CN115096274B - Ocean observation system and mapping scheme evaluation method and system - Google Patents
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Abstract
The invention relates to a marine mapping scheme and a subsurface observation system evaluation method and system, and relates to the field of marine observation, wherein the method comprises the following steps: resampling three kinds of ocean data at preset sampling positions according to the space-time distribution of the preset sampling positions in the EN4.2.2 profile data set to obtain a composite profile of ocean analysis data, a composite profile of ocean circulation pattern data and a composite profile of weather coupling pattern ocean component data; randomly selecting a synthetic section, randomly selecting a scheme from the mapping schemes in step 3, sequentially adding disturbance to the selected synthetic section, and lattice-ordering the selected synthetic section after disturbance is added by adopting the selected mapping scheme to obtain N reconstruction fields; comparing the N reconstructed fields with the corresponding selected synthesized sections to determine evaluation indexes; and adjusting the distribution of the marine data observation instrument according to the evaluation index. The effectiveness of the distribution setting of the ocean data observation instrument is improved.
Description
Technical Field
The invention relates to the technical field of ocean observation, in particular to an ocean observation system and a mapping scheme evaluation method and system.
Background
For hundreds of years, due to the consumption of fossil fuels such as coal, petroleum and the like, CO 2 Isothermal chamber gases are discharged into the atmosphere in large quantities, causing unbalance of energy balance of the earth climate system, which absorbs energy cleanly, and causing global warming. Of these, about 93% of the extra heat is stored by the ocean (Hakuba et al 2021). Ocean, with its huge specific heat capacity, wide coverage area and huge volume, becomes the biggest heat storage reservoir of the earth climate system. Ocean heat content is one of the most important and stable (trend change is large compared with natural change rate) indices for global climate change (von Schuckmann et al, 2020). The accurate estimation of ocean heat content is a fundamental problem of global climate change research, and is also an important basis for evaluating climate patterns, researching energy balance of the earth system and improving the season-to-year prediction skills of the climate patterns, so that scientific reference can be provided for government to formulate a policy for coping with climate changes (Cheng et al, 2019 b).
Currently, internationally different research institutions have released sets of objective analysis products that characterize global and regional ocean heat content with major divergence (Wang et al, 2018). This difference comes from 5 related data processing flows in the heat content estimation (Cheng et al, 2017): 1) Correcting systematic deviations of the scope, such as depth deviations of disposable temperature probes (XBT) (Cheng et al,2016 b); 2) Profile quality control (Tan et al 2022); 3) Vertical interpolation of the profile to standard layers (Li et al 2020);
4) -selection of climatic states (Bagnell and DeVries, 2021); 5) The unobserved grid points are interpolated using a mathematical scheme, i.e. applying a certain "mapping" scheme, the heat content of the unobserved areas is "guessed" according to a certain principle. Systematic evaluation showed that: the difference in different mapping schemes is the most significant cause of the large difference in global ocean subsurface temperature and thermal content reconstruction (Boyer et al, 2016).
In recent years, different research institutions have been working on optimizing mapping schemes in an effort to reduce the uncertainty of the mapping scheme. Although the estimated global ocean heat content long-term trends have been quite consistent (Cheng et al, 2019 b), the difference in ocean heat content estimated by different mapping schemes on a regional scale is still large, especially in the edge sea, deep sea (below 2000 meters), and regions where mesoscale phenomena are active (meysignac et al, 2019).
The calculation of the dispersion of heat content in time and space by means of transverse comparison between sets of data is a major way of studying uncertainty in mapping schemes in the past (Boyer et al, 2016; liang et al, 2021;Savita et al, 2022). These studies do not quantify the uncertainty or bias size of a particular mapping scheme nor what mapping scheme is better. Because of the inability to obtain true values (trunk) of historical ocean thermal conditions, nor to re-observe historical ocean, the lack of direct, independent "third party data" as a benchmark, presents significant difficulties in assessing ocean thermal content mapping scheme uncertainty (Palmer et al, 2019).
It is desirable to find relatively independent, global coverage, continuously sampled, and physically coordinated "third party data" as the "true value" for evaluating mapping scheme uncertainty. Some research groups select "two-dimensional" satellite remote sensing Sea Level Anomaly (SLA) data as "true values", which have the advantage of high global coverage and independence, but in middle and high latitude sea areas where the salt specific volume effect is not negligible, the correspondence between sea level anomalies and ocean heat content is not good, and the "three-dimensional" sampling characteristics of the true observation profile cannot be restored (Lyman and Johnson,2008;Durack et al, 2014). Still another group evaluated the uncertainty of the EN4-mapping scheme using the output data of the coupled mode HadGEM3-GC2 as a "true value" (Allison et al, 2019). However, their studies still have the following disadvantages: firstly, only one true value is used, and whether the obtained conclusion is stable or not depends on the selection of the true value or not, so that the conclusion is worth further discussion; secondly, besides EN4-mapping schemes (Good et al, 2013), there are other widely used internationally such as IAP-mapping schemes (Cheng and Zhu,2016 a), ISAS-mapping schemes (Gaillard et al, 2016) and nci-mapping schemes (Levitus et al, 2009) etc. which estimate the magnitude of uncertainty and the space-time characteristics of ocean heat content, and are to be further discussed; finally, the physical interpretation of the uncertainty is insufficient, and the relation between the uncertainty (noise) and the heat content space-time variability (signal) is not deeply analyzed, so that the reliability of the corresponding objective analysis data is further clarified.
