Empirical grid model construction method and device for improving specific wet profile precision
By iteratively updating the AVP specific wet and building an empirical grid model, the problem of insufficient accuracy of the wet profile in meteorological satellites is solved, and higher specific wet profile accuracy and data practicality are achieved.
Patent Information
- Application Number
- CN202411763271.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-03
- Publication Date
- 2025-05-06
AI Technical Summary
In the prior art, the specific wet profile accuracy provided by meteorological satellites still need to be improved, especially affected by clouds and precipitation.
By obtaining historical AVP product data and ERA5 reanalysis data, the water vapor density of each air pressure layer is calculated, and the AVP specific wet is iteratively updated based on the preset convergence conditions to determine the updated specific wetness; then the specific wetness adjustment factor is calculated based on the specific wetness before and after the update, and the construction of the empirical grid model is completed.
Improves the accuracy of the specific wet profile, reduces the impact of clouds and precipitation, and enhances the practicality of the data.
Smart Images
Figure CN119937056A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing technology, in particular to the field of meteorological application research technology, and specifically to a method and device for constructing an empirical grid model for improving the accuracy of specific humidity profiles. Background Art
[0002] Atmospheric water vapor is mainly stored in the troposphere. Although it accounts for a small proportion, it plays a leading role in atmospheric changes such as the evolution of weather systems, the earth's water cycle and atmospheric circulation. Specific humidity is often used to characterize the state of atmospheric water vapor and is a key parameter in climate research and extreme weather warning.
[0003] Fengyun-4B (codename: FY-4B) is the first operational satellite of the Fengyun-4 series of meteorological satellites. FY-4B drifted from 133 degrees east longitude to 105 degrees east longitude. The design of the geostationary orbit interferometric infrared detector was optimized, and the spatial resolution was further improved, which can provide more accurate atmospheric vertical temperature and humidity profile products (AVP). The spatial resolution, temporal resolution, radiometric calibration accuracy, and spectral calibration accuracy of FY-4B AVP are 12km, 45min, 0.7K, and better than 10ppm respectively. The product content includes the longitude, latitude, surface pressure, surface temperature, temperature profile and specific humidity profile of each field of view point, among which the temperature profile and specific humidity profile are divided into 101 layers according to the pressure. Although the specific humidity profile provided by FY-4B can make up for the spatial discontinuity of the sounding site, it is greatly affected by clouds and precipitation, and the detection accuracy still needs to be further improved. Summary of the invention
[0004] The present disclosure provides a method and device for constructing an empirical grid model for improving the accuracy of specific humidity profiles.
[0005] According to a first aspect of the present disclosure, a method for constructing an empirical grid model for improving the accuracy of a specific humidity profile is provided. The method comprises:
[0006] Access historical AVP product data and ERA5 reanalysis data;
[0007] Calculate the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data;
[0008] Based on a preset convergence condition, the AVP specific humidity of the historical AVP product data is iteratively updated according to the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data to determine an updated AVP specific humidity;
[0009] Calculate the humidity adjustment factor based on the AVP humidity before and after the update;
[0010] According to the specific humidity adjustment factor, the model coefficient of each grid in the preset annual cycle, semi-annual cycle and daily cycle model is calculated to complete the construction of the empirical grid model for improving the accuracy of the specific humidity profile.
[0011] According to the above aspects and any possible implementation, an implementation is further provided, wherein the preset annual cycle, semi-annual cycle, and daily cycle model include:
[0012]
[0013] Wherein, w(k) represents the humidity adjustment factor of the kth pressure layer, A0 represents the constant term, A1 and A2 represent the annual cycle coefficients, A3 and A4 represent the semi-annual cycle coefficients, A5 and A6 represent the daily cycle coefficients, doy represents the annual accumulated day, and hour represents the hour.
[0014] According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein the preset convergence condition includes:
[0015] |P vA (k,n+1)-P vA (k,n)|<α
[0016] Among them, α represents the convergence factor, P vA (k,n+1) represents the AVP water vapor density at the n+1th iteration of the kth pressure layer, P vA (k,n) represents the AVP water vapor density at the nth iteration of the kth pressure layer.
