Method and device for estimating bias of simulated brightness temperature, electronic equipment and storage medium

Abstract

The application provides an estimation method and device for simulating brightness temperature deviation, electronic equipment and storage medium, the method comprises: obtaining the simulation parameters at the current time, the simulation parameters comprise atmospheric parameters, ground parameters and cloud parameters; according to the simulation parameters, the simulation brightness temperature data under the cloud condition and the cloud-free condition is calculated; according to the relationship of the simulation brightness temperature data under the cloud condition and the cloud-free condition, the corresponding grouping of the simulation parameters is determined; according to the simulation parameters and the fitting relationship corresponding to the corresponding grouping, the simulation brightness temperature deviation under the cloud condition at the current time is determined; wherein, one grouping corresponds to one fitting relationship, the fitting relationships corresponding to different groupings are different, the fitting relationship corresponding to one grouping is obtained according to the historical simulation parameters and the historical simulation brightness temperature deviation under the cloud condition corresponding to the grouping, and the grouping represents the influence of the cloud on the historical simulation brightness temperature deviation under the cloud condition.

Description

Technical Field

[0001] This application relates to the field of meteorological forecasting, and in particular to a method, apparatus, electronic device, and storage medium for estimating simulated brightness temperature deviation. Background Technology

[0002] When predicting atmospheric brightness temperature, simulated atmospheric parameters are generally used. However, there is a discrepancy between the simulation results and the actual satellite measurements; this discrepancy is called the simulated brightness temperature bias. To correct the simulated brightness temperature data, the simulated brightness temperature bias needs to be obtained.

[0003] Current technologies generally only calculate simulated brightness temperature deviations under clear-sky conditions. The typical calculation method involves using a set of simulated parameters to establish a fitting relationship with historical simulated brightness temperature deviations, and then using this fitting relationship to predict future simulated brightness temperature deviations.

[0004] Because existing technologies for simulating brightness temperature deviation generally only consider clear sky conditions, the resulting simulated brightness temperature deviation is inaccurate and has too narrow an applicability. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, electronic device, and storage medium for estimating simulated brightness temperature deviation, so as to solve the problems that existing simulated brightness temperature deviations are not accurate enough and have too narrow an application.

[0006] This application provides a method for estimating simulated brightness temperature deviation, comprising: acquiring simulated parameters at the current moment, the simulated parameters including atmospheric parameters, surface parameters, and cloud parameters; calculating simulated brightness temperature data under cloudy and cloudless conditions based on the simulated parameters; determining the grouping corresponding to the simulated parameters based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions; and determining the simulated brightness temperature deviation under cloudy conditions at the current moment based on the fitting relationship between the simulated parameters and the corresponding group; wherein, one group corresponds to one fitting relationship, different groups correspond to different fitting relationships, and the fitting relationship corresponding to one group is obtained by fitting the historical simulated parameters and historical simulated brightness temperature deviation under cloudy conditions corresponding to that group, the grouping characterizing the influence of clouds on the historical simulated brightness temperature deviation under cloudy conditions.

[0007] In the above implementation process, since the grouping characterizes the influence of cloud factors on historical simulated brightness temperature data, and the fitting relationship is obtained by fitting the historical simulated parameters and historical simulated brightness temperature deviation under the cloud conditions corresponding to the group, the simulated brightness temperature deviation under the cloud conditions at the current moment is determined by determining the group corresponding to the simulated parameters and then determining the simulated brightness temperature deviation under the cloud conditions at the current moment based on the fitting relationship corresponding to the group. The influence of cloud factors on simulated brightness temperature deviation is considered. Since the grouping and the fitting relationship are in a one-to-one correspondence, the fitting relationship corresponding to each group is not completely the same. Compared with the existing technology that does not consider cloud factors and uses the same set of fitting relationships for all time predictions, the simulated brightness temperature deviation under the cloud conditions can be obtained more accurately.

[0008] Further, before determining the grouping of the simulation parameters based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions, the method further includes: acquiring multiple historical simulation parameters and their corresponding historical observed brightness temperature data corresponding to multiple times prior to the current time, wherein the historical simulation parameters include atmospheric parameters, surface parameters, and cloud parameters; for each historical simulation parameter, calculating historical simulated brightness temperature data under cloudy and cloudless conditions based on the historical simulation parameter; for each historical simulated brightness temperature data under cloudy conditions, calculating the historical simulated brightness temperature deviation under cloudy conditions based on the historical simulated brightness temperature data under cloudy conditions and its corresponding historical observed brightness temperature data; grouping multiple historical simulation parameters and their corresponding historical simulated brightness temperature deviations under cloudy conditions based on the relationship between the historical simulated brightness temperature data under cloudy and cloudless conditions calculated using the same historical simulation parameter; and for each group, determining the fitting relationship corresponding to that group based on the historical simulation parameters and historical simulated brightness temperature deviations under cloudy conditions corresponding to that group.

[0009] In the above implementation process, since the simulated brightness temperature deviation in the fitting relationship is calculated using historical observed brightness temperature data and historical simulated brightness temperature data under cloudy conditions, the historical simulated brightness temperature deviation in the fitting relationship takes into account the influence of cloud factors. Because the influence of cloud factors on the simulated brightness temperature deviation under cloudy conditions is complex, the overall relationship between the simulated parameters and the simulated brightness temperature deviation under cloudy conditions is non-linear. However, after detailed grouping according to the contribution of cloud factors, the historical simulated brightness temperature deviation under cloudy conditions changes with the historical simulated parameters in a relatively regular and approximately linear manner. Therefore, each group can obtain a relatively accurate fitting relationship.

