Sub-tropical high-voltage parameter extraction method based on meteorological satellite multispectral imaging observation

Abstract

The invention discloses a meteorological satellite multispectral imaging observation-based subtropical high voltage parameter extraction method, which comprises the following steps of: S1, projecting target data into a static meteorological satellite observation view field to calculate a subtropical high voltage influence area, and performing space-time matching on the subtropical high voltage influence area, historical observation data and sea-land template data; s2, randomly sampling the space-time matching data set, selecting time or space discrete points, and dividing a plurality of sampling areas according to different sky states; s3, sample sets of the multiple sampling areas are divided into a training set and a test set, an XGBoost classification decision tree is adopted to establish an identification inversion model, and Bayesian iterative optimization is adopted to establish an identification optimal model; s4, inputting real-time static observation data, and identifying the subtropical high-voltage influence area by using the model to obtain the influence probability of each point; s5, dividing according to a probability threshold value; s6, performing projection conversion on a division result, and applying Canny edge detection on a specified range to extract a subtropical high-voltage outer boundary; and S7, calculating the area index or the strength index of the subtropical high pressure.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of meteorological remote sensing technology, and specifically to a method for extracting subtropical high pressure parameters based on multispectral imaging observations of meteorological satellites. Background Art

[0002] The subtropical high pressure (hereinafter referred to as the "subtropical high") is a key large-scale weather system that controls the tropical and subtropical regions. It has a close interactive relationship with the tropical and mid-high latitude circulation systems. A large number of studies have shown that the intensity and position changes of the subtropical high not only regulate the distribution of droughts and floods and the activities of the main rain belt in the subtropical region, but also have a significant impact on the movement paths of low-latitude weather systems such as typhoons and climate anomalies. my country is located on the west coast of the Pacific Ocean and the southeastern end of Eurasia. Weather and climate changes are closely related to the dynamics of the subtropical high pressure in the western Pacific. This system not only dominates the spatial distribution of my country's rain belts, but also plays a vital role in the total amount of precipitation and the start and end time of the rainy season. Therefore, accurately identifying the location, structure and intensity evolution characteristics of the subtropical high pressure and in-depth understanding of its activity patterns play a key role in improving the refinement of short-term climate prediction and weather forecasting.

[0003] In weather and climate analysis, the influence of a subtropical high is generally defined as the area enclosed by the 5880 gpm contour line in the 500 hPa geopotential height field. This method is simple and straightforward and is a common expression used in weather analysis and forecasting. In the past, identification of subtropical highs was typically based on manual analysis of radiosonde data collected twice daily, morning and evening. Due to the sparse distribution of radiosonde stations and the lack of offshore observations, radiosonde observations provide only a rough estimate of the subtropical high's influence, making it difficult to accurately characterize its internal structure, particularly its structural characteristics over the offshore region. With the advancement of numerical forecasting technology, numerical forecasts based on 500 hPa geopotential height analysis are now increasingly used. Although numerical forecast models can produce meteorological fields with higher temporal and spatial resolution than conventional radiosonde observations, significant discrepancies between forecasts and observations persist due to issues with the model's initial values. Reanalysis data, which integrates observations with numerical forecasts, is currently considered the "optimal" reflection of atmospheric conditions. However, reanalysis data are typically only available with a lag of at least one day, failing to meet the timeliness requirements of weather monitoring and forecasting.

[0004] The Fengyun-4 (FY-4) series of geostationary satellites is my country's second-generation Fengyun geostationary meteorological satellites. This series is designed to meet the meteorological observation needs of the China Meteorological Administration around 2020. Its instrument design specifications are comparable to those of the latest generation of geostationary meteorological satellites from the United States and Japan, the GOES-R and Himawari series. The FY-4A and FY-4B satellites have been launched. The Advanced Geosynchronous Radiation Imager (AGRI) (hereinafter referred to as the imager) on board the FY-4B satellite has a spectral range of 0.45-13.6 μm and 15 channels, including three visible and near-infrared channels (spectral range 0.45-0.9 μm), three short-wave infrared channels (spectral range 1.36-2.35 μm), and nine medium- and long-wave infrared channels (spectral range 3.5-13.8 μm). In addition to the visible and near-infrared channels, the medium- and long-wave infrared channels have a sub-satellite horizontal resolution of 4 km. In addition, the FY-4 satellite's infrared imager can also quantitatively measure the outgoing longwave radiation (OLR) of the Earth-atmosphere system as observed from space. This outgoing longwave radiation can well reflect large-scale upward (low OLR) and downward (high OLR) motions in the atmosphere, making it highly useful for revealing subtropical high pressure activity.