[0017] According to the above aspects and any possible implementation, an implementation is further provided, wherein the calculating the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data comprises:
[0018] The ERA5 pressure, ERA5 temperature and ERA5 specific humidity at the AVP target point are calculated based on the four nearest ERA5 grid points around the AVP target point;
[0019] The water vapor density of each pressure layer of the ERA5 reanalysis data is calculated based on the ERA5 pressure, ERA5 temperature and ERA5 specific humidity; the water vapor density of each pressure layer of the historical AVP product data is calculated based on the AVP pressure, AVP temperature and AVP specific humidity of the AVP target point.
[0020] According to the above aspects and any possible implementation, there is further provided an implementation, wherein the historical AVP product data includes a temperature quality identifier;
[0021] When the temperature quality mark of the kth pressure layer of the AVP is the preset temperature quality mark, the water vapor density of each pressure layer of the historical AVP product data is calculated using the ERA5 temperature instead of the AVP temperature, wherein the ERA5 temperature is:
[0022]
[0023] Among them, T E represents the temperature of ERA5 in the kth pressure layer of AVP, T 1E and T 2E denote the temperatures of the two adjacent ERA5 pressure layers containing the kth AVP pressure layer, P k represents the k-th layer air pressure of AVP, and P1 and P2 represent the two adjacent layers of ERA5 air pressure including the k-th layer air pressure of AVP.
[0024] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the historical AVP product data includes a specific humidity mass identifier; and the specific humidity mass identifier is used to mark the model coefficient.
[0025] According to a second aspect of the present disclosure, a method for improving the accuracy of a specific humidity profile is provided. The method comprises:
[0026] Obtain the longitude, latitude, and humidity quality mark of the AVP humidity profile to be processed, input a pre-built empirical grid model for improving the accuracy of the humidity profile, and output the corresponding model coefficients;
[0027] Calculate the specific humidity adjustment factor based on the model coefficient and the annual accumulation of days and hours of the AVP specific humidity profile to be processed;
[0028] The optimized AVP specific humidity profile is calculated according to the specific humidity adjustment factor and the AVP specific humidity profile to be processed.
[0029] According to a third aspect of the present disclosure, there is provided an empirical grid model construction device for improving the accuracy of specific humidity profile. The device comprises:
[0030] Acquisition module, used to obtain historical AVP product data and ERA5 reanalysis data;
[0031] A calculation module, used to calculate the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data;
[0032] An updating module, configured to iteratively update the AVP specific humidity of the historical AVP product data based on a preset convergence condition and according to the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data, and determine an updated AVP specific humidity;
[0033] The calculation module is also used to calculate the specific humidity adjustment factor according to the AVP specific humidity before and after the update;
[0034] The calculation module is also used to calculate the model coefficients of each grid in the preset annual cycle, semi-annual cycle, and daily cycle models according to the specific humidity adjustment factor, so as to complete the construction of an empirical grid model for improving the accuracy of the specific humidity profile.
[0035] According to a fourth aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.
[0036] According to a fifth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described above is implemented.
[0037] An empirical grid model construction method and device for improving the accuracy of specific humidity profiles provided in an embodiment of the present application can obtain and calculate the water vapor density of each pressure layer of historical AVP product data and ERA5 reanalysis data; then based on a preset convergence condition, the AVP specific humidity of the AVP product data is iteratively updated according to the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data to determine the updated AVP specific humidity; then the specific humidity adjustment factor is calculated according to the AVP specific humidity before and after the update; then, based on the specific humidity adjustment factor, the model coefficient of each grid in the preset annual cycle, semi-annual cycle, and daily cycle model is calculated to complete the construction of an empirical grid model for improving the accuracy of the specific humidity profile; based on this, the advantage of the stable accuracy of the ERA5 reanalysis data can be utilized, and the AVP specific humidity profile is iteratively updated by calculating the water vapor density and constraining it using the convergence condition, thereby establishing an empirical grid model for improving the accuracy of the specific humidity profile, thereby improving the accuracy of the AVP specific humidity profile and increasing the practicality of the data.
[0038] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:
[0040] Figure 1A flow chart of a method for constructing an empirical grid model for improving the accuracy of specific humidity profiles according to an embodiment of the present disclosure is shown;
[0041] Figure 2 A flow chart showing a method for improving the accuracy of a specific humidity profile according to an embodiment of the present disclosure is shown;
[0042] Figure 3 A block diagram of an empirical grid model construction device for improving the accuracy of specific humidity profile according to an embodiment of the present disclosure is shown;
[0043] Figure 4 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION
[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.
