CN109241632B - Method for evaluating ocean numerical model simulation capability by adopting warm salt mirror image layer - Google Patents
Method for evaluating ocean numerical model simulation capability by adopting warm salt mirror image layer Download PDFInfo
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- CN109241632B CN109241632B CN201811060813.4A CN201811060813A CN109241632B CN 109241632 B CN109241632 B CN 109241632B CN 201811060813 A CN201811060813 A CN 201811060813A CN 109241632 B CN109241632 B CN 109241632B
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Abstract
The invention discloses a method for evaluating ocean numerical mode simulation capability by adopting a warm salt mirror image layer. Calculating the spatial correlation coefficients of ocean temperature and salinity layer by layer; calculating the spatial correlation coefficient of temperature and salinity in each water layer of the model or data product to obtain a sequence of the spatial correlation coefficients; finding out the water layer with the maximum spatial correlation coefficient of temperature and salinity; comparing the numerical mode with the correlation coefficients of the temperature-salt mirror image layer depth, the temperature on the mirror image layer and the salt spatial distribution of the objective analysis field; and calculating the temperature and salinity mirror image layer depth of the numerical mode result to be evaluated and the temperature and salinity spatial correlation coefficient on the temperature and salinity mirror image layer, comparing the temperature and salinity spatial correlation coefficient with the corresponding result on the reference data set, and taking the relative error as a measurement of the mode simulation level. The invention has the beneficial effects that: the temperature-salinity mirror image layer directly derived through the spatial correlation of temperature and salinity has higher spatial similarity; the warm salt mirror layer is an appropriate reference surface for evaluation of the modal capability outside the ocean surface.
Description
Technical Field
The invention belongs to the technical field of oceanology, and relates to a method for evaluating ocean numerical mode simulation capability by adopting a thermohaline mirror image layer.
Background
Temperature and salinity are two important physical properties of seawater, but they are essentially independent of each other: changes in temperature have little effect on salinity and vice versa.
The correlation of the spatial average time sequence of the temperature and salinity of Argo lattice point marine products is calculated layer by Chenge et al (Chen, G., D.Geng.A. "mirror layer" of temperature and salinity in the ocean.Climate Dynamics,2018, in press) of China ocean university, the correlation of the temperature and salinity of seawater reaches the maximum value in the water layer between 200-300m under the sea surface, and the spatial distribution forms of the two are also very similar. Chenge et al refer to the water layer with the highest time series correlation of sea water temperature and salt space average as the warm salt mirror layer.
Since the temperature and salinity mirror image layer is characterized by similar spatial distribution forms of temperature and salinity, the spatial correlation can be used for directly measuring the similarity of the spatial distribution of the temperature and the salinity. A plurality of sets of ocean site observation and reanalysis temperature and salinity data products are adopted to directly calculate the temperature and salinity spatial correlation coefficients of each water layer. In all data products, the spatial dependence of temperature and salinity is at a maximum around 150m layers (this depth is slightly different for different data sets), i.e. the spatial distribution of temperature and salinity is most similar at this depth. Compared with mirror image layers derived from Chenge and the like, the warm salt mirror image layer determined by spatial correlation has obviously higher spatial similarity and conforms to the basic characteristics of the warm salt mirror image layer.
Disclosure of Invention
The invention aims to provide a method for evaluating ocean numerical mode simulation capability by adopting a thermohaline mirror layer. The invention has the beneficial effects that: the warm salt mirror image layer directly derived through the spatial correlation of temperature and salinity has higher spatial similarity. The method is simple and the evaluation result is accurate.
The technical scheme adopted by the invention is carried out according to the following steps:
step 1: calculating the space correlation coefficient of ocean temperature and salinity layer by layer
The spatial correlation coefficient r of the temperature field T and the salinity field S is calculated by the following formula:
where the indices m, n represent two-dimensional spatial positions,
and &>
The spatial average values of T and S, respectively;
the spatial correlation coefficients of temperature and salinity are calculated at each water layer of the model or data product to obtain a sequence of spatial correlation coefficients.
Step 2: finding out the water layer with the maximum spatial correlation coefficient of temperature and salinity;
and searching maximum value points in the sequence of the correlation coefficients of the temperature and the salt space of each water layer.
And 3, step 3: comparing the numerical mode with the temperature-salt mirror image layer depth of the objective analysis field and the correlation coefficient of temperature and salt spatial distribution on the mirror image layer; and calculating the temperature-salinity mirror image layer depth of the numerical mode result to be evaluated and the temperature-salinity spatial correlation coefficient on the temperature-salinity mirror image layer, comparing the temperature-salinity mirror image layer depth with the corresponding result on the reference data set, and taking the relative error as a measurement of the mode simulation level.
