A method and system for optimizing sounding data based on time-varying characteristics

Abstract

This specification discloses a method and system for optimizing radiosonde data based on time-varying characteristics, comprising: acquiring radiosonde data to be processed; obtaining minute-level radiosonde data based on the radiosonde data to be processed using a linear interpolation method; obtaining minute-level reanalysis data based on the location reanalysis data of the radiosonde station to be processed using a linear interpolation method; and obtaining final radiosonde data based on the minute-level radiosonde data and its weights, and the minute-level reanalysis data and its weights, thereby optimizing the radiosonde data to be processed.

Description

Technical Field

[0001] This specification relates to the field of weather forecasting technology, and in particular to a method and system for optimizing radiosonde data based on time-varying characteristics. Background Technology

[0002] Atmospheric temperature and humidity profiles describe the vertical distribution of temperature and humidity with altitude in the atmosphere and are widely used in weather forecasting, climate research, and environmental monitoring. Methods for obtaining atmospheric temperature and humidity profiles mainly include radiosonde observation, satellite remote sensing inversion, numerical model simulation, and ground-based vertical sounding instrument inversion. Among these, radiosonde observation, which involves deploying radiosonde balloons carrying radiosondes to obtain in-situ atmospheric observation data, is the most representative "true value" of atmospheric conditions.

[0003] While radiosonde data is of high quality, it is scarce and observed infrequently. In contrast, satellite inversion products offer advantages such as high spatiotemporal resolution and comprehensive regional coverage. However, due to factors such as instrument performance, radiometric calibration, and inversion algorithms, the accuracy of satellite atmospheric temperature and humidity profile products may be low. To reduce errors in satellite products and optimize calibration and inversion algorithms, a comprehensive evaluation of satellite product quality is necessary, and radiosonde data serves as the most important source of verification.

[0004] When radiosonde data is used to verify satellite products, spatiotemporal matching with the satellite products is required. The general procedure involves searching for satellite inversion results within a 50-300 km radius of the radiosonde station, with a time difference of 1-6 hours from the radiosonde data. The choice of spatiotemporal threshold is relatively broad and has no fixed requirements, but it is generally necessary to consider the spatiotemporal resolution of the satellite product being verified to ensure an appropriate number of matching samples. If the threshold is too high, although there will be many matching samples, it is easy to introduce matching errors caused by changes in atmospheric conditions; if the threshold is too strict, there will be too few matching samples, and the verification results will not be representative or statistically significant.

[0005] Besides radiosonde data, gridded reanalysis data is also frequently used as a source for verifying satellite atmospheric temperature and humidity profile products. Reanalysis data has good integrity and high quality, and usually also has high spatiotemporal resolution (generally 1-3h, 10-50km). When comparing with satellite products, data is usually selected according to the nearest neighbor method, that is, the reanalysis data closest to the satellite transit time and the satellite observation area is selected. Sometimes, several nearest neighbor reanalysis data are interpolated to the satellite transit time and observation center location to generate reference data for comparison with satellite products.

[0006] In existing technologies, when verifying satellite products using radiosonde data, because satellite products are observed instantaneously, a fixed value is assigned to the observation time of the radiosonde balloon for easy comparison with the satellite's transit time. However, this does not match reality. For example, if the satellite's transit time coincides with the radiosonde observation time in the lower atmosphere, it will certainly differ from the radiosonde observation time in the upper atmosphere. Operational radiosonde observations can only provide relatively reliable verification sources for satellite inversion products at early morning and late evening. However, a large number of profile products originate from polar-orbiting satellites transiting in the morning or afternoon, and the time difference between the radiosonde data and these products exceeds 4-6 hours, causing significant uncertainty in the verification results. To verify satellite products outside of operational radiosonde times, the only option is to deploy denser radiosonde balloons at the satellite's transit time. However, this is very costly, so reanalysis data is often relied upon. However, the quality of reanalysis data may be low, affecting the accurate verification and evaluation of satellite products. To obtain verification data that is time-synchronized with satellite products at different altitudes, it is necessary to calculate the observation time of a radiosonde profile at each altitude layer, and then interpolate to the satellite's transit time based on the observation times of multiple profiles at the same altitude layer. However, due to the very sparse temporal distribution of radiosonde data (twice a day), it cannot accurately reflect the changes in the atmosphere within a single day, resulting in poor performance from simple linear interpolation based solely on radiosonde data. Reanalysis data, with its high temporal resolution, can accurately characterize the time-varying features of each atmospheric layer. Therefore, the advantages of high precision in radiosonde data and rich time-varying features in reanalysis data can be combined to improve the interpolation accuracy of atmospheric temperature and humidity, generating high-quality atmospheric profile data for satellite transit times. It is evident that in existing technologies, due to the very sparse temporal distribution of radiosonde data, when verifying satellite products, if the satellite transit time coincides with the radiosonde observation time of the lower atmosphere, it will inevitably differ from the radiosonde observation time of the upper atmosphere. Operational radiosonde observations can only provide a relatively reliable source of verification for satellite inversion products at early and late hours, but cannot provide a reliable source of detection for other satellite products, resulting in inaccurate detection results and failing to provide accurate radiosonde data for weather forecasting.