[0045] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.
[0046] In the present disclosure, the advantage of the stable accuracy of the ERA5 reanalysis data can be utilized, and the AVP specific humidity profile is iteratively updated by calculating the water vapor density and constraining it using the convergence conditions, thereby establishing an empirical grid model for improving the accuracy of the specific humidity profile, thereby improving the accuracy of the AVP specific humidity profile and increasing the practicality of the data.
[0047] Figure 1 A flow chart of an empirical grid model construction method 100 for improving the accuracy of specific humidity profile according to an embodiment of the present disclosure is shown.
[0048] At block 110, historical AVP product data and ERA5 reanalysis data are acquired.
[0049] In some embodiments, the historical AVP product data and the ERA5 reanalysis data are corresponding data based on the same time and space matching. Among them, the AVP product data can be FY-4B AVP product data; the ERA5 reanalysis data is the fifth generation reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF), which can provide global atmospheric, sea and land parameters since 1950, and the product delay time is only 5 days. Based on the advantage of the stable accuracy of the ERA5 reanalysis data, the ERA5 reanalysis data can be used to iteratively update the AVP specific humidity profile, thereby improving the accuracy of the AVP specific humidity profile.
[0050] In some embodiments, the FY-4B AVP products and ERA5 reanalysis data of a certain region for several years may be obtained as historical AVP product data and its corresponding ERA5 reanalysis data.
[0051] At block 120, the water vapor density of each pressure layer is calculated for the historical AVP product data and the ERA5 reanalysis data.
[0052] In some embodiments, the above calculation of water vapor density of each pressure layer of historical AVP product data and ERA5 reanalysis data includes:
[0053] The ERA5 pressure, ERA5 temperature and ERA5 specific humidity at the AVP target point are calculated based on the four nearest ERA5 grid points around the AVP target point;
[0054] The water vapor density of each pressure layer of the ERA5 reanalysis data is calculated based on the ERA5 pressure, ERA5 temperature and ERA5 specific humidity. The water vapor density of each pressure layer of the historical AVP product data is calculated based on the AVP pressure, AVP temperature and AVP specific humidity of the AVP target point.
[0055] In some embodiments, bilinear interpolation may be performed on the data of the four closest ERA5 grid points around the AVP field of view point, ie, the target point, to obtain the ERA5 air pressure, ERA5 temperature and ERA5 specific humidity of the ERA5 at the field of view point.
[0056] In some embodiments, the water vapor density of each pressure layer of the ERA5 reanalysis data and the water vapor density of each pressure layer of the historical AVP product data can be calculated according to equations (1) and (2). Equations (1), (2) and (3) are as follows:
[0057]
[0058]
[0059]
[0060] Among them, R v represents the specific gas constant of water vapor (461.5 J / (kg·K)), P v Indicates water vapor density (kg / m 3 ), e represents water vapor pressure (hPa), P represents air pressure (hPa), T represents temperature (℃), and q represents specific humidity (g / kg).
[0061] In some embodiments, the historical AVP product data includes a temperature quality indicator;
[0062] When the temperature quality indicator of the kth pressure layer of AVP is the preset temperature quality indicator, the ERA5 temperature is used instead of the AVP temperature to calculate the water vapor density of each pressure layer of the historical AVP product data, where the ERA5 temperature is:
[0063]
[0064] Among them, T E represents the temperature of ERA5 in the kth pressure layer of AVP, T 1E and T 2E denote the temperatures of the two adjacent ERA5 pressure layers containing the kth AVP pressure layer, P k represents the k-th layer air pressure of AVP, and P1 and P2 represent the two adjacent layers of ERA5 air pressure including the k-th layer air pressure of AVP.
[0065] In some embodiments, the temperature quality indicator is used to characterize the quality of temperature data, and the temperature quality indicator includes 0-4, where 0 represents the best, 1 represents good, 2 represents bad, 3 represents not used, and 4 represents poor data.
[0066] In some embodiments, the preset temperature quality indicator can be set according to the actual needs of the user, such as setting the preset temperature quality indicator to 2 or 3.
