolution rime and ice simulation or prediction result that takes into account the complex underlying surface.

[0016] Preferably, the preprocessing includes: projection transformation, resampling and binary format conversion.

[0017] The projection transformation is expressed by the following formula (1):

[0018] C(x, y) = LU(i, j) (1)

[0019] Where:

[0020] C represents the original land cover data, (x, y) represents the location coordinates of the raster data after projection transformation, LU represents the projection function, and (i, j) represents the location coordinates of the raster data before projection transformation;

[0021] The original land cover data C is resampled to a new grid using the four-point interpolation function IN, which is expressed as follows:

[0022] C′(m,n)=IN[C(x,y)] (2)

[0023] Where:

[0024] C(x, y) represents the original land cover data at the location (x, y), IN represents the four-point interpolation function, and C′(m, n) represents the resampled land cover grid data;

[0025] The conversion function Tr is used to convert the m×n grid matrix into a binary file A for WRF-ARW pattern recognition, which is expressed as follows:

[0026] A=Tr(C′) (3)

[0027] Where:

[0028] A represents a binary file that can be recognized by the WRF-ARW model, and Tr represents a conversion function used to convert to a binary file that can be recognized by the WRF-ARW model.

[0029] Preferably, the MAKKONEN rime and icing model is fully coupled with the cloud microphysical parameterization scheme in the WRF-ARW model to obtain the small and medium-scale numerical model WRF-Icing-Fog that can directly calculate rime and icing, including:

[0030] The meteorological element data of each layer of the vertical layer of the model are calculated based on the Thompson-aero-MP cloud microphysics scheme in WRF-ARW;

[0031] The coupled MAKKONEN rime and icing model is used to calculate the ice thickness. The meteorological element data of each layer is used as the input of the MAKKONEN rime and icing model, and the ice thickness variable is used as the output of the MAKKONEN rime and icing model. A small and medium-scale numerical model WRF-Icing-Fog that can directly calculate rime and icing is constructed.

[0032] Preferably, the meteorological element data of each layer of the vertical layer of the Thompson-aero-MP cloud microphysics scheme calculation model includes: using a mathematical model to describe multi-level radial wind U, zonal wind V, liquid water content Q, temperature T, precipitation P, air density M, expressed by the following formulas (4) to (9):

[0033] U=w u(t，i，j，k) (4)

[0034] V=w v(t，i，j，k) (5)

[0035] Q=w q(t，i，j，k ) (6)

[0036] M=w m(t，i，j，k) (7)

[0037] T=w t(t，i，j，k) (8)

[0038] P=w p(t，i，j，k) (9)

[0039] Where:

[0040] t represents time, i, j, k represent the spatial coordinates of small and medium scale patterns, W u , W v , W q , W m , W t , W p It is a function that describes the changes of six meteorological elements over time and space.

[0041] Preferably, coupling the MAKKONEN rime and ice accumulation model to calculate ice accumulation thickness includes:

[0042] The MAKKONEN rime ice accumulation model is used to calculate the ice accumulation diameter D, which is expressed as follows:

[0043] D=w d(U,V,Q,M,T,P) (10)

[0044] Where:

[0045] W d Function representing the relationship between ice accumulation thickness and six four-dimensional meteorological elements calculated by the Thompson-aero-MP cloud microphysics scheme;

[0046] The rime ice diameter at each time step is calculated based on the ice diameter, which is expressed as follows:

[0047] D(t+Δt)=D(t)+ΔD (11)

[0048] Where:

[0049] Δt is the time step, and ΔD represents the ice accumulation increment within the time step Δt.

[0050] Preferably, the generation area high-resolution rime and ice numerical prediction model includes:

[0051] The preprocessed USGS annual MODIS satellite land cover data with a resolution of 500m×500m are used to update and replace the inaccurate and older land cover data in the small and medium-scale numerical model WRF-Icing-Fog, which can directly calculate rime and ice.