[0005] In summary, the current traditional methods for identifying subtropical high pressure based on soundings or numerical forecasts and reanalysis data cannot accurately and efficiently reflect the structure and position changes of the subtropical high pressure. Therefore, it is necessary to propose an automatic identification method for subtropical high pressure based on high temporal and spatial resolution geostationary meteorological satellite observation data to meet urgent business needs. Summary of the Invention

[0006] To this end, the present invention provides a method for extracting subtropical high pressure parameters based on observations by a multispectral imager of a meteorological satellite. Currently, the identification of subtropical high pressure mainly relies on numerical forecast data. There are no similar publicly published results for automatic identification and parameter extraction of subtropical high pressure based entirely on data from a multispectral imager of a geostationary meteorological satellite. The method of the present invention fills this gap.

[0007] In order to achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A method for extracting subtropical high pressure parameters based on multispectral imaging observations of meteorological satellites comprises the following steps: S1. The target data is projected onto the observation field of view of the geostationary meteorological satellite using the bilinear interpolation method. The observed subtropical high pressure influence area is calculated based on the set potential height as the division standard. Based on the data time of the observed subtropical high pressure influence area, it is temporally and spatially matched with the historical long-sequence geostationary meteorological satellite observation data and the sea and land template data to obtain the temporal and spatial matching dataset.

[0008] S2. A random sampling method is used for the spatiotemporal matching dataset to select discrete sample points in time or space. In the spatial sampling process, the longwave radiation (OLR) emitted by the geostationary meteorological satellite is used as the dividing indicator, and the dataset is divided into multiple sampling areas according to the different celestial states in the meteorological classification.

[0009] S3. The sample sets of multiple sampling areas are divided into training sets and test sets according to a certain ratio, and the XGBoost classification decision tree method is used to establish the identification inversion model. The Bayesian iterative optimization method is used in the training process, and the cross-validated AUC mean is used as the objective function to construct the optimal model for subtropical high pressure identification.

[0010] S4. Input real-time geostationary meteorological satellite imager observation data and use the optimal model for identifying subtropical high pressure to identify the subtropical high pressure affected area within the satellite observation field of view according to the following formula, and obtain the subtropical high pressure influence probability at each point.

[0011] Where x i Represents the prediction characteristic factor of the i-th point; represents the predicted probability of the kth tree; is the sum of the predicted probabilities of the K trees in the i-th point.

[0012] S5. Based on the probability of subtropical high pressure influence at each point, each point is divided into the subtropical high pressure influence area and the non-subtropical high pressure influence area according to the set probability threshold. After traversing all points, the subtropical high pressure identification result is obtained.

[0013] S6. The subtropical high pressure identification result is converted from the satellite nominal projection to equal longitude and latitude grid data through projection conversion, and the subtropical high pressure affected area in the equal longitude and latitude grid data is regionally segmented and the segmented areas are numbered. The Canny edge detection method is applied to the segmented areas within the specified range to extract the outer boundary of the subtropical high pressure.

[0014] S7. Calculate the subtropical high index based on the subtropical high identification results and the grid spacing of the segmented area, including the western ridge point index, northern boundary index, area index, or intensity index.

[0015] The embodiments of the present invention have the following advantages: The present invention provides a method for extracting subtropical high pressure parameters based on observations by a meteorological satellite multispectral imager. The method uses the spatial and temporal continuous distribution of subtropical high pressure products obtained from geostationary meteorological satellite data, which has important application prospects for studying the multi-scale structure of subtropical high pressure and improving the level of short-term climate prediction and weather forecast. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other implementation drawings based on the provided drawings without inventive effort.

[0017] The structures, proportions, sizes, etc. illustrated in this specification are intended only to complement the contents disclosed herein and to facilitate understanding and reading by persons familiar with the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall still fall within the scope of the technical contents disclosed herein.

[0018] Figure 1 A flow chart of a method for extracting subtropical high pressure parameters based on observations by a meteorological satellite multispectral imager; Figure 2 A schematic flow chart of a method for extracting subtropical high pressure parameters based on observations by a meteorological satellite multispectral imager according to the present invention; Figure 3 The ROC curve of the subtropical high pressure area identification result obtained by the ERA5 reanalysis data is used as the benchmark truth value and the method of the present invention is used to generate the ROC curve of the subtropical high pressure area identification result based on the FY4B / AGRI satellite data. DETAILED DESCRIPTION

[0019] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0020] like Figure 1-2 As shown in FIG, a method for extracting subtropical high pressure parameters based on observations by a meteorological satellite multispectral imager is described, and the steps are as follows: S1. The target data is projected onto the observation field of view of the geostationary meteorological satellite using the bilinear interpolation method. The observed subtropical high pressure influence area is calculated based on the set potential height as the division standard. Based on the data time of the observed subtropical high pressure influence area, it is temporally and spatially matched with the historical long-sequence geostationary meteorological satellite observation data and the sea and land template data to obtain the temporal and spatial matching dataset.

