CN115096274A - Ocean observation system and mapping scheme evaluation method and system - Google Patents
Ocean observation system and mapping scheme evaluation method and system Download PDFInfo
- Publication number
- CN115096274A CN115096274A CN202210682706.5A CN202210682706A CN115096274A CN 115096274 A CN115096274 A CN 115096274A CN 202210682706 A CN202210682706 A CN 202210682706A CN 115096274 A CN115096274 A CN 115096274A
- Authority
- CN
- China
- Prior art keywords
- data
- ocean
- marine
- synthetic
- profile
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000013507 mapping Methods 0.000 title claims abstract description 105
- 238000011156 evaluation Methods 0.000 title claims abstract description 66
- 230000008878 coupling Effects 0.000 claims abstract description 73
- 238000010168 coupling process Methods 0.000 claims abstract description 73
- 238000005859 coupling reaction Methods 0.000 claims abstract description 73
- 238000012950 reanalysis Methods 0.000 claims abstract description 65
- 238000005070 sampling Methods 0.000 claims abstract description 49
- 238000012952 Resampling Methods 0.000 claims abstract description 13
- 230000009466 transformation Effects 0.000 claims abstract description 10
- 238000004458 analytical method Methods 0.000 claims description 15
- 238000004873 anchoring Methods 0.000 claims description 7
- 239000002131 composite material Substances 0.000 claims description 6
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 19
- 238000012544 monitoring process Methods 0.000 description 15
- 238000011160 research Methods 0.000 description 8
- 230000008859 change Effects 0.000 description 6
- 238000002474 experimental method Methods 0.000 description 5
- 230000002123 temporal effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000003908 quality control method Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 239000000047 product Substances 0.000 description 3
- 238000011158 quantitative evaluation Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000013139 quantization Methods 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 240000001549 Ipomoea eriocarpa Species 0.000 description 1
- 235000005146 Ipomoea eriocarpa Nutrition 0.000 description 1
- 241001280304 Pirata Species 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- MXCPYJZDGPQDRA-UHFFFAOYSA-N dialuminum;2-acetyloxybenzoic acid;oxygen(2-) Chemical compound [O-2].[O-2].[O-2].[Al+3].[Al+3].CC(=O)OC1=CC=CC=C1C(O)=O MXCPYJZDGPQDRA-UHFFFAOYSA-N 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 238000005338 heat storage Methods 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 150000003839 salts Chemical class 0.000 description 1
- 239000013535 sea water Substances 0.000 description 1
- 230000001568 sexual effect Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- GPRLSGONYQIRFK-MNYXATJNSA-N triton Chemical compound [3H+] GPRLSGONYQIRFK-MNYXATJNSA-N 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Remote Sensing (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Physics & Mathematics (AREA)
- Hydrology & Water Resources (AREA)
- Atmospheric Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Environmental Sciences (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a method and a system for evaluating an ocean mapping scheme and a subsurface observation system, which relate to the field of ocean observation, and the method comprises the following steps: according to the spatial-temporal distribution of preset sampling positions in the EN4.2.2 profile data set, resampling is respectively carried out on three ocean data of the preset sampling positions, and a synthetic profile of ocean reanalysis data, a synthetic profile of ocean circulation mode data and a synthetic profile of climate coupling mode ocean component data are obtained; randomly selecting a synthetic section, randomly selecting a scheme from mapping schemes in 3, sequentially adding disturbance to the selected synthetic section, and performing lattice transformation on the selected synthetic section after the disturbance is added by adopting the selected mapping scheme to obtain N reconstructed fields; comparing the N reconstructed fields with the corresponding selected synthetic section to determine an evaluation index; and adjusting the distribution of the marine data observation instrument according to the evaluation index. The effectiveness of the distribution setting of the marine data observation instrument is improved.
Description
Technical Field
The invention relates to the technical field of marine observation, in particular to a marine observation system and a mapping scheme evaluation method and system.
Background
For more than a hundred years, CO is consumed in large quantities due to fossil fuels such as coal, petroleum and the like 2 The isothermal chamber gas is discharged into the atmosphere in a large amount, so that the energy balance of the earth climate system is unbalanced, and the earth climate system is netAbsorb energy and cause global warming. Therein, about 93% of the additional heat is stored by the sea (Hakuba et al, 2021). The ocean is the largest heat storage reservoir of the earth climate system due to the huge specific heat capacity, wide coverage area and huge volume of the ocean. Ocean heat content is one of the most important and stable indicators of global climate change (trend changes are large compared to natural variability) (von Schuckmann et al, 2020). Accurate estimation of ocean heat content is a fundamental problem of global climate change research, is also an important support for evaluating climate modes, researching energy balance of the earth system and improving the climate mode season to chronologic prediction skills, and can provide scientific reference for governments to formulate climate change coping policies (Cheng et al, 2019 b).
Currently, different internationally instituted research has issued sets of objective analysis products that have a large divergence in the global and regional ocean heat content they depict (Wang et al, 2018). This difference is from 5 relevant data processing flows in the estimation of heat content (Cheng et al, 2017): 1) correcting systematic deviation of the observation instrument, such as depth deviation (Cheng et al,2016b) of a disposable temperature detector (XBT); 2) profile quality control (Tan et al, 2022); 3) vertical interpolation of the profile into a standard layer (Li et al, 2020);
4) selecting climatic conditions (Bagnell and DeVries, 2021); 5) interpolating the non-observed grid points using a mathematical scheme, i.e. applying a certain "mapping" scheme, and "guessing" the heat content of the non-observed region according to a certain principle. The systematic evaluation showed that: the differences in different mapping protocols are the most significant cause of the large differences in global ocean subsurface temperature and heat content reconstitution (Boyer et al, 2016).
In recent years, different research institutes have been working on optimizing mapping schemes in an effort to reduce the uncertainty of mapping schemes. Although the estimated global ocean heat content long term trends have been quite consistent (Cheng et al, 2019b), the differences in ocean heat content estimated by different mapping programs are still large on a regional scale, especially in marginal seas, deep seas (below 2000 meters), and areas where mesoscale phenomena are active (meysignac et al, 2019).