[0052] Preferably, the generation area high-resolution rime and ice numerical prediction model includes:

[0053] Use OneAPI / Intel compiler to compile the small and medium-scale numerical model WRF-Icing-Fog in the Rocky Linux system to generate a regional high-resolution numerical prediction model for rime and ice. Use the obtained reanalysis meteorological data or NCEP / GDAS data drive, process it through the WRF-ARW pre-processing system WPS, and input it into the regional high-resolution numerical prediction model for rime and ice for parallel calculation.

[0054] Preferably, the prediction results are optimized, including: analyzing the prediction results using the root mean square error RMSE,

[0055]

[0056] Among them, D is the predicted value and O is the actual value;

[0057] If the RMSE value is less than the first threshold, the model predictive accuracy system is evaluated as good. If the RMSE is 0, it means that the model prediction is excellent. If the RMSE is greater than the first threshold, the quality of the data, the parameters of the model or the model structure is checked.

[0058] Preferably, when constructing a high-resolution numerical prediction model for rime and ice accumulation on the plateau, the vertical scheme of the model is set to Hybrid mixed coordinates, the number of vertical layers is set to 51, and the terrain following quality Euler coordinate system in WRF-ARW is replaced;

[0059] When establishing a high-resolution numerical prediction model for rime and ice accumulation on the plateau, the data output dependency library is set to NETCDF4 to reduce the volume of the output results and the storage space occupied by the output data.

[0060] The second aspect of the present invention provides a rime and ice accretion forecasting system taking into account underlying surface and cloud physical parameterization, and runs the rime and ice accretion forecasting method taking into account underlying surface and cloud physical parameterization, comprising: a data preprocessing module, a numerical simulation / forecasting module, a model configuration module, and a result processing module;

[0061] The data preprocessing module acquires, preprocesses and converts data formats and projections to make them suitable for reading small-scale patterns in WRF-ARW;

[0062] The numerical simulation / forecast module uses the WRF-ARW model and the MAKKONEN model to calculate multiple meteorological element parameters and the thickness of rime and ice accumulation;

[0063] The model configuration module configures and establishes a rime forecast model according to a specific area and resolution;

[0064] The ice accumulation result processing module processes the calculation results of the rime ice accumulation diameter and generates rime ice coverage related products with high temporal and spatial resolution.

[0065] A third aspect of the present invention provides a high-performance computer device, comprising a memory and a processor, wherein the memory stores a computer Fortran program, and the processor executes the steps of the method described above when executing the computer Fortran program.

[0066] A fourth aspect of the present invention provides a computer readable and writable storage medium having a computer Fortran program stored thereon, which, when executed by a processor, implements the steps of the method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization.

[0067] Compared with the prior art, the beneficial effects of the present invention include at least:

[0068] (1) The present invention combines the small and medium-scale numerical weather forecast model WRF-ARW and the rime and ice accumulation model MAKKONEN, and fully couples the cloud microphysical parameterization scheme in the WRF-ARW model, the Thompson-aero-MP scheme, and the MAKKONEN rime and ice accumulation model, and calculates the historical ice diameter data product with an output interval of 1 hour and a spatial resolution of 2km×2km. Due to the complex terrain and high mountains in my country, the rime problem occurs as the temperature decreases due to the increase in altitude. When establishing the model, the rime and ice accumulation forecast with seasons and terrain is considered. To this end, the Thompson-aero-MP cloud microphysical parameterization scheme in the WRF-ARW small and medium-scale model and the MAKKONEN rime and ice accumulation model are fully coupled to construct the WRF-Icing-Fog fully coupled rime and ice accumulation numerical prediction model, which significantly reduces the calculation steps, and the ice diameter and ice quality detection or forecast data can be directly obtained from the output results of the WRF-ARW model.

[0069] (2) The rime and icing data generated by the dynamic downscaling of the small and medium-scale model WRF-ARW have a higher temporal and spatial resolution (1 hour, 2 km × 2 km), which fully considers the complex underlying surface of the plateau. Therefore, it is possible to obtain more detailed spatiotemporal distribution characteristics of rime and icing on the plateau under complex underlying surface conditions.