[0067] In some embodiments, when the water vapor density of each pressure layer of the historical AVP product data is obtained by formula (3), when the temperature quality mark of the kth pressure layer of the AVP is 2 or 3, the ERA5 temperature is used instead of the AVP temperature, and the ERA5 temperature can be obtained by interpolation by formula (5).
[0068] In box 130, based on a preset convergence condition, the AVP specific humidity of the historical AVP product data is iteratively updated according to the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data to determine an updated AVP specific humidity.
[0069] In some embodiments, the AVP specific humidity profile may be iteratively updated using equation (4), which is shown as follows:
[0070] q1(k)=q0(k)·P vE (k) / P vA (k) (4)
[0071] Where k represents the kth pressure layer of AVP, q1(k) represents the updated AVP specific humidity (g / kg) of the kth pressure layer, q0(k) represents the AVP specific humidity (g / kg) before the update of the kth pressure layer, P vE (k) represents the water vapor density of ERA5 at the kth layer pressure (g / m 3 ), P vE (k) is the mean water vapor density of the two adjacent ERA5 pressure layers containing the kth AVP pressure layer, obtained by formula (3), P vA (k) represents the water vapor density of the kth layer AVP (g / m 3 ).
[0072] In some embodiments, the updated AVP specific humidity profile can be used to calculate the updated AVP water vapor density P according to equation (3): vA (k,n) (n represents the nth iteration), set the convergence condition to end the iterative update.
[0073] In some embodiments, the above-mentioned preset convergence condition includes:
[0074] |P vA (k,n+1)-P vA (k,n)|<α (6)
[0075] Among them, α represents the convergence factor, P vA (k,n+1) represents the AVP water vapor density at the n+1th iteration of the kth pressure layer, P vA (k,n) represents the AVP water vapor density at the nth iteration of the kth pressure layer.
[0076] In some embodiments, the convergence factor can be set to 1×10 -8 g / m 3 .
[0077] In block 140 , a humidity adjustment factor is calculated based on the AVP humidity before and after the update.
[0078] In some embodiments, after the iterative update is completed, the specific humidity adjustment factor is calculated according to formula (7), which is as follows:
[0079] w(k)=q1(k) / q0(k) (7)
[0080] Wherein, w(k) represents the humidity adjustment factor of the kth pressure layer.
[0081] In box 150, the model coefficients of each grid in the preset annual cycle, semi-annual cycle, and daily cycle models are calculated according to the specific humidity adjustment factor, and the construction of the empirical grid model for improving the accuracy of the specific humidity profile is completed.
[0082] In some embodiments, the grid resolution may be set to 0.25°×0.25°, and the w(k) in each grid point may be expressed using preset annual cycle, semi-annual cycle, and daily cycle models.
[0083] In some embodiments, the preset annual cycle, semi-annual cycle, and daily cycle models include:
[0084]
[0085] Wherein, w(k) represents the humidity adjustment factor of the kth pressure layer, A0 represents the constant term, A1 and A2 represent the annual cycle coefficients, A3 and A4 represent the semi-annual cycle coefficients, A5 and A6 represent the daily cycle coefficients, doy represents the annual accumulated day, and hour represents the hour.
[0086] In some embodiments, the above historical AVP product data includes a specific humidity quality indicator; the specific humidity quality indicator is used to mark the model coefficient. The specific humidity quality indicator is used to characterize the quality of the specific humidity data.
[0087] In some embodiments, the specific humidity quality flag is used to characterize the quality of the specific humidity data, and the specific humidity quality flag includes 0-4, where 0 represents the best, 1 represents good, 2 represents bad, 3 represents not used, and 4 represents poor data.
[0088] In some embodiments, due to different specific humidity mass identifiers, that is, the quality of historical AVP product data varies, the specific humidity adjustment factors for the specific humidity mass identifiers of 0 and 1, and the quality identifiers of 2 and 3 can be calculated respectively. For example, the least square method can be used to solve the model coefficients of each grid in formula (8), and finally two sets of model coefficients are obtained, one set corresponding to the specific humidity mass identifiers of 0 and 1, and the other set corresponding to the specific humidity mass identifiers of 2 and 3.