Further, in step 1, the water layer is a z-coordinate layer, and the Sigma layer is firstly converted into the z-coordinate layer.
Further, the temperature salt mirror image layer in the step 2 is located in a water layer where the maximum value of the temperature and salt space correlation coefficient is located at 500m above the ocean.
Further, in step 3, the plurality of mode results are compared with the reference data set to evaluate the quality of the mode simulation level or respectively compare the simulation errors of the temperature, salinity and flow rate variables on the temperature-salinity mirror image layer by adopting a traditional comparison mode.
Drawings
FIG. 1 is a temperature saline image layer depth derived from four datasets WOA, EN4, ishii, argo, etc.;
FIG. 2 is a spatial distribution of temperature and salinity on a warm salt mirror image layer derived from spatial correlation coefficients of warm salt for four data sets of WOA, EN4, ishii, argo, etc.;
FIG. 3 is a distribution of the correlation coefficient of the warm salt space with depth in the WOA climatic seasonal warm salt field data.
Detailed Description
The present invention will be described in detail with reference to the following embodiments.
The invention comprises the following steps:
step 1: calculating the space correlation coefficient of ocean temperature and salinity layer by layer
The spatial correlation coefficient r of the temperature field T and the salinity field S is calculated by the following formula:
where the indices m, n represent two-dimensional spatial positions,
and &>
The spatial averages of T and S, respectively.
The temperature and salinity data is limited to the ocean, so the land sites and the defect sites are filled with their respective averages of temperature and salinity, respectively, before calculating the spatial correlation coefficients. According to equation (1), these points do not affect the correlation coefficient.
The spatial correlation coefficients for temperature and salinity are calculated at each water layer (z-coordinate slice, if Sigma, converted to z-coordinate slice) of the model or data product to obtain a sequence of spatial correlation coefficients (with respect to water depth).
And 2, step: finding out the water layer with the maximum spatial correlation coefficient of temperature and salinity
And searching a maximum value point in the sequence of the correlation coefficients of the warm-salt space. The warm salt mirror image layer is positioned in a water layer with a maximum value of a warm salt space correlation coefficient of 500m on the ocean. In the deep water layer at 2000m, the spatial correlation coefficient may reach a maximum value, even a maximum value. This water layer may not act as a warm saline mirror layer. The reason is that: (1) 2000m, observed values in deep sea are rare, and the temperature-salt space field error after difference is large; (2) The medium temperature in the deep ocean at 2000m, the salt structure is relatively stable, the space change is small, and meaningful information contained in the space distribution is less.
And 3, step 3: comparing the depth of the temperature-salt image layer of the numerical mode and the objective analysis field, and the correlation coefficient of the temperature and salt spatial distribution on the image layer
The depth of the temperature-salt image layer of the numerical model result to be evaluated and the temperature-salt spatial correlation coefficient on the temperature-salt image layer are calculated, and compared with the corresponding result on a reference data set (usually an objective analysis field), and the relative error is used as a measure of the model simulation level. The plurality of pattern results may be compared to a reference data set to assess the goodness of the pattern simulation level.
And the simulation errors of variables such as temperature, salinity, flow velocity and the like can be respectively compared on the temperature-salt mirror image layer by adopting a traditional comparison mode.
As shown in fig. 1, four data sets such as WOA, EN4, ishi, argo, etc. are taken as examples.
Firstly, calculating the temperature-salt spatial correlation coefficient layer by layer in each data set, and solving respective temperature-salt mirror image layers. Compared with a temperature salt mirror image layer derived according to the correlation of the temperature salt space average time sequence, the mirror image layer provided by the method is more stable. The depth of the mirror image layer obtained by directly calculating the spatial correlation coefficient is stable in four data sets and is between 100m and 200 m; whereas the mirror layers, derived from the correlation of the spatially averaged time series, differ greatly in different data sets. In addition, the temperature and salt space structures of the mirror image layers derived from the latter are not the most similar.
Second, the spatial distribution of temperature and salinity over the warm salt mirror is given. Each pair of temperature and salt spatial distributions was similar (see figure 2). The left column is the spatial distribution of the temperature of the four datasets over the mirror layer, and the right column is the respective salinity distribution.