[0070] (3) The WRF-Icing-Fog fully coupled rime and ice numerical forecast model takes into account the complex underlying surface effect and describes the local rime and ice data caused by complex plateau underlying vegetation cover more accurately and in line with reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0072] in:

[0073] Figure 1 A schematic flow chart of a numerical prediction method for rime and ice accumulation taking into account a complex underlying surface provided by an embodiment of the present invention;

[0074] Figure 2 A schematic flow chart of a numerical prediction system for rime and ice accumulation taking into account complex underlying surfaces provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The described embodiments are only embodiments of a part of the present invention, not all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0076] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0077] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0078] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0079] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0080] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0081] like Figure 1 As shown, embodiment 1 of the present invention provides a method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization, comprising the following steps:

[0082] Step 1: Collect high-resolution MODIS (Moderate Resolution Imaging Spectroradiometer) land cover data, stitch and preprocess them to obtain a high-precision underlying surface dataset.

[0083] In a preferred but non-limiting embodiment of the present invention, step 1 specifically comprises:

[0084] Step 1.1: Obtain the annual MODIS satellite 500m × 500m resolution land cover dataset from USGS (US Geological Survey) and stitch it together to obtain the stitched high-resolution land cover data.

[0085] Step 1.2: Python3, Geopandas, tiff, GDAL and other tools can be used to preprocess the spliced ​​high-resolution land cover data obtained in step 1.1, and convert the data format into a binary format that can be read by the WRF-ARW model to obtain a high-precision underlying surface dataset.

[0086] The preprocessing includes projection transformation and resampling. The projection transformation is expressed by the following formula (1):

[0087] C(x, y) = LU(i, j) (1)

[0088] Where:

[0089] C represents the original land cover data;

[0090] (x, y) represents the location coordinates of the raster data after projection transformation;

[0091] LU represents the projection function;

[0092] (i, j) represents the location coordinates of the raster data before projection conversion.

[0093] The original land cover data C is resampled to a new grid using the four-point interpolation function IN, which is expressed as follows:

[0094] C′(m,n)=IN[C(x,y)] (2)

[0095] Where:

[0096] C(x, y) represents the original land cover data of the location (x, y);

[0097] IN represents the four-point interpolation function;

[0098] C′(m, n) represents the preprocessed land cover raster data;

[0099] The conversion function Tr is used to convert the m×n grid matrix into a binary file A that can be recognized by the WRF-ARW model, which is expressed as the following formula (3):

[0100] A=Tr(C′) (3)

[0101] Where:

[0102] A indicates binary files that can be recognized by the WRF-ARW model;

[0103] Tr represents the conversion function used to convert into a binary file that can be recognized by the WRF-ARW model.

[0104] Step 2: Integrate the MAKKONEN rime and icing model into the Thompson-aero-MP cloud microphysics parameterization scheme of the WRF-ARW model to obtain the small and medium-scale numerical model WRF-Icing-Fog that can directly calculate rime and icing.

[0105] Preferably but not limiting, step 2 specifically comprises:

[0106] Step 2.1: Based on the Thompson-aero-MP cloud microphysics scheme in WRF-ARW, calculate meteorological data such as wind direction, wind speed, liquid water content, air density, air temperature and precipitation for each vertical layer of the model.

[0107] Specifically, the WRF-ARW variables for constructing the small- and medium-scale numerical model for ice calculation include: using a mathematical model to describe multi-level radial wind U, zonal wind V, liquid water content Q, air temperature T, precipitation P, and air density M, expressed as the following formulas (4) to (9):

[0108] U=w u(t，i，j，k) (4)

[0109] V=w v(t，i，j，k) (5)

[0110] Q=w q(t，i，j，k) (6)

[0111] M=w m(t，i，j，k) (7)

[0112] T=w t(t，i，j，k) (8)

[0113] P=w p(t，i，j，k) (9)

[0114] Where:

[0115] t represents time, i, j, k represent the spatial coordinates of small and medium scale patterns, W u , W v , W q , W m , W t , W p It is a function that describes the changes of six meteorological elements over time and space.

