A method, device and equipment for constructing a climate state data set for the Arctic Ocean

Abstract

The present invention discloses a method, device and equipment for constructing a climate state data set in the Arctic sea area, and the technical field of sea area data analysis, including: obtaining original observation data from multiple data sources in the Arctic sea area, and removing duplicates from the original observation data according to preset rules to obtain first valid data; performing standardized preprocessing on the first valid data, and performing first quality control based on high-latitude temperature-salinity characteristics to obtain second valid data; interpolating the second valid data to a standard layer, and performing second quality control on the interpolated data based on high-latitude temperature-salinity characteristics to obtain third valid data; based on the third valid data, performing spatiotemporal standardized grid interpolation, and adopting a cross-objective analysis method to construct a climate state data set in the Arctic sea area. The present invention establishes an accurate, comprehensive and unified gridded climate state data set in the Arctic sea area.

Description

Technical Field

[0001] The present invention relates to the technical field of sea area data analysis, and in particular to a method, device and equipment for constructing a climate state data set for an Arctic sea area. Background Art

[0002] The geographical location of the Arctic Ocean is in a high-latitude area, and its special environmental characteristics make it more difficult to obtain on-site ocean environment temperature and salinity observation data in the Arctic than in other oceans. However, a large amount of on-site observation data is needed to help understand the ice-sea dynamic and thermal processes in this sea area. For a long time, a series of observation plans in many countries / internationalities have included on-site observation data of marine elements in the Arctic Ocean: Copernicus Marine Environment Monitoring Service (CMEMS) data, World Ocean Database (WOD), Ice-Moored Profiler (ITP), etc. provide important data resources for studying the long-term and historical characteristics of global oceanography, climatology and environmental research. The Argo observation program also covers some Arctic sea areas. In addition, researchers have carried out a series of scientific expeditions in some special phenomenon sea areas and accumulated a large amount of first-hand on-site marine environment observation data. However, the quality control methods and element storage formats of these data (sets) lack uniformity.

[0003] Therefore, there is an urgent need to provide a more reliable solution for constructing a climate state dataset for the Arctic Ocean. Summary of the invention

[0004] The purpose of the present invention is to provide a method, device and equipment for constructing a climate data set for the Arctic Ocean, which is used to construct a multi-source temperature and salinity observation data set for the Arctic Ocean with accurate quality control, comprehensive distribution area and unified storage format.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a method for constructing an Arctic sea climate data set, the method comprising:

[0007] Acquire original observation data from multiple data sources in the Arctic Ocean, and remove duplicates from the original observation data according to preset rules to obtain first valid data;

[0008] Performing standardization preprocessing on the first valid data, and performing first quality control based on high-latitude temperature-salinity characteristics, to obtain second valid data;

[0009] interpolating the second valid data to the standard layer, and performing a second quality control on the interpolated data based on the high-latitude temperature-salinity characteristics to obtain third valid data;

[0010] Based on the third effective data, spatiotemporal standardized grid interpolation is performed, and a cross-objective analysis method is used to construct a climate state dataset for the Arctic Ocean.

[0011] Optionally, interpolating the second valid data to a standard layer specifically includes:

[0012] Using the formula:

[0013] ;

[0014] Modified Akima cubic Hermite interpolation; where, represents the slope of the sampling point, For interval The slope on , are weight coefficients, , ;

[0015] interpolating the quality-controlled observation profile to the standard layer along the depth based on the second valid data;

[0016] The interpolated data is subjected to a second quality control based on the high-latitude temperature-salinity characteristics, including:

[0017] Using the formula:

[0018] ;

[0019] ;

[0020] A second quality control is performed; wherein, , They are the peak values ​​of temperature and salinity data obtained based on the standard climate state temperature and salinity and standard deviation, , are the temperature and salinity values ​​on the standard layer of the standard climate state, , are the standard deviations of the standard climatic temperature and salinity.

[0021] Optionally, performing spatiotemporal normalized grid interpolation based on the third valid data specifically includes:

[0022] Divide the Arctic Ocean area into grids of preset sizes;

[0023] In each grid, averaging a plurality of the third valid data;

[0024] The third effective data of the SODAO spatial grid average is classified monthly to form a spatiotemporal gridded data set.

[0025] Optionally, a cross-objective analysis method is used to construct a climate state dataset for the Arctic Ocean, including:

[0026] Based on the spatiotemporal gridded dataset, a background field of a preset resolution is generated by gridding all temperature and salinity data at each vertical standard layer in space using an objective analysis method;

[0027] The background field is corrected using a stepwise correction method to obtain a climate data set for the Arctic Ocean.