[0021] Specifically, the target data in this embodiment is the data of the set potential height in the reanalysis data or the sounding data, such as 500hPa (Z H The target data is projected onto the geopotential field of view of a geostationary meteorological satellite using bilinear interpolation, generating data with the same resolution as the infrared channel observations of the geostationary meteorological satellite. The formula for calculating the observed subtropical high (STH) area is: ; Wherein, G is the set potential height, the unit is geopotential meter (gpm), wherein G is preferably 5880 gpm.

[0022] Then, based on the time of the observed data of the subtropical high-pressure affected area, they were temporally and spatially matched with the historical long-series geostationary meteorological satellite observation data (including the brightness temperature (Tb) of the medium-wave and long-wave infrared channels of the geostationary meteorological satellite imager, the satellite zenith angle (θ), and the long-wave radiation (OLR) emitted by the geostationary meteorological satellite) as well as the land and sea template information to obtain the temporal and spatial matching dataset.

[0023] S2. A random sampling method is used for the spatiotemporal matching dataset to select discrete sample points in time or space. In the spatial sampling process, the long-wave radiation OLR emitted by the geostationary meteorological satellite is used as the division indicator, and the dataset is divided into three sampling areas according to different sky conditions in meteorological classification, namely, clear sky sampling area, few cloud sampling area, and cloudy sampling area. In order to ensure sample balance, samples are collected according to the principle that the number of samples in the subtropical high pressure and non-subtropical high pressure areas is equal under three different sky conditions, and finally three sample datasets are generated, namely, clear sky sample set, few cloud sample set, and cloudy sample set. This technology uses OLR as the threshold to divide the clear sky, few cloud, and cloudy areas as follows:

[0024] Based on the extraction of characteristic parameters of geostationary meteorological satellite observation data, according to the time and location of the sampling point, the collected information includes: satellite observation time, longitude, latitude, sea and land identification, satellite zenith angle cosine (cosθ), satellite zenith angle sine (sinθ), upper water vapor channel (WV1) brightness temperature (wavelength 5.8-6.7μm), middle water vapor channel (WV2) brightness temperature (wavelength 6.75-7.15μm), lower water vapor channel (WV3) brightness temperature (wavelength 7.24-7.60μm), long-wave infrared channel 1 (IR1) brightness temperature (wavelength 8.3- 8.8μm), Brightness Temperature of Wavelength Infrared Channel 2 (IR2) (Wavelength 10.3-11.3μm), Brightness Temperature of Long-Wave Infrared Channel 3 (IR3) (Wavelength 11.5-12.5μm), Brightness Temperature Difference 1 (BTD1) (WV1-WV3), Brightness Temperature Difference 2 (BTD2) (WV3-IR1), Brightness Temperature Difference 3 (BTD3) (IR1-IR2), Brightness Temperature Difference 4 (BTD4) (WV1-IR2), Brightness Temperature Difference 5 (BTD5) (IR2-IR3), Brightness Temperature Difference 1 (TBTD1) of Three Channels ((WV1-WV3)+(WV2-WV3)) and two types of OLR indices: one is the 24-hour, 12-hour, 6-hour, 3-hour, and 1-hour OLR abnormality index; the second is the 10-day, 5-day, 24-hour, 12-hour, 6-hour, 3-hour, and 1-hour OLR standardized deviation index, totaling 30 parameters. The calculation formulas for the OLR abnormal index (OAI: OLR abnormal index) and the standardized deviation index (SOI: Standard OLR deviation index) are as follows:

[0025] Where, Indicates the satellite-observed OLR at a certain location at the current moment; Indicates the satellite-observed OLR for a certain location in the period before the current moment; It represents the standard deviation of the OLR observed by satellite at a certain location in the period before the current moment.

[0026] t is taken as 10 days, 5 days, 24 hours, 12 hours, 6 hours, 3 hours and 1 hour respectively.

[0027] S3. The sample sets of the three sampling areas (clear sky, partly cloudy, and overcast) were divided into training sets and test sets according to a certain ratio. The XGBoost classification decision tree method was used to establish the identification inversion model. The Bayesian iterative optimization method was used during the model training process. The cross-validated AUC mean (classification model performance evaluation indicator) was used as the objective function to construct the optimal model for identifying subtropical high pressure. If the AUC value increases, the identification inversion model will undergo a new round of iteration and add new feature parameters. When the AUC value stops increasing, the iteration ends and the corresponding current model is the optimal model for identifying subtropical high pressure. The calculation formula for the AUC mean is:

[0028] Where, Represents the ranking number of positive sample i; represents the number of positive samples; Indicates the number of negative samples.

[0029] S4. Input the real-time geostationary meteorological satellite imager observation data, use the optimal model for identifying subtropical high pressure, identify the subtropical high pressure affected area within the satellite observation field of view, and obtain the subtropical high pressure influence probability at each point.