The calculation of the dispersion of the heat content in time and space by means of lateral comparisons between sets of data is the main way of studying the uncertainty of mapping schemes in the past (Boyer et al, 2016; Liang et al, 2021; Savita et al, 2022). These studies cannot quantify the uncertainty or deviation magnitude of a particular mapping scheme, nor what mapping scheme is better. Since true values (truth) of historical ocean thermal conditions are not available, nor can historical oceans be re-observed, lacking direct, independent "third party data" as a benchmark (benchmark), great difficulties are encountered in assessing mapping scheme uncertainty of ocean thermal content (Palmer et al, 2019).
It is desirable to look for relatively independent, globally covering, continuously sampled and physically coordinated "third party data" as a "true value" for assessing mapping scheme uncertainty. Some research groups select two-dimensional satellite remote sensing Sea Level Anomaly (SLA) data as a true value, which has the advantages of global coverage and high independence, but in high-latitude sea areas with non-negligible salt specific volume effect, the correspondence between sea level anomaly and ocean heat content is not good, and the three-dimensional sampling characteristic of a real observation section cannot be restored (Lyman and Johnson, 2008; Durack et al, 2014). A further group evaluated the uncertainty of the EN4-mapping scheme using the output data of the coupling mode HadGEM3-GC2 as a "true value" (Allison et al, 2019). However, their research still has the following disadvantages: firstly, only one true value is used, whether the obtained conclusion is stable or not depends on the selection of the true value or not is worth further discussion; secondly, besides the EN4-mapping scheme (Good et al, 2013), there are other mapping schemes with wide application internationally, such as the IAP-mapping scheme (Cheng and Zhu,2016a), the ISAS-mapping scheme (Gaillard et al, 2016), and the ncii-mapping scheme (Levitus et al, 2009), which estimate the size of uncertainty and how the spatio-temporal characteristics of the ocean heat content, to be further discussed; finally, the physical explanation of the uncertainty is not enough, and the relationship between uncertainty (noise) and the spatiotemporal variability of heat content (signal) is not deeply analyzed, so that the reliability of corresponding objective analysis data is further clarified.
In principle, continuous encryption of ocean observations to achieve satisfactory densities will forever remove the uncertainty introduced by the mapping scheme. However, carrying out marine observations is a costly project, and humans have little ability to fully observe all scales of variability of the sea for a long period of time in the future. At present, Argo observation nets (liu Zeng et al, 2016) have been built in the upper 2000 m ocean in open areas without ice, and further construction of buoy observation systems in deep ocean (2000 m or less) is a common consensus of scientific community (wu standing new and morning glory, 2013). A very natural problem is: the current ocean subsurface observation system can monitor the change of ocean heat content to a large extent, and how to design the future observation system is enough to track the redistribution process of heat in the whole ocean, for example, research shows that the trend of the ocean heat content at the upper layer of 2000 meters in 2005-2017 and the change characteristics of annual scale signals of different international data product descriptions still have great disputes (Bindoffet al, 2019; Cheng et al, 2019 a).
By means of independent 'third-party data', an objective analysis method is used, and the method is an effective means for evaluating the monitoring capacity of the ocean subsurface observation system on ocean heat content. Some of the previous approaches (AchutaRao et al, 2007; Good, 2017; Gregory et al, 2004) have been pursued but still suffer from the following limitations: (1) the mapping scheme used is very simple (e.g. weighted average scheme, where values of non-observed regions are replaced by weighted averages of values of observed regions) or only one mapping scheme is used, the conclusion of which may depend on the choice of mapping scheme; (2) the "third party data" comes from the lattice data, without random observation errors, and without subgrid variability (signals below the spatio-temporal resolution are smoothed out), it is difficult to "simulate" a real observation system. Furthermore, historically the major observation instruments of the marine subsurface have undergone an evolution from mechanical thermometers (MBT), disposable thermometers (XBT) to Argo buoys, and today marine subsurface observation systems have evolved into a complex network mixing multiple instruments. The contribution of each instrument to monitoring global and regional ocean temperature and heat content changes is a basic problem of ocean science research and needs to be deeply researched.
Disclosure of Invention
The invention aims to provide an ocean observation system, a mapping scheme evaluation method and a mapping scheme evaluation system, which are used for realizing the quantitative evaluation of the monitoring capability of the ocean mapping scheme and a subsurface observation system, providing an adjustment reference for the future release of an ocean data observation instrument and improving the rationality and effectiveness of the arrangement of the ocean subsurface observation system.
In order to achieve the purpose, the invention provides the following scheme:
a marine observation system evaluation method comprises the following steps:
acquiring climate coupling mode ocean component data, ocean reanalysis data and ocean circulation mode data of a preset sampling position;
according to the temporal-spatial distribution of the preset sampling positions in the EN4.2.2 profile data set, resampling is carried out on the ocean reanalysis data, the ocean circulation mode data and the climate coupling mode ocean component data respectively, and a synthetic profile of the ocean reanalysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data are obtained;
repeatedly constructing the lattice reconstruction field for N times to obtain N reconstruction fields;
the constructing of the lattice-structured reconstruction field specifically includes: randomly selecting one synthetic section from the synthetic sections of the ocean reanalysis data, the synthetic sections of the ocean circulation mode data and the synthetic sections of the climate coupling mode ocean component data as a selected synthetic section, randomly selecting one 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 section to obtain a selected synthetic section after disturbance is added, and performing lattice transformation on the selected synthetic section after disturbance is added by adopting the selected mapping scheme to obtain a reconstruction field;
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 sequence of each reconstruction field and the weather coupling mode marine component data, the marine reanalysis data or the marine circulation mode data corresponding to the selected synthetic profile;
and increasing or decreasing the release of the marine data observation instrument on the preset sampling position according to the evaluation index.
Optionally, the number of M is 3, and the 3 mapping schemes are an IAP-mapping scheme, an ISAS-mapping scheme, and an ncii-mapping scheme, respectively.
Optionally, the climate coupling mode ocean component data adopts high-resolution variability mode comparison plan CNRM-CM6-1-HR ocean component data in phase 6 of the coupling mode comparison plan, the ocean reanalysis data adopts C-GLORSv7 vortex compatible resolution ocean reanalysis data, and the ocean circulation mode data adopts LICOM vortex resolution ocean mode data.