In principle, the constant encryption of marine observations to a satisfactory density will once and forever remove the uncertainty introduced by mapping schemes. However, the implementation of ocean observation is a costly project, and it is very difficult for humans to have the ability to fully observe the variability of all dimensions of the ocean for a long time in the future. Currently, the construction of Argo observatory nets (Liu Zenghong, etc., 2016) in the upper 2000 meters of the open ocean without ice, and further construction of deep sea (below 2000 meters) buoy observational systems, is a consensus of the scientific community (Wu Lixin and Chen Chaohui, 2013). One very natural problem is: current marine subsurface observation systems are able to monitor changes in marine heat content to how much, and future observation systems need to be designed to adequately track the heat redistribution process throughout the ocean, for example, studies have shown that the 2005-2017 annual upper 2000 meter marine heat content trend and the annual scale signal change characteristics, characterized by internationally different data products, remain highly controversial (Bindoffet al, 2019; cheng et al, 2019 a).
By means of independent third party data, the objective analysis method is an effective means for evaluating the monitoring capability of the ocean subsurface observation system to ocean heat content. Some previous studies have been conducted (achotarao et al 2007;Good,2017;Gregory et al, 2004), but the following limitations still exist: (1) The mapping scheme used is very simple (e.g. a weighted average scheme, where the values of no observation area are replaced by a weighted average of the values of the observation area) or only one mapping scheme is used, the conclusion of which may depend on the choice of the mapping scheme; (2) The third party data comes from the grid point data, has no random observation error, also lacks the subgrid variability (the signal lower than the space-time resolution is smoothed out), and is difficult to 'simulate' a real observation system. In addition, historically the main observation of marine subsurface layers has undergone evolution from mechanical temperature probes (MBT), disposable temperature probes (XBT) to Argo buoy, and today's marine subsurface observation systems have evolved into a complex network that incorporates multiple instruments. The quantitative evaluation of the contribution of each instrument to the monitoring of global and regional ocean temperature and heat content changes is a fundamental problem of ocean science research and needs to be studied deeply.
Disclosure of Invention
The invention aims to provide a marine observation system, a mapping scheme evaluation method and a marine observation system, and the marine mapping scheme evaluation method and the marine mapping scheme evaluation system realize quantitative evaluation of the monitoring capability of the marine mapping scheme and the subsurface observation system, provide adjustment references for the throwing of future marine data observation instruments and improve the rationality and effectiveness of the arrangement of the marine subsurface observation system.
In order to achieve the above object, the present invention provides the following solutions:
a marine observation system evaluation method, comprising:
acquiring climate coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position;
resampling the ocean analysis data, the ocean circulation pattern data and the weather coupling pattern ocean component data according to the space-time distribution of the preset sampling positions in the EN4.2.2 profile data set to obtain a synthesized profile of the ocean analysis data, the synthesized profile of the ocean circulation pattern data and the synthesized profile of the weather coupling pattern ocean component data;
repeatedly constructing the latticed reconstruction fields for N times to obtain N reconstruction fields;
the reconstruction field for constructing lattice comprises the following specific steps: randomly selecting a synthetic profile from the synthetic profile of the ocean analysis data, the synthetic profile of the ocean circulation pattern data and the synthetic profile of the weather coupling pattern ocean component data as a selected synthetic profile, randomly selecting a scheme from M mapping schemes as a selected mapping scheme, sequentially adding random observation error disturbance, spatial disturbance and time disturbance to the selected synthetic profile to obtain a disturbed selected synthetic profile, and latticing the disturbed selected synthetic profile by adopting the selected mapping scheme to obtain a reconstruction field;
Determining an evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time sequence of each reconstruction field and the time sequence of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected synthesis profile;
and increasing or decreasing the release of the marine data observation instrument at the preset sampling position according to the evaluation index.
Alternatively, the number of M is 3, and the 3 mapping schemes are IAP-mapping scheme, ISAS-mapping scheme, and NCEI-mapping scheme, respectively.
Optionally, the climate coupling mode ocean component data adopts ocean component data of CNRM-CM6-1-HR in a high-resolution variation mode comparison plan in a phase 6 of the coupling mode comparison plan, the ocean analysis data adopts C-GLORSv7 vortex compatible resolution ocean analysis data, and the ocean circulation mode data adopts LICOM vortex resolution ocean mode data.
Optionally, the determining the evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time series of each reconstructed field and the time series of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected composite profile specifically includes:
According to the formulaCalculating skill scores of the marine subsurface observation system at the preset sampling positions to obtain N skill scores;
taking the average value of N skill scores as the evaluation index;
wherein ,SSi Skill score representing the ith reconstruction field, R i Correlation coefficient representing time series of i-th reconstructed field and time series of weather coupling mode ocean component data, ocean analysis data or ocean circulation mode data corresponding to i-th reconstructed field, SDR i The ratio of the standard deviation of the time series of the i-th reconstruction field to the standard deviation of the time series of the weather-coupling mode ocean component data, ocean analysis data, or ocean circulation mode data corresponding to the i-th reconstruction field is represented.
Optionally, the selected composite profile after the added perturbation is expressed as:
wherein ,representing the selected composite profile after the added perturbation, PS k Representing said selected composite profile, +.>Representing random observation error disturbance,/->Representing spatial disturbance, ++>Representing time disturbance, ε 1 、ε 2 and ε3 All represent gaussian random numbers.