[0116] Step 2.2: Integrate the MAKKONEN rime and icing model into the Thompson-aero-MP cloud microphysics parameterization scheme of the WRF-ARW model, including: using the six meteorological elements provided by the Thompson-aero-MP cloud microphysics scheme of the WRF-ARW model as input for calculating the icing thickness of the MAKKONEN rime and icing model, calculating the rime and icing thickness at each time step according to the rime and icing thickness calculation formula in the MAKKONEN rime and icing model, and outputting the icing thickness variable.

[0117] Specifically, the MAKKONEN rime ice accumulation model is used to calculate the ice accumulation diameter D, which is expressed as follows:

[0118] D=w d(U,V,Q，M,T,P) (10)

[0119] Where:

[0120] W d Represents the relationship function between ice thickness and the six four-dimensional meteorological elements calculated by the WRF-ARW cloud microphysical parameterization scheme. Step 2.3: Calculate the rime ice diameter at each time step based on the ice diameter, expressed as the following formula (11):

[0121] D(t+Δt)=D(t)+ΔD (11)

[0122] Where:

[0123] Δt is the time step, and ΔD represents the ice accumulation increment within the time step Δt.

[0124] Preferably but not restrictively, Lambert projection is used when establishing the meteorological model; vertical layer: both nestings have 51 layers, of which 26 layers are below 1 km vertically, and a hybrid coordinate system is used to reduce the impact of gravity waves brought by plateaus and mountains on the high-altitude meteorological field and its impact on the surrounding and downstream weather. The nested inner layer has a spatial resolution of 2km×2km and a time step of 60 seconds; the cloud microphysics schemes screened are Thompson-aero-MP, boundary layer transmission scheme: MYNN 2.5, near-surface scheme: MYNN 2.5, shortwave radiation scheme: RRTMG, longwave radiation scheme: RRTMG, land surface parameterization scheme: Noah-MP scheme, cloud droplet / fog droplet gravity sedimentation scheme turned on, and terrain surface wind correction scheme Topo_Wind turned on. Each parameterization scheme selects the physical scheme shown in Table 1 in detail, and the calculation results are output hour by hour.

[0125] Table 1 Physical scheme combinations of the WRF-Icing-Fog model

[0126] Microphysics Solutions Thompson-aero-MP Long wave radiation scheme RRTMG Shortwave radiation solution RRTMG Boundary layer scheme MYNN 2.5 Land Plan Noah - MP Near-surface solution MYNN 2.5 Cumulus Convective Parameterization Scheme GF (Inner layer closed) Topographic surface wind correction scheme Open Vertical coordinate systems Hybrid Topo_Wind Open grav_settling Open

[0127] Step 3: Collect atmospheric reanalysis meteorological data or forecast system data as required by the small and medium-scale numerical model WRF-Icing-Fog.

[0128] Preferably, but not restrictively, obtain and download the global atmospheric reanalysis meteorological data or global forecast system data commonly available from WMO or domestic and foreign (Europe, America, China, etc.): download the public global atmospheric reanalysis meteorological data or global forecast system data, such as ERA5, NCEP GFS (GlobalForecast System), NCEP FNL, etc., through the website of the WMO World Meteorological Center or the website of the European and American meteorological department.

[0129] The meteorological data driving the WRF-ARW model must include: U, V, air pressure, temperature, humidity (no less than 30 vertical layers), ground 0cm temperature, soil moisture, soil temperature, snow depth, snow cover and other information to drive the fully coupled rime and ice numerical forecast model WRF-Icing-Fog.