[0030] Specifically, S3 obtains the optimal models for identifying subtropical high pressure under clear sky, few clouds and cloudy conditions respectively. The prediction factors in the optimal models for identifying subtropical high pressure under clear sky and few clouds include: satellite observation time, longitude, latitude, land and sea identification, satellite zenith angle cosine (cosθ), satellite zenith angle sine (sinθ), upper water vapor channel (WV1) brightness temperature (wavelength 5.8-6.7μm), middle water vapor channel (WV2) brightness temperature (wavelength 6.75-7.15μm), lower water vapor channel (WV3) brightness temperature (wavelength 7.24-7.60μm), long-wave infrared channel 1 (IR 1) Brightness temperature (wavelength 8.3-8.8 μm), brightness temperature of long-wave infrared channel 2 (IR2) (wavelength 10.3-11.3 μm), brightness temperature of long-wave infrared channel 3 (IR3) (wavelength 11.5-12.5 μm), dual-channel brightness temperature difference 1 (BTD1) (WV1-WV3), dual-channel brightness temperature difference 2 (BTD2) (WV3-IR1), dual-channel brightness temperature difference 3 (BTD3) (IR1-IR2), dual-channel brightness temperature difference 4 (BTD4) (WV1-IR2), dual-channel brightness temperature difference 5 (BTD5) (IR2-IR3), triple-channel brightness temperature difference 1 (TBTD1) ((WV1-WV3)+(WV2-WV3)), and 24-hour, 12-hour, 6-hour, 3-hour, and 1-hour OLR anomaly indices, totaling 23 parameters. The optimal model under cloudy conditions includes, in addition to the above prediction factors, 10-day, 5-day, 24-hour, 12-hour, 6-hour, 3-hour, and 1-hour OLR standardized deviation indices, for a total of 30 parameters.

[0031] The optimal model for identifying subtropical high pressure under clear sky, few clouds and cloudy conditions is respectively used. The time, longitude and latitude, satellite zenith angle, channel brightness temperature, OLR and sea and land template information of the real-time geostationary meteorological satellite imager are input. The optimal model for identifying subtropical high pressure under clear sky, few clouds and cloudy conditions is used to identify the subtropical high pressure influence area within the satellite observation field according to the following formula, and the subtropical high pressure influence probability of each point is obtained:

[0032] Where x i represents the predictive characteristic factor; represents the predicted probability of the kth tree; The final prediction result at a certain point is the sum of the predicted probabilities of K trees.

[0033] S5. Based on the probability of subtropical high pressure influence at each point, divide each point into a subtropical high pressure influence area and a non-subtropical high pressure influence area according to the set probability threshold. After traversing all points, the subtropical high pressure identification result is obtained. Specifically, based on the subtropical high pressure influence probability of each point obtained in S4, using a probability of 50% as the threshold, points with a probability <50% are classified as non-subtropical high pressure influence areas and marked as 0; otherwise, they are subtropical high pressure influence areas and marked as 1. After traversing all points, the subtropical high pressure identification result is obtained.

[0034] In this step, in order to ensure the spatial continuity of the subtropical high pressure and remove spatially isolated small-scale high-pressure bodies, the morphological closing operation is used to fill the void area, and then the Gaussian convolution filter is used to low-pass filter the subtropical high pressure area to remove noise and isolated points.

[0035] The formula of the Gaussian convolution filter is as follows:

[0036] Where x and y represent the row and column coordinates relative to the center point of the filter window respectively; Represents the standard deviation of the Gaussian distribution.

[0037] S6. Convert the subtropical high pressure identification result from the satellite nominal projection to isometric grid data through projection conversion. Segment the subtropical high pressure affected area in the isometric grid data, number the segmented areas, and extract the outer boundary of the subtropical high pressure using the Canny edge detection method for the segmented areas within a specified range. The specified range is a user-specified area, such as north of 10°N and west of 150°E.

[0038] S7. Calculate the area index or intensity index of the subtropical high pressure based on the subtropical high pressure identification result and the grid spacing of the segmented area. The area index is calculated based on the latitude and longitude range of the specified area, and the calculation formula is:

[0039] Where, The subtropical high pressure and non-subtropical high pressure influence areas determined by the subtropical high pressure probability obtained by inversion in step 8 are represented by 0 and 1; is latitude; dx and dy are the distances between grids.

[0040] The intensity index is calculated based on the latitude and longitude of the specified area, and is expressed by the relative size of the subtropical high pressure area in the area. The calculation formula is:

[0041] Where, The subtropical high pressure and non-subtropical high pressure influence areas determined by the subtropical high pressure probability obtained by inversion in step 8 are represented by 0 and 1; is latitude; dx and dy are the distances between grids; is the probability size of each grid point in the subtropical high pressure identification area.

[0042] In S7, the west ridge index of the subtropical high pressure in the specified area is obtained based on the extracted outer boundary and affected area of ​​the subtropical high pressure; the west ridge index is to find the longitude value of the westernmost point of the outer boundary of the main body of the subtropical high pressure in the area according to the longitude and latitude range of the specified area. If the longitude of the westernmost point is less than the longitude of the western boundary of the area, the west ridge index is set as the longitude of the western boundary of the area; if it does not exist, the west ridge index is the longitude of the eastern boundary of the area.