Optionally, the marine data scope comprises a disposable temperature and depth gauge, an anchor buoy, an Argo buoy, and a submarine glider.
The invention also discloses an ocean observation system evaluation system, which comprises:
the ocean data acquisition module is used for acquiring weather coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position; the preset sampling position is an ocean subsurface observation section;
the composite profile module is used for resampling the ocean analysis data, the ocean circulation pattern data and the weather coupling pattern ocean component data according to the space-time distribution of the preset sampling positions in the EN4.2.2 profile data set to obtain a composite profile of the ocean analysis data, a composite profile of the ocean circulation pattern data and a composite profile of the weather coupling pattern ocean component data;
a reconstruction field module is constructed and used for repeatedly constructing the lattice reconstruction fields for N times to obtain N reconstruction fields;
the reconstruction field for constructing lattice comprises the following specific steps: randomly selecting a synthetic profile from the synthetic profile of the ocean analysis data, the synthetic profile of the ocean circulation pattern data and the synthetic profile of the weather coupling pattern ocean component data as a selected synthetic profile, randomly selecting a scheme from M mapping schemes as a selected mapping scheme, sequentially adding random observation error disturbance, spatial disturbance and time disturbance to the selected synthetic profile to obtain a disturbed selected synthetic profile, and latticing the disturbed selected synthetic profile by adopting the selected mapping scheme to obtain a reconstruction field;
The evaluation index determining module is used for determining an evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time sequence of each reconstruction field and the time sequence of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected synthesis profile;
and the marine data observation instrument distribution adjustment module is used for increasing or decreasing the throwing of the marine data observation instrument at the preset sampling position according to the evaluation index.
Alternatively, the number of M is 3, and the 3 mapping schemes are IAP-mapping scheme, ISAS-mapping scheme, and NCEI-mapping scheme, respectively.
Optionally, the climate coupling mode ocean component data adopts ocean component data of CNRM-CM6-1-HR in a high-resolution variation mode comparison plan in a phase 6 of the coupling mode comparison plan, the ocean analysis data adopts C-GLORSv7 vortex compatible resolution ocean analysis data, and the ocean circulation mode data adopts LICOM vortex resolution ocean mode data.
The invention also discloses a mapping scheme evaluation method, which is characterized by comprising the following steps:
acquiring climate coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position;
Resampling the ocean analysis data, the ocean circulation pattern data and the weather coupling pattern ocean component data according to the space-time distribution of the preset sampling positions in the EN4.2.2 profile data set to obtain a synthesized profile of the ocean analysis data, the synthesized profile of the ocean circulation pattern data and the synthesized profile of the weather coupling pattern ocean component data;
repeatedly constructing the latticed reconstruction fields for N times to obtain N reconstruction fields;
the reconstruction field for constructing lattice comprises the following specific steps: randomly selecting one synthetic profile from the synthetic profile of the ocean analysis data, the synthetic profile of the ocean circulation pattern data and the synthetic profile of the weather coupling pattern ocean component data as a selected synthetic profile, and latticing the selected synthetic profile by adopting a mapping scheme to be evaluated to obtain a reconstruction field;
determining an evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time sequence of each reconstruction field and the time sequence of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected synthesis profile;
And evaluating the uncertainty of the mapping scheme to be evaluated according to the evaluation index.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a method and a system for evaluating an ocean subsurface observation system, which are used for acquiring three types of acquired data of a preset sampling position, respectively resampling and synthesizing a profile, determining an evaluation index of the ocean subsurface observation system by constructing a lattice reconstruction field for a plurality of times, realizing quantitative evaluation of the monitoring capability of the ocean subsurface observation system, providing an adjustment basis for the distribution of an ocean data observation instrument, and improving the effectiveness and rationality of the distribution setting of the ocean data observation instrument.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an evaluation method of a marine observation system according to the present invention;
FIG. 2 is a schematic diagram of the invention for extracting a composite section from lattice points;
FIG. 3 is a schematic flow chart of the present invention for constructing a reconstructed field;
FIG. 4 is a schematic diagram of a marine observation system evaluation system according to the present invention;
FIG. 5 is a schematic flow chart of a mapping scheme evaluation method of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a marine observation system, a mapping scheme evaluation method and a marine observation system, and the marine mapping scheme evaluation method and the marine mapping scheme evaluation system realize quantitative evaluation of the monitoring capability of the marine mapping scheme and the subsurface observation system, provide adjustment references for the throwing of future marine data observation instruments and improve the rationality and effectiveness of the arrangement of the marine subsurface observation system.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
FIG. 1 is a schematic flow chart of an evaluation method of an ocean subsurface observation system according to the present invention, as shown in FIG. 1, the evaluation method of an ocean subsurface observation system comprises:
step 101: and acquiring climate coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position.
The step 101 specifically includes: climate-coupling-mode ocean component data, ocean-analysis data, and ocean-circulation-mode data capable of covering the global ocean are acquired.
The climate coupling mode marine component data, marine analysis data and marine circulation mode data are in particular temperature data and salinity data.
Ocean heat content is calculated from temperature and salinity.
The climate coupling mode ocean component data, ocean analysis data and ocean circulation mode data are used as 3 'true' fields, so that (1) power coordination is satisfied, basic ocean phenomena in physical ocean are included, and the ocean motion law is met; (2) The space-time coverage is complete, i.e. all grid points of the ocean have continuous values; (3) The space-time resolution is as fine as possible, so that the space-time variability of the 'synthetic profile' obtained by resampling is ensured to be closer to the real ocean state. To overcome the dependence of the evaluation method on the choice of "true values", 3 independent data classes were selected as "true values".