[0028] Optionally, based on the spatiotemporal gridded dataset, all temperature and salinity data are gridded using an objective analysis method at each vertical standard layer in space to generate a background field of a preset resolution, specifically including:

[0029] Using the formula:

[0030] ;

[0031] ;

[0032] All temperature and salinity data are gridded to generate a background field with a preset resolution; is the analytical field at point i after n iterations, is the search radius after the nth iteration The b-th observation in is the estimated value at position b after the nth iteration, is the influence radius of the observation point after the nth iteration, is the number of observations searched in the nth iteration, is the weight coefficient, Represents the distance between the observation point and the analysis point.

[0033] Optionally, the background field is corrected using a stepwise correction method to obtain an Arctic Ocean climate state dataset, specifically including:

[0034] Using the formula:

[0035] ;

[0036] The background field is corrected to obtain the Arctic Ocean climate data set; wherein, is the filter constant, is the convergence factor, where Take 0.2, Represents an exponential function.

[0037] Optionally, performing standardized preprocessing on the first valid data and performing first quality control based on high-latitude temperature-salinity characteristics to obtain second valid data specifically includes:

[0038] Performing standardized preprocessing on the first valid data, removing abnormal files, and obtaining preprocessed observed temperature-salinity profile data;

[0039] The preprocessed observed temperature-salinity profile data is subjected to a first quality control; the first quality control includes a position-time test, a depth reversal test, a repeatability test, a temperature-salinity extreme value test, a gradient test, and a density gradient test to obtain second valid data; the second valid data is a standard in-situ observation data set for the Arctic Ocean area that conforms to the actual ocean temperature-salinity structure.

[0040] Compared with the prior art, the present invention provides a method for constructing a climate state data set for the Arctic sea area, which obtains the original observation data from multiple data sources in the Arctic sea area, removes the duplicates of the original observation data according to preset rules, and obtains the first valid data; performs standardized preprocessing on the first valid data, and performs a first quality control based on the high-latitude temperature-salinity characteristics to obtain the second valid data; interpolates the second valid data to the standard layer, and performs a second quality control on the interpolated data based on the high-latitude temperature-salinity characteristics to obtain the third valid data; based on the third valid data, performs spatiotemporal standardized grid interpolation, and adopts a cross-objective analysis method to construct a climate state data set for the Arctic sea area. The present invention uses the established Arctic sea area temperature-salinity observation data set, and the high-latitude temperature-salinity element characteristics and distribution characteristics as constraints, and establishes a set of accurate, comprehensive, and unified gridded climate state data sets for the Arctic sea area through a cross-objective analysis method.

[0041] In a second aspect, the present invention provides a device for constructing a climate state dataset for an Arctic sea area, the device comprising:

[0042] A data deduplication module is used to obtain original observation data from multiple data sources in the Arctic Ocean area, and to dedupe the original observation data according to preset rules to obtain first valid data;

[0043] A first quality control module is used to perform standardization preprocessing on the first valid data and perform first quality control based on high-latitude temperature-salinity characteristics to obtain second valid data;

[0044] A second quality control module is used to interpolate the second valid data to a standard layer, and perform a second quality control on the interpolated data based on high-latitude temperature-salinity characteristics to obtain third valid data;

[0045] The module for constructing the Arctic Ocean climate state data set is used to perform spatiotemporal standardized grid interpolation based on the third effective data, and to construct the Arctic Ocean climate state data set by adopting a cross-objective analysis method.

[0046] In a third aspect, the present invention provides a device for constructing a climate state dataset for an Arctic sea area, the device comprising:

[0047] A memory, a processor, and a communication interface coupled to the processor; the memory stores a computer program that can be executed by the processor; when the processor runs the computer program, the above-mentioned method for constructing a climate state data set for the Arctic sea area is executed.

[0048] In a fourth aspect, the present invention provides a computer storage medium having instructions stored therein, which, when executed, implement the above-mentioned method for constructing a climate state dataset for the Arctic Ocean.

[0049] The technical effects achieved by the device type solution provided in the second aspect, the equipment type solution provided in the third aspect, and the computer storage medium solution provided in the fourth aspect are the same as those of the method type solution provided in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0051] Figure 1 A schematic diagram of a method for constructing a climate state dataset for the Arctic Ocean provided by the present invention;

[0052] Figure 2 A schematic diagram of the quality control process of high-latitude sea area observation data provided by the present invention;

[0053] Figure 3 A flow chart for making a climate state data set provided by the present invention;

[0054] Figure 4 A schematic diagram of a device for constructing a climate state data set for the Arctic Ocean provided by the present invention;

[0055] Figure 5 A schematic diagram of a device for constructing an Arctic sea area climate state data set provided by the present invention. DETAILED DESCRIPTION

[0056] In order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and effects. For example, the first threshold and the second threshold are only used to distinguish different thresholds, and their order is not limited. Those skilled in the art can understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit them to be different.