[0007] Therefore, a method and system for optimizing sounding data based on time-varying characteristics is needed. Summary of the Invention

[0008] This specification provides a method and system for optimizing radiosonde data based on time-varying characteristics to address the following technical problem: In the prior art, due to the very sparse temporal nature of radiosonde data, when verifying satellite products, if the satellite's transit time coincides with the radiosonde observation time in the lower atmosphere, it will definitely not coincide with the radiosonde observation time in the upper atmosphere. Operational radiosonde observations can only provide relatively reliable verification sources for early and late-night satellite inversion products, but cannot provide reliable detection sources for other satellite products, resulting in inaccurate detection results and failing to provide accurate radiosonde data for weather forecasting.

[0009] To solve the above-mentioned technical problems, the embodiments in this specification are implemented as follows:

[0010] This specification provides an embodiment of a method for optimizing radiosonde data based on time-varying characteristics, including:

[0011] Acquire the sounding data to be processed;

[0012] Based on the radiosonde data to be processed, a linear interpolation method is used to obtain minute-level radiosonde data;

[0013] Based on the location reanalysis data of the radiosonde station to be processed, a linear interpolation method was used to obtain minute-level reanalysis data;

[0014] Based on the minute-level radiosonde data and its weights, as well as the minute-level reanalysis data and its weights, the final radiosonde data is obtained, thereby optimizing the radiosonde data to be processed.

[0015] This specification also provides an embodiment of a sounding data optimization system based on time-varying characteristics, comprising:

[0016] The acquisition module acquires the sounding data to be processed.

[0017] The minute-level radiosonde data acquisition module obtains minute-level radiosonde data based on the radiosonde data to be processed using a linear interpolation method.

[0018] The minute-level reanalysis data acquisition module uses linear interpolation to obtain minute-level reanalysis data based on the location reanalysis data of the radiosonde station to be processed.

[0019] The optimization module obtains the final sounding data based on the minute-level sounding data and its weights, as well as the minute-level reanalysis data and its weights, thereby optimizing the sounding data to be processed.

[0020] The radiosonde data optimization method based on time-varying characteristics provided in this specification involves: acquiring radiosonde data to be processed; obtaining minute-level radiosonde data using linear interpolation based on the radiosonde data to be processed; obtaining minute-level reanalysis data using linear interpolation based on the location reanalysis data of the radiosonde station to be processed; and obtaining final radiosonde data based on the minute-level radiosonde data and its weights, as well as the minute-level reanalysis data and its weights. This method optimizes the radiosonde data to be processed, extending morning and evening operational radiosonde data to the entire day, generating time-continuous, minute-level resolution all-day atmospheric temperature and humidity profiles. For satellite products passing over the radiosonde station at any time of day, it can provide verification data with time synchronization at each layer in the vertical direction, fully leveraging the accuracy of radiosonde data and the high time-frequency advantage of reanalysis data, thereby improving the accuracy of satellite product verification and weather forecasting. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A schematic diagram of the system architecture for a time-varying feature-based sounding data optimization method provided in the embodiments of this specification;

[0023] Figure 2 A flowchart illustrating a time-varying feature-based method for optimizing sounding data, provided in an embodiment of this specification.

[0024] Figure 3 A flowchart illustrating another method for optimizing radiosonde data based on time-varying characteristics, provided in the embodiments of this specification;

[0025] Figure 4 A schematic diagram of a sounding data optimization system based on time-varying characteristics provided in the embodiments of this specification;

[0026] Figure 5 This is a schematic diagram of a radiosonde data optimization system based on time-varying characteristics, provided as an embodiment of this specification. Detailed Implementation

[0027] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this application.