The climate coupling mode ocean component data, in particular climate system mode eddy current compatible resolution ocean component data. The marine component data of the weather system mode vortex compatible resolution ratio adopts the marine component data of CNRM-CM6-1-HR in the high-resolution ratio mode comparison plan in the 6 th stage of the coupling mode comparison plan, the marine mode is NEMO3.6, the spatial resolution ratio is 1/4 degree, the time resolution ratio is month by month, the vertical direction is extended to about 5500 meters deep from the surface layer, and the time span is 1970-2021 years. Wherein, tier2 history forced experiments were used in 1970-2014, and HighRes-Future experimental results were used after 2015.
Marine analysis data using C-GLORSv7 vortex compatible resolution marine analysis data. The C-GLORSv7 vortex compatible resolution ocean analysis data was developed by Italian theoretical physical research center (CMCC) and the ocean subsurface profile data, ocean satellite remote sensing data (sea surface temperature, sea surface altitude) were assimilated into NEMO ocean patterns using a three-dimensional variational (3 DVar) assimilation scheme. The spatial resolution of the dataset was 1/4 degree, the temporal resolution was month by month, and the time span was from 1993 to 2021, vertically from sea level to about 5500 meters deep.
The ocean loop pattern data employs LICOM eddy resolution ocean pattern data. The LICOM vortex resolution ocean model data is based on an ocean circulation model LICOM which is independently developed by the national institute of science and technology of atmospheric physics, and participates in the phase 2 (OMIP-2) of the International ocean model comparison plan, the spatial resolution is 1/10 degree, the time resolution is in two versions of month by month and day by day, the vertical direction is from sea surface to about 5500 meters, and the time span is from 1958 to 2018. Unlike the climate system mode marine component, this data is obtained by atmospheric analysis data driven marine mode integration.
Step 102: and resampling the ocean analysis data, the ocean circulation pattern data and the climate coupling pattern ocean component data according to the space-time distribution of the historical sampling positions in the EN4.2.2 profile data set to obtain a synthesized profile of the ocean analysis data, a synthesized profile of the ocean circulation pattern data and a synthesized profile of the climate coupling pattern ocean component data.
The step 102 specifically includes: the 3 "true" fields were resampled separately from the spatiotemporal distribution of the observed profile in the EN4.2.2 profile dataset published by the hadley center, uk. In the sampling process, grid point data in a 'true value' field are respectively interpolated to longitude, latitude and depth of a space-time distribution section of an observation section in a EN4.2.2 section data set, the horizontal of the space-time distribution section adopts bilinear interpolation, and vertical reinitiator-Ross interpolation is used. The composite profile dataset is evaluated for rationality and then the data is stored according to the format suggested by the international marine data quality control program (IQuOD).
As shown in FIG. 2, when the composite profile (scatter) is extracted using pattern data of 1/4 degree resolution as "true" (lattice) for the observation profile PO falling in the (i-1, j-1) grid k (P represents the profile, O represents the observation, subscript k represents the profile number), the profiles of the surrounding 4 adjacent grid points (reference numbers 1-4) are interpolated to the observation profile PO using bilinear interpolation k The longitude and latitude of the position are interpolated on the vertical layer of the observation section by using a reinitiator-Ross method to obtain a corresponding composite section (marked as PS) k P represents the profile, S represents the syntic, subscript k represents the profile number), and the resampling process is completed. Resampling points are points denoted in-situ profile in fig. 2.
Step 103: and repeatedly constructing the latticed reconstructed fields N times to obtain N reconstructed fields.
N is 50.
The 3 mapping schemes are IAP-mapping scheme, ISAS-mapping scheme and NCEI-mapping scheme, respectively.
To overcome the dependency of the technical scheme on the mapping scheme, the uncertainty size and the space-time characteristics of the international mainstream IAP-mapping, ISAS-mapping and NCEI-mapping schemes are selected.
The basic idea of IAP-mapping scheme is to combine historical observations with multimode outputs of the fifth stage of the coupled mode comparison plan (CMIP 5) using ensemble optimal interpolation (Ensemble Optimal Interpolation, enOI), build a relationship between observed and unobserved areas using the results of the multiple modes (background field error covariance matrix, B matrix), project the observed information to the unobserved areas, and use the multimode ensemble average of the power coordinated CMIP5 as the initial guess (similar to the "off-line assimilation" technique).
The core of the ISAS-mapping scheme is an optimal interpolation algorithm, observation information is projected to a background field (climatic state) grid by constructing a static B matrix, interpolation of a non-observation grid is achieved, and the B matrix is determined by the distance between space points and the variability (standard deviation) of respective time sequences.
The core algorithm of the NCEI-mapping scheme is a Barnes gradual correction scheme, the distance is used as a weight to carry out weighted summation on observed signals in an influence radius, the influence radius is updated repeatedly from large to small, and signals from large scale to small scale are analyzed sequentially.