[0057] It should be noted that, in the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0058] In the present invention, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, the combination of a and b, the combination of a and c, the combination of b and c, or the combination of a, b and c, where a, b, c can be single or multiple.

[0059] Environmental characteristics of the Arctic Ocean, such as sea ice covering the sea area, lead to uneven quality of observation data, and the quality control standards of observation data collected through different data sources are not unified, and there are objective errors in the observation process, which is not conducive to the establishment and use of standardized data. Therefore, it is necessary to uniformly control and evaluate the quality of data from different sources. According to the high-latitude ocean-sea ice characteristics of the Arctic Ocean, a standardized data quality control method and a climate state production method based on high-latitude temperature-salinity characteristic constraints are used to establish a set of multi-source temperature-salinity observation data sets for the Arctic Ocean with accurate quality control, comprehensive distribution area, and unified storage format. Based on this data set, a monthly 1 / 4° resolution climate state product is established for the Arctic Ocean for use in oceanographic research. The data set established by this unified method can be used as a standard reference for future observation data quality control, and can also be used to correct the temperature-salinity results of the existing ice-sea coupling model. Next, the scheme provided in the embodiments of this specification is described in conjunction with the accompanying drawings:

[0060] like Figure 1 As shown, the process may include the following steps:

[0061] Step 110: Acquire original observation data from multiple data sources in the Arctic Ocean region, and remove duplicates from the original observation data according to preset rules to obtain first valid data.

[0062] The collected data are original observation data corresponding to various data sources in the Arctic Ocean. There are some duplicate observation data in these data, so they need to be deduplicated. Specifically, strict data deduplication can be performed on the duplicate observation data in the original collected data sources. The deduplication method is within the spatial error of 0.1°×0.1° and the time error is within 5 hours. The data within this time and space repetition range is processed as duplicate data by default, and finally the first valid data is obtained. The first valid data can refer to the data remaining after removing the duplicate observation data from the original observation data.

[0063] This step removes duplicates from the collected data from different databases. Since the sources of field observation data are different, the repeatability of the data needs to be considered. The present invention uses thresholds set in time and space to control the repeatability of the data (within the spatial error of 0.1°×0.1° and the time error of 5 hours, the data within this time and space repeatability range is defaulted to repeated data processing) to ensure the authenticity and reliability of the data set and the rationality of the climate state data set product.

[0064] Step 120: performing standardization preprocessing on the first valid data, and performing first quality control based on high-latitude temperature-salinity characteristics to obtain second valid data.

[0065] The second valid data may refer to the data after the abnormal data is eliminated from the first valid data, the data is converted into a rate format, and the first quality control is performed based on the high-latitude temperature-salinity characteristics.

[0066] Specifically, the first valid data collected in step 110 is subjected to standardized preprocessing, abnormal files are removed, and the format of the preprocessed observed temperature-salinity profile data is converted, and the temperature-salinity profile data is used to calculate ocean elements such as depth, pressure, and density.

[0067] Quality control plays an important role in improving the correct use of observational data. Generally, quality control can be divided into near real-time quality control (NRQC) and delayed mode QC (DMQC). Near real-time quality control refers to the preliminary quality control performed immediately after the observational data is acquired, and the delay time is usually between 1 hour and 1 week. In contrast, delayed quality control uses more standardized and rigorous techniques to conduct a more in-depth quality assessment of the observational data, which takes a longer time, usually delayed by 6 to 12 months.

[0068] In step 120, the first quality control is performed on the preprocessed data; the first quality control may mainly include position time test, depth reversal and repeatability test, temperature and salinity extreme value test, gradient test and density gradient test. Through preprocessing and quality control, a high-quality standard in situ observations dataset of the arctic ocean (SODAO) that conforms to the actual ocean temperature and salinity structure is finally obtained.

[0069] Step 130: interpolate the second valid data to the standard layer, and perform a second quality control on the interpolated data based on the high-latitude temperature-salinity characteristics to obtain third valid data.

[0070] The quality control process method of the data set in the present invention is a method for automatically checking and marking the physical variables of each profile on the basis of quality control through automated detection and WOD technical manual, specifically including standardized preprocessing, quality control of high-latitude temperature-salinity characteristics, interpolation to standard layers, and secondary quality control of high-latitude temperature-salinity characteristics.

[0071] Among them, since the observation depths of the original data from different sources vary due to the different designs of the observation instruments, in order to unify the observation depth and facilitate subsequent use, the observation profile after the secondary quality control in step 130 can be interpolated to the standard layer along the depth. The deepest is 5500m, with a total of 102 layers. In order to make the profile interpolated to the standard layer more consistent with the original observation profile curve, specifically, the modified Akima cubic Hermite interpolation can be selected. This interpolation produces smaller fluctuations than the cubic spline interpolation, but does not flatten the data sharply like the conformal piecewise cubic interpolation.