[0028] Figure 1 This diagram illustrates the system architecture of a time-varying feature-based sounding data optimization method provided in this specification. Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0029] Terminal devices 101, 102, and 103 interact with server 105 via network 104 to receive or send messages, etc. Various client applications can be installed on terminal devices 101, 102, and 103, such as dedicated programs for optimizing radiosonde data based on time-varying characteristics.

[0030] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various dedicated or general-purpose electronic devices, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services) or as a single software program or software module.

[0031] Server 105 can be a server that provides various services, such as a backend server that provides services to client applications installed on terminal devices 101, 102, and 103. For example, the server can perform radiosonde data optimization based on time-varying characteristics so that the optimization results can be displayed on terminal device servers 101, 102, and 103.

[0032] Server 105 can be either hardware or software. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services), or as a single software program or software module.

[0033] Figure 2 This is a flowchart illustrating a method for optimizing radiosonde data based on time-varying characteristics, provided in an embodiment of this specification. From a programming perspective, the execution entity of the process can be a program hosted on an application server or application terminal. It is understood that this method can be executed by any device, equipment, platform, or cluster of devices with computing and processing capabilities. Figure 2 As shown, the sounding data optimization method includes:

[0034] Step S201: Obtain the radiosonde data to be processed.

[0035] In the embodiments described in this specification, the radiosonde data to be processed is radiosonde data that requires optimization, and includes at least four sets of radiosonde data. Specifically, it includes four data points: 8:00 AM and 8:00 PM Beijing time on the same day, 8:00 PM the previous night, and 8:00 AM the following morning. If there are encrypted radiosonde observations at the station on the same day (e.g., at noon), these can also be used for subsequent data processing. If there are two radiosonde data points per day for several consecutive days, minute-level interpolation results for the corresponding day can be generated, using the same method as for a single day.

[0036] Step S203: Based on the radiosonde data to be processed, obtain minute-level radiosonde data using a linear interpolation method.

[0037] In the embodiments of this specification, obtaining minute-level radiosonde data based on the radiosonde data to be processed using a linear interpolation method specifically includes:

[0038] Obtain the radiosonde observation results for each height layer in the preset vertical height layer;

[0039] Based on the radiosonde observation results of each altitude layer, and according to the observation time, linear interpolation is performed to the whole day with minutes as the time resolution to obtain the minute-level radiosonde data.

[0040] In the embodiments of this specification, the preset vertical height layers are the vertical height layers of the ground-based microwave radiometer. In one specific embodiment of this specification, the ground-based microwave radiometer has 83 vertical height layers, with a denser number of layers near the ground and sparser data further away from the ground. The heights of each layer from the ground are: 0.00 (km), 0.03 (km), 0.05 (km), 0.08 (km), 0.10 (km), 0.13 (km), 0.15 (km), 0.18 (km), 0.20 (km), 0.23 (km), 0.25 (km), 0.28 (km), 0.30 (km), 0.33 (km), 0.35 (km), 0.3... 8(km), 0.40(km), 0.43(km), 0.45(km), 0.48(km), 0.50(km), 0.55(km), 0.60(km), 0.65(km), 0.70(km), 0.75(km), 0.80( km), 0.85(km), 0.90(km), 0.95(km), 1.00(km), 1.05(km), 1.10(km), 1.15(km), 1.20(km), 1.25(km), 1.30(km), 1.35(km) , 1.40(km), 1.45(km), 1.50(km), 1.55(km), 1.60(km), 1.65(km), 1.70(km), 1.75(km), 1.80(km), 1.85(km), 1.90(km), 1 .95(km), 2.00(km), 2.25(km), 2.50(km), 2.75(km), 3.00(km), 3.25(km), 3.50(km), 3.75(km), 4.00(km), 4.25(km), 4.50 (km), 4.75(km), 5.00(km), 5.25(km), 5.50(km), 5.75(km), 6.00(km), 6.25(km), 6.50(km), 6.75(km), 7.00(km), 7.25(km ), 7.50(km), 7.75(km), 8.00(km), 8.25(km), 8.50(km), 8.75(km), 9.00(km), 9.25(km), 9.50(km), 9.75(km), 10.00(km).