With reference to the instrument random observation errors provided by the international ocean data quality control program (IQuoD) (Table 1), according to a certain synthetic profile PS k Corresponding true section PO k Determining random observed error amplitude(measurementerror). Using the daily data of the glirsy 12 vortex resolution (level 1/12 degrees), the spatial standard deviation of all 1/12 degree grid values contained in the 1/4 degree grid was calculated, the daily standard deviation in month was time averaged, as the amplitude of the spatial uncertainty, one value per month, labeled +.>(spatluancerty); using the daily data of the glirsy 12 vortex resolution (level 1/12 degrees), the time-dimensional standard deviation of the daily average increment (2 nd day per month minus 1 st day, 3 rd day minus 2 nd day, and so on) per grid point was calculated, and then Interpolation onto a 1/4 degree resolution grid, 1 value per month, labeled +.>(temporaluncertainty)。
Assuming that random observation errors, temporal uncertainty, and spatial uncertainty are independent of one another, then the synthetic profile data is used to account for random observation errorsTime uncertainty +.>And spatial uncertainty amplitudeGaussian perturbation (mean 0, standard deviation 1) was applied to the composite profile (PS k ) On the above, a "random disturbance composite profile" is obtained>The true profile is "simulated" to the maximum extent.
TABLE 1 different instrument observations accuracy published by International ocean data quality control plan (IQuOD)
The monitoring capability of the historical observation system on ocean heat content can be quantified by using 3 independent composite profiles, 3 different mapping schemes (IAP-mapping scheme, ISAS-mapping scheme and NCEI-mapping scheme), respectively repeating random observation error disturbance (5 times), subgrid variability disturbance (5 times) and intra-month variability disturbance (5 times), and comparing a 'reconstruction' field with a 'true value' field. If all possible permutations are combined, 1125 experiments (3X 5X 3) will be performed, and the resulting data is bulky and computationally expensive. Thus, the present invention N is 50.
The construction method of the latticed reconstruction field specifically comprises the following steps: randomly selecting a synthetic profile from the synthetic profile of ocean analysis data, the synthetic profile of ocean circulation mode data and the synthetic profile of weather coupling mode ocean component data as a selected synthetic profile, randomly selecting a scheme from an IAP-mapping scheme, an ISAS-mapping scheme and an NCEI-mapping scheme as a selected mapping scheme, sequentially adding random observation error disturbance, spatial disturbance and time disturbance to the selected synthetic profile to obtain a disturbed selected synthetic profile, and latticing the disturbed selected synthetic profile by adopting the selected mapping scheme to obtain a reconstruction field. The 50 times are repeated, obtaining 50 reconstructed fields. 50 sets of reconstruction data setsAs shown in fig. 3. The concept of 'random disturbance+set' is used, dependence on 'true value' selection, random observation errors, space-time subgrid variability loss and mapping schemes is overcome, and a robustness assessment conclusion on the monitoring capability of the ocean subsurface observation system is obtained.
The selected composite profile after the addition of the perturbation is expressed as:
wherein ,representing the selected composite profile after the addition of the perturbation, PS k Representing the selected composite profile->Representing random observation error disturbance,/->Representing spatial disturbance, ++>Representing time disturbance, ε 1 、ε 2 and ε3 All represent gaussian random numbers.
Step 104: and determining an evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time sequence of each reconstruction field and the time sequence of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected synthesis profile.
Step 104 specifically includes:
according to the formulaCalculating Skill scores of a marine subsurface observation system at preset sampling positions to obtain 50 Skill scores (SS, skill Score);
when R is i Less than 0.5 or failing 95% saliency test, or SDR i Greater than 2 or SDR i SS below 0.5 i The value is 0.
Taking the average value of the 50 skill scores as an evaluation index;
wherein ,SSi Skill score representing the ith reconstruction field, R i Correlation coefficient representing time series of i-th reconstructed field and time series of weather coupling mode ocean component data, ocean analysis data or ocean circulation mode data corresponding to i-th reconstructed field, SDR i A ratio of a standard deviation of the time series representing the i-th reconstruction field to a standard deviation of the time series of the weather-coupling mode marine component data, the marine analysis data, or the marine circulation mode data corresponding to the i-th reconstruction field, i=1, 2, …,50.
Evaluation of the ability of an observation system to monitor ocean heat content using skill scores, for a certain period of time, when R i Less than 0.5 or failing 95% saliency test, or SDR i Greater than 2 or less than 0.5 ("reconstructed" amplitude exceeds "true" by a factor of 2 or less than half of true ") SS i The value is 0. Skill scores were calculated for each of the 50 experiments, and the arithmetic averages were calculated as evaluation indexes. It can be seen that the greater the skill score value, which is between 0 and 100, the better the performance of the monitoring is represented, and vice versa. Further, a spatial correlation coefficient and a temporal correlation coefficient are used as the auxiliary quantization index.
Step 105: and adding or reducing the input of the marine data observation instrument at the preset sampling position according to the evaluation index.
Step 105 specifically includes: and (3) increasing or reducing the throwing of the marine data observation instrument at the preset sampling position by comparing the evaluation indexes of each lattice point on the reconstructed lattice point field.
By removing, redistributing and encrypting (adding) 4 main observers such as XBT, argo, anchoring buoy and Glider, an idealized experiment is carried out, and a reference is provided for adjusting and optimizing the distribution of the marine data observers.
Marine data observers include disposable temperature and depth gauges, anchoring buoys, argo buoys, and underwater gliders.