[0130] Step 4: The original static data of the underlying surface of the WRF-ARW model is replaced by the high-precision underlying surface dataset obtained in step 1, and then the atmospheric reanalysis meteorological data or forecast system data obtained in step 3 is processed by the WRF-ARW pre-processing system WPS to compile and generate a regional high-resolution rime and ice numerical prediction model, thereby obtaining a regional, high-temporal and spatial resolution rime and ice simulation or prediction result that takes into account the complex underlying surface.

[0131] Preferably but not limiting, step 4 specifically comprises:

[0132] Step 4.1: Obtain the static data set of the underlying surface prepared by the small and medium-scale model WRF-ARW, which contains inaccurate and older underlying surface land cover, and use the USGS annual MODIS satellite 500m×500m resolution land cover data preprocessed in step 1 to update and replace the inaccurate and older land cover data in the small and medium-scale numerical model WRF-Icing-Fog that can directly calculate rime and ice, so that calculations can be performed based on higher resolution and higher precision land use data.

[0133] Step 4.2: Use the WRF-ARW pre-processing system WPS settings and design configurations to obtain the simulation or forecast area configuration for the specified research or area of ​​interest and resolution, and generate the static underlying surface data file of the regional high-resolution rime and icing numerical prediction model: geo_em.d0X.nc, which will be read by the subsequently generated regional high-resolution rime and icing numerical prediction model.

[0134] Step 4.3: Use the OneAPI / Intel compiler to compile the small and medium-scale numerical model WRF-Icing-Fog in the Rocky Linux system to generate a regional high-resolution rime and ice numerical prediction model.

[0135] Step 4.4: Use the obtained reanalysis meteorological data or NCEP / GDAS data to drive, after processing by the WRF-ARW pre-processing system WPS, input the regional high-resolution rime and ice numerical prediction model for large-scale parallel calculation, obtain the high temporal and spatial resolution rime and ice diameter prediction results considering the complex underlying surface of the plateau in the coverage area, and improve the ice diameter prediction results.

[0136] When constructing a high-resolution numerical prediction model for rime and ice on the plateau, set the mode vertical scheme to Hybrid mixed coordinates, set the number of vertical layers to 51, and replace the terrain following quality Euler coordinate system in WRF-ARW; when establishing a high-resolution numerical prediction model for rime and ice on the plateau, set the WRF-Icing-Fog model data output dependency library to NETCDF4 (supported by hdf5) to reduce the volume of the output results and achieve the purpose of reducing the storage space occupied by the output data.

[0137] Preferably, but not limiting, optimizing the prediction results includes analyzing the prediction results using a root mean square error (RMSE), which can be expressed as follows:

[0138]

[0139] Where:

[0140] D is the predicted value, O is the actual value;

[0141] If the RMSE value is less than the first threshold, the model predictive accuracy system is evaluated as good. If the RMSE is 0, it means that the model prediction is excellent. If the RMSE is greater than the first threshold, the quality of the data, the parameters of the model or the model structure is checked.

[0142] refer to Figure 2 Embodiment 2 of the present invention provides a rime and icing forecasting system taking into account underlying surface and cloud physical parameterization, and runs the rime and icing forecasting method taking into account underlying surface and cloud physical parameterization described in embodiment 1, including: a data preprocessing module, a numerical simulation / forecasting module, a model configuration module, and a result processing module; wherein,

[0143] The data preprocessing module acquires, preprocesses and converts data formats and projections to make them suitable for reading small-scale patterns in WRF-ARW;

[0144] The numerical simulation / forecast module uses the WRF-ARW model and the MAKKONEN model to calculate six meteorological element parameters and the thickness of rime and ice accumulation;

[0145] The model configuration module configures and establishes a rime forecast model according to a specific area and resolution;

[0146] The ice accumulation result processing module processes the calculation results of the rime ice accumulation diameter and generates rime ice coverage related products with high temporal and spatial resolution.

[0147] Embodiment 3 of the present invention provides a high-performance computer device, including a memory and a processor, wherein the memory stores a computer Fortran program, and when the processor executes the computer Fortran program, the steps of the method for forecasting rime and ice accumulation taking into account the underlying surface and cloud physical parameterization described in Embodiment 1 are implemented.