[0010] Furthermore, the time closest to the current time among the plurality of times is spaced apart from the current time by a preset number of times. The method further includes: for each group in the group, excluding the corresponding group, determining the fitting relationship corresponding to the group based on the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions corresponding to the group.

[0011] In the above implementation process, since the life cycle of a cloud is between a few hours and a dozen hours, the fitting relationship corresponding to each group at multiple moments within the life cycle can be determined in advance. When calculating the simulated brightness temperature data under cloud conditions at the current moment, the calculation is performed based on the pre-determined fitting relationship of each group, without having to calculate the fitting relationship of the corresponding group at multiple moments before the current moment again in real time, which reduces the amount of calculation and improves efficiency.

[0012] Furthermore, the step of calculating historical simulated brightness temperature data under cloud conditions and cloudless conditions based on each historical simulation parameter includes: for each historical simulation parameter, in mesoscale mode, calculating historical simulated brightness temperature data under cloud conditions and cloudless conditions based on the historical simulation parameter and a preset simulated brightness temperature model.

[0013] In the above implementation process, the mesoscale model is used to calculate historical simulated brightness temperature data under both cloud-covered and cloudless conditions, which enables the calculation of historical simulated brightness temperature data for a specified area at a more appropriate temporal and spatial scale.

[0014] Furthermore, the step of grouping multiple historical simulation parameters and corresponding historical simulation brightness temperature deviations under cloudy conditions based on the relationship between historical simulation brightness temperature data calculated under the same historical simulation parameter and under cloudy conditions includes: calculating the absolute value of the difference between the historical simulation brightness temperature data calculated under the same historical simulation parameter and under cloudy conditions; grouping the absolute values ​​that are greater than a preset threshold according to a preset interval; and grouping the historical simulation parameters and historical simulation brightness temperature deviations under cloudy conditions corresponding to the absolute values ​​in each group into a single group.

[0015] In the above implementation process, since the absolute value of the difference between the historical simulated brightness temperature data under cloud conditions and cloudless conditions is used to characterize the influence of cloud factors on the historical simulated brightness temperature data, grouping according to the size of the absolute value can more accurately obtain groups with different contributions of cloud factors.

[0016] Furthermore, determining the fitting relationship corresponding to the group based on the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions corresponding to the group includes: inputting the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions corresponding to the group into a preset model for training to obtain the fitting relationship corresponding to the group.

[0017] In the above implementation process, by training the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions of the grouped data using a preset model, the fitting relationship between the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions can be obtained simply and efficiently.

[0018] Further, determining the grouping corresponding to the simulation parameters and simulation brightness temperature deviation based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions includes: calculating the absolute value of the difference between the simulated brightness temperature data under cloudy and cloudless conditions; comparing the absolute value with the absolute value interval corresponding to the preset grouping; and determining the group of the absolute value interval where the absolute value is located as the corresponding grouping.

[0019] In the above implementation process, the grouping of the simulation parameters and the simulation brightness temperature deviation is determined by the absolute value of the difference between the simulated brightness temperature data under cloudy and cloudless conditions, which can more accurately determine the corresponding fitting relationship.

[0020] This application provides an apparatus for estimating simulated brightness temperature deviation, comprising: an acquisition module for acquiring simulated parameters at the current moment, the simulated parameters including atmospheric parameters, surface parameters, and cloud parameters; a first calculation module for calculating simulated brightness temperature data under cloudy and cloudless conditions based on the simulated parameters; a first determination module for determining a group corresponding to the simulated parameters and the simulated brightness temperature deviation based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions; and a second determination module for determining the simulated brightness temperature deviation under cloudy conditions at the current moment based on the fitting relationship between the simulated parameters and the corresponding group; wherein, one group corresponds to one fitting relationship, different groups correspond to different fitting relationships, and the fitting relationship corresponding to one group is obtained by fitting historical simulated parameters and historical simulated brightness temperature deviations under cloudy conditions corresponding to that group, the grouping representing the influence of clouds on the historical simulated brightness temperature deviation under cloudy conditions.

[0021] This application provides an electronic device, including: a processor and a memory; the processor is used to execute a program stored in the memory to implement the simulation brightness temperature deviation estimation method described in any of the above claims.

[0022] This application provides a computer-readable storage medium that stores one or more programs that can be executed by one or more processors to implement the simulation brightness temperature deviation estimation method described in any of the above claims. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a method for determining simulated brightness temperature deviation provided in an embodiment of this application;

[0025] Figure 2 A schematic diagram illustrating the process of determining the group fitting relationship in a method for determining simulated brightness temperature deviation provided in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of a simulated brightness temperature deviation determination device provided in an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0029] To facilitate understanding, the following is a definition of the terms:

[0030] Brightness temperature: When the radiance of an object is equal to that of a blackbody, the physical temperature of the blackbody is called the brightness temperature of the object. Therefore, brightness temperature has the dimension of temperature, but does not have the physical meaning of temperature. It is a representative term for the radiance of an object.