Meanwhile, the present invention intends to divide a time series into: the monitoring capability of the historical marine observation system on signals of different time scales is respectively examined by signals of an annual scale (12 months), an annual scale (13-84 months) and an annual scale (linear trend is deducted firstly and then low-pass filtering is carried out for 84 months). The time variability of the "true" sequence is defined as "signal", and the root mean square error of the "true" and reconstructed sequences is defined as "noise". The ability of the observation system to monitor this time scale "signal" is quantified by introducing a signal-to-noise ratio (SNR, signal/noise).
To evaluate the contribution of each subsystem in a historical observation system (marine subsurface observation system), the present invention will remove the data of XBT, argo, anchoring buoy (mainly TAO/TRITON, RAMA and PIRATA arrays in tropical sea areas) and Glider, respectively, to simulate "subtracting" part of the observations in the subsurface observation system. The influence of a specific observation instrument on global and regional scale ocean heat change monitoring is clear by comparing the heat content estimation obtained by all the observation data and the heat content estimation obtained by removing a certain part of observation, so that the purpose is clear: (1) The global impact of different observation systems (e.g., how much impact on the reconstruction of ocean heat content signals at different time scales); (2) determining whether the quantization is possible or not, which areas are greatly affected.
The invention achieves the technical effects that:
advantage 1: the uncertainty introduced in the mapping process can be objectively and quantitatively evaluated, and the method is an improvement clear direction of the mainstream objective analysis product.
The method is characterized in that a composite profile data set (scattered points) is constructed based on the weather system mode vortex compatible resolution ocean component data and the ocean analysis data for the first time, uncertainty of the mapping schemes of 3 main streams is systematically evaluated, the advantages and disadvantages of different mapping schemes are quantified, and a foundation is laid for continuously optimizing the mapping schemes and improving ocean heat content estimation accuracy.
Advantage 2: the monitoring capability of the historical marine subsurface observation system on the marine heat content can be quantitatively evaluated, and scientific references are provided for maintenance, design and construction of the marine subsurface observation system in the future.
Based on an objective analysis method, the monitoring capability of a historical marine observation system on the marine heat content of different time scales (annual, chronologic and long-term trend) of the world and the region is evaluated, and the dependence of a conclusion on 'true value' selection, random observation errors, space-time subgrid variability deletion and mapping schemes is overcome through the thought of 'random disturbance+set'; the relative contribution of 4 main observation instruments such as XBT, argo, anchoring buoy and Glider is quantized by removing, rearranging and encrypting the main observation instruments, so that scientific reference is provided for the maintenance, design and construction of the marine subsurface observation system in the future. The method does not need to run a marine power mode, and the operation amount of the global marine observation system is estimated to be obviously lower than that of target observation technologies such as Observation System Simulation Experiments (OSSE) in the field of data assimilation, and the method is a beneficial supplement for estimating the marine subsurface observation system.
FIG. 4 is a schematic structural diagram of an evaluation system for a marine subsurface observation system according to the present invention, as shown in FIG. 4, comprising:
the ocean data acquisition module 201 is used for acquiring weather coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position; the preset sampling position is an ocean subsurface observation section;
the composite profile module 202 is configured to resample ocean analysis data, ocean circulation pattern data and weather coupling pattern ocean component data according to the spatial-temporal distribution of historical preset sampling positions in the EN4.2.2 profile data set, so as to obtain a composite profile of the ocean analysis data, a composite profile of the ocean circulation pattern data and a composite profile of the weather coupling pattern ocean component data;
the reconstruction field constructing module 203 is configured to repeatedly construct the lattice reconstruction fields N times to obtain N reconstruction fields;
the construction of the latticed reconstruction field specifically comprises the following steps: randomly selecting a synthetic profile from the synthetic profile of ocean analysis data, the synthetic profile of ocean circulation mode data and the synthetic profile of weather coupling mode ocean component data as a selected synthetic profile, randomly selecting a scheme from M mapping schemes as a selected mapping scheme, sequentially adding random observation error disturbance, spatial disturbance and time disturbance to the selected synthetic profile to obtain a disturbed selected synthetic profile, and latticing the disturbed selected synthetic profile by adopting the selected mapping scheme to obtain a reconstruction field.
The number of M is 3, and the 3 mapping schemes are IAP-mapping scheme, ISAS-mapping scheme and NCEI-mapping scheme, respectively.
An evaluation index determining module 204, configured to determine an evaluation index of the marine subsurface observation system at the preset sampling position according to a correlation coefficient and a standard deviation of the time series of each reconstructed field and the time series of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected composite profile;
and the marine data observation instrument distribution adjustment module 205 is configured to increase or decrease the delivery of the marine data observation instrument at the preset sampling position according to the evaluation index.
The ocean component data of the climate coupling mode adopts ocean component data of CNRM-CM6-1-HR in a high-resolution variation mode comparison plan in a phase 6 of the coupling mode comparison plan, the ocean analysis data adopts ocean analysis data of C-GLORSv7 vortex compatible resolution, and the ocean circulation mode data adopts ocean mode data of LICOM vortex resolution.
Determining an evaluation index of the marine subsurface observation system at the preset sampling position according to a correlation coefficient and a standard deviation of a time sequence of each reconstruction field and a time sequence of weather coupling mode marine component data, marine analysis data or marine circulation mode data corresponding to the selected synthesis profile, wherein the evaluation index specifically comprises the following steps:
According to the formulaCalculating skill scores of a marine subsurface observation system at preset sampling positions to obtain N skill scores;
taking the average value of the N skill scores as an evaluation index;
wherein ,SSi Skill score representing the ith reconstruction field, R i Correlation coefficient representing time series of i-th reconstructed field and time series of weather coupling mode ocean component data, ocean analysis data or ocean circulation mode data corresponding to i-th reconstructed field, SDR i The ratio of the standard deviation of the time series of the i-th reconstruction field to the standard deviation of the time series of the weather-coupling mode ocean component data, ocean analysis data, or ocean circulation mode data corresponding to the i-th reconstruction field is represented.