[0148] Embodiment 4 of the present invention provides a computer readable and writable storage medium on which a computer Fortran program is stored. When the computer Fortran program is executed by a processor, the steps of the method for forecasting rime and ice accumulation taking into account the underlying surface and cloud physical parameterization described in Embodiment 1 are implemented.

[0149] It is understandable that in embodiments 3 and 4 of the present invention, if the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0150] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0151] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0152] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0153] It can be understood that, through the above introduction, the present invention has outstanding substantive features and significant progress, especially compared with the prior art documents 1 and 2, including at least: the present invention directly couples the icing model to the cloud microphysical parameterization scheme of the small and medium-scale models, and realizes the direct output of ice accumulation models at different height levels. There is no need to calculate and output meteorological elements from the small and medium-scale models and then input them into the icing model. The icing prediction results at different vertical heights can also be output simultaneously.

[0154] In order to more clearly introduce the outstanding essential features of the technical solution of the present invention and the significant progress it brings to the prior art, a verification example of a numerical prediction method for rime and ice accumulation considering a complex underlying surface is provided below.

[0155] The rime and ice prediction method taking into account the underlying surface and cloud physical parameterization in the verification example includes the following steps:

[0156] Step 1: Based on the annual MODIS satellite 500m×500 resolution underlying surface dataset obtained by USGS, a high-precision underlying surface dataset covering the plateau that can be recognized and utilized by WRF-ARW is constructed;

[0157] Step 2: Based on the cloud microphysical parameterization scheme in the WRF-ARW model, the Thompson-aero-MP scheme and the MAKKONEN rime and icing model, a fully coupled numerical rime and icing prediction model WRF-Icing-Fog is established.

[0158] Step 3. Obtain and download WMO or common global atmospheric reanalysis meteorological data or global forecast system data at home and abroad: Download public global atmospheric reanalysis meteorological data or global forecast system data through the WMO World Meteorological Center website or the European and American meteorological department website, such as ERA5, NCEP GFS, NCEP FNL, etc. The meteorological data driving the WRF-ARW model must include: U, V, air pressure, temperature, humidity (no less than 30 vertical layers), ground 0cm temperature, soil moisture, soil temperature, snow depth, snow cover and other information to drive the fully coupled rime and ice numerical prediction model WRF-Icing-Fog;

[0159] Step 4: Input the underlying surface obtained in Step 1 and Step 3 and the global atmospheric reanalysis meteorological data or the global forecast system data into the regional high-resolution rime and ice numerical prediction model WRF-Icing-Fog for large-scale parallel calculation to obtain regional rime and ice products with high temporal and spatial resolution that take into account the complex underlying surface of the plateau, including icing time, icing space, and icing diameter. Lambert projection is used when establishing the meteorological model; vertical layer: both nestings have 51 layers, of which 26 layers are below 1 km vertically. Hybrid coordinate system is used to reduce the impact of gravity waves brought by plateaus and mountains on the high-altitude meteorological field and its impact on the surrounding and downstream weather. The nested inner layer spatial resolution is 2km×2km, and the time step is 60 seconds; the cloud microphysics schemes screened are Thompson-aero-MP, boundary layer transmission scheme: MYNN 2.5, near-surface scheme: MYNN 2.5, shortwave radiation scheme: RRTMG, longwave radiation scheme: RRTMG, land surface parameterization scheme: Noah-MP scheme, cloud droplet / fog droplet gravity sedimentation scheme is turned on, and terrain surface wind correction scheme Topo_Wind is turned on. The physical schemes shown in Table 1 are selected in detail for each parameterization scheme, and the calculation results are output hour by hour.

[0160] The 20-year global atmospheric reanalysis meteorological data NCEP FNL was input into the WRF-Icing-Fog model for large-scale parallel computing, and a regional rime and ice accumulation dataset with a spatial resolution of 2km×2km and 1 hour was obtained.