[0072] For secondary quality control, considering the influence of mathematical interpolation method on original data, the present invention performs secondary temperature and salinity test on the interpolated data, including temperature and salinity range test and temperature and salinity gradient test in high-latitude sea area. The present invention automatically checks and marks the physical variables of each profile, including standardization preprocessing, quality control, interpolation to standard layer and secondary quality control after interpolation. The authenticity, reliability and rationality of standard observation data set are guaranteed. And the climatological standard deviation test is used to ensure the accuracy of data to the greatest extent.

[0073] Step 140: Based on the third valid data, perform spatiotemporal standardized grid interpolation, and use a cross-objective analysis method to construct an Arctic sea area climate state dataset.

[0074] Since the standard in situ observations dataset of the arctic ocean (SODAO) is irregularly distributed in horizontal space, this type of data clustering will seriously affect the results of objective analysis. Therefore, spatiotemporal standardized grid interpolation is used to solve this problem. Then, the cross-objective analysis method is used to construct the Arctic climate state dataset.

[0075] Figure 1 The method in the method obtains the original observation data from multiple data sources in the Arctic sea area, and removes the duplicates of the original observation data according to preset rules to obtain the first valid data; the first valid data is subjected to standardized preprocessing, and the first quality control is performed based on the high-latitude temperature-salinity characteristics to obtain the second valid data; the second valid data is interpolated to the standard layer, and the interpolated data is subjected to the second quality control based on the high-latitude temperature-salinity characteristics to obtain the third valid data; based on the third valid data, time-space standardized grid interpolation is performed, and a cross-objective analysis method is used to construct a climate state data set for the Arctic sea area. The present invention is based on the established Arctic sea area temperature-salinity observation data set, and the high-latitude temperature-salinity element characteristics and distribution characteristics are used as constraints, and a set of accurate, comprehensive and unified gridded climate state data sets for the Arctic sea area is established through a cross-objective analysis method.

[0076] based on Figure 1 The method, the embodiments of this specification also provide some specific implementation methods of the method, which are described below.

[0077] First, if Figure 2 As shown in FIG. 1 , the quality control of high-latitude sea area observation data, compared with the prior art, the present invention also adopts secondary quality control, and the quality control method adopted in the first quality control is also different from that in the prior art. Specifically, for the input original observation data ( Figure 2The original observation data in the default is the first valid data obtained after the duplicate removal process), firstly perform standardization processing, and then perform the first quality control. The first quality control at this time includes position time test, depth reversal and repeatability test, temperature and salinity extreme value test, gradient test and density gradient test. After the first quality control, data interpolation is performed, and then the secondary quality control is continued to obtain the standard in-situ observation data set. The standard in-situ observation data set at this time is the third valid data obtained in step 130.

[0078] Next, Figure 2 The steps in step 120 are described in detail: The first quality control in step 120 may specifically include:

[0079] Performing standardized preprocessing on the first valid data, removing abnormal files, and obtaining preprocessed observed temperature-salinity profile data;

[0080] The first quality control is performed on the pre-processed observed temperature-salinity profile data; the first quality control includes position time test, depth reversal test, repeatability test, temperature-salinity extreme value test, gradient test and density gradient test to obtain the second valid data; the second valid data is the standard in-situ observation data set of the Arctic Ocean area that conforms to the actual ocean temperature-salinity structure. The following are the descriptions of several tests:

[0081] (1) Position and time verification: The position of each observation profile must meet the requirements, specifically: the latitude range is 60°N to 90°N, and the longitude range is 180°W to 180°E. The position is compared with the global land position. If the buoy position is not at sea, the buoy position is considered wrong. The date and time of each profile must meet the requirements.

[0082] (2) Depth reversal and repeatability test: Observation points where the depth of one observation is shallower than the depth of the previous observation (depth reversal) and where one reading has the same depth as the reading before it (depth repeat) are eliminated.

[0083] (3) Temperature and salinity extreme value test in high-latitude sea areas: According to the temperature characteristics of high-latitude sea areas, the extreme value is between -3 ℃ and 20 ℃; the maximum value of salinity is 40 psu, and due to the existence of sea ice, the minimum value of salinity is 0 psu. Range check is used to screen extreme values ​​in the data. The temperature and salinity range varies with depth. The original data observation depth varies, and the extreme value standard layer with the smallest distance to the specific observation layer is dynamically searched, so as to perform extreme value test on each observation value.

[0084] (4) Temperature-salinity gradient test in high-latitude sea areas:

[0085] According to the positive and negative gradient values, two types of gradients were checked:

[0086] Excessive negative gradients: The gradient value decreases too much in the depth direction. Any value exceeding the "maximum gradient value" (MGV) will be marked as a gradient anomaly and removed.