[0043] In S7, the northern boundary index of the subtropical high pressure in the specified area is obtained based on the extracted outer boundary and affected area of ​​the subtropical high pressure; the northern boundary index is the latitude average of the outer boundary of the main body of the subtropical high pressure toward the polar side in the specified area calculated based on the longitude and latitude range of the specified area.

[0044] Experiments using this technology: Taking the western Pacific subtropical high as an example, a flowchart of extracting the subtropical high west ridge index, northern boundary index, area index, and intensity index is shown. The processing process is as follows: 1. Read FY4B / AGRI L1B data and positioning information; this data is in HDF5 format with a 15-minute temporal resolution. The subsatellite point is located at (0°N, 105°E), covering the entire Eastern Hemisphere. This data includes channel counts, calibration coefficients, satellite zenith angles, and latitude and longitude data for channels 9 (wavelength 5.8-6.7μm), 10 (wavelength 6.75-7.15μm), 11 (wavelength 7.24-7.60μm), 12 (wavelength 8.3-8.8μm), 13 (wavelength 10.3-11.3μm), and 14 (wavelength 11.5-12.5μm).

[0045] 2. Read the FY4B / AGRI Outgoing Longwave Radiation product; this data is in netcdf format, with a temporal resolution of 15 minutes, and is located at (0°N, 105°E) for full-disk observations covering the Eastern Hemisphere. The spatial resolution is 4 km. This data contains the row and column numbers of each pixel in the satellite's field of view, as well as the Outgoing Longwave Radiation (OLR) parameter values ​​derived from FY4B / AGRI, in W m². -2 According to the method of the present invention, it is necessary to read the OLR product data with a time interval of 15 minutes including the current time and the previous 10 days.

[0046] 3. Read the land and sea template data; this data is static, with a spatial resolution of 4km and global coverage. The data includes latitude and longitude information, as well as land and sea identification information.

[0047] 4. Using the channel count values ​​and calibration coefficients of channel 9 (wavelength 5.8-6.7μm), channel 10 (wavelength 6.75-7.15μm), channel 11 (wavelength 7.24-7.60μm), channel 12 (wavelength 8.3-8.8μm), channel 13 (wavelength 10.3-11.3μm), and channel 14 (wavelength 11.5-12.5μm) read from the FY4B / AGRI L1B data, calculate the brightness temperature (Tb) of channels 9, 10, 11, 12, 13, and 14. ch9 、Tb ch10 、Tb ch11 、Tb ch12 、Tb ch13 、Tb ch14 On this basis, the dual-channel brightness temperature difference between channels 9 and 11 (BTD1), the dual-channel brightness temperature difference between channels 11 and 12 (BTD2), the dual-channel brightness temperature difference between channels 12 and 13 (BTD3), the dual-channel brightness temperature difference between channels 9 and 13 (BTD4), the dual-channel brightness temperature difference between channels 13 and 14 (BTD5), and the three-channel brightness temperature difference (TBTD1) of channels 9, 10, and 11 are calculated.

[0048] 5. Using the OLR data read from the FY4B / AGRI data, calculate the 24-hour (OAI24h), 12-hour (OAI12h), 6-hour (OAI6h), 3-hour (OAI3h), and 1-hour (OAI1h) OLR anomaly indices; as well as the 10-day (SOI10d), 5-day (SOI5d), 24-hour (SOI24h), 12-hour (SOI12h), 6-hour (SOI6h), 3-hour (SOI3h), and 1-hour (SOI1h) OLR standardized deviation indices. The calculation formulas are as follows:

[0049] 6. Constructing the FY4B satellite subtropical high pressure inversion model under clear, partly cloudy and overcast conditions: Based on the subtropical high pressure area extracted from the FY4B / AGRI long-term satellite observation data and OLR products and ERA5 reanalysis data, a subtropical high pressure sample dataset under clear, partly cloudy and overcast sky conditions was established through spatiotemporal matching and random sampling methods. Among them, the clear, partly cloudy and overcast sky conditions are based on the OLR at the current observation time as the indicator, and the OLR is ≥ 250W m -2 , 235W m -2 <OLR<250W m -2 , OLR ≦ 235W m -2 The threshold division standard is used to distinguish. The time range covered by the matching dataset is from March 1, 2024 to January 31, 2025, and the spatial coverage is taken from the area with the FY4B / AGRI satellite zenith angle ≤ 70°. Based on the constructed sample set, the training set and test set are divided into a ratio of 7:3, and the XGBoost classification decision tree method is used to establish the recognition inversion model. The Bayesian iterative optimization method is used in the model training process, and the AUC mean (classification model performance evaluation index) of 5 cross-validations is used as the objective function to obtain the optimal models: STH_clearsky_bestmodel.m (clear sky), STH_cloudy_bestmodel.m (few clouds), STH_heavycld_bestmodel.m (cloudy). The main parameters of the optimal inversion model of subtropical high pressure under clear sky, few clouds and cloudy conditions obtained based on the XGBoost method in this example are shown in the following table: Table 1. Main parameters of the optimal inversion model for subtropical high pressure under clear, partly cloudy and cloudy conditions