[0161] The meanings of the abbreviations used in the present invention are explained as follows:

[0162] WMO: World Meteorological Organization, World Meteorological Organization;

[0163] ECMWF: European Centre for Medium-Range Weather Forecasts, European Centre for Medium-Range Weather Forecasts;

[0164] NCEP: National Centers for Environmental Prediction, U.S. National Center for Environmental Prediction;

[0165] GDAS: Global Data Assimilation System, Global Data Assimilation System;

[0166] MODIS: Moderate Resolution Imaging Spectroradiometer, Moderate Resolution Imaging Spectroradiometer;

[0167] USGS: USGeological Survey, United States Geological Survey;

[0168] NOAA: National Oceanic and Atmospheric Administration, National Oceanic and Atmospheric Administration of the United States.

Claims

1. A method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization, characterized in that: The following steps are involved: Collect high-resolution MODIS land cover data, stitch and preprocess them to obtain a high-precision underlying surface dataset; The MAKKONEN rime and ice accumulation model is fully coupled with the cloud microphysical parameterization scheme in the WRF-ARW model to obtain the small and medium-scale numerical model WRF-Icing-Fog, which can directly calculate rime and ice. Collect atmospheric reanalysis meteorological data or forecast system data according to the data requirements of the small and medium-scale numerical model WRF-Icing-Fog; The original underlying surface static data of the WRF-ARW model is replaced by the high-precision underlying surface data set, and then the atmospheric reanalysis meteorological data or forecast system data is processed by the WRF-ARW pre-processing system WPS to compile and generate a regional high-resolution rime and ice numerical prediction model, thereby obtaining a regional, high-temporal and spatial resolution rime and ice simulation or prediction result that takes into account the complex underlying surface.

2. The method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to claim 1, characterized in that: The preprocessing includes: projection transformation, resampling and binary format conversion, The projection transformation is expressed by the following formula (1): C(x,y)=LU(i,j) (1) Where: C represents the original land cover data, (x, y) represents the location coordinates of the raster data after projection transformation, LU represents the projection function, and (i, j) represents the location coordinates of the raster data before projection transformation; The original land cover data C is resampled to a new grid using the four-point interpolation function IN, which is expressed as follows: C'(m,n)=IN[C(x,y)] (2) Where: C(x,y) represents the original land cover data at the location (x,y), IN represents the four-point interpolation function, and C'(m,n) represents the resampled land cover grid data; The conversion function Tr is used to convert the m×n grid matrix into a binary file A for WRF-ARW pattern recognition, which is expressed as follows: A=Tr(C') (3) Where: A represents a binary file that can be recognized by the WRF-ARW model, and Tr represents a conversion function used to convert to a binary file that can be recognized by the WRF-ARW model.

3. The method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to claim 1, characterized in that: The MAKKONEN rime and icing model is fully coupled with the cloud microphysical parameterization scheme in the WRF-ARW model to obtain the small and medium-scale numerical model WRF-Icing-Fog that can directly calculate rime and icing, including: The meteorological element data of each layer of the vertical layer of the model are calculated based on the Thompson-aero-MP cloud microphysics scheme in WRF-ARW; The coupled MAKKONEN rime and icing model is used to calculate the ice thickness. The meteorological element data of each layer is used as the input of the MAKKONEN rime and icing model, and the ice thickness variable is used as the output of the MAKKONEN rime and icing model. A small and medium-scale numerical model WRF-Icing-Fog that can directly calculate rime and icing is constructed.

4. The method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to claim 3, characterized in that: The meteorological element data of each layer of the vertical layer of the Thompson-aero-MP cloud microphysics scheme calculation model include: the use of mathematical models to describe the multi-level radial wind U, zonal wind V, liquid water content Q, temperature T, precipitation P, air density M, expressed by the following formulas (4) to (9): U=w u(t,i,j,k) (4) V=w v(t,i,j,k) (5) Q=w q(t,i,j,k) (6) M=w m(t,i,j,k) (7) T=w t(t,i,j,k) (8) P=w p(t,i,j,k) (9) Where: t represents time, i, j, k represent the spatial coordinates of small and medium scale patterns, W u , W v , W q , W m , W t , W p It is a function that describes the changes of six meteorological elements over time and space.