FIG. 3 is the distribution of temperature-salt space correlation coefficient with depth in the WOA climatic seasonal temperature-salt field data. The spatial correlation coefficient reaches a maximum of 0.6 at 150m layer, which means that the spatial distribution of temperature and salinity of the water layer is similar. The temperature and salt space correlation coefficient can reach a maximum value even at the depth of 2000m, and the temperature and salt space correlation coefficient does not serve as a temperature and salt mirror image layer.
The invention also has the advantages that:
(1) The temperature and salinity spatial correlation is used as a basis to determine the temperature and salinity mirror image layer, so that a water layer with higher temperature and salinity spatial distribution similarity can be obtained, and the basic characteristics of the mirror image layer are better met;
(2) The method can be used for testing the simulation effect of the ocean numerical mode. When the ocean numerical model capability is evaluated by the traditional method, the ocean surface layer is often used as a reference surface. Because the ocean mode fuses more ocean surface observation data (such as satellite remote sensing data) in the assimilation process, the ocean element deviation of the surface layer is used as the measurement of the mode simulation level, and a certain dispute exists. The thermohaline mirror layer provides another reference surface for the evaluation of the mode simulation effect, and the reference surface is less influenced by assimilation factors;
(3) The temperature and salinity mirror image layer can be compared with the ocean surface in the same single variable comparison mode (such as independent comparison of temperature, salinity and the like) and can also be compared with the comprehensive simulation effect of the temperature and the salinity (such as the spatial correlation of the temperature and the salinity, the depth of the temperature and salinity mirror image layer and the like).
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not intended to limit the present invention in any way, and all simple modifications, equivalent variations and modifications made to the above embodiments according to the technical spirit of the present invention are within the scope of the present invention.
Claims (3)
1. The method for evaluating the ocean numerical model simulation capability by adopting the warm salt mirror image layer is characterized by comprising the following steps of:
step 1: calculating the spatial correlation coefficients of ocean temperature and salinity layer by layer;
the spatial correlation coefficient r of the temperature field T and the salinity field S is calculated by the following formula:
where the indices m, n represent two-dimensional spatial positions,
and &>
The spatial average values of T and S, respectively;
calculating the spatial correlation coefficient of temperature and salinity in each water layer of the model or data product to obtain a sequence of the spatial correlation coefficients;
step 2: finding out the water layer with the maximum spatial correlation coefficient of temperature and salinity;
searching a maximum value point in the sequence of the correlation coefficients of the temperature and salt space, wherein a temperature and salt mirror image layer is positioned in a water layer where the maximum value of the correlation coefficients of the temperature and salt space is 500m above the ocean;
and step 3: comparing the numerical mode with the temperature-salt mirror image layer depth of the objective analysis field and the correlation coefficient of temperature and salt spatial distribution on the mirror image layer; and calculating the temperature-salinity mirror image layer depth of the numerical mode result to be evaluated and the temperature-salinity spatial correlation coefficient on the temperature-salinity mirror image layer, comparing the temperature-salinity mirror image layer depth with the corresponding result on the reference data set, and taking the relative error as a measurement of the mode simulation level.
2. The method for evaluating the ocean numerical model simulation ability by adopting the warm salt mirror image layer according to claim 1, which is characterized by comprising the following steps of: in the step 1, the water layer is a z-coordinate layer, and the Sigma layer is firstly converted into the z-coordinate layer.
3. The method for evaluating the ocean numerical model simulation ability by adopting the thermohaline mirror layer according to claim 1, wherein: comparing the plurality of pattern results with a reference data set in the step 3 to evaluate the quality of the pattern simulation level; or the traditional comparison mode is adopted, and the simulation errors of the temperature, the salinity and the flow velocity variables are respectively compared on the temperature-salinity mirror image layer.
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| US9811614B2 (en) * | 2013-03-13 | 2017-11-07 | The United States Of America, As Represented By The Secretary Of The Navy | System and method for correcting a model-derived vertical structure of ocean temperature and ocean salinity based on velocity observations |
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| CN103940781A (en) * | 2014-04-24 | 2014-07-23 | 南京大学 | SMOS (Soil Moisture And Ocean Salinity) satellite inversion surface seawater salinity correction method based on underway data |
| CN107300561A (en) * | 2016-04-15 | 2017-10-27 | 北京空间飞行器总体设计部 | Ocean Salinity satellite based on many remote sensor combined detections |
| CN106886024A (en) * | 2017-03-31 | 2017-06-23 | 上海海洋大学 | Deep-sea multi-beam sound ray precise tracking method |
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