Optionally, the determining, according to a correlation coefficient and a standard deviation of the time series of each reconstructed field and the time series of the climate coupling mode marine component data, the marine reanalysis data, or the marine circulation mode data corresponding to the selected synthetic profile, an evaluation index of the marine subsurface observation system at the preset sampling position specifically includes:
according to the formula
Calculating skill scores of the marine subsurface observation system at the preset sampling position to obtain N skill scores;
taking the average value of the N skill scores as the evaluation index;
wherein ,SSi Indicates the skill score, R, of the ith reconstruction field i Correlation coefficient representing the time series of the ith reconstruction field and the time series of the climate coupling mode ocean component data, ocean reanalysis data or ocean circulation mode data corresponding to the ith reconstruction field, SDR i And the standard deviation of the time series of the ith reconstruction field is expressed as the ratio of the standard deviation of the time series of the ith reconstruction field to the standard deviation of the time series of the climate coupling mode ocean component data, the ocean reanalysis data or the ocean circulation mode data corresponding to the ith reconstruction field.
Optionally, the selected synthetic profile after adding the perturbation is represented as:
wherein ,
representing the selected synthetic profile, PS, after said added perturbation k Representing the selected composite cross-section,
representing a disturbance of a random observation error,
the spatial perturbation is represented by a spatial perturbation,
representing a temporal disturbance, epsilon 1 、ε 2 and ε3 Both represent gaussian random numbers.
Optionally, the marine data observation instrument comprises a disposable depth and temperature gauge, an anchoring buoy, an Argo buoy and an underwater glider.
The invention also discloses an ocean observation system evaluation system, which comprises:
the marine data acquisition module is used for acquiring climate coupling mode marine component data, marine reanalysis data and marine circulation mode data of a preset sampling position; the preset sampling position is an ocean subsurface observation profile;
the synthetic profile module is used for resampling the ocean re-analysis data, the ocean circulation mode data and the climate coupling mode ocean component data respectively according to the temporal-spatial distribution of the preset sampling positions in the EN4.2.2 profile data set, and acquiring a synthetic profile of the ocean re-analysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data;
a reconstruction field building module for repeatedly building the lattice reconstruction field for N times to obtain N reconstruction fields;
the constructing of the lattice-structured reconstruction field specifically includes: randomly selecting one synthetic section from the synthetic sections of the ocean reanalysis data, the synthetic sections of the ocean circulation mode data and the synthetic sections of the climate coupling mode ocean component data as a selected synthetic section, randomly selecting one 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 section to obtain a selected synthetic section after disturbance is added, and performing lattice transformation on the selected synthetic section after disturbance is added by adopting the selected mapping scheme to obtain a reconstruction field;
the evaluation index determining module is used for determining the evaluation index of the ocean subsurface observation system at the preset sampling position according to the correlation coefficient and the standard deviation of the time sequence of each reconstruction field and the time sequence of the climate coupling mode ocean component data, the ocean reanalysis data or the ocean circulation mode data corresponding to the selected synthetic profile;
and the distribution adjustment module of the marine data observation instrument is used for increasing or reducing the throwing of the marine data observation instrument on the preset sampling position according to the evaluation index.
Optionally, the number of M is 3, and the 3 mapping schemes are an IAP-mapping scheme, an ISAS-mapping scheme, and an ncii-mapping scheme, respectively.
Optionally, the climate coupling mode ocean component data adopts high-resolution variability mode comparison plan CNRM-CM6-1-HR ocean component data in phase 6 of the coupling mode comparison plan, the ocean reanalysis data adopts C-GLORSv7 vortex compatible resolution ocean reanalysis 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 reanalysis data and ocean circulation mode data of a preset sampling position;
according to the temporal-spatial distribution of the preset sampling positions in the EN4.2.2 profile data set, resampling is carried out on the ocean reanalysis data, the ocean circulation mode data and the climate coupling mode ocean component data respectively, and a synthetic profile of the ocean reanalysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data are obtained;
repeatedly constructing the lattice reconstruction field for N times to obtain N reconstruction fields;
the constructing of the lattice-structured reconstruction field specifically includes: randomly selecting one synthetic section from the synthetic section of the ocean reanalysis data, the synthetic section of the ocean circulation mode data and the synthetic section of the climate coupling mode ocean component data as a selected synthetic section, and performing lattice transformation on the selected synthetic section by adopting a mapping scheme to be evaluated to obtain a reconstruction field;
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 sequence of each reconstruction field and the weather coupling mode marine component data, the marine reanalysis data or the marine circulation mode data corresponding to the selected synthetic 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 an evaluation method and an evaluation system for a marine subsurface observation system, which are used for acquiring three kinds of acquired data of preset sampling positions, resampling and synthesizing a section respectively, determining evaluation indexes of the marine subsurface observation system by constructing a lattice reconstruction field for multiple times, realizing quantitative evaluation of monitoring capability of the marine subsurface observation system, providing an adjustment basis for distribution of marine data observation instruments, and improving effectiveness and rationality of distribution setting of the marine data observation instruments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
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 principle of extracting a synthetic section from a lattice point according to the present invention;
FIG. 3 is a schematic flow chart of the present invention for constructing a reconstructed field;
FIG. 4 is a schematic structural diagram of an evaluation system of the marine observation system of the present invention;
FIG. 5 is a flow chart illustrating an evaluation method of mapping scheme according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a marine observation system, a mapping scheme evaluation method and a mapping scheme evaluation system, which are used for realizing quantitative evaluation of monitoring capability of the marine mapping scheme and a subsurface observation system, providing an adjustment reference for future release of marine data observation instruments and improving the reasonability and effectiveness of the layout of the marine subsurface observation system.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a schematic flow diagram of an evaluation method of an ocean subsurface observation system of the present invention, and as shown in fig. 1, the evaluation method of the ocean subsurface observation system includes:
step 101: and acquiring climate coupling mode ocean component data, ocean reanalysis data and ocean circulation mode data of a preset sampling position.
Wherein, step 101 specifically includes: obtaining climate coupling mode ocean component data, ocean reanalysis data and ocean circulation mode data which can cover global oceans.
The climate coupling mode ocean component data, the ocean reanalysis data and the ocean circulation mode data are temperature data and salinity data.
Ocean heat content is calculated from temperature and salinity.
The climate coupling mode ocean component data, ocean reanalysis data and ocean circulation mode data are used as 3 true value fields, power coordination is met, basic ocean phenomena in physical ocean are included, and the motion rule of the seawater is met; (2) the space-time coverage is complete, namely all grid points of the global ocean have continuous numerical values; (3) the space-time resolution is as fine as possible, and the space-time variability of the 'synthetic profile' obtained by resampling is ensured to be closer to the real ocean state. In order to overcome the dependency of the evaluation method on the selection of the 'true value', 3 types of independent data are selected as the 'true value'.