[0089] It should be noted that due to differences in AVP product data quality, whether solving the model coefficients with specific humidity mass identification of 0 and 1, or solving the model coefficients with specific humidity mass identification of 2 and 3, when the temperature quality identification of the AVP kth pressure layer is 2 or 3, the ERA5 temperature is used instead of the AVP temperature.
[0090] According to the embodiments of the present disclosure, the following technical effects are achieved:
[0091] It can obtain and calculate the water vapor density of each pressure layer of the historical AVP product data and ERA5 reanalysis data; then based on the preset convergence conditions, the AVP specific humidity of the AVP product data is iteratively updated according to the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data to determine the updated AVP specific humidity; then the specific humidity adjustment factor is calculated according to the AVP specific humidity before and after the update; then according to the specific humidity adjustment factor, the model coefficient of each grid in the preset annual cycle, semi-annual cycle, and daily cycle model is calculated to complete the construction of an empirical grid model for improving the accuracy of the specific humidity profile; based on this, the advantage of the stable accuracy of the ERA5 reanalysis data can be used to iteratively update the AVP specific humidity profile by calculating the water vapor density and using the convergence conditions to constrain it, thereby establishing an empirical grid model for improving the accuracy of the specific humidity profile, so as to improve the accuracy of the AVP specific humidity profile and increase the practicality of the data.
[0092] Figure 2 A flow chart of a method 200 for improving the accuracy of a specific humidity profile according to an embodiment of the present disclosure is shown.
[0093] In box 210, the longitude, latitude, and humidity quality indicator of the AVP humidity profile to be processed are obtained, and a pre-built empirical grid model for improving the accuracy of the humidity profile is input, and the corresponding model coefficients are output.
[0094] In some embodiments, the empirical grid model for improving the accuracy of the specific humidity profile queries and outputs the grid where the specific humidity profile is located and its corresponding model coefficients according to the longitude, latitude, and specific humidity quality identifier of the input AVP specific humidity profile.
[0095] In block 220, a humidity adjustment factor is calculated based on the model coefficients and the annualized day and hourly specific humidity profile of the AVP to be processed.
[0096] In some embodiments, the specific humidity adjustment factor can be calculated using equation (8) based on the model coefficients provided by the grid and the annual accumulation of days and hours of the input AVP specific humidity profile.
[0097] In block 230 , an optimized AVP specific humidity profile is calculated based on the specific humidity adjustment factor and the AVP specific humidity profile to be processed.
[0098] In some embodiments, the specific humidity adjustment factor may be multiplied by the original specific humidity to obtain the optimized specific humidity.
[0099] According to an embodiment of the present disclosure, based on the empirical grid model constructed for improving the accuracy of the specific humidity profile, the user only needs to input the AVP specific humidity profile to be optimized to improve the accuracy of the AVP specific humidity profile.
[0100] To facilitate understanding, the following will combine specific data, such as the FY-4B AVP product and ERA5 reanalysis data from January 16, 2023 to January 16, 2024, to further illustrate the method of improving the accuracy of the specific humidity profile.
[0101] In some embodiments, the ERA5 reanalysis data product used is atmospheric stratified meteorological data (divided into 37 layers from 1000hPa to 1hPa according to air pressure), the product has a spatial resolution of 0.25°×0.25°, a temporal resolution of 2h (01h, 03h, 05h, 07h, 09h, 11h, 13h, 15h, 17h, 19h, 21h and 23h), and the range is 103°E to 113°E, 18°N to 27°N, with a total of 1440 grid points.
[0102] In some embodiments, the construction of an empirical grid model for improving the accuracy of specific humidity profiles includes:
[0103] Step (1): Obtain FY-4B AVP product data and ERA5 reanalysis data from January 16, 2023 to January 16, 2024 at 103°E to 113°E, 18°N to 27°N.
[0104] Step (2): Perform bilinear interpolation on the data of the four closest ERA5 grid points around the AVP field of view point to obtain the air pressure, temperature and specific humidity of ERA5 at the field of view point.
[0105] Step (3): Calculate the water vapor density of each pressure layer of ERA5 and AVP according to equations (1), (2) and (3).
[0106] Step (4): Iteratively update the specific humidity profile of AVP according to formula (4). When the water vapor density of AVP is obtained by formula (3), when the temperature quality mark of the kth pressure layer of AVP is 2 or 3, the ERA5 temperature is used instead of the AVP temperature, and the ERA5 temperature can be obtained by interpolation by formula (5).