[0031] Cloud conditions: This refers to the fact that cloud parameters are included in the input simulation parameters when calculating simulated brightness temperature data.

[0032] Cloudless condition: This means that when calculating simulated brightness temperature data, the input simulation parameters do not include cloud parameters.

[0033] Example 1:

[0034] To address the issues of inaccurate and limited applicability of existing simulated brightness temperature deviation techniques, this application provides a method for estimating simulated brightness temperature deviation. See also... Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for estimating simulated brightness temperature deviation provided in an embodiment of this application, including:

[0035] S101: Obtain the simulation parameters at the current moment. The simulation parameters include atmospheric parameters, surface parameters, and cloud parameters.

[0036] The simulation parameter model is not limited; for example, a climate model, mesoscale model, or microscale model can be selected. The temporal and spatial resolutions of the simulation parameters differ depending on the model. The method of obtaining the simulation parameters is not limited; they can be obtained from a database, from a meteorological simulation parameter generation module, or by connecting to external meteorological simulation software to obtain simulation parameters for the current moment. Simulation parameters include atmospheric parameters, surface parameters, and cloud parameters. Atmospheric parameters include temperature and humidity profiles; cloud parameters include cloud cover and cloud height; and surface parameters include surface reflectance and surface temperature. Each simulation parameter has frequency, coordinate, and temporal attributes; that is, a simulation parameter represents the simulation parameters at a specific coordinate, time, and frequency.

[0037] Optionally, simulation parameters can be obtained in a mesoscale meteorological model.

[0038] S102: Calculate simulated brightness temperature data under cloudy and cloudless conditions based on simulation parameters;

[0039] Optionally, the simulation parameters are based on a mesoscale meteorological model.

[0040] Specifically, the formula for calculating simulated brightness temperature data under cloudless conditions is as follows:

[0041]

[0042] Where B(v, T) is Planck's function of frequency v and temperature T, and τ s (v, θ) represents the transmittance from the surface of the blackbody into space, and ε s (v, θ) represents the emissivity of the blackbody surface. Here, v is the satellite receiving frequency, and θ is the zenith angle. B(v, T) s (v) represents frequency v, and T represents surface temperature. s Planck's function.

[0043] Among them, B(v, T) and ε are obtained from the simulation parameters. s (v, θ), τ s The method of (v, θ) is existing technology and will not be elaborated here.

[0044] Specifically, the formula for calculating simulated brightness temperature data under complete cloud coverage is as follows:

[0045]

[0046] Where, τ cld (v, θ) is the transmittance above the cloud top, where v is the satellite receiving frequency and θ is the zenith angle. B(v, T) cld (v) represents frequency v, and T represents cloud top temperature. cldThe Planck function is given by B(v, T), where B(v, T) is the Planck function for frequency v and temperature T.

[0047] Among them, B(v,T) and B(v,T) are obtained based on the simulation parameters. cld ), τ cld The method of (v, θ) is existing technology and will not be elaborated here.

[0048] Specifically, the formula for calculating simulated brightness temperature data under cloud conditions is as follows:

[0049] L(v, θ) = (1-N)L clr (v, θ) + NL cld (v,θ) (3)

[0050] Where N is the cloud cover, with a value in the range [0, 1]. v is the satellite receiving frequency, and θ is the zenith angle.

[0051] Specifically, formula (1) is used to batch calculate the simulated brightness temperature data of each coordinate value in the specified area under cloudless conditions. Based on formulas (1) and (2), formula (3) is used to batch calculate the simulated brightness temperature data of each coordinate value in the specified area under cloudy conditions. Wherein, N is the cloud cover of the model background field at the specific coordinate value of the specified area at the current time. The value range of N is [0,1], and the coordinate value includes longitude, latitude, and altitude information.

[0052] S103: Determine the grouping corresponding to the simulation parameters based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions;

[0053] S104: Determine the simulated brightness temperature deviation under cloud conditions at the current moment based on the simulation parameters and the fitting relationship corresponding to the corresponding group; wherein, one group corresponds to one fitting relationship, and different groups correspond to different fitting relationships. The fitting relationship corresponding to one group is obtained by fitting the historical simulation parameters and historical simulated brightness temperature deviation under cloud conditions corresponding to that group. The group represents the influence of clouds on the historical simulated brightness temperature deviation under cloud conditions.

[0054] To facilitate understanding of the grouping in step S103 and the corresponding fitting relationships in step S104, the following will explain in detail how each group and its corresponding fitting relationship are determined.

[0055] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the process of determining the group fitting relationship in the brightness temperature deviation determination method of this application. This process can be executed by an electronic device that performs the simulated brightness temperature deviation determination method, or by other devices. After obtaining the fitting relationship, it is sent to the electronic device that performs the simulated brightness temperature deviation determination method. This application does not impose specific limitations on the embodiments.

[0056] Specifically, such as Figure 2 As shown, before step S103, that is, before determining the grouping of the simulation parameters based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions, the following technical solution is also included:

[0057] S201: Obtain multiple historical simulation parameters and their corresponding historical observed brightness temperature data for multiple times prior to the current time. The historical simulation parameters include atmospheric parameters, surface parameters, and cloud parameters.