The climate coupling mode ocean component data is particularly climate system mode vortex compatible resolution ocean component data. The climate system mode vortex compatible resolution ocean component data adopts the ocean component data of CNRM-CM6-1-HR in the high resolution variability mode comparison plan in the 6 th stage of the coupled mode comparison plan, the adopted ocean mode is NEMO3.6, the spatial resolution is 1/4 degrees, the time resolution is month by month, the vertical direction extends from the surface to about 5500 meters in depth, and the time span is from 1970 and 2021 years. Among them, the history forced experiment using Tier2 was used in 1970-2014, and the result of HighRes-Future experiment was used after 2015.
The marine reanalysis data was analyzed using C-GLORSv7 vortex compatible resolution marine reanalysis data. C-GLORSv7 vortex compatible resolution ocean reanalysis data was developed by the Italian theory physical research center (CMCC) and three-dimensional variate (3DVar) assimilation scheme was used to assimilate ocean subsurface profile data and ocean satellite remote sensing data (sea surface temperature, sea surface height) into NEMO ocean patterns. The spatial resolution of the data set was 1/4 degrees, the temporal resolution was monthly, the vertical direction was from the surface to about 5500 meters depth, and the time span was 1993 to 2021.
The ocean circulation mode data adopts LICOM vortex resolution ocean mode data. The LICOM vortex resolution ocean mode data is based on an ocean circulation mode LICOM independently developed by atmospheric physics research institute of Chinese academy of sciences, participates in the 2 nd stage (OMIP-2) of the international ocean mode comparison plan, the spatial resolution is 1/10 degrees, the time resolution has two versions of month-by-month and day-by-day, the vertical direction is from the sea surface to about 5500 meters, and the time span is from 1958 and 2018. Unlike the climate system mode ocean component, this data is derived from the atmospheric re-analysis data driving the ocean mode integration.
Step 102: according to the spatial-temporal distribution of historical sampling positions in the EN4.2.2 profile data set, ocean re-analysis data, ocean circulation mode data and climate coupling mode ocean component data are re-sampled respectively, and a synthetic profile of the ocean re-analysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data are obtained.
Wherein, step 102 specifically comprises: the 3 "true" fields were resampled individually based on the spatio-temporal distribution of the observed profiles in the EN4.2.2 profile dataset published by the Hardley center, UK. In the sampling process, grid point data in a 'true value' field are respectively interpolated to the longitude and latitude and the depth of a space-time distribution profile of an observation profile in an EN4.2.2 profile data set, the level of the space-time distribution profile adopts bilinear interpolation, and Reiniger-Ross interpolation is vertically used. The synthetic profile data set is evaluated for plausibility and then stored according to the format suggested by the international quality control of ocean data program (IQuOD).
As shown in FIG. 2, when the synthetic profile (scatter) is extracted using the pattern data of 1/4 degrees resolution as the "true value" (lattice point), the observation profile PO falling on the (i-1, j-1) grid k (P represents a section, O represents an observation, and subscript k represents a section number), the section of 4 neighboring lattice points (reference numerals 1-4) around it is interpolated to an observation section PO using bilinear interpolation k The longitude and latitude of the position are interpolated on the vertical layer of the observation profile by using a Reiniger-Ross method to obtain a corresponding synthetic profile (marked as PS) k P represents profile, S represents synthetic, and subscript k represents profile number), completing the resampling process. The resampling points are the points indicated by in-situ profile in FIG. 2.
Step 103: and repeatedly constructing the lattice reconstruction field for N times to obtain N reconstruction fields.
And taking 50 as N.
The 3 mapping schemes are respectively an IAP-mapping scheme, an ISAS-mapping scheme and an NCEI-mapping scheme.
In order to overcome the dependency of the technical scheme on the mapping scheme, the uncertainty and the time-space characteristics of the international mainstream IAP-mapping, ISAS-mapping and NCEI-mapping schemes are selected and used.
The basic idea of the IAP-mapping scheme is to combine the historical observations with the multi-mode output of the coupling mode comparison planning stage five (CMIP5) using Ensemble Optimal Interpolation (EnOI), establish the relationship between observed and non-observed regions using the multi-mode results (background field error covariance matrix, B matrix), project the observation information to the non-observed regions, and use the multi-mode Ensemble average of the dynamically 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 (climate state) grid by constructing a static B matrix, and interpolation of a grid without observation is realized, wherein 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 the weight to carry out weighted summation on the observation signals in the 'influence radius', the 'influence radius' is updated from large to small in a plurality of iterations, and the signals from large scale to small scale are analyzed in sequence.
According to a certain synthetic profile PS, with reference to the instrument random observation error provided by the International Marine data quality control program (IQuoD) (Table 1) k Corresponding real profile PO k Determining random observation error amplitude
(measurementerror). Using the day-by-day data of GLORSY12 vortex resolution (horizontal 1/12 degrees), the spatial standard deviation of all 1/12 degree grid values contained within the 1/4 degree grid was calculated, the standard deviations of each day of the month were time averaged as the amplitude of the spatial uncertainty, one value per month, labeled as the amplitude of the spatial uncertainty
(spatialuncintaint); using the day-by-day data for GLORSY12 vortex resolution (horizontal 1/12 degrees), the time-dimensional standard deviation of the daily average increment (day 2 minus day 1, day 3 minus day 2, and so on) for each grid point was calculated and then interpolated onto a 1/4 degree resolution grid as the amplitude of the time uncertainty, 1 value per month, labeled as the amplitude of the time uncertainty
(temporaluncertainty)。
Assuming that random observation errors, time uncertainty and spatial uncertainty are independent of each other, then using the synthetic profile data, the random observation errors are corrected
Uncertainty of time
And amplitude of spatial uncertainty
Gaussian perturbation (mean 0, standard deviation 1) was performed and added to the synthetic Profile (PS) k ) In the above, a "randomly perturbed synthetic section" was obtained "
The real section is maximally 'simulated'.