[0107] Step (5): Calculate the updated AVP water vapor density using the updated AVP specific humidity profile according to formula (3), set the convergence condition to end the iterative update, and the convergence condition is shown in formula (6).
[0108] Step (6): After the iterative update is completed, the humidity adjustment factor is calculated according to formula (7).
[0109] Step (7): Set the grid resolution to 0.25°×0.25°, and use the annual cycle, semi-annual cycle, and daily cycle models to express w(k) in each grid point. The annual cycle, semi-annual cycle, and daily cycle models are shown in formula (8).
[0110] Step (8): Calculate the humidity adjustment factors for humidity mass identifications of 0 and 1 and humidity mass identifications of 2 and 3 respectively through steps (3) to obtain the model coefficients of each grid in formula (8) using the least squares method. Finally, two sets of model coefficients are obtained, one set corresponding to humidity mass identifications of 0 and 1, and the other set corresponding to humidity mass identifications of 2 and 3.
[0111] In some embodiments, a method for improving the accuracy of a specific humidity profile includes:
[0112] Step (9): The empirical grid model used to improve the accuracy of the specific humidity profile queries the grid where the specific humidity profile is located and its corresponding model coefficients based on the longitude, latitude, and specific humidity quality identifier of the input AVP specific humidity profile.
[0113] Step (10): Calculate the humidity adjustment factor using formula (8) based on the model coefficients provided by the grid and the annual accumulation of days and hours of the input AVP humidity profile.
[0114] Step (11): Multiply the specific humidity adjustment factor by the original specific humidity to obtain the optimized specific humidity
[0115] It should be noted that, for the sake of simplicity, the formulas (1) to (8) involved in the above steps (1) to (11) can refer to the specific contents involved in the corresponding processes in the aforementioned method embodiments, and will not be repeated here.
[0116] In some embodiments, in order to verify the accuracy of the above method, the ERA5 reanalysis data from March 5, 2024 to March 8, 2024 that are not involved in the modeling can be regarded as the true value, and the accuracy of the FY-4B AVP specific humidity profile from March 5, 2024 to March 8, 2024 before and after optimization is evaluated. The final statistical results are shown in Table 1.
[0117] Table 1: Comparison of humidity profile accuracy
[0118]
[0119] Table 1 shows that compared with the original specific humidity profile, the root mean square error (RMSE) value of the FY-4B AVP specific humidity profile optimized by the empirical grid model used to improve the accuracy of the specific humidity profile has decreased, indicating that the optimized specific humidity profile is closer to the ERA5 reanalysis data, and its correlation coefficient (CC) has increased, indicating that the optimized specific humidity profile has a higher degree of correlation with the ERA5 reanalysis data. Therefore, the empirical grid model for improving the accuracy of the specific humidity profile constructed by the above method can improve the accuracy of the FY-4B AVP specific humidity profile and increase the practicability of the data.
[0120] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.
[0121] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.
[0122] Figure 3 FIG. 3 is a block diagram of an empirical grid model construction device 300 for improving the accuracy of specific humidity profile according to an embodiment of the present disclosure. Figure 3 As shown, the device 300 includes:
[0123] An acquisition module 310 is used to acquire historical AVP product data and ERA5 reanalysis data;
[0124] The calculation module 320 is used to calculate the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data;
[0125] An updating module 330 is used to iteratively update the AVP specific humidity of the historical AVP product data based on a preset convergence condition and according to the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data, and determine an updated AVP specific humidity;
[0126] The calculation module 320 is further used to calculate the specific humidity adjustment factor according to the AVP specific humidity before and after the update;
[0127] The calculation module 320 is also used to calculate the model coefficients of each grid in the preset annual cycle, semi-annual cycle, and daily cycle models according to the specific humidity adjustment factor, and complete the construction of the empirical grid model for improving the accuracy of the specific humidity profile.
[0128] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.
[0129] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.
[0130] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
[0131] Figure 4 A block diagram of an exemplary electronic device 400 capable of implementing an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.
[0132] The electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in the ROM 402 or a computer program loaded from the storage unit 408 into the RAM 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An I / O interface 405 is also connected to the bus 404.
[0133] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0134] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408.
[0135] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the method 100 in any other appropriate manner (e.g., by means of firmware).