[0058] Specifically, it obtains historical simulation parameters and historical observed brightness temperature data for the most recent consecutive hours.

[0059] Optionally, historical simulation parameters in the mesoscale mode can be obtained.

[0060] For example, if the current time is 7 o'clock, then historical simulation parameters for 0 o'clock, 1 o'clock, 2 o'clock, 3 o'clock, 4 o'clock, 5 o'clock, and 6 o'clock can be obtained. This step is similar to step S101, and will not be described in detail here.

[0061] Optionally, since the lifespan of a cloud is generally between a few hours and a dozen hours, it is also possible to obtain historical simulation parameters from multiple consecutive moments with a preset number of intervals between the most recent moment and the current moment, where the preset number is determined according to the lifespan of the cloud.

[0062] S202: For each historical simulation parameter, calculate the historical simulated brightness temperature data under cloudy and cloudless conditions based on the historical simulation parameter.

[0063] Optionally, in mesoscale mode, historical simulated brightness temperature data under cloud-covered and cloudless conditions can be calculated based on the historical simulation parameters and the preset simulated brightness temperature model.

[0064] The preset simulated brightness temperature model is formula (1), formula (2) and formula (3) in step S102.

[0065] The method for calculating historical simulated brightness temperature data under cloudy and cloudless conditions is as described in step S102, and will not be repeated here.

[0066] S203: For each historical simulated brightness temperature data under cloud conditions, calculate the historical simulated brightness temperature deviation under cloud conditions based on the historical simulated brightness temperature data under the cloud conditions and its corresponding historical observed brightness temperature data.

[0067] Specifically, the historical observed brightness temperature data at each time point and at each coordinate value is compared with the historical simulated brightness temperature data under corresponding cloud conditions to obtain the historical simulated brightness temperature deviation under cloud conditions at each time point and at each coordinate value.

[0068] S204: Based on the relationship between historical simulated brightness temperature data under cloudy and cloudless conditions calculated using the same historical simulated parameter, group multiple historical simulated parameters under cloudy conditions and the corresponding historical simulated brightness temperature deviations under cloudy conditions.

[0069] Specifically, the relationship between historical simulated brightness temperature data under cloud conditions and cloudless conditions can be a difference relationship or a quotient relationship.

[0070] In one feasible implementation, step S204 includes: calculating the absolute value of the difference between historical simulated brightness temperature data under cloudy and cloudless conditions, calculated based on the same historical simulation parameters. Absolute values ​​greater than a preset threshold are grouped according to preset intervals. The historical simulation parameters corresponding to the absolute values ​​in each group and their corresponding historical simulated brightness temperature deviations under cloudy conditions are grouped together.

[0071] The preset threshold is the critical value at which cloud factors have a significant impact on the simulated brightness-temperature deviation. The preset interval is a numerical range grouped by the absolute values ​​of the difference; the size of the preset interval is not limited.

[0072] Specifically, for each historical simulation parameter, the absolute value of the difference between historical simulated brightness and temperature data under cloudy and cloudless conditions is calculated. The relationship between the absolute value of each difference and a preset threshold is determined. When the absolute value of the difference is higher than the preset threshold, the absolute values ​​of the difference are grouped using a preset interval, and the corresponding historical simulation parameters and historical simulated brightness and temperature deviations under cloudy conditions are assigned to the corresponding groups.

[0073] For example, when the absolute values ​​of the historical simulated brightness temperature data differences under cloudy and cloudless conditions are 4k, 6k, 8k, 12k, 15k, and 18k, the preset threshold is 3k, where k is the temperature unit Kelvin. The preset range is 5k, resulting in several absolute value ranges: 3k-8k, 9k-14k, and 15k-20k. The historical simulated parameters corresponding to 4k, 6k, and 8k, along with their corresponding historical simulated brightness temperature deviations under cloudy conditions, are grouped into the 3k-8k group; the historical simulated parameters corresponding to 12k, along with their historical simulated brightness temperature deviations under cloudy conditions, are grouped into the 9k-14k group; and the historical simulated parameters corresponding to 15k and 18k, along with their historical simulated brightness temperature deviations under cloudy conditions, are grouped into the 15k-20k group.

[0074] Optionally, the historical simulation parameters and historical simulation brightness temperature deviations under cloud conditions can be grouped according to the quotient of historical simulation brightness temperature data under cloud conditions and cloudless conditions.

[0075] Specifically, the historical simulated brightness and temperature data under cloudy and cloudless conditions are used to calculate a quotient. The relationship between the quotient and a second preset threshold is then determined. When the quotient is greater than the second preset threshold, the data is grouped according to a second preset interval, and the corresponding historical simulated parameters and historical simulated brightness and temperature deviations under cloudy conditions are assigned to the corresponding group. The second preset threshold is obtained based on prior experience, and its value range is not specifically limited. The value range of the second preset interval is also not specifically limited.

[0076] S205: For each group, determine the fitting relationship for that group based on the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions corresponding to that group.

[0077] Specifically, since the simulation parameters at the current moment can uniquely determine the corresponding group, it is only necessary to calculate the fitting relationship for the group corresponding to the simulation parameters at the current moment. There are no restrictions on the calculation of the fitting relationship. For example, the fitting relationship can be calculated using the least squares method or multiple linear regression, or it can be determined using neural network training.