[0087] Excessive positive inversions: The gradient value increases too much in the depth direction. Data exceeding the "maximum inversion value" (MIV) will be marked as inversion anomaly and removed.

[0088] Table 1 Gradient test standard

[0089]

[0090] Density gradient test in high latitude sea areas

[0091] The density under different pressures must meet certain restrictions. When the pressure is 0-30dbar, the density gradient should be greater than -0.03; when the pressure is 30-400dbar, the density gradient should be greater than -0.02; when the pressure is greater than 400dbar, the density gradient should be greater than -0.01.

[0092] For the interpolation of the second valid data to the standard layer in step 130, the third valid data is obtained: the standard layer is defined as the WOA annual average climate state standard layer, with a maximum depth of 5500m and a total of 102 layers. In order to make the profile interpolated to the standard layer more consistent with the original observed profile curve, the present invention selects the modified Akima cubic Hermite interpolation. This interpolation produces smaller fluctuations than the cubic spline interpolation, but does not flatten the data sharply like the shape-preserving piecewise cubic interpolation. More specifically, the slope of the sampling point in the modified Akima cubic Hermite interpolation is calculated by formula (1).

[0093] ;

[0094] in, represents the slope of the sampling point, For interval The slope on , is the weight coefficient, calculated by equations (2) and (3).

[0095] ;

[0096] ;

[0097] For the second quality control of the interpolated data based on the high-latitude temperature-salinity characteristics in step 130, generally speaking, the error threshold of the climate state standard deviation test is 3 times the standard deviation. Considering the scarcity of data in high-latitude sea areas, it is inappropriate to continue to use the error threshold of mid- and low-latitudes. Therefore, the present invention adopts 5 times the standard deviation as the climate state test standard. Specifically:

[0098] Using formulas (4) and (5):

[0099] (4);

[0100] (5);

[0101] A second quality control is performed; wherein, , They are the peak values ​​of temperature and salinity data obtained based on the standard climate state temperature and salinity and standard deviation, , are the temperature and salinity values ​​on the standard layer of the standard climate state, , are the standard deviations of the standard climatic temperature and salinity.

[0102] For step 140, when constructing the Arctic Ocean climate state data set, since the climate state product can intuitively display the average value, distribution, and change trend of climate elements in a specific region and time period, the climate state product established based on the field observation data set can more realistically reflect the phenomenon characteristics within a certain time scale period and be used as a reference standard. Figure 3 As shown, Figure 3 The third valid data in is the standardized observation data set. In the specific implementation of the present invention, the time-space standardized grid interpolation can be performed first to obtain the monthly average spatial grid distribution data set, and then the temperature-salinity grid background field is obtained by an objective analysis method, and the corrected grid background field is obtained by step-by-step correction, and finally the Arctic Ocean climate state data set is constructed. Next, they are explained separately:

[0103] (1) Spatiotemporal standardized grid interpolation: Since the standard in situ observations dataset of the arctic ocean (SODAO) is irregularly distributed in horizontal space, this type of data clustering will seriously affect the results of objective analysis. To solve the whole problem, we divide the sea area into 0.25°×0.25° grids, average multiple data in each grid to make the spatial distribution of the observation profile more uniform, and then classify the SODAO spatial grid average data month by month to form a monthly average dataset.

[0104] (2) Production of high-latitude temperature-salinity field climate state based on cross-objective analysis method: Based on the above spatiotemporal gridded data sets, all temperature and salinity data are gridded at each vertical standard layer in space using an objective analysis method to generate a background field with a resolution of 0.25° × 0.25°, as shown in Formulas (6) and (7).

[0105] On this basis, the background field is corrected using the stepwise correction method to obtain the Arctic Ocean climate state product with a monthly horizontal resolution of 0.25°×0.25° and 57 vertical layers, as shown in formulas (6) and (8).

[0106] (6)

[0107] (7)

[0108] is the analytical field at point i after n iterations, is the search radius after the nth iteration In view of the fact that the high-latitude sea area has a vast space and relatively sparse observations, we set three iterations. They are 1665km, 999km and 333km respectively, so that the background field covers the entire sea area. is the estimated value at position b after the nth iteration, is the influence radius of the observation point after the nth iteration, is the number of observations searched in the nth iteration, is the weight coefficient, represents the distance between the observation point and the analysis point, which is calculated by equation (4). The weight coefficient of the stepwise correction method is calculated by equation (5).

[0109] (8)

[0110] in is the filter constant, is the convergence factor. Take 0.2, They are and km 2 .