[0041] In this embodiment, the radiosonde observation results include air pressure, temperature, humidity, and observation time. For each altitude layer, based on the observation time of the radiosonde data at that layer, the temperature, humidity, and air pressure data are linearly interpolated to the whole day with a time resolution of minutes, until all altitude layers have been interpolated, resulting in radiosonde minute-level interpolated data, denoted by Di,t (where i represents the layer and t represents the time).

[0042] Step S205: Based on the location reanalysis data of the radiosonde station to be processed, use linear interpolation to obtain minute-level reanalysis data.

[0043] Since the reanalysis data is gridded, the location reanalysis data of the radiosonde station to be processed needs to be extracted based on the station location, either using the nearest neighbor method or bilinear spatial interpolation. The distribution of the vertical height layers in the reanalysis data is relatively sparse and inconsistent with the vertical height layer settings of the microwave radiometer. Therefore, the reanalysis data needs to be linearly interpolated vertically to the preset vertical height layers.

[0044] In the embodiments of this specification, the method of obtaining minute-level reanalysis data based on the location reanalysis data of the radiosonde to be processed using linear interpolation specifically includes:

[0045] If the vertical layer setting of the location reanalysis data of the radiosonde to be processed is consistent with the preset vertical height layer, then the location reanalysis data of the radiosonde to be processed is interpolated to the minute level with an hourly time resolution and a linear interpolation method to obtain the minute-level reanalysis data.

[0046] For example, for two temperature observation data X0 and X1 that are 60 minutes apart, taking the observation time of X0 as the starting point, the method for calculating the temperature at minute t is as follows:

[0047] , where the range of values ​​for t is 1≤t≤59.

[0048] Since in most cases the vertical layer settings of the reanalysis data are inconsistent with the preset vertical height layers, in the embodiments of this specification, the process of obtaining minute-level reanalysis data based on the location reanalysis data of the radiosonde to be processed using a linear interpolation method includes the following steps beforehand:

[0049] If the vertical layer setting of the reanalysis data of the radiosonde to be processed is inconsistent with the preset vertical height layer, then a linear interpolation method is used to interpolate the reanalysis data to the preset vertical height layer in order to update the reanalysis data of the radiosonde to be processed.

[0050] In the embodiments of this specification, the step of obtaining minute-level reanalysis data based on the location reanalysis data of the radiosonde to be processed using a linear interpolation method further includes:

[0051] Based on the linear customization model, the minute-level reanalysis data is biased to obtain corrected minute-level reanalysis data, which is then used to update the minute-level reanalysis data.

[0052] In the embodiments of this specification, the linear customized model is a univariate linear regression model established based on minute-level reanalysis data of each height layer and its two adjacent height layers in a preset vertical height layer.

[0053] Considering the potential systematic bias in the reanalysis data, data were extracted from the corresponding layer and time period of the reanalysis data based on the actual observation time of the radiosonde data at each layer. Then, a linear correction model was established layer by layer to correct the minute-level reanalysis interpolation results obtained in the previous step. Using the four radiosonde observation data at layer i as the dependent variable y, and the four reanalysis data corresponding to the radiosonde observation time at that layer as the independent variable x, a univariate linear regression model was established between the two.

[0054]

[0055] The slope (b) and intercept (a) in the model are calculated using the least squares method.

[0056]

[0057]

[0058] Building a correction model layer by layer may result in insufficient sample size (for example, in a four-sound study, there would only be four pairs of matching data, i.e., n=4 in the above formula). To increase the number of samples for modeling, and considering that adjacent layers should have similar correction coefficients, data from the upper and lower layers are used when modeling each layer. For example, when modeling the i-th layer, data from three layers (i-1, i, i+1) can be used, resulting in 12 pairs of matching data. Alternatively, data from five layers (i-2, i-1, i, i+1, i+2) can be used, resulting in 20 pairs of matching data. The specific choice depends on the situation. For the topmost layer, data from the adjacent next layer can be used during modeling; for the bottommost layer, data from the adjacent previous layer can be used, depending on the specific circumstances.

[0059] This bias correction ensures that when the two are subsequently linearly weighted, neither will produce a significant error due to overall overestimation or underestimation. The minute-level reanalysis data after bias correction is then processed using E... i,t express.

[0060] Step S207: Based on the minute-level radiosonde data and its weight, the minute-level reanalysis data and its weight, the final radiosonde data is obtained, thereby optimizing the radiosonde data to be processed.

[0061] Each layer (i) has two weight values ​​at each time point (t), with the minute-level sounding data weight W. i,t Minute-level reanalysis data weights 1-W i,t Weight Wi,t The calculation mainly relies on the current considered time t and the observation time of the nearest radiosonde data at layer i ( The differences between the current time t and the actual observation time of the nearest neighbor radiosonde data, as well as the temporal variability of the atmosphere itself. Specifically: if the current time t and the actual observation time of the nearest neighbor radiosonde data... If they are very close, then W will be given. i,t Larger values, with increasing time differences, result in higher weights W. i,t The temperature will then drop, and the rate of drop mainly depends on the temporal variability of the atmosphere. The smaller (larger) the temporal variability of the atmosphere, the slower (faster) the drop. If the current t and If consistent, then W i,t The value is 1, meaning that the final weighted average result only considers sounding data.

[0062] In the embodiments of this specification, obtaining the final radiosonde data based on the minute-level radiosonde data and minute-level radiosonde data weights, the minute-level reanalysis data and minute-level re-analysis data weights, specifically includes:

[0063] The final sounding data is obtained by summing the product of the minute-level sounding data and the weight of the minute-level sounding data, and the product of the weight of the minute-level reanalysis data and the weight of the minute-level reanalysis data.

[0064] The final sounding data was calculated as follows:

[0065]

[0066] in,

[0067] This refers to the final sounding data;

[0068] i represents the layer number;

[0069] t represents time.

[0070] This represents minute-level sounding data at time t in the i-th layer;

[0071] This represents the minute-level sounding data weight at time t in the i-th layer;

[0072] This represents the minute-level reanalysis data at time t in the i-th layer;

[0073] ( This represents the minute-level reanalysis data weight at time t in the i-th layer.

[0074] In the embodiments described in this specification, the minute-level sounding data weights are obtained using a logistic function;

[0075] The formula for calculating the weight of the minute-level radiosonde data is as follows:

[0076] when hour,

[0077] ;

[0078] when hour,

[0079] 1;

[0080] in,

[0081] This represents the minute-level sounding data weight at time t in the i-th layer;

[0082] t represents time.

[0083] i represents the layer number;

[0084] This represents the actual observation time of the radiosonde data closest to time t at the i-th layer;

[0085] It represents the absolute value of the time difference between time t and the actual observation time of the nearest radiosonde data in the i-th layer;

[0086] This represents the time difference during which the weights of the i-th layer decrease to half.

[0087] This indicates the rate at which the weight decreases.

[0088] In the embodiments described in this specification, and This is for location-based reanalysis data setup. Specifically, It can be set to a value between 1 and 2 (1.5 is recommended). Based on the standard deviation setting of the reanalysis data at layer i, the larger the standard deviation, the faster the atmospheric conditions change on that day, and the smaller the influence of radiosonde data on interpolation results far from the observation time. The smaller the value, the better. Taking temperature as an example, the standard deviation and... The relationship between them can be estimated using the table below.

[0089]

[0090] Taking relative humidity as an example, the standard deviation and The relationship between them can be estimated using the table below.

[0091]

[0092] Figure 3 This is a flowchart illustrating another method for optimizing sounding data based on time-varying characteristics, provided as an embodiment of this specification. (For example...) Figure 3 As shown, the methods for optimizing radiosonde data include:

[0093] Step S301: Obtain the radiosonde data to be processed.

[0094] Step S303: Based on the radiosonde data to be processed, obtain minute-level radiosonde data using a linear interpolation method.

[0095] Step S305: Based on the location reanalysis data of the radiosonde station to be processed, use linear interpolation to obtain minute-level reanalysis data.

[0096] Step S307: Based on the linear customization model, perform deviation correction on the minute-level reanalysis data to obtain corrected minute-level reanalysis data. The corrected minute-level reanalysis data is used to update the minute-level reanalysis data to obtain updated minute-level reanalysis data.

[0097] Step S309: Based on the minute-level radiosonde data and its weights, the updated minute-level reanalysis data and its weights, the final radiosonde data is obtained, thereby optimizing the radiosonde data to be processed.