[0136] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0137] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0138] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0139] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0140] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.
[0141] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.
[0142] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0143] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.
Claims
1. A method for constructing an empirical grid model for improving the accuracy of specific humidity profiles, characterized in that: include: Access historical AVP product data and ERA5 reanalysis data; Calculate the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data; Based on a preset convergence condition, the AVP specific humidity of the historical AVP product data is iteratively updated according to the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data to determine an updated AVP specific humidity; Calculate the humidity adjustment factor based on the AVP humidity before and after the update; According to the specific humidity adjustment factor, the model coefficient of each grid in the preset annual cycle, semi-annual cycle and daily cycle model is calculated to complete the construction of the empirical grid model for improving the accuracy of the specific humidity profile.
2. The method according to claim 1, characterized in that The preset annual cycle, semi-annual cycle, and daily cycle models include: Wherein, w(k) represents the humidity adjustment factor of the kth pressure layer, A0 represents the constant term, A1 and A2 represent the annual cycle coefficients, a3 and a4 represent the semi-annual cycle coefficients, a5 and a6 represent the daily cycle coefficients, doy represents the annual cumulative day, and hour represents the hour.
3. The method according to claim 1, characterized in that: The preset convergence conditions include: |P vA (k,n+1)-P vA (k,n)|<α Among them, α represents the convergence factor, P vA (k,n+1) represents the AVP water vapor density at the n+1th iteration of the kth pressure layer, P vA (k,n) represents the AVP water vapor density at the nth iteration of the kth pressure layer.
4. The method according to claim 1, characterized in that: The calculation of the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data includes: The ERA5 pressure, ERA5 temperature and ERA5 specific humidity at the AVP target point are calculated based on the four nearest ERA5 grid points around the AVP target point; The water vapor density of each pressure layer of the ERA5 reanalysis data is calculated based on the ERA5 pressure, ERA5 temperature and ERA5 specific humidity; the water vapor density of each pressure layer of the historical AVP product data is calculated based on the AVP pressure, AVP temperature and AVP specific humidity of the AVP target point.
5. The method according to claim 4, characterized in that The historical AVP product data includes a temperature quality indicator; When the temperature quality mark of the kth pressure layer of the AVP is the preset temperature quality mark, the water vapor density of each pressure layer of the historical AVP product data is calculated using the ERA5 temperature instead of the AVP temperature, wherein the ERA5 temperature is: Among them, T E represents the temperature of ERA5 in the kth pressure layer of AVP, T 1E and T 2E denote the temperatures of the two adjacent ERA5 pressure layers containing the kth AVP pressure layer, P k represents the k-th layer air pressure of AVP, and P1 and P2 represent the two adjacent layers of ERA5 air pressure including the k-th layer air pressure of AVP.
6. The method according to claim 5, characterized in that The historical AVP product data includes a specific humidity mass identifier; the specific humidity mass identifier is used to mark the model coefficient.
7. A method for improving the accuracy of a specific humidity profile, characterized in that: include: Obtain the longitude, latitude, and humidity quality mark of the AVP humidity profile to be processed, input a pre-built empirical grid model for improving the accuracy of the humidity profile, and output the corresponding model coefficients; Calculate the specific humidity adjustment factor based on the model coefficient and the annual accumulation of days and hours of the AVP specific humidity profile to be processed; The optimized AVP specific humidity profile is calculated according to the specific humidity adjustment factor and the AVP specific humidity profile to be processed.
8. An empirical grid model construction device for improving the accuracy of specific humidity profile, characterized in that: include: Acquisition module, used to obtain historical AVP product data and ERA5 reanalysis data; A calculation module, used to calculate the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data; An updating module, configured to iteratively update the AVP specific humidity of the historical AVP product data based on a preset convergence condition and according to the water vapor density of each pressure layer of the historical AVP product data and the ERA5 reanalysis data, and determine an updated AVP specific humidity; The calculation module is also used to calculate the specific humidity adjustment factor according to the AVP specific humidity before and after the update; The calculation module is also used to calculate the model coefficients of each grid in the preset annual cycle, semi-annual cycle, and daily cycle models according to the specific humidity adjustment factor, so as to complete the construction of an empirical grid model for improving the accuracy of the specific humidity profile.
9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.
10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.