[0111] The solution provided by the present invention is a set of quality control and establishment methods for the temperature and salinity observation data set of the Arctic Ocean with unified standards and reliable methods, aiming at the characteristics of high-latitude temperature and salinity data and the difficulties and various types of observation data in high-latitude observation. A set of real, scientific and reasonable temperature and salinity observation data sets of the Arctic Ocean has been established. Based on the characteristics and distribution characteristics of high-latitude temperature and salinity elements as constraints, a standard in situ observations dataset of the arctic ocean (SODAO) was established through interactive objective analysis experiments. Based on this dataset, a standard in situ observations climatology dataset of the arctic ocean (SOCAO) with a horizontal resolution of 0.25°×0.25°, 57 vertical layers, and monthly Arctic Ocean standard climate state dataset was established for use in oceanographic research.

[0112] The observation data set generated by the present invention covers long-term, multi-type and multi-national field observation data, as well as observation data from China's Arctic scientific expeditions over the years, with a wide range, long time and complete coverage.

[0113] In addition, due to the environmental characteristics of the Arctic Ocean, it is difficult to obtain a large amount of field observation data, and due to the uneven quality of the observation data caused by the characteristics of the sea area covered with sea ice, it is necessary to uniformly control and evaluate the data from different sources. Therefore, based on the existing observation data that can be collected, the present invention establishes a set of data processing methods such as standardized processing for analysis and elimination, and establishes a data set and a climatological data set. The data set established by this unified method can be used as a standard reference for future observation data quality control, and can be used to correct the temperature and salinity results of the existing ice-sea coupling model.

[0114] Based on the same idea, the present invention also provides a device for constructing a climate state data set in the Arctic Ocean. Figure 4 As shown, the device may include:

[0115] The data deduplication module 410 is used to obtain the original observation data from multiple data sources in the Arctic Ocean area, and deduplication the original observation data according to a preset rule to obtain the first valid data;

[0116] A first quality control module 420 is used to perform standardization preprocessing on the first valid data and perform first quality control based on high-latitude temperature-salinity characteristics to obtain second valid data;

[0117] A second quality control module 430 is used to interpolate the second valid data to a standard layer, and perform a second quality control on the interpolated data based on high-latitude temperature-salinity characteristics to obtain third valid data;

[0118] The Arctic Ocean climate state data set construction module 440 is used to perform spatiotemporal standardized grid interpolation based on the third effective data, and to construct an Arctic Ocean climate state data set using a cross-objective analysis method.

[0119] based on Figure 4 The device in the embodiment may further include some specific implementation units:

[0120] Optionally, the data interpolation unit in the second quality control module 430 may be specifically used for:

[0121] Using the formula:

[0122] ;

[0123] Modified Akima cubic Hermite interpolation; where, represents the slope of the sampling point, For interval The slope on , are weight coefficients, , ;

[0124] interpolating the quality-controlled observation profile to the standard layer along the depth based on the second valid data;

[0125] The secondary quality control unit in the second quality control module 430 can be specifically used for:

[0126] Using the formula:

[0127] ;

[0128] ;

[0129] A second quality control is performed; wherein, , They are the peak values ​​of temperature and salinity data obtained based on the standard climate state temperature and salinity and standard deviation, , are the temperature and salinity values ​​on the standard layer of the standard climate state, , are the standard deviations of the standard climatic temperature and salinity.

[0130] Optionally, the spatiotemporal standardized grid interpolation unit in the Arctic Ocean climate state dataset construction module 440 can be specifically used for:

[0131] Divide the Arctic Ocean area into grids of preset sizes;

[0132] In each grid, averaging a plurality of the third valid data;

[0133] The third effective data of the SODAO spatial grid average is classified monthly to form a spatiotemporal gridded data set.

[0134] The Arctic sea area climate state data set construction unit in the Arctic sea area climate state data set construction module 440 may specifically include:

[0135] A background field generation subunit is used to generate a background field of a preset resolution by gridding all temperature and salinity data in each vertical standard layer in space using an objective analysis method based on the spatiotemporal gridded data set;

[0136] The background field correction subunit is used to correct the background field using a step-by-step correction method to obtain a climate state data set for the Arctic Ocean.

[0137] Optionally, the background field generation subunit may be specifically used for:

[0138] Using the formula:

[0139] ;

[0140] ;

[0141] All temperature and salinity data are gridded to generate a background field with a preset resolution; is the analytical field at point i after n iterations, is the search radius after the nth iteration The b-th observation in is the estimated value at position b after the nth iteration, is the influence radius of the observation point after the nth iteration, is the number of observations searched in the nth iteration, is the weight coefficient.

[0142] The background field correction subunit can be specifically used for:

[0143] Using the formula:

[0144] ;

[0145] The background field is corrected to obtain the Arctic Ocean climate data set; wherein, is the filter constant, is the convergence factor, where Take 0.2, Represents an exponential function.