[0050]

[0051] The subtropical high pressure inversion model under clear, partly cloudy and cloudy conditions obtained in step 6 and the current prediction factors obtained in steps 3-5, including satellite observation time, longitude, latitude, land and sea identification, satellite zenith angle cosine (cosθ), satellite zenith angle sine (sinθ), brightness temperature (Tb) of channels 9, 10, 11, 12 and channels 13 and 14, are used. ch9 , Tb ch10 , Tb ch11 , Tb ch12 , Tb ch13 , Tb ch14), dual-channel brightness temperature difference (BTD1, BTD2, BTD3, BTD4, BTD5) and three-channel brightness temperature difference (TBTD1), OLR anomaly indices (OAI24h, OAI12h, OAI6h, OAI3h and OAI1h) and OLR standardized deviation indices (SOI10d, SOI5d, SOI24h, SOI12h, SOI6h, SOI3h and SOI1h), and the probability of subtropical high pressure influence at the current observation time of FY4B / AGRI are inverted.

[0052] The inversion results are output by pixels, and different colors are used to represent the probability of subtropical high pressure influence. The value range is between 0%-100%. The smaller the probability, the lower the possibility that the area is affected by subtropical high pressure. The subtropical high pressure and non-subtropical high pressure areas are divided with a probability of 50% as the threshold, and the morphological closing operation is applied to the inversion results to fill the void area. Then, the Gaussian convolution filter is used to perform a low-pass filter on the subtropical high pressure area to remove noise and isolated points. According to the spatial resolution of FY4B / AGRI, the kernel in the closing operation is selected as a 3×3 rectangular template, as shown below. The Gaussian kernel size in the Gaussian filter is selected as 15×15, ;

[0053] The denoised subtropical high pressure identification results are converted from the nominal satellite projection to a 0.25° × 0.25° latitude and longitude grid through projection conversion. Based on this, the subtropical high pressure is segmented and numbered. The Canny edge detection method is applied to the subtropical high pressure area within the specified region to extract the outer boundary of the subtropical high pressure within the specified area. In this example, the Canny edge detection uses a 3×3 kernel window.

[0054] Based on the extracted outer boundary and affected area of ​​the subtropical high, this example uses the western Pacific subtropical high as the target and calculates its western ridge index (GD), northern boundary index (GB), area index (GM), and intensity index (GQ). By definition, the western Pacific region is the area west of 150°E and north of 10°N. The calculation method is as follows: West Ridge Index (GD): With 10°N and 150°E as the southern and eastern boundaries, find the longitude of the westernmost point of the outer boundary of the subtropical high pressure body. If the longitude of the westernmost point is less than 70°E, the West Ridge Index is set to 70°E. If it does not exist, the West Ridge Index is 150°E.

[0055] Northern Boundary Index (GB): With 10°N and 150°E as the southern and eastern boundaries, the latitude average of the northern outer boundary of the main body of the subtropical high pressure is calculated.

[0056] Area index (GM): With 10°N and 150°E as the southern and eastern boundaries, the area of ​​the main subtropical high pressure region is calculated using the following formula:

[0057] in, The subtropical high pressure and non-subtropical high pressure influence areas are determined by the subtropical high pressure probability obtained by inversion in step 8, and are represented by 0 and 1. is the latitude, dx and dy are the distances between grids.

[0058] Intensity Index (GQ): Calculates the relative volume of the main subtropical high pressure area, with 10°N and 150°E as the southern and eastern boundaries. Based on the relationship between probability and intensity, this paper uses the relative size of the probability value to represent the relative volume of the subtropical high pressure. The calculation formula is as follows:

[0059] in, The subtropical high pressure and non-subtropical high pressure influence areas are determined by the inversion of the subtropical high pressure probability, and are represented by 0 and 1; is the latitude, dx and dy are the distances between grids; is the probability size of each grid point in the subtropical high pressure identification area.

[0060] Experimental results This experiment uses FY4B observation data around April 8, 2025, which was not used in model training. The data includes: ① FY4B / AGRI L1B product at 0000 UTC on April 8, 2025, the data file name is: FY4B-_AGRI--_N_DISK_1050E_L1-_FDI-_MULT_NOM_20250408000000_20250408001459_4000M_V0001.HDF. (The data contains the brightness temperature data of the six infrared channels required by this invention); ② FY4B / AGRI GEO data at 00000 UTC on April 8, 2025 (including satellite zenith angle information). The data file name is: FY4B-_AGRI--_N_DISK_1050E_L1-_GEO-_MULT_NOM_20250408000000_20250408001459_4000M_V0001.HDF; ③ Continuous FY4B OLR inversion products within the 10 days before and at the current time with a 15-minute interval, totaling 960 data files, the data file names are: FY4B-_AGRI--_N_DISK_1050E_L2-_OLR-_MULT_NOM_20250408000000_20250408001459_4000M_V0001.NC, FY4B-_AGRI--_N_DISK_1050E_L2-_OLR-_MULT_NOM_20250408000000_20250408001459_4000M_V0001.NC, LR-_MULT_NOM_20250407234500_20250407235959_4000M_V0001.NC, FY4B-_AGRI--_N_ DISK_1050E_L2-_OLR-_MULT_NOM_20250407233000_20250407234459_4000M_V0001.NC