5. The method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to claim 4, characterized in that: The coupled MAKKONEN rime ice model is used to calculate ice thickness including: The MAKKONEN rime ice accumulation model is used to calculate the ice accumulation diameter D, which is expressed as follows: D=w d(U,V,Q,M,T,P) (10) Where: w d Function representing the relationship between ice accumulation thickness and six four-dimensional meteorological elements calculated by the Thompson-aero-MP cloud microphysics scheme; The rime ice diameter at each time step is calculated based on the ice diameter, which is expressed as follows: D(t+Δt)=D(t)+ΔD (11) Where: Δt is the time step, and ΔD represents the ice accumulation increment within the time step Δt.

6. The method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to claim 1, characterized in that: The high-resolution rime and ice numerical prediction model for the generation area includes: The preprocessed USGS annual MODIS satellite land cover data with a resolution of 500m×500m are used to update and replace the inaccurate and older land cover data in the small and medium-scale numerical model WRF-Icing-Fog, which can directly calculate rime and ice.

7. The method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to claim 1 or 6, characterized in that: The high-resolution rime and ice numerical prediction model for the generation area includes: Use OneAPI / Intel compiler to compile the small and medium-scale numerical model WRF-Icing-Fog in the Rocky Linux system to generate a regional high-resolution numerical prediction model for rime and ice. Use the obtained reanalysis meteorological data or NCEP / GDAS data drive, process it through the WRF-ARW pre-processing system WPS, and input it into the regional high-resolution numerical prediction model for rime and ice for parallel calculation.

8. The method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to claim 1, characterized in that: Optimize the prediction results, including: using the root mean square error RMSE to analyze the prediction results, Among them, D is the predicted value and O is the actual value; If the RMSE value is less than the first threshold, the model predictive accuracy system is evaluated as good. If the RMSE is 0, it means that the model prediction is excellent. If the RMSE is greater than the first threshold, the quality of the data, the parameters of the model or the model structure is checked.

9. The method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to claim 1, characterized in that: When constructing a high-resolution numerical prediction model for rime and ice accumulation on the plateau, set the vertical scheme of the model to Hybrid mixed coordinates, set the number of vertical layers to 51, and replace the terrain-following quality Euler coordinate system in WRF-ARW; When establishing a high-resolution numerical prediction model for rime and ice accumulation on the plateau, the data output dependency library is set to NETCDF4 to reduce the volume of the output results and the storage space occupied by the output data.

10. A rime and icing forecasting system taking into account underlying surface and cloud physical parameters, running the rime and icing forecasting method taking into account underlying surface and cloud physical parameters as claimed in any one of claims 1 to 9, characterized in that: include: Data preprocessing module, numerical simulation / forecasting module, model configuration module, result processing module; The data preprocessing module acquires, preprocesses and converts data formats and projections to make them suitable for reading small-scale patterns in WRF-ARW; The numerical simulation / forecast module uses the WRF-ARW model and the MAKKONEN model to calculate multiple meteorological element parameters and the thickness of rime and ice accumulation; The model configuration module configures and establishes a rime forecast model according to a specific area and resolution; The ice accumulation result processing module processes the calculation results of the rime ice accumulation diameter and generates rime ice coverage related products with high temporal and spatial resolution.

11. A high-performance computer device, comprising a memory and a processor, wherein the memory stores a computer Fortran program, characterized in that: When the processor executes the computer Fortran program, the steps of the method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to any one of claims 1 to 9 are implemented.

12. A computer readable and writable storage medium having a computer Fortran program stored thereon, characterized in that: When the computer Fortran program is executed by a processor, the steps of the method for forecasting rime and ice accumulation taking into account underlying surface and cloud physical parameterization according to any one of claims 1 to 9 are implemented.

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