[0146] Optionally, the first quality control module 420 may be specifically used for:

[0147] Performing standardized preprocessing on the first valid data, removing abnormal files, and obtaining preprocessed observed temperature-salinity profile data;

[0148] The preprocessed observed temperature-salinity profile data is subjected to a first quality control; the first quality control includes a position-time test, a depth reversal test, a repeatability test, a temperature-salinity extreme value test, a gradient test, and a density gradient test to obtain second valid data; the second valid data is a standard in-situ observation data set for the Arctic Ocean area that conforms to the actual ocean temperature-salinity structure.

[0149] Based on the same idea, the embodiment of this specification also provides a device for constructing a climate state dataset for the Arctic Ocean. Figure 5 As shown, the device includes:

[0150] A memory, a processor, and a communication interface coupled to the processor; the memory stores a computer program that can be executed by the processor; when the processor runs the computer program, it executes the aforementioned method for constructing a climate state data set for the Arctic sea area.

[0151] like Figure 5 As shown, the processor may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention. The communication interface may be one or more. The communication interface may use any transceiver or other device for communicating with other devices or communication networks.

[0152] like Figure 5 As shown, the terminal device may further include a communication line. The communication line may include a path to transmit information between the components.

[0153] Optional, such as Figure 5 As shown, the terminal device may further include a memory. The memory stores a computer program that can be executed by the processor; when the processor executes the computer program, the method provided by the embodiment of the present invention is implemented.

[0154] like Figure 5As shown, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor through a communication line. The memory may also be integrated with the processor.

[0155] Optionally, the computer-executable instructions in the embodiment of the present invention may also be referred to as application program codes, which is not specifically limited in the embodiment of the present invention.

[0156] In a specific implementation, as an example, Figure 5 As shown, the processor may include one or more CPUs, such as Figure 5 CPU0 and CPU1 in.

[0157] In a specific implementation, as an example, Figure 5 As shown, the terminal device may include multiple processors, such as Figure 5 Each of these processors can be a single-core processor or a multi-core processor.

[0158] Based on the same idea, the embodiments of this specification also provide a computer storage medium corresponding to the above embodiments, and the computer storage medium stores instructions, and when the instructions are executed, the method in the above embodiments is implemented.

[0159] The above mainly introduces the solution provided by the embodiment of the present invention from the perspective of the interaction between the various modules. It is understandable that, in order to realize the above functions, each module includes a hardware structure and / or software unit corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0160] The embodiment of the present invention can divide the functional modules according to the above method example. For example, each functional module can be divided according to each function, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware or in the form of software functional modules. It should be noted that the division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0161] The processor in this specification may also have the function of a memory. The memory is used to store computer-executable instructions for executing the solution of the present invention, and the execution is controlled by the processor. The processor is used to execute the computer-executable instructions stored in the memory, thereby implementing the method provided by the embodiment of the present invention.

[0162] The memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to this. The memory may exist independently and be connected to the processor through a communication line. The memory may also be integrated with the processor.

[0163] Optionally, the computer-executable instructions in the embodiment of the present invention may also be referred to as application program codes, which is not specifically limited in the embodiment of the present invention.

[0164] The method disclosed in the above embodiment of the present invention can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a digital signal processor (digital signal processing, DSP), an ASIC, a field-programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor are combined to be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0165] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0166] Although the present invention has been described in conjunction with specific features and embodiments thereof, it is apparent that various modifications and combinations may be made thereto without departing from the spirit and scope of the present invention. Accordingly, this specification and the accompanying drawings are merely exemplary illustrations of the present invention as defined by the appended claims and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present invention. Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such modifications and variations if they fall within the scope of the claims of the present invention and their equivalents.

Claims

1. A method for constructing an Arctic sea climate data set, characterized in that: Methods include: Acquire original observation data from multiple data sources in the Arctic Ocean, and remove duplicates from the original observation data according to preset rules to obtain first valid data; Performing standardization preprocessing on the first valid data, and performing first quality control based on high-latitude temperature-salinity characteristics, to obtain second valid data; interpolating the second valid data to the standard layer, and performing a second quality control on the interpolated data based on the high-latitude temperature-salinity characteristics to obtain third valid data; Based on the third effective data, space-time standardized grid interpolation is performed, and a cross-objective analysis method is used to construct a climate state dataset for the Arctic Ocean; Interpolating the second valid data to the standard layer specifically includes: Using the formula: ; Modified Akima cubic Hermite interpolation; where, represents the slope of the sampling point, For interval The slope on , are weight coefficients, , ; interpolating the quality-controlled observation profile to the standard layer along the depth based on the second valid data; The interpolated data is subjected to a second quality control based on the high-latitude temperature-salinity characteristics, including: Using the formula: ; ; A second quality control is performed; wherein, It is the peak value of temperature-salinity data obtained based on the standard climate state temperature-salinity. is the peak value of temperature and salinity data obtained based on the standard deviation, is the temperature-salinity value of the standard climate state, is the temperature-salinity value on the standard layer, is the standard deviation of the standard climate state temperature and salinity, is the standard deviation of the standard layer.