[0061] ④ Sea and land template data, the data file name is: landseamask_FY4BNOM.nc The model file name is: STH_clearsky_bestmodel.m STH_cloudy_bestmodel.m STH_heavycld_bestmodel.m In addition, in order to verify the reliability of the experimental results, the experiment also used the subtropical high pressure area obtained by the formula in S1 based on the ERA5 reanalysis data to evaluate the accuracy of the experimental results. The data file name is: ERA5_STH_20250408000000.nc Figure 3 This is a receiver operating characteristic (ROC) plot comparing FY4B's identification of the subtropical high pressure at 0000 UTC on April 8, 2025, with ERA5's identification results. The AUC value is 0.916 (AUC values ​​typically range from 0 to 1, with values ​​closer to 1 indicating greater consistency between the two data sets). The test results show good consistency between the subtropical high pressure identification results obtained by the proposed method and ERA5's.

[0062] The method of the present invention is based on the influence area and contour line of the western Pacific subtropical high at 0000 UTC on April 8, 2025, identified by the FY4B satellite. Based on the extraction results, the western ridge point index, northern boundary index, area index and intensity index calculation method of the western Pacific subtropical high in the present invention are applied to obtain the western ridge point index of the western Pacific subtropical high at that time as 74°E, the northern boundary index as 20.48°N, and the area index as 110×10 5km 2 , the strength index is 382.96×10 6 km 2 .

[0063] To verify the credibility of the results, we calculated the parameters of the western Pacific subtropical high based on the ERA5 reanalysis data and the revised calculation method of the western Pacific subtropical high western ridge index, northern boundary index, area index, and intensity index of the western Pacific subtropical high. The calculation method is as follows: West Ridge Index: The westernmost longitude of the 5880 gpm contour line west of the main body of the subtropical high, located north of 10°N and west of 150°E on the 500 hPa weather chart. If the westernmost longitude is less than 70°E, the West Ridge Index is set to 70°E; if it does not exist, the West Ridge Index is 150°E. Northern Boundary Index: The latitude average of the 5880 gpm contour line on the north side of the subtropical high within the range north of 10°N and west of 150°E on the 500 hPa weather map; Area Index: The area enclosed by the 5880 gpm contour line north of 10°N and west of 150°E on a 500 hPa weather map. The calculation formula is as follows:

[0064] in, is the latitude, dx and dy are the distances between grids; ; Hij represents the potential height value of a grid point in the 500 hPa geopotential height field.

[0065] Intensity Index: The relative volume of the subtropical high pressure body with a geopotential height greater than the 5880 gpm isohyet on the 500 hPa weather chart north of 10°N and west of 150°E. Calculated as follows:

[0066] in, ,dx,dy,n ij and H ij Same meaning as the parameters in the area index.

[0067] The results show that the western ridge index of the western Pacific subtropical high pressure calculated based on the ERA5 reanalysis data is 70°E, the northern boundary index is 21.68°N, and the area index is 86×10 5 km 2 , the strength index is 326.39×10 6 km 2The numerical values ​​are basically equivalent to those of the western Pacific subtropical high estimated based on the subtropical high identified by the FY4B satellite.

[0068] The above examples show that since the spatial and temporal resolutions of geostationary meteorological satellites (the spatial resolution of the FY4B infrared channel is 4 km, and the temporal resolution is 15 minutes; the spatial resolution of the Himawari-8 or GOES-R infrared channel is 2 km, and the temporal resolution is 5 minutes to 30 minutes) are much higher than those of current numerical models and sounding observations, the subtropical high pressure products with continuous temporal and spatial distribution obtained using geostationary meteorological satellite data can provide multi-scale structural information of the subtropical high pressure with higher temporal and spatial resolution, which has important application prospects for analyzing and studying the short-term structural change characteristics of the subtropical high pressure and improving short-term climate prediction and weather forecast.