[0078] For example, if the absolute value of the difference between the simulated brightness temperature data under cloud conditions and cloudless conditions corresponding to the simulated parameters at the current moment is 7k, corresponding to a grouping of absolute values ​​between 3k and 8k, then the fitting relationship corresponding to this grouping can be calculated using the method of multiple linear regression.

[0079] In other words, in this embodiment, historical data from the most recent few moments can be processed only when S104 is executed, thereby obtaining the fitting relationship of the group corresponding to the current moment. And at each prediction moment, steps S201-205 are re-executed.

[0080] In one feasible implementation, since the cloud's lifespan ranges from several hours to over ten hours, the fitting relationships for all groups within the lifespan can be predetermined. Specifically, the time closest to the current time among the multiple time points is spaced a predetermined number of time points away from the current time. The method further includes: for each group (excluding the corresponding group), determining the fitting relationship corresponding to that group based on the historical simulation parameters and historical simulation brightness-temperature deviations. In other words, within the lifespan, the fitting relationship for each group only needs to be calculated once and can be used multiple times within the lifespan. For example, when calculating the fitting relationship for each group within 0-6 hours, the fitting relationship for each group within 0-6 hours can be used to calculate the simulation brightness-temperature deviations at the 7th, 8th, or even 10th hour within the same lifespan. In this case, the 10th hour at the current time is 4 time points away from the 6th time point among the 6 time points. The predetermined number is determined based on the cloud's lifespan.

[0081] Optionally, in this embodiment, steps S201-S205 and the determination of fitting relationships for other groups can be performed before step S101, and the fitting relationship can be directly used in the next few prediction times (i.e., the current time), instead of performing steps S201-S205 and determining fitting relationships for other groups at each current time (prediction time).

[0082] In one feasible implementation, regardless of when the fitting relationship of the corresponding group is determined, the fitting relationship of the group is determined based on the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions corresponding to the group, including: inputting the historical simulation parameters and historical simulation brightness temperature deviation corresponding to the group into a preset model for training to obtain the fitting relationship of the group.

[0083] The preset model is not specifically limited and can be a linear regression model or various neural network models. For example, it can be a fully connected neural network.

[0084] Specifically, by taking the historical simulation parameters within the group as independent variables and the historical simulation brightness temperature deviation as dependent variables, and inputting the above historical data into the preset model for training, the fitting parameters of the historical simulation parameters and historical simulation brightness temperature deviation of the group can be obtained, and thus the fitting relationship corresponding to the group can be obtained.

[0085] After determining the groupings and their corresponding fitting relationships through the methods described above or other means, the following section will continue. Figure 1 Steps S103 and S104 in the method shown.

[0086] S103: Determine the grouping of simulation parameters based on the relationship between simulated brightness temperature data under cloudy and cloudless conditions;

[0087] Specifically, the relationship between simulated brightness and temperature data under cloudy and cloudless conditions can be either a difference relationship or a quotient relationship. If the absolute value of the difference is used when acquiring the groups, then the absolute value of the difference is also used when determining the corresponding groups; if the quotient relationship is used when acquiring the groups, then the quotient relationship is also used when determining the corresponding groups.

[0088] In one possible implementation, step S103 includes: calculating the absolute value of the difference between simulated brightness temperature data under cloudy and cloudless conditions; comparing the absolute value with the absolute value interval corresponding to a preset group; and determining the group of the absolute value interval where the absolute value is located as the corresponding group.

[0089] Specifically, the simulated brightness temperature data under cloudy and cloudless conditions corresponding to each coordinate value are subtracted, and the absolute value of the difference is calculated. The relationship between this absolute value and a preset threshold is then determined. This preset threshold is the critical value at which cloud factors significantly contribute to the simulated brightness temperature deviation; above this threshold, cloud factors have a significant impact on the simulated brightness temperature deviation. The preset threshold is not specifically limited and is generally obtained based on prior experience. When this absolute value is greater than the preset threshold, it is determined whether the absolute value is within the absolute value interval of each group. If the absolute value is within the absolute value interval of a certain group, then this group is determined to be the group corresponding to the simulated parameter corresponding to that absolute value.

[0090] For example, the absolute values ​​of the simulated brightness temperature data differences under cloudy and cloudless conditions at multiple coordinate values ​​at the current time are 2k, 4k, 7k, 13k, and 18k, respectively, with a preset threshold of 3k. Here, k represents the temperature unit Kelvin, and the grouping intervals are 3k-8k, 9k-14k, and 15k-20k, respectively. Therefore, the simulated parameters corresponding to values ​​below 3k are not grouped, and their simulated brightness temperature deviation is predicted according to the simulated brightness temperature deviation under cloudless conditions in existing technology. For values ​​greater than 3k, it can be determined that 4k and 7k fall within the group with absolute values ​​between 3k and 8k, 13k falls within the group with absolute values ​​between 9k and 14k, and 18k falls within the group with absolute values ​​between 15k and 20k. The simulated parameters at the current time corresponding to 4k, 7k, 13k, and 18k are added to the corresponding absolute value interval groups.