2. The method for constructing an Arctic sea climate data set according to claim 1, characterized in that: Based on the third valid data, performing spatiotemporal normalized grid interpolation specifically includes: Divide the Arctic Ocean area into grids of preset sizes; In each grid, averaging a plurality of the third valid data; The third effective data of the SODAO spatial grid average is classified monthly to form a spatiotemporal gridded data set.

3. The method for constructing an Arctic sea climate data set according to claim 2, characterized in that: The cross-objective analysis method is used to construct the Arctic Ocean climate data set, which includes: Based on the spatiotemporal gridded dataset, a background field of a preset resolution is generated by gridding all temperature and salinity data at each vertical standard layer in space using an objective analysis method; The background field is corrected using a stepwise correction method to obtain a climate data set for the Arctic Ocean.

4. The method for constructing an Arctic sea climate data set according to claim 3, characterized in that: Based on the spatiotemporal gridded dataset, all temperature and salinity data are gridded in each vertical standard layer in space using an objective analysis method to generate a background field with a preset resolution, including: Using the formula: ; ; All temperature and salinity data are gridded to generate a background field with a preset resolution; is the analytical field at point i after n iterations, is the search radius after the nth iteration The b-th observation in is the estimated value at position b after the nth iteration, is the influence radius of the observation point after the nth iteration, is the number of observations searched in the nth iteration, is the weight coefficient, Represents the distance between the observation point and the analysis point.

5. The method for constructing an Arctic sea climate data set according to claim 4, characterized in that: The background field is corrected using the stepwise correction method to obtain the Arctic Ocean climate data set, which specifically includes: Using the formula: ; The background field is corrected to obtain the Arctic Ocean climate data set; wherein, is the filter constant, is the convergence factor, where Take 0.2, Represents an exponential function.

6. The method for constructing an Arctic sea climate data set according to claim 1, characterized in that: The first valid data is subjected to standardization preprocessing, and a first quality control is performed based on the high-latitude temperature-salinity characteristics to obtain second valid data, specifically including: Performing standardized preprocessing on the first valid data, removing abnormal files, and obtaining preprocessed observed temperature-salinity profile data; The preprocessed observed temperature-salinity profile data is subjected to a first quality control; the first quality control includes a position-time test, a depth reversal test, a repeatability test, a temperature-salinity extreme value test, a gradient test, and a density gradient test to obtain second valid data; the second valid data is a standard in-situ observation data set for the Arctic Ocean area that conforms to the actual ocean temperature-salinity structure.

7. A device for constructing a climate data set for the Arctic Ocean, characterized in that: The device includes: A data deduplication module is used to obtain original observation data from multiple data sources in the Arctic Ocean area, and to dedupe the original observation data according to preset rules to obtain first valid data; A first quality control module is used to perform standardization preprocessing on the first valid data and perform first quality control based on high-latitude temperature-salinity characteristics to obtain second valid data; A second quality control module is used to interpolate the second valid data to a standard layer, and perform a second quality control on the interpolated data based on high-latitude temperature-salinity characteristics to obtain third valid data; An Arctic sea area climate state data set construction module is used to perform spatiotemporal standardized grid interpolation based on the third effective data, and to construct an Arctic sea area climate state data set using a cross objective analysis method; The data interpolation unit in the second quality control module is specifically used to: Using the formula: ; Modified Akima cubic Hermite interpolation; where, represents the slope of the sampling point, For interval The slope on , Both are weight coefficients , ; interpolating the quality-controlled observation profile to the standard layer along the depth based on the second valid data; Among them, the secondary quality control unit in the second quality control module is specifically used for: Using the formula: ; ; A second quality control is performed; wherein, , They are the peak values ​​of temperature and salinity data obtained based on the standard climate state temperature and salinity and standard deviation, , are the temperature and salinity values ​​on the standard layer of the standard climate state, , are the standard deviations of the standard climatic temperature and salinity.

8. A device for constructing a climate data set for the Arctic Ocean, characterized in that the device include: A memory, a processor, and a communication interface coupled to the processor; The memory stores a computer program executable by the processor; When the processor runs the computer program, it executes the method for constructing an Arctic sea area climate state dataset as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that The computer storage medium stores instructions, and when the instructions are executed, the method for constructing an Arctic sea area climate state data set as described in any one of claims 1 to 6 is implemented.

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