[0069] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observations, characterized in that: include: S1. Project the target data onto the observation field of view of a geostationary meteorological satellite. Calculate the affected area of ​​the subtropical high pressure using the set geopotential height as the division standard. Based on the data time of the affected area, perform spatiotemporal matching with the historical long-sequence geostationary meteorological satellite observation data and sea and land template data to obtain a spatiotemporal matching dataset. S2. Randomly sample the spatiotemporal matching dataset, select discrete sample points in time or space, and divide the spatial sampling process into multiple sampling areas according to different sky states in meteorological classification; S3. Divide the sample sets of multiple sampling areas into training sets and test sets, and use XGBoost classification decision trees to establish recognition inversion models, and use Bayesian iterative optimization to construct the optimal recognition model; S4. Input real-time geostationary meteorological satellite imager observation data and use the optimal identification model to identify the subtropical high pressure influence area within the satellite observation field of view according to the following formula, and obtain the influence probability of each point; ； Where, xi is the prediction characteristic factor of point i; is the predicted probability of the kth tree; is the sum of the predicted probabilities of the K trees in the i-th point; S5. Based on the comparison of the influence probability of each point with the probability threshold, each point is divided into the influence area and the non-influence area of ​​the subtropical high pressure, thereby obtaining the identification result of the subtropical high pressure; S6. Convert the identification results from the satellite nominal projection into equal latitude and longitude grid data, segment and number the subtropical high pressure affected area in the equal latitude and longitude grid data, apply Canny edge detection to the segmented areas within the specified range, and extract the outer boundary of the subtropical high pressure; S7. Calculate a subtropical high pressure index based on the subtropical high pressure identification result and the grid spacing of the segmented area, including at least one of a west ridge point index, a northern boundary index, an area index, or an intensity index.

2. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, wherein: The formula for calculating the observed subtropical high pressure influence area in S1 is: ; Where G is the set geopotential height, and the unit is geopotential meter (gpm).

3. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, wherein: In S2, different sky conditions in meteorological classification are divided into clear sky, partly cloudy and cloudy, i.e., three sampling areas. The regional division standard of the three sampling areas is performed with OLR as the threshold. 。 4. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, wherein: The information collected by the sampling area in S2 includes: satellite observation time, sampling area longitude, sampling area latitude, sea and land identification, satellite zenith angle cosθ, satellite zenith angle sine sinθ, upper water vapor channel brightness temperature, middle water vapor channel brightness temperature, lower water vapor channel brightness temperature, long-wave infrared channel brightness temperature, dual-channel brightness temperature difference, three-channel brightness temperature difference, and OLR anomaly index and OLR standardized deviation index for multiple time periods.

5. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, wherein: If the AUC value increases in S3, the model undergoes a new round of iteration and adds new characteristic parameters. When the AUC value stops increasing, the iteration ends and the optimal model for identifying subtropical high pressure is obtained. The AUC calculation formula is: ； Where, Represents the ranking number of positive sample i; represents the number of positive samples; Indicates the number of negative samples.

6. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, characterized in that: In order to ensure the spatial continuity of the subtropical high pressure, the spatially isolated small-scale high-pressure bodies are removed in S5, and the hole areas are filled by morphological closing operation. Then, the subtropical high pressure area is low-pass filtered using Gaussian convolution filtering to remove noise and isolated points. The formula of the Gaussian convolution filtering is as follows: ； Where x and y represent the row and column coordinates relative to the center point of the filter window respectively; Represents the standard deviation of the Gaussian distribution.

7. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, characterized in that: The area index of the subtropical high pressure in S7 is calculated based on the longitude and latitude range of the specified area, and the calculation formula is: ； Where, The subtropical high pressure and non-subtropical high pressure influence areas determined by the subtropical high pressure probability obtained by inversion in step 8 are represented by 0 and 1; is latitude; dx and dy are the distances between grids.

8. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, characterized in that: The intensity index of the subtropical high pressure in S7 is calculated based on the longitude and latitude range of the specified area, and is expressed by the relative size of the probability value. The calculation formula is: ； Where, The subtropical high pressure and non-subtropical high pressure influence areas determined by the subtropical high pressure probability obtained by inversion in step 8 are represented by 0 and 1; is latitude; dx and dy are the distances between grids; is the probability size of each grid point in the subtropical high pressure identification area.

9. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, characterized in that: In said S7, according to the extracted outer boundary and affected area of ​​the subtropical high pressure, a western ridge point index of the subtropical high pressure in the specified area is obtained; The west ridge point index is to find the longitude value of the westernmost point of the outer boundary of the subtropical high pressure body in the specified area according to the longitude and latitude range of the area. If the longitude of the westernmost point is less than the longitude of the western boundary of the area, the west ridge point index is set as the longitude of the western boundary of the area. If it does not exist, the west ridge point index is the longitude of the eastern boundary of the area.

10. The method for extracting subtropical high pressure parameters based on meteorological satellite multispectral imaging observation according to claim 1, characterized in that: In said S7, based on the extracted outer boundary and affected area of ​​the subtropical high pressure, a northern boundary index of the subtropical high pressure in the specified area is obtained; The northern boundary index is calculated based on the longitude and latitude range of a specified area, and is the average latitude of the outer boundary of the main body of the subtropical high pressure toward the polar side in the area.

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