The selected composite profile after the addition of the perturbation is expressed as:
wherein ,representing the selected composite profile after the addition of the perturbation, PS k Representing the selected composite profile->Representing random observation error disturbance,/->Representing spatial disturbance, ++>Representing time disturbance, ε 1 、ε 2 and ε3 All represent gaussian random numbers.
Marine data observers include disposable temperature and depth gauges, anchoring buoys, argo buoys, and underwater gliders.
As shown in fig. 5, a mapping scheme evaluation method includes:
Step 301: and acquiring climate coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position.
Step 302: and resampling the ocean analysis data, the ocean circulation pattern data and the weather coupling pattern ocean component data according to the space-time distribution of the preset sampling positions in the EN4.2.2 profile data set to obtain a synthesized profile of the ocean analysis data, the synthesized profile of the ocean circulation pattern data and the synthesized profile of the weather coupling pattern ocean component data.
Step 303: and repeatedly constructing the latticed reconstructed fields N times to obtain N reconstructed fields.
The reconstruction field for constructing lattice comprises the following specific steps: and randomly selecting one synthetic profile from the synthetic profile of the ocean analysis data, the synthetic profile of the ocean circulation pattern data and the synthetic profile of the weather coupling pattern ocean component data as a selected synthetic profile, and latticing the selected synthetic profile by adopting a mapping scheme to be evaluated to obtain a reconstruction field.
Step 304: and determining an evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time sequence of each reconstruction field and the time sequence of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected synthesis profile.
Step 305: and evaluating the uncertainty of the mapping scheme to be evaluated according to the evaluation index.
The invention can objectively and quantitatively evaluate the uncertainty of different mapping schemes. And providing a reference basis for developers of the mapping scheme to be evaluated.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (9)
1. A marine observation system evaluation method, comprising:
Acquiring climate coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position;
resampling the ocean analysis data, the ocean circulation pattern data and the weather coupling pattern ocean component data according to the space-time distribution of the preset sampling positions in the EN4.2.2 profile data set to obtain a synthesized profile of the ocean analysis data, the synthesized profile of the ocean circulation pattern data and the synthesized profile of the weather coupling pattern ocean component data;
repeatedly constructing the latticed reconstruction fields for N times to obtain N reconstruction fields;
the reconstruction field for constructing lattice comprises the following specific steps: randomly selecting a synthetic profile from the synthetic profile of the ocean analysis data, the synthetic profile of the ocean circulation pattern data and the synthetic profile of the weather coupling pattern ocean component data as a selected synthetic profile, randomly selecting a scheme from M mapping schemes as a selected mapping scheme, sequentially adding random observation error disturbance, spatial disturbance and time disturbance to the selected synthetic profile to obtain a disturbed selected synthetic profile, and latticing the disturbed selected synthetic profile by adopting the selected mapping scheme to obtain a reconstruction field;
Determining an evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time sequence of each reconstruction field and the time sequence of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected synthesis profile;
adding or reducing the throwing of the marine data observation instrument at the preset sampling position according to the evaluation index;
determining an evaluation index of the marine subsurface observation system at the preset sampling position according to a correlation coefficient and a standard deviation of a time sequence of each reconstruction field and a time sequence of weather coupling mode marine component data, marine analysis data or marine circulation mode data corresponding to the selected synthesis profile, wherein the evaluation index specifically comprises the following steps:
according to the formulaCalculating skill scores of the marine subsurface observation system at the preset sampling positions to obtain N skill scores;
taking the average value of N skill scores as the evaluation index;
wherein ,SSi Skill score representing the ith reconstruction field, R i Correlation coefficient representing time series of i-th reconstructed field and time series of weather coupling mode ocean component data, ocean analysis data or ocean circulation mode data corresponding to i-th reconstructed field, SDR i A ratio of a standard deviation of a time series representing an i-th reconstruction field to a standard deviation of a time series of weather-coupling mode ocean component data, ocean-analysis data, or ocean-circulation mode data corresponding to the i-th reconstruction field;
m is 3 and N is 50.
2. The marine observation system evaluation method according to claim 1, wherein the 3 mapping schemes are an IAP-mapping scheme, an ISAS-mapping scheme, and an ncii-mapping scheme, respectively.
3. The marine observation system evaluation method according to claim 1, wherein the climate coupling mode marine component data employs marine component data of CNRM-CM6-1-HR in a high resolution mode comparison plan in a coupling mode comparison plan 6 th stage, the marine analysis data employs marine analysis data of C-GLORSv7 vortex compatible resolution, and the marine circulation mode data employs marine pattern data of limom vortex resolution.
4. The marine observation system evaluation method according to claim 1, wherein the post-disturbance selected composite profile is expressed as:
wherein ,representing the selected composite profile after the added perturbation, PS k Representing the selected composite profile of the object, Representing random observation error disturbance,/->Representing spatial disturbance, ++>Representing time disturbance, ε 1 、ε 2 and ε3 All represent gaussian random numbers.