TABLE 1 Observation accuracies of different instruments published by the International Marine data quality control plan (IQOD)
3 independent synthetic profiles and 3 different mapping schemes (IAP-mapping scheme, ISAS-mapping scheme and NCEI-mapping scheme) are used, random observation error disturbance (5 times), sub-grid variability disturbance (5 times) and intra-month variability disturbance (5 times) are repeated respectively, a 'reconstructed' field is compared with a 'true value' field, and the monitoring capability of a historical observation system on ocean heat content can be quantified. If 1125 experiments (3 × 5 × 5 × 3) were performed for all possible permutations, the resulting data would be voluminous and computationally expensive. Therefore, the N in the invention is 50.
The method for constructing the lattice reconstruction field specifically comprises the following steps: randomly selecting a synthetic section from the synthetic section of the ocean reanalysis data, the synthetic section of the ocean circulation mode data and the synthetic section of the climate coupling mode ocean component data as a selected synthetic section, 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 section to obtain the selected synthetic section after the disturbance is added, and performing lattice transformation on the selected synthetic section after the disturbance is added by adopting the selected mapping scheme to obtain a reconstruction field. Repeat 50 times, obtain 50 reconstruction fields. 50 sets of reconstruction data
As shown in fig. 3. The idea of 'random disturbance + set' is used, the dependence on 'true value' selection, random observation error, space-time sub-grid variability loss and mapping scheme is overcome, and the robustness of the monitoring capability of the ocean subsurface observation system is obtainedAnd (5) judging the sexual evaluation.
The selected composite profile after adding the perturbation is represented as:
wherein ,
representing the selected synthetic profile, PS, after adding perturbation k A representation of the selected composite cross-section,
representing a disturbance of a random observation error,
the spatial perturbation is represented by a spatial perturbation,
representing a temporal disturbance, epsilon 1 、ε 2 and ε3 Both represent gaussian random numbers.
Step 104: and determining the evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and the standard deviation of the time sequence of each reconstruction field and the weather coupling mode marine component data, the marine reanalysis data or the marine circulation mode data corresponding to the selected synthetic profile.
Wherein, step 104 specifically includes:
according to the formula
Calculating Skill scores of the ocean subsurface observation system at preset sampling positions to obtain 50 Skill scores (SS, Skill Score);
when R is i Less than 0.5 or fails the 95% significance test, or SDR i Greater than 2 or SDR i Less than 0.5 time SS i The value is 0.
Taking the average value of 50 skill scores as an evaluation index;
wherein ,SSi Indicates the skill score, R, of the ith reconstruction field i Correlation coefficient representing the time series of the ith reconstruction field and the time series of climate coupling mode ocean component data, ocean reanalysis data or ocean circulation mode data corresponding to the ith reconstruction field, SDR i And (3) representing the ratio of the standard deviation of the time series of the ith reconstruction field to the standard deviation of the time series of the climate coupling mode ocean component data, the ocean reanalysis data or the ocean circulation mode data corresponding to the ith reconstruction field, wherein i is 1,2, … and 50.
Evaluating the monitoring capability of an observation system on ocean heat content by adopting skill scoring, and when R is within a certain determined time period i Less than 0.5 or failing to pass the 95% significance test, or SDR i Greater than 2 or less than 0.5 (the amplitude of the "reconstruction" exceeds the "true value" by a factor of 2 or less than half the true value), SS i The value is 0. And calculating skill scores for 50 experiments respectively, and calculating the arithmetic mean to be used as an evaluation index. Therefore, the skill score value is between 0 and 100, the larger the skill score value is, the better the monitoring capability is represented, and the worse the monitoring capability is, the vice versa. Further, the spatial correlation coefficient and the temporal correlation coefficient are used as auxiliary quantization indexes.
Step 105: and increasing or decreasing the throwing of the marine data observation instrument on the preset sampling position according to the evaluation index.
Wherein, step 105 specifically comprises: and increasing or reducing the throwing of the marine data observation instrument at the preset sampling position by comparing the evaluation indexes on each lattice point on the reconstructed lattice point field.
The idealized experiment is carried out by removing, redeploying and encrypting (adding) 4 main observation instruments such as XBT, Argo, anchoring buoy, Glider and the like, and reference is provided for adjusting and optimizing the distribution of the marine data observation instruments.
The marine data observation instrument comprises a disposable temperature and depth measuring instrument, an anchoring buoy, an Argo buoy and an underwater glider.
Meanwhile, the invention intends to use a filter to divide the time series into: and (3) signals of an intra-year scale (12 months), an inter-year scale (13-84 months) and an inter-year scale (linear trend is deducted and low-pass filtering is carried out for 84 months) are respectively inspected on the monitoring capability of the historical marine observation system on the signals of different time scales. The time variability defining the "true" sequence is the "signal" and the root mean square error of the "true" and reconstructed sequences is the "noise". Signal-to-noise ratio (SNR, signal/noise) is introduced to quantify the monitoring ability of the observation system for this time scale "signal".
To evaluate the contribution of each subsystem in the historical observation system (marine subsurface observation system), the present invention will remove the data of XBT, Argo, anchoring buoys (primarily TAO/TRITON, RAMA, and PIRATA arrays in tropical sea), and Glider, respectively, and simulate a partial observation in a "subtractive" subsurface observation system. The influence of a specific observation instrument on the global and regional scale ocean heat change monitoring is determined by comparing the heat content estimation obtained by all observation data and removing a certain part of observed heat content estimation, and the method aims at clarifying: (1) global impact of different observation systems (e.g., how much impact is on reconstruction of ocean heat content signals at different time scales); (2) and determining which regions are influenced greatly and whether quantization can be performed.
The invention achieves the technical effects that:
the method has the advantages that: the uncertainty introduced by the mapping process can be objectively and quantitatively evaluated, and the method is the clear direction for improving the mainstream objective analysis product.
A synthetic profile data set (scatter points) is constructed for the first time based on climate system mode vortex compatible resolution ocean component data, ocean reanalysis data and vortex resolution ocean mode data, the uncertainty of 3 mainstream mapping schemes is systematically evaluated, the advantages and the disadvantages of different mapping schemes are favorably quantified, and a foundation is laid for continuously optimizing the mapping schemes and improving the ocean heat content estimation precision.
The method has the advantages that: the monitoring capability of the historical ocean subsurface observation system on the ocean heat content can be quantitatively evaluated, and scientific reference is provided for maintenance, design and construction of the ocean subsurface observation system in the future.