[0098] The radiosonde data optimization method based on time-varying characteristics provided in this specification involves: acquiring radiosonde data to be processed; obtaining minute-level radiosonde data using linear interpolation based on the radiosonde data to be processed; obtaining minute-level reanalysis data using linear interpolation based on the location reanalysis data of the radiosonde station to be processed; and obtaining final radiosonde data based on the minute-level radiosonde data and its weights, as well as the minute-level reanalysis data and its weights. This method optimizes the radiosonde data to be processed, extending morning and evening operational radiosonde data to the entire day, generating time-continuous, minute-level resolution all-day atmospheric temperature and humidity profiles. For satellite products passing over the radiosonde station at any time of day, it can provide verification data with time synchronization at each layer in the vertical direction, fully leveraging the accuracy of radiosonde data and the high time-frequency advantage of reanalysis data, thereby improving the accuracy of satellite product verification and weather forecasting.

[0099] The above describes in detail a method for optimizing radiosonde data based on time-varying characteristics. Correspondingly, this specification also provides a system for optimizing radiosonde data based on time-varying characteristics, such as... Figure 4 As shown. Figure 4 This is a schematic diagram of a radiosonde data optimization system based on time-varying characteristics, provided in an embodiment of this specification. The radiosonde data optimization system includes:

[0100] Module 401 acquires the radiosonde data to be processed;

[0101] The minute-level radiosonde data acquisition module 403 obtains minute-level radiosonde data based on the radiosonde data to be processed using a linear interpolation method.

[0102] The minute-level reanalysis data acquisition module 405 obtains minute-level reanalysis data based on the location reanalysis data of the radiosonde station to be processed, using a linear interpolation method.

[0103] The optimization module 407 obtains the final sounding data based on the minute-level sounding data and its weight, as well as the minute-level reanalysis data and its weight, thereby optimizing the sounding data to be processed.

[0104] Figure 5 This is a schematic diagram of another time-varying feature-based sounding data optimization system provided in the embodiments of this specification. The sounding data optimization system includes:

[0105] Module 501 acquires the radiosonde data to be processed;

[0106] The minute-level radiosonde data acquisition module 503 obtains minute-level radiosonde data based on the radiosonde data to be processed using a linear interpolation method.

[0107] The minute-level reanalysis data acquisition module 505 obtains minute-level reanalysis data based on the location reanalysis data of the radiosonde station to be processed, using a linear interpolation method.

[0108] The deviation correction module 507, based on the linear customization model, corrects the deviation of the minute-level reanalysis data to obtain corrected minute-level reanalysis data. The corrected minute-level reanalysis data is used to update the minute-level reanalysis data to obtain updated minute-level reanalysis data.

[0109] The optimization module 509 obtains the final radiosonde data based on the minute-level radiosonde data and its weights, the updated minute-level reanalysis data and its weights, thereby optimizing the radiosonde data to be processed.

[0110] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0111] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for apparatus, electronic devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0112] The apparatus, electronic device, and non-volatile computer storage medium and method provided in the embodiments of this specification are corresponding. Therefore, the apparatus, electronic device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, electronic device, and non-volatile computer storage medium will not be repeated here.

[0113] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0114] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0115] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0116] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0117] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0121] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0122] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0123] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0124] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0125] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside on local and remote computer storage media, including storage devices.

[0126] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0127] The above description is merely an embodiment of this specification and is not intended to limit 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 principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for optimizing sounding data based on time-varying characteristics, characterized in that, The sounding data optimization method comprises: obtaining sounding data to be processed; based on the sounding data to be processed, using a linear interpolation method to obtain minute-level sounding data, specifically comprising: obtaining sounding observation results of each height layer in a preset vertical height layer; based on the sounding observation results of each height layer, linearly interpolating to the whole day with minutes as the time resolution according to the observation time to obtain the minute-level sounding data; based on the position reanalysis data of the sounding station to be processed, using a linear interpolation method to obtain minute-level reanalysis data, specifically comprising: if the vertical layer setting of the position reanalysis data of the sounding station to be processed is consistent with the preset vertical height layer, then the position reanalysis data of the sounding station to be processed is interpolated to the minute level with hours as the time resolution according to the linear interpolation method to obtain the minute-level reanalysis data; Based on the minute sounding data and the weight of the minute sounding data, the minute reanalysis data and the weight of the minute reanalysis data, final sounding data is obtained, the optimization of the sounding data to be processed is realized, and specifically includes: the product of the minute sounding data and the weight of the minute sounding data, the product of the minute reanalysis data and the weight of the minute reanalysis data are added to obtain the final sounding data; the calculation of the final sounding data is: wherein, represents the final sounding data; i represents the layer number; t represents the time; represents the minute sounding data of the i-th layer at time t; represents the weight of the minute sounding data of the i-th layer at time t; represents the minute reanalysis data of the i-th layer at time t; represents the weight of the minute reanalysis data of the i-th layer at time t, wherein the weight of the minute sounding data is obtained by using a logistic function. the calculation formula of the weight of the minute-level sounding data is: When time, ； When time, 1； wherein, represents the minute-level sounding data weight of the ith layer at time t; t represents the time; i represents the layer number; tactual(i) represents the actual observation time of the sounding data most adjacent to time t on the i-th layer; |t - t0| represents the absolute value of the time difference between time t and the actual observation time of the most adjacent sounding data on the i-th layer; ti represents the time difference for the weight of the i-th layer to drop to half; represents the speed of the weight drop.

2. The sounding data optimization method of claim 1, wherein, the method of obtaining the minute-level reanalysis data based on the position reanalysis data of the sounding station to be processed using the linear interpolation method further comprises the following steps: if the vertical layer setting of the position reanalysis data of the sounding station to be processed is inconsistent with the preset vertical height layer, then the reanalysis data is interpolated to the preset vertical height layer using the linear interpolation method to update the position reanalysis data of the sounding station to be processed.

3. The sounding data optimization method of claim 1, wherein, the method of obtaining the minute-level reanalysis data based on the position reanalysis data of the sounding station to be processed using the linear interpolation method further comprises: based on a linear customization model, the minute-level reanalysis data is corrected for deviation to obtain corrected minute-level reanalysis data, which is used to update the minute-level reanalysis data.

4. The sounding data optimization method of claim 3, wherein, The linear customization model is a linear regression model established based on the minute-level reanalysis data of each adjacent two height layers in the preset vertical height layer.

5. The sounding data optimization method of claim 1, wherein, With For setting based on position reanalysis data.

6. A time-varying feature-based sounding data optimization system, comprising: The sounding data optimization system comprises: an acquisition module for acquiring sounding data to be processed; a minute-level sounding data acquisition module for obtaining minute-level sounding data based on the sounding data to be processed using a linear interpolation method, specifically comprising: obtaining sounding observation results of each height layer in a preset vertical height layer; based on the sounding observation results of each height layer, linearly interpolating to the whole day with minutes as the time resolution according to the observation time to obtain the minute-level sounding data; a minute-level reanalysis data acquisition module for obtaining minute-level reanalysis data based on the position reanalysis data of the sounding station to be processed using a linear interpolation method, specifically comprising: if the vertical layer setting of the position reanalysis data of the sounding station to be processed is consistent with the preset vertical height layer, then the position reanalysis data of the sounding station to be processed is interpolated to the minute level with hours as the time resolution according to the linear interpolation method to obtain the minute-level reanalysis data; An optimization module is configured to obtain final sounding data based on the minute-level sounding data and the minute-level sounding data weight, the minute-level reanalysis data and the minute-level reanalysis data weight, and to realize optimization of the sounding data to be processed, and specifically includes: adding the product of the minute-level sounding data and the minute-level sounding data weight, the product of the minute-level reanalysis data and the minute-level reanalysis data weight, to obtain the final sounding data; the calculation of the final sounding data is: wherein, represents the final sounding data; i represents a layer number; t represents a time point; represents minute-level sounding data of the i-th layer at the t-th time point; represents a minute-level sounding data weight of the i-th layer at the t-th time point; represents minute-level reanalysis data of the i-th layer at the t-th time point; represents a minute-level reanalysis data weight of the i-th layer at the t-th time point, wherein the minute-level sounding data weight is obtained by using a logistic function. the calculation formula of the weight of the minute-level sounding data is: When time, ； When time, 1； wherein, represents the minute-level sounding data weight of the ith layer at time t; t represents the time; i represents the layer number; tactual(i) represents the actual observation time of the sounding data most adjacent to time t on the i-th layer; |ti - t| represents the absolute value of the time difference between time t and the actual observation time of the most adjacent sounding data on the i-th layer; ti represents the time difference for the weight of the i-th layer to drop to half; represents the speed of the weight drop.

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