5. The marine observation system evaluation method of claim 1, wherein the marine data observation instrument comprises a disposable temperature and depth gauge, an anchor buoy, an Argo buoy, and a submarine glider.
6. A marine observation system evaluation system, comprising:
the ocean data acquisition module is used for acquiring weather coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position; the preset sampling position is an ocean subsurface observation section;
the composite profile module is used for resampling the ocean analysis data, the ocean circulation pattern data and the weather coupling pattern ocean component data according to the space-time distribution of the preset sampling positions in the EN4.2.2 profile data set to obtain a composite profile of the ocean analysis data, a composite profile of the ocean circulation pattern data and a composite profile of the weather coupling pattern ocean component data;
a reconstruction field module is constructed and used for repeatedly constructing the lattice reconstruction fields for N times to obtain N reconstruction fields;
The reconstruction field for constructing lattice comprises the following specific steps: randomly selecting a synthetic profile from the synthetic profile of the ocean analysis data, the synthetic profile of the ocean circulation pattern data and the synthetic profile of the weather coupling pattern ocean component data as a selected synthetic profile, randomly selecting a scheme from M mapping schemes as a selected mapping scheme, sequentially adding random observation error disturbance, spatial disturbance and time disturbance to the selected synthetic profile to obtain a disturbed selected synthetic profile, and latticing the disturbed selected synthetic profile by adopting the selected mapping scheme to obtain a reconstruction field;
the evaluation index determining module is used for determining an evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time sequence of each reconstruction field and the time sequence of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected synthesis profile;
the marine data observation instrument distribution adjustment module is used for increasing or decreasing the throwing of the marine data observation instrument at the preset sampling position according to the evaluation index;
Determining an evaluation index of the marine subsurface observation system at the preset sampling position according to a correlation coefficient and a standard deviation of a time sequence of each reconstruction field and a time sequence of weather coupling mode marine component data, marine analysis data or marine circulation mode data corresponding to the selected synthesis profile, wherein the evaluation index specifically comprises the following steps:
according to the formulaCalculating skill scores of the marine subsurface observation system at the preset sampling positions to obtain N skill scores;
taking the average value of N skill scores as the evaluation index;
wherein ,SSi Skill score representing the ith reconstruction field, R i Correlation coefficient representing time series of i-th reconstructed field and time series of weather coupling mode ocean component data, ocean analysis data or ocean circulation mode data corresponding to i-th reconstructed field, SDR i Weather-coupling mode ocean component number representing standard deviation of time series of i-th reconstructed field and corresponding to i-th reconstructed fieldThe ratio of standard deviations of time series of data, marine analysis data or marine circulation pattern data;
m is 3 and N is 50.
7. The marine observation system evaluation system according to claim 6, wherein the 3 mapping schemes are IAP-mapping scheme, ISAS-mapping scheme, and ncii-mapping scheme, respectively.
8. The marine observation system evaluation system according to claim 6, wherein the climate coupling mode marine component data employs marine component data of CNRM-CM6-1-HR in a high resolution mode comparison plan in stage 6 of a coupling mode comparison plan, the marine analysis data employs C-GLORSv7 vortex compatible resolution marine analysis data, and the marine circulation mode data employs limcom vortex resolution marine mode data.
9. A mapping scheme evaluation method, characterized by comprising:
acquiring climate coupling mode ocean component data, ocean analysis data and ocean circulation mode data of a preset sampling position;
resampling the ocean analysis data, the ocean circulation pattern data and the weather coupling pattern ocean component data according to the space-time distribution of the preset sampling positions in the EN4.2.2 profile data set to obtain a synthesized profile of the ocean analysis data, the synthesized profile of the ocean circulation pattern data and the synthesized profile of the weather coupling pattern ocean component data;
repeatedly constructing the latticed reconstruction fields for N times to obtain N reconstruction fields;
the reconstruction field for constructing lattice comprises the following specific steps: randomly selecting one synthetic profile from the synthetic profile of the ocean analysis data, the synthetic profile of the ocean circulation pattern data and the synthetic profile of the weather coupling pattern ocean component data as a selected synthetic profile, and latticing the selected synthetic profile by adopting a mapping scheme to be evaluated to obtain a reconstruction field;
Determining an evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and standard deviation of the time sequence of each reconstruction field and the time sequence of the weather coupling mode marine component data, the marine analysis data or the marine circulation mode data corresponding to the selected synthesis profile;
evaluating the uncertainty of the mapping scheme to be evaluated according to the evaluation index;
determining an evaluation index of the marine subsurface observation system at the preset sampling position according to a correlation coefficient and a standard deviation of a time sequence of each reconstruction field and a time sequence of weather coupling mode marine component data, marine analysis data or marine circulation mode data corresponding to the selected synthesis profile, wherein the evaluation index specifically comprises the following steps:
according to the formulaCalculating skill scores of the marine subsurface observation system at the preset sampling positions to obtain N skill scores;
taking the average value of N skill scores as the evaluation index;
wherein ,SSi Skill score representing the ith reconstruction field, R i Correlation coefficient representing time series of i-th reconstructed field and time series of weather coupling mode ocean component data, ocean analysis data or ocean circulation mode data corresponding to i-th reconstructed field, SDR i A ratio of a standard deviation of a time series representing an i-th reconstruction field to a standard deviation of a time series of weather-coupling mode ocean component data, ocean-analysis data, or ocean-circulation mode data corresponding to the i-th reconstruction field;
m is 3 and N is 50.
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