Based on an objective analysis method, the monitoring capability of a historical marine observation system on the ocean heat content of different global and regional time scales (annual, annual generation and long-term trends) is evaluated, and the dependence of conclusion on 'true value' selection, random observation error, space-time secondary grid variability loss and mapping scheme is overcome through the thought of 'random disturbance + aggregation'; by removing, redeploying and encrypting 4 types of main observation instruments such as XBT, Argo, anchoring buoy, Glider and the like, the relative contribution of the observation instruments is quantified, and 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 operate an ocean power mode, has the calculation amount for evaluating the global ocean observation system which is obviously lower than that of target observation technologies such as an Observation System Simulation Experiment (OSSE) in the field of data assimilation, and is beneficial supplement for evaluating the ocean subsurface observation system.
Fig. 4 is a schematic structural diagram of an evaluation system of an ocean subsurface observation system of the present invention, and as shown in fig. 4, the evaluation system of the ocean subsurface observation system includes:
the marine data acquisition module 201 is used for acquiring climate coupling mode marine component data, marine reanalysis data and marine circulation mode data of a preset sampling position; presetting a sampling position as an ocean subsurface observation profile;
the synthetic profile module 202 is used for resampling the ocean reanalysis data, the ocean circulation mode data and the climate coupling mode ocean component data respectively according to the time-space distribution of the historical preset sampling positions in the EN4.2.2 profile data set, and acquiring a synthetic profile of the ocean reanalysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data;
a reconstructed field constructing module 203, configured to repeatedly construct a lattice reconstructed field N times, to obtain N reconstructed fields;
the constructing of the lattice reconstruction field specifically comprises: randomly selecting a synthetic section from the synthetic section of the ocean reanalysis data, the synthetic section of the ocean circulation mode data and the synthetic section of the climate coupling mode ocean component data as a selected synthetic section, randomly selecting a scheme from M mapping schemes as the selected mapping scheme, sequentially adding random observation error disturbance, spatial disturbance and time disturbance to the selected synthetic section to obtain the selected synthetic section after disturbance is added, and performing lattice transformation on the selected synthetic section after disturbance is added by adopting the selected mapping scheme to obtain a reconstruction field.
The number of M is 3, and the 3 mapping schemes are respectively an IAP-mapping scheme, an ISAS-mapping scheme and an NCEI-mapping scheme.
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 sequence of each reconstruction field and the time sequence of the climate coupling mode marine component data, the marine reanalysis data, or the marine circulation mode data corresponding to the selected synthetic profile;
and the marine data observation instrument distribution adjusting module 205 is configured to increase or decrease the number of marine data observation instruments placed at the preset sampling position according to the evaluation index.
The climate coupling mode ocean component data adopt ocean component data of CNRM-CM6-1-HR in a high-resolution variability mode comparison plan in the 6 th stage of a coupling mode comparison plan, ocean reanalysis data adopt C-GLORSv7 vortex compatible resolution ocean reanalysis data, and ocean circulation mode data adopt LICOM vortex resolution ocean mode data.
Determining the evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and the standard deviation of the time sequence of each reconstruction field and the time sequence of the climate coupling mode marine component data, the marine reanalysis data or the marine circulation mode data corresponding to the selected synthetic profile, and specifically comprises the following steps:
according to the formula
Calculating skill scores of the marine subsurface observation system at the preset sampling position to obtain N skill scores;
taking the average value of the N skill scores as an evaluation index;
wherein ,SSi Indicates the skill score, R, of the ith reconstruction field i Indicating the ith reconstructionCorrelation coefficient of time series of field and climate coupling mode ocean component data, ocean reanalysis data or ocean circulation mode data corresponding to ith reconstruction field, SDR i And the standard deviation of the time series of the ith reconstruction field is expressed as the ratio of the standard deviation of the time series of the ith reconstruction field to the standard deviation of the time series of the climate coupling mode ocean component data, the ocean reanalysis data or the ocean circulation mode data corresponding to the ith reconstruction field.
The selected composite profile after adding the perturbation is represented as:
wherein ,
representing the selected synthetic profile, PS, after adding perturbations k A representation of the selected composite cross-section,
representing a disturbance of a random observed error,
the spatial perturbation is represented by a spatial perturbation,
representing a temporal disturbance, epsilon 1 、ε 2 and ε3 Both represent gaussian random numbers.
The marine data observation instrument comprises a disposable temperature and depth measuring instrument, an anchoring buoy, an Argo buoy and an underwater glider.
As shown in fig. 5, a mapping scheme evaluation method includes:
step 301: and acquiring climate coupling mode ocean component data, ocean reanalysis data and ocean circulation mode data of a preset sampling position.
Step 302: and according to the temporal-spatial distribution of the preset sampling positions in the EN4.2.2 profile data set, respectively resampling the ocean reanalysis data, the ocean circulation mode data and the climate coupling mode ocean component data, and acquiring a synthetic profile of the ocean reanalysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data.
Step 303: and repeatedly constructing the lattice reconstruction field for N times to obtain N reconstruction fields.
The constructing of the lattice-structured reconstruction field specifically includes: and randomly selecting one synthetic section from the synthetic section of the ocean reanalysis data, the synthetic section of the ocean circulation mode data and the synthetic section of the climate coupling mode ocean component data as a selected synthetic section, and performing lattice transformation on the selected synthetic section by adopting a mapping scheme to be evaluated to obtain a reconstruction field.
Step 304: and determining the evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and the standard deviation of the time sequence of each reconstruction field and the weather coupling mode marine component data, the marine reanalysis data or the marine circulation mode data corresponding to the selected synthetic profile.
Step 305: and evaluating the uncertainty of the mapping scheme to be evaluated according to the evaluation index.
The method can objectively and quantitatively evaluate the uncertainty of different mapping schemes. And providing reference basis for developers of mapping schemes to be evaluated.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention are explained herein using specific examples, which are provided only to help understand the method and the core idea of the present invention; also, it will be apparent to those skilled in the art that variations may be made in the embodiments and applications without departing from the spirit of the invention. In view of the foregoing, the description should not be construed as limiting the invention.