[0091] S104: Determine the simulated brightness temperature deviation under cloud conditions at the current moment based on the simulation parameters and the fitting relationship corresponding to the corresponding group; where, one group corresponds to one fitting relationship, and different groups correspond to different fitting relationships. The fitting relationship corresponding to one group is obtained by fitting the historical simulation parameters and historical simulated brightness temperature deviation under cloud conditions corresponding to that group. The grouping characterizes the influence of clouds on the historical simulated brightness temperature deviation under cloud conditions.

[0092] Specifically, the simulation parameters at the current moment are input into the fitting relationship corresponding to the corresponding group to obtain the simulated brightness temperature deviation under cloud conditions at the current moment.

[0093] The method for estimating simulated brightness temperature deviation provided in this application determines the corresponding grouping by the relationship between simulated brightness temperature data under cloudy and cloudless conditions. Then, based on the fitting relationship between the simulated parameters corresponding to the grouping and the simulated brightness temperature deviation, the method achieves accurate determination of the brightness temperature deviation under cloudy conditions at the current moment, thus expanding the applicable range of brightness temperature deviation.

[0094] Furthermore, determining the grouping based on the absolute value of the difference between simulated brightness temperature data under cloudy and cloudless conditions allows for a faster and more accurate determination of the corresponding fitting relationship.

[0095] Furthermore, since the simulated brightness temperature deviation in the fitting relationship is calculated using historical observed brightness temperature data and historical simulated brightness temperature data under cloudy conditions, the historical simulated brightness temperature deviation in the fitting relationship takes into account the influence of cloud factors. Because the influence of cloud factors on the simulated brightness temperature deviation is complex, the overall relationship between the simulated parameters and the simulated brightness temperature deviation under cloudy conditions is non-linear. However, after detailed grouping according to the contribution of cloud factors, the brightness temperature deviation under cloudy conditions changes relatively regularly with the simulated parameters. Therefore, each group can obtain a relatively accurate fitting relationship.

[0096] Furthermore, since the lifespan of a cloud is between a few hours and a dozen hours, the fitting relationship corresponding to each group at multiple moments within the lifespan can be predetermined. When calculating the simulated brightness temperature data under cloud conditions at the current moment, the calculation is performed based on the predetermined fitting relationship of each group, without having to recalculate the fitting relationship of the corresponding groups at multiple moments before the current moment in real time, thus reducing the amount of computation and improving efficiency.

[0097] Furthermore, by using a mesoscale model to calculate historical simulated brightness temperature data under both cloud-covered and cloudless conditions, historical simulated brightness temperature data for a specified area can be calculated at a more suitable temporal and spatial scale.

[0098] Furthermore, by taking the absolute value of the difference between historical simulated brightness temperature data under cloudy and cloudless conditions and grouping them according to preset intervals, it is possible to obtain groups of historical simulated parameters and historical simulated brightness temperature deviations with different contributions from cloud factors. This allows for a more convenient acquisition of the fitting relationship between historical simulated parameters and historical simulated brightness temperature deviations under cloudy conditions within a smaller data range.

[0099] Furthermore, by training the historical simulation parameters and historical simulation brightness temperature deviations of the group using a pre-set model, the fitting relationship between the historical simulation parameters and historical simulation brightness temperature deviations can be obtained simply and efficiently.

[0100] Example 2

[0101] Based on the same inventive concept, this application also provides a device 300 for estimating simulated brightness temperature deviation. Please refer to... Figure 3 As shown, Figure 3 It shows the use of Figure 1 The method shown describes a device for estimating simulated brightness temperature deviation. It should be understood that the specific functions of the simulated brightness temperature deviation estimation device 300 are described above; to avoid repetition, detailed descriptions are omitted here. The simulated brightness temperature deviation estimation device 300 includes at least one software function module that can be stored in memory or embedded in the operating system of the simulated brightness temperature deviation estimation device 300 in the form of software or firmware. Specifically:

[0102] See Figure 3 As shown, the device 300 for estimating the simulated brightness temperature deviation may include:

[0103] The acquisition module 301 is used to acquire the simulation parameters at the current moment. The simulation parameters include atmospheric parameters, surface parameters, and cloud parameters.

[0104] The first calculation module 302 is used to calculate simulated brightness temperature data under cloud conditions and cloudless conditions based on simulation parameters;

[0105] The first determining module 303 is used to determine the grouping of the simulation parameters based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions.

[0106] The second determining module 304 is used to determine the simulated brightness temperature deviation under cloud conditions at the current moment based on the simulation parameters and the fitting relationship corresponding to the corresponding group. Here, each group corresponds to a fitting relationship, and the fitting relationships corresponding to different groups are different. The fitting relationship corresponding to a group is obtained by fitting the historical simulation parameters and historical simulated brightness temperature deviation under cloud conditions corresponding to that group. The grouping characterizes the influence of clouds on the historical simulated brightness temperature deviation under cloud conditions.

[0107] In this embodiment of the application, the first determining module 303 can be specifically used to calculate the absolute value of the difference between simulated brightness temperature data under cloudy conditions and cloudless conditions; compare the absolute value with the absolute value interval corresponding to the preset group; and determine the group of the absolute value interval where the absolute value is located as the corresponding group.

[0108] In this embodiment, the simulated brightness temperature deviation estimation device 300 may further include a fitting relationship determination module. The fitting relationship determination module is used to acquire multiple historical simulation parameters and their corresponding historical observed brightness temperature data corresponding to multiple times prior to the current time. The historical simulation parameters include atmospheric parameters, surface parameters, and cloud parameters. For each historical simulation parameter, historical simulated brightness temperature data under cloudy and cloudless conditions are calculated based on the historical simulation parameter. For each historical simulated brightness temperature data under cloudy conditions, the historical simulated brightness temperature deviation under cloudy conditions is calculated based on the historical simulated brightness temperature data under cloudy conditions and its corresponding historical observed brightness temperature data. Multiple historical simulation parameters and historical simulated brightness temperature deviations under cloudy conditions are grouped according to the relationship between the historical simulated brightness temperature data under cloudy and cloudless conditions calculated using the same historical simulation parameter. For each group, the fitting relationship corresponding to that group is determined based on the historical simulation parameters and historical simulated brightness temperature deviations under cloudy conditions corresponding to that group.

[0109] In this embodiment of the application, the fitting relationship determination module is further used to determine the fitting relationship corresponding to each group, excluding the corresponding group, based on the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions corresponding to that group.

[0110] In this embodiment of the application, the fitting relationship determination module is specifically used to calculate, in mesoscale mode, historical simulated brightness temperature data under cloud conditions and cloudless conditions for each historical simulated parameter.

[0111] In this embodiment, the fitting relationship determination module is specifically used to calculate the absolute value of the difference between historical simulated brightness temperature data under cloudy and cloudless conditions calculated based on the same historical simulation parameters; group the absolute values ​​greater than a preset threshold according to a preset interval; and group the historical simulation parameters and historical simulated brightness temperature deviations under cloudy conditions corresponding to the absolute values ​​in each group into one group.

[0112] In this embodiment, the fitting relationship determination module is specifically used to input the historical simulation parameters and historical simulation brightness temperature deviation corresponding to the group into a preset model for training, so as to obtain the fitting relationship corresponding to the group.

[0113] It should be understood that, for the sake of brevity, some of the content described in Embodiment 1 will not be repeated in this embodiment.

[0114] Example 3:

[0115] This embodiment provides an electronic device, see [link / reference] Figure 4 As shown, it includes a processor 401 and a memory 402. Wherein:

[0116] The processor 401 is used to execute one or more programs stored in the memory 402 to implement the brightness temperature deviation determination method described in Embodiment 1 above.

[0117] Understandable. Figure 4 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 4 The more or fewer components shown, or having the same Figure 4 The different configurations shown.

[0118] For example, the processor 401 and the memory 402 may be connected via a communication bus. As another example, the electronic device may also include components such as a display, mouse, and keyboard.

[0119] In this embodiment, the processor 401 can be a central processing unit, a microprocessor, a microcontroller, etc., but this is not a limitation. The memory 402 can be a random access memory, a read-only memory, a programmable read-only memory, an erasable read-only memory, an electrically erasable read-only memory, etc., but this is not a limitation.

[0120] In the embodiments of this application, the electronic device can be, but is not limited to, physical devices such as desktop computers, laptops, smartphones, smart wearable devices, and in-vehicle devices, or virtual devices such as virtual machines. Furthermore, the electronic device is not necessarily a single device; it can be a combination of multiple devices, such as a server cluster, etc.

[0121] This embodiment also provides a computer-readable storage medium, such as a floppy disk, optical disk, hard disk, flash memory, USB flash drive, SD (Secure Digital Memory Card), MMC (Multimedia Card), etc., in which one or more programs implementing the above steps are stored. These one or more programs can be executed by one or more processors to implement the brightness temperature deviation determination method of Embodiment 1. Further details will not be elaborated here.

[0122] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0123] In the various embodiments of this application, the functional modules can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0124] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0125] In this article, "multiple" refers to two or more.

[0126] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for estimating simulated brightness temperature deviation, characterized in that, include: Obtain the simulation parameters at the current moment, including atmospheric parameters, surface parameters, and cloud parameters; Simulated brightness temperature data under cloudy and cloudless conditions are calculated based on the simulation parameters. The grouping of the simulation parameters is determined based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions; Based on the simulation parameters and the fitting relationship corresponding to the corresponding group, the simulated brightness temperature deviation under the current cloud conditions is determined; wherein, one group corresponds to one fitting relationship, and different groups correspond to different fitting relationships. The fitting relationship corresponding to one group is obtained by fitting the historical simulation parameters and historical simulated brightness temperature deviation under the cloud conditions corresponding to that group. The group represents the influence of clouds on the historical simulated brightness temperature deviation under the cloud conditions. Before determining the grouping of the simulation parameters based on the relationship between simulated brightness temperature data under cloudy and cloudless conditions, the method further includes: acquiring multiple historical simulation parameters and their corresponding historical observed brightness temperature data corresponding to multiple times prior to the current time, wherein the historical simulation parameters include atmospheric parameters, surface parameters, and cloud parameters; for each historical simulation parameter, calculating historical simulated brightness temperature data under cloudy and cloudless conditions based on the historical simulation parameter; for each historical simulated brightness temperature data under cloudy conditions, calculating the historical simulated brightness temperature deviation under cloudy conditions based on the historical simulated brightness temperature data under cloudy conditions and its corresponding historical observed brightness temperature data; grouping multiple historical simulation parameters and their corresponding historical simulated brightness temperature deviations under cloudy conditions based on the relationship between the historical simulated brightness temperature data under cloudy and cloudless conditions calculated using the same historical simulation parameter; the relationship between the historical simulated brightness temperature data under cloudy and cloudless conditions is a difference relationship or a quotient relationship. Before determining the grouping corresponding to the simulation parameters based on the relationship between the simulated brightness temperature data under cloud conditions and cloudless conditions, or when performing the fitting relationship based on the simulation parameters and the corresponding grouping, the method further includes: for the grouping, determining the fitting relationship corresponding to the corresponding group based on the historical simulation parameters and historical simulated brightness temperature deviation under cloud conditions corresponding to the grouping.