Claims (10)
1. A marine observation system evaluation method is characterized by comprising the following steps:
acquiring climate coupling mode ocean component data, ocean reanalysis data and ocean circulation mode data of a preset sampling position;
according to the temporal-spatial distribution of the preset sampling positions in the EN4.2.2 profile data set, resampling is carried out on the ocean reanalysis data, the ocean circulation mode data and the climate coupling mode ocean component data respectively, and a synthetic profile of the ocean reanalysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data are obtained;
repeatedly constructing the lattice reconstruction field for N times to obtain N reconstruction fields;
the constructing of the lattice-structured reconstruction field specifically includes: randomly selecting a synthetic section from the synthetic section of the ocean reanalysis data, the synthetic section of the ocean circulation mode data and the synthetic section of the climate coupling mode ocean component data as a selected synthetic section, 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 section to obtain a selected synthetic section after disturbance is added, and performing lattice transformation on the selected synthetic section after disturbance is added by adopting the selected mapping scheme to obtain a reconstruction field;
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 sequence of each reconstruction field and the weather coupling mode marine component data, the marine reanalysis data or the marine circulation mode data corresponding to the selected synthetic profile;
and increasing or decreasing the release of the marine data observation instrument on the preset sampling position according to the evaluation index.
2. The ocean observation system evaluation method according to claim 1, wherein the number of M is 3, and the 3 mapping schemes are an IAP-mapping scheme, an ISAS-mapping scheme and an NCEI-mapping scheme, respectively.
3. The marine observation system evaluation method of claim 1, wherein the climate coupling mode marine component data is marine component data of CNRM-CM6-1-HR in a high-resolution variability mode comparison plan in stage 6 of a coupling mode comparison plan, the marine reanalysis data is marine reanalysis data of C-GLORSv7 vortex compatible resolution, and the marine circulation mode data is marine mode data of LICOM vortex resolution.
4. The marine observation system evaluation method according to claim 1, wherein the determining of the evaluation index of the marine subsurface observation system at the preset sampling position according to the correlation coefficient and the standard deviation of the time series of each reconstruction field and the time series of the climate coupling mode marine component data, the marine re-analysis data or the marine circulation mode data corresponding to the selected synthetic profile specifically comprises:
according to the formula
Calculating skill scores of the marine subsurface observation system at the preset sampling position to obtain N skill scores;
taking the average value of the N skill scores as the evaluation index;
wherein ,SSi Skill score, R, representing the ith reconstruction field i Correlation coefficient representing the time series of the ith reconstruction field and the time series of climate coupling mode ocean component data, ocean reanalysis data or ocean circulation mode data corresponding to the ith reconstruction field, SDR i Showing the standard deviation of the time sequence of the ith reconstruction field and the climate coupling mode ocean component data and ocean reanalysis number corresponding to the ith reconstruction fieldOr the ratio of the standard deviations of the time series of marine circulation pattern data.
5. The marine observation system evaluation method of claim 1, wherein the selected synthetic profile after adding the perturbation is represented as:
wherein ,
representing the selected synthetic profile, PS, after said added perturbation k Representing the selected composite cross-section,
representing a disturbance of a random observation error,
the spatial perturbation is represented by a spatial perturbation,
represents a time perturbation, ε 1 、ε 2 and ε3 Both represent gaussian random numbers.
6. The marine observation system evaluation method of claim 1, wherein the marine data observation instrument comprises a disposable thermo-depth gauge, an anchoring buoy, an Argo buoy, and an underwater glider.
7. An ocean observation system evaluation system, comprising:
the marine data acquisition module is used for acquiring climate coupling mode marine component data, marine reanalysis data and marine circulation mode data of a preset sampling position; the preset sampling position is an ocean subsurface observation profile;
the synthetic profile module is used for resampling the ocean re-analysis data, the ocean circulation mode data and the climate coupling mode ocean component data respectively according to the temporal-spatial distribution of the preset sampling positions in the EN4.2.2 profile data set, and acquiring a synthetic profile of the ocean re-analysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data;
a reconstruction field building module for repeatedly building the lattice reconstruction field for N times to obtain N reconstruction fields;
the constructing of the lattice-structured reconstruction field specifically includes: randomly selecting one synthetic section from the synthetic sections of the ocean reanalysis data, the synthetic sections of the ocean circulation mode data and the synthetic sections of the climate coupling mode ocean component data as a selected synthetic section, randomly selecting one 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 section to obtain a selected synthetic section after disturbance is added, and performing lattice transformation on the selected synthetic section after disturbance is added by adopting the selected mapping scheme to obtain a reconstruction field;
the evaluation index determining module is used for determining the evaluation index of the ocean subsurface observation system at the preset sampling position according to the correlation coefficient and the standard deviation of the time sequence of each reconstruction field and the time sequence of the climate coupling mode ocean component data, the ocean reanalysis data or the ocean circulation mode data corresponding to the selected synthetic profile;
and the distribution adjustment module of the marine data observation instrument is used for increasing or reducing the release of the marine data observation instrument on the preset sampling position according to the evaluation index.
8. The marine observation system evaluation system of claim 7, wherein the number of M is 3, and the 3 mapping schemes are an IAP-mapping scheme, an ISAS-mapping scheme, and an NCEI-mapping scheme, respectively.
9. The marine observation system evaluation system of claim 7, wherein the climate coupling mode marine component data is marine component data of CNRM-CM6-1-HR in the high resolution variability mode comparison plan at stage 6 of the coupling mode comparison plan, the marine re-analysis data is marine re-analysis data of C-GLORSv7 vortex compatible resolution, and the marine circulation mode data is marine mode data of LICOM vortex resolution.