2. The method according to claim 1, characterized in that, The method further includes: The moment closest to the current moment among the plurality of moments is spaced from the current moment by a preset number of moments. For each group other than the corresponding group, the fitting relationship for that group is determined based on the historical simulation parameters and historical simulation brightness temperature deviation under the cloud conditions.

3. The method according to claim 1, characterized in that, For each historical simulation parameter, the calculation of historical simulated brightness temperature data under cloudy and cloudless conditions based on that historical simulation parameter includes: For each historical simulation parameter, in mesoscale mode, historical simulated brightness temperature data under cloud conditions and cloudless conditions are calculated based on the historical simulation parameter and the preset simulated brightness temperature model.

4. The method according to claim 1, characterized in that, The relationship between historical simulated brightness temperature data under cloudy and cloudless conditions, calculated based on the same historical simulated parameter, is used to group multiple historical simulated parameters under cloudy conditions, including: The absolute value of the difference between the historical simulated brightness temperature data under cloudy and cloudless conditions, calculated based on the same historical simulation parameter, is obtained. The absolute values ​​greater than a preset threshold are grouped according to a preset interval; The historical simulation parameters and historical simulation brightness temperature deviations under cloud conditions corresponding to the absolute values ​​in each group are grouped together.

5. The method according to claim 1 or 2, characterized in that, The step of determining the fitting relationship corresponding to the group based on the historical simulation parameters and historical simulation brightness temperature deviation under cloud conditions includes: The historical simulation parameters and historical simulation brightness-temperature deviations under cloud conditions corresponding to this group are input into a preset model for training to obtain the fitting relationship corresponding to this group.

6. The method according to claim 1, characterized in that, The step of determining the grouping corresponding to the simulation parameters based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions includes: Calculate the absolute value of the difference between the simulated brightness temperature data under cloudy and cloudless conditions; The absolute value is compared with the absolute value range corresponding to the preset group; The group of the absolute value interval containing the absolute value is determined as the corresponding grouping.

7. A device for estimating simulated brightness temperature deviation, characterized in that, include: The acquisition module is used to acquire the simulation parameters at the current moment, including atmospheric parameters, surface parameters, and cloud parameters; The first calculation module is used to calculate simulated brightness temperature data under cloudy and cloudless conditions based on the simulation parameters. The first determining module is used to determine the grouping corresponding to the simulation parameters based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions; The second determining module is used to determine the simulated brightness temperature deviation under cloud conditions at the current moment based on the simulation parameters and the fitting relationship corresponding to the corresponding group; wherein, one group corresponds to one fitting relationship, and different groups correspond to different fitting relationships. The fitting relationship corresponding to one group is obtained by fitting the historical simulation parameters and historical simulated brightness temperature deviation under cloud conditions corresponding to that group. The group represents the influence of clouds on the historical simulated brightness temperature deviation. The fitting relationship determination module is used to obtain multiple historical simulation parameters and their corresponding historical observed brightness temperature data corresponding to multiple times before the current time, before determining the grouping of the simulation parameters based on the relationship between the simulated brightness temperature data under cloudy and cloudless conditions. The historical simulation parameters include atmospheric parameters, surface parameters, and cloud parameters. For each historical simulation parameter, historical simulated brightness temperature data under cloudy and cloudless conditions are calculated based on the historical simulation parameter. For each historical simulated brightness temperature data under cloudy conditions, the historical simulated brightness temperature deviation under cloudy conditions is calculated based on the historical simulated brightness temperature data under cloudy conditions and its corresponding historical observed brightness temperature data. Multiple historical simulation parameters and their corresponding historical simulated brightness temperature deviations under cloudy conditions are grouped according to the relationship between the historical simulated brightness temperature data under cloudy and cloudless conditions calculated based on the same historical simulation parameter. The relationship between the historical simulated brightness temperature data under cloudy and cloudless conditions is a difference relationship or a quotient relationship. The fitting relationship determination module is further configured to, before determining the grouping corresponding to the simulation parameters based on the relationship between the simulated brightness temperature data under the cloud conditions and the cloudless conditions, or, when executing the fitting relationship corresponding to the simulation parameters and the corresponding group, determine the fitting relationship corresponding to the group based on the historical simulation parameters and historical simulated brightness temperature deviation under the cloud conditions corresponding to the group.

8. An electronic device, characterized in that, include: Processor, memory; The processor is used to execute the program stored in the memory to implement the method for estimating the simulated brightness temperature deviation as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs that can be executed by one or more processors to implement the method for estimating simulated brightness temperature deviation as described in any one of claims 1-6.

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