10. A mapping scheme evaluation method is characterized by comprising the following steps:
acquiring climate coupling mode ocean component data, ocean reanalysis data and ocean circulation mode data of a preset sampling position;
according to the temporal-spatial distribution of the preset sampling positions in the EN4.2.2 profile data set, resampling is carried out on the ocean reanalysis data, the ocean circulation mode data and the climate coupling mode ocean component data respectively, and a synthetic profile of the ocean reanalysis data, a synthetic profile of the ocean circulation mode data and a synthetic profile of the climate coupling mode ocean component data are obtained;
repeatedly constructing the lattice reconstruction field for N times to obtain N reconstruction fields;
the constructing of the lattice-structured reconstruction field specifically includes: randomly selecting one synthetic section from the synthetic section of the ocean reanalysis data, the synthetic section of the ocean circulation mode data and the synthetic section of the climate coupling mode ocean component data as a selected synthetic section, and performing lattice transformation on the selected synthetic section by adopting a mapping scheme to be evaluated to obtain a reconstruction field;
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 sequence of each reconstruction field and the weather coupling mode marine component data, the marine reanalysis data or the marine circulation mode data corresponding to the selected synthetic profile;
and evaluating the uncertainty of the mapping scheme to be evaluated according to the evaluation index.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210682706.5A CN115096274B (en) | 2022-06-16 | 2022-06-16 | Ocean observation system and mapping scheme evaluation method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210682706.5A CN115096274B (en) | 2022-06-16 | 2022-06-16 | Ocean observation system and mapping scheme evaluation method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115096274A true CN115096274A (en) | 2022-09-23 |
| CN115096274B CN115096274B (en) | 2023-08-18 |
Family
ID=83291171
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210682706.5A Active CN115096274B (en) | 2022-06-16 | 2022-06-16 | Ocean observation system and mapping scheme evaluation method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115096274B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5808964A (en) * | 1995-06-06 | 1998-09-15 | Institut Francais Du Petrole | Processing method for obtaining zero-offset seismic data by depth domain stacking |
| US20130158874A1 (en) * | 2011-12-14 | 2013-06-20 | The Government Of The United States, As Represente D By The Secretary Of Navy | Automated system and method for vertical gradient correction |
| CN113051795A (en) * | 2021-03-15 | 2021-06-29 | 哈尔滨工程大学 | Three-dimensional temperature-salinity field analysis and prediction method for offshore platform guarantee |
| CN113051824A (en) * | 2021-03-26 | 2021-06-29 | 福州大学 | Ocean internal temperature field reconstruction method based on profile observation data |
| KR102317005B1 (en) * | 2021-04-13 | 2021-10-26 | 주식회사 그린블루 | Method for making electronic nautical chart by measuring the submarine topography |
| CN113935249A (en) * | 2021-11-23 | 2022-01-14 | 中国海洋大学 | Upper-layer ocean thermal structure inversion method based on compression and excitation network |
-
2022
- 2022-06-16 CN CN202210682706.5A patent/CN115096274B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5808964A (en) * | 1995-06-06 | 1998-09-15 | Institut Francais Du Petrole | Processing method for obtaining zero-offset seismic data by depth domain stacking |
| US20130158874A1 (en) * | 2011-12-14 | 2013-06-20 | The Government Of The United States, As Represente D By The Secretary Of Navy | Automated system and method for vertical gradient correction |
| CN113051795A (en) * | 2021-03-15 | 2021-06-29 | 哈尔滨工程大学 | Three-dimensional temperature-salinity field analysis and prediction method for offshore platform guarantee |
| CN113051824A (en) * | 2021-03-26 | 2021-06-29 | 福州大学 | Ocean internal temperature field reconstruction method based on profile observation data |
| KR102317005B1 (en) * | 2021-04-13 | 2021-10-26 | 주식회사 그린블루 | Method for making electronic nautical chart by measuring the submarine topography |
| CN113935249A (en) * | 2021-11-23 | 2022-01-14 | 中国海洋大学 | Upper-layer ocean thermal structure inversion method based on compression and excitation network |
Non-Patent Citations (4)
| Title |
|---|
| GUINEHUT, S. LE TRAON, P.Y. LARNICOL, G. PHILIPPS, S.: "Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields—a first approach based on simulated observations", JOURNAL OF MARINE SYSTEMS, vol. 46, no. 1 * |
| 仝长亮: "海洋测绘在西南浅滩海砂资源探测中的应用", 海洋测绘, vol. 42, no. 3 * |
| 张敏;赵昌;张远凌;姜文正;王刚;: "EN4两套分析数据中海洋热含量的差异", 海洋科学进展, no. 03 * |
| 李纲, 孙曼: "大数据思想在遥感测绘中的应用前景分析", 科技创新导报 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115096274B (en) | 2023-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Spatial interpolation of temperature in the United States using residual kriging | |
| Griffies et al. | An assessment of global and regional sea level for years 1993–2007 in a suite of interannual CORE-II simulations | |
| Davis et al. | The temperature of Europe during the Holocene reconstructed from pollen data | |
| Roemmich et al. | Argo: the challenge of continuing 10 years of progress | |
| Chen et al. | Seasonal sea level change from TOPEX/Poseidon observation and thermal contribution | |
| Woodroffe et al. | Holocene sea-level changes in the Indo-Pacific | |
| Shen et al. | Groundwater depletion in the Hai River Basin, China, from in situ and GRACE observations | |
| Dudei et al. | Using ecological niche modeling to assess biogeographic and niche response of brachiopod species to the Richmondian Invasion (Late Ordovician) in the Cincinnati Arch | |
| Xu et al. | Projections of future meteorological droughts in China under CMIP6 from a three‐dimensional perspective | |
| Gao et al. | Revealing ecosystem services relationships and their driving factors for five basins of Beijing | |
| CN106597484B (en) | The precise quantification method that thermal expansion effects influence GPS coordinate time series | |
| Liao et al. | Numerical simulation and forecasting of water level for Qinghai Lake using multi-altimeter data between 2002 and 2012 | |
| Wang et al. | Variability of sea surface height and circulation in the North Atlantic: Forcing mechanisms and linkages | |
| Fu et al. | Estimation of sea level variability in the South China Sea from satellite altimetry and tide gauge data | |
| Li et al. | A global gridded ocean salinity dataset with 0.5° horizontal resolution since 1960 for the upper 2000 m | |
| Chang et al. | Neural network-based estimates of Southern Ocean net community production from in situ O 2/Ar and satellite observation: a methodological study | |
| CN116758224B (en) | Fusion assimilation method and device for multi-source ocean observation data | |
| He et al. | An investigation of atmospheric temperature and pressure using an improved spatio-temporal Kriging model for sensing GNSS-derived precipitable water vapor | |
| CN115096274A (en) | Ocean observation system and mapping scheme evaluation method and system | |
| Gusev et al. | Modeling the components of heat and water balance for the land surface of the globe | |
| Chang et al. | Basin patterns of global sea level changes for 2004–2007 | |
| Naumov et al. | Quality assessment of a satellite altimetry data product DT18 in the Norwegian Sea: A comparison to tide gauge records and drifters data | |
| Zhang et al. | Comparisons of OCO-2 satellite derived XCO2 with in situ and modeled data over global ocean | |
| Zhang et al. | Gridded reconstruction of the population in the traditional cultivated region of China from 1776 to 1953 | |
| Limareva et al. | The climate change of the Caucasus as a result of the global warming |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |