Analytical method and device for the impact of sea temperature anomaly on precipitation in maritime continental regions

Abstract

The present invention relates to a method and apparatus for analyzing the impact of sea temperature anomalies in maritime continental regions on precipitation, comprising the following steps: obtaining multi-source climate grid historical data for a target region; analyzing the relationship between sea temperature anomalies in the maritime continental region and East Asian summer monsoon anomalies based on the multi-source climate grid historical data to obtain a first result; analyzing the impact of summer sea temperature anomalies in the maritime continental region, independent of ENSO, on precipitation in the target region based on the multi-source climate grid historical data to obtain a second result; analyzing the impact of summer sea temperature anomalies in the maritime continental region on precipitation in the target region, while filtering out ENSO and IOD based on the multi-source climate grid historical data, to obtain a third result; and outputting the first, second, and third results. The present invention proposes a method for analyzing the impact of sea temperature anomalies in maritime continental regions on precipitation, which can effectively analyze the impact of sea temperature anomalies in maritime continental regions on the climate of a specific region and reveal the mechanism of the impact.

Description

Technical Field

[0001] The present invention relates to the technical field related to meteorological analysis, and in particular to a method and device for analyzing the impact of abnormal sea temperature on precipitation in a maritime continental region. Background Art

[0002] Major meteorological disasters are often associated with abnormal monsoon activity. Within the target region lies a unique region known as the Maritime Continental Area, located between [90°E–150°E, 10°S–20°N]. Its climatic characteristics are closely linked to the East Asian monsoon. Currently, most studies focus on climate variability in the Maritime Continental Area associated with Indian and Pacific Ocean sea temperature anomalies, while little analysis has been conducted on changes in the Maritime Continental Area itself. Consequently, there is currently no clear analytical method for climate analysis in this region.

[0003] Since studying the variability of sea temperature in maritime continental regions is crucial for understanding climate variability in these regions and their impact on regional and global climate, a method is needed to analyze the impact of sea temperature anomalies in these regions on precipitation. This method can effectively analyze the impact of sea temperature anomalies in these regions on the climate of specific regions and reveal their influencing mechanisms. Summary of the Invention

[0004] The purpose of the present invention is to solve at least one of the deficiencies of the prior art and to provide a method and device for analyzing the impact of abnormal sea temperature on precipitation in a maritime continental region.

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

[0006] Specifically, an analytical method for the impact of SST anomalies on precipitation in maritime continental regions is proposed, including the following:

[0007] Obtain multi-source historical climate grid data for the target area;

[0008] The first result is obtained by analyzing the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly based on the multi-source climate grid historical data;

[0009] The second result is obtained by analyzing the impact of the abnormal changes in sea surface temperature in the maritime continental region in summer, which is independent of ENSO, on the precipitation in the target area based on the multi-source climate grid historical data.

[0010] The third result is obtained by analyzing the historical data of multi-source climate grids and filtering out the impact of ENSO and IOD sea temperature anomalies in the maritime continental region on the precipitation in the target area.

[0011] The first result, the second result, and the third result are output to complete the analysis of the impact of the sea temperature anomaly in the maritime continental region on the precipitation in the target area.

[0012] Furthermore, specifically, the multi-source climate grid historical data includes:

[0013] The OISST V2 data from the National Oceanic and Atmospheric Administration of the United States includes daily sea surface temperature data with a grid resolution of 0.25°×0.25° and monthly mean SST data with a grid resolution of 1.0°×1.0°; the GODAS data includes monthly mean sea surface height, subsurface sea temperature, and ocean current data with a grid resolution of 0.33°×1.0°; the CMAP data includes monthly mean precipitation data and monthly mean outward longwave radiation data with a grid resolution of 2.5°×2.5°; the NCEP / NCAR reanalysis data includes variable The data include monthly mean data of wind field, wind stress, temperature, height field and humidity field at 10m, lower troposphere (850hPa), middle troposphere (500hPa) and upper troposphere (200hPa), monthly mean data of sea level pressure, net longwave radiation, net shortwave radiation, net sensible heat flux, net latent heat flux and total cloud cover, with a grid resolution of 2.5°×2.5°. The precipitation data in the target area are derived from the grid data of CN07, with a grid resolution of 0.25°×0.25°.

[0014] Furthermore, specifically, based on the multi-source climate grid historical data, the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly is analyzed to obtain the first result, including:

[0015] The sea surface temperature anomaly data in the region [90°E-150°E, 10°S-20°N] and the sea level pressure anomaly data in the region [105°E-150°E, 20°N-45°N] are selected. The singular value decomposition (SVD) method is used to calculate the main singular value decomposition modes SVD1 and SVD2 between the sea surface temperature anomaly (SSTA) in the maritime continental region and the sea level pressure anomaly in East Asia. The correlation index I is constructed based on the first two modes of the main modes. svd1 and I svd2 , and then analyze the relationship between the target area and the SSTA in the tropical region in the previous and same period, and then obtain the first result;

[0016] According to the time series of the left and right fields of SVD1 and SVD2, the time series of the left and right fields are normalized and their average values ​​are taken, which are defined as the SVD joint index, which are I svd1 and I svd2 :

[0017]

[0018]

[0019] PC1 L 、PC1 R are the time series of the left and right fields of SVD1, PC2L 、PC2 R are the time series of the left and right fields of SVD2, respectively.

[0020] The singular value decomposition (SVD) method can effectively correlate and analyze the coordinated changes in climate anomalies between the Maritime Continent and the target region. The SVD joint index can more intuitively and conveniently reflect the degree of coordinated changes between the two regions.

[0021] Furthermore, specifically, based on the multi-source climate grid historical data, the second result is obtained by analyzing the impact of the abnormal changes in sea temperature in the summer maritime continental region independent of ENSO on the precipitation in the target area, including:

[0022] Using a simple linear regression to filter out the ENSO signal, which is subtracted from all other time series data, such as sea surface temperature anomalies, EOF analysis was performed on the summer sea surface temperature anomalies over the maritime continent after filtering out the ENSO signal, yielding the first two modes. The spatial morphology of the main mode of sea surface temperature anomalies over the maritime continent before and after filtering out the ENSO signal was then compared. By calculating the composite difference field of precipitation and outward longwave radiation over the target region, the possible impact of the main mode of sea surface temperature anomalies over the maritime continent, independent of ENSO, on precipitation in China was analyzed, yielding the second result.

[0023] Let Y′ represent the time series of SST anomalies. Then, after filtering out the ENSO signal, the SST anomaly Yr can be expressed as:

[0024] Y′ r =Y′-αI′ N3JJA -βI′ N3DJF ,

[0025] where I′ N3JJA and I′ N3DJF The Nino3 index is calculated by screening the sea surface temperature data in the range of 150°W-90°W, 5°S-5°N. α and β are the regression coefficients of the Nino3 index and sea surface temperature anomaly in summer and previous winter, respectively.

[0026] Furthermore, based on the analysis of the multi-source climate grid historical data, the third result was obtained by filtering out the impact of the abnormal changes in sea temperature in the maritime continental region caused by ENSO and IOD on the precipitation in the target area. Specifically,

[0027] After filtering out the ENSO signal, a simple linear regression was used to further filter out the IOD signal. EOF analysis was performed on the SST anomalies over the maritime continental region after further filtering out the IOD signal to obtain its main mode. By calculating the synthetic difference field of precipitation and outward longwave radiation over the target region, the possible impact of the main mode of SST anomalies over the maritime continental region, independent of ENSO and IOD, on the precipitation in the target region was analyzed.

[0028] When filtering out the IOD signal of the JJA in the same summer, the IOD index of the JJA in the same summer is first filtered out, which is the time coefficient of the difference between the seasonal mean sea surface temperature anomalies in the region [50°E-70°W, 10°S-10°N] and the region [90°E-110°E, 10°S-EQ]. N3JJA and I′ N3DJF , and obtain the IOD index I′ independent of ENSO influence IODJJAr :

[0029] I′ IODJJAr =I′ IODJJA -λI′ N3JJA -τI′ N3DJF

[0030] The SST anomaly after filtering out ENSO and IOD signals is defined as Y′ r :

[0031] Y′ r =Y′-αI′ N3JJA -βI′ N3DJF -ηI′ IODJJAr

[0032] where I′ N3JJA and I′ N3DJF The Nino3 index is calculated by screening the sea surface temperature data in the range of 150°W-90°W, 5°S-5°N. α and β are the regression coefficients of the Nino3 index and sea surface temperature anomaly in summer and previous winter, respectively; I′ IODJJAr represents the IOD index independent of the influence of ENSO, and η is the regression coefficient of the IOD index independent of the influence of ENSO in summer and the SST anomaly.

[0033] The present invention also proposes an analysis device for the impact of sea temperature anomalies on precipitation in a maritime continental region, comprising the following:

[0034] Data acquisition module, used to obtain multi-source climate grid historical data of the target area;

[0035] A first analysis module is configured to analyze the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly based on the multi-source climate grid historical data to obtain a first result;

[0036] The second analysis module is used to analyze the impact of the abnormal change of sea surface temperature in the maritime continental region in summer, which is independent of ENSO, on the precipitation in the target area based on the multi-source climate grid historical data to obtain a second result;

[0037] A third analysis module is used to analyze the multi-source climate grid historical data and filter out the impact of the abnormal changes in sea temperature in the maritime continental region caused by ENSO and IOD on the precipitation in the target area to obtain a third result;

[0038] The result output module is used to output the first result, the second result and the third result to complete the analysis of the impact of the sea temperature anomaly in the maritime continental region on the precipitation in the target area.

[0039] The beneficial effects of the present invention are:

[0040] This paper proposes a method and device for analyzing the impact of SST anomalies on precipitation in maritime continental regions. This method conducts a multi-dimensional analysis of the impact of SST anomalies on precipitation in target regions, analyzing the relationship between SST anomalies and East Asian summer monsoon anomalies, filtering out ENSO signals, and filtering out both ENSO and IOD signals. This method effectively reveals the impact of SST anomalies in maritime continental regions on the climate of specific regions. Furthermore, this method can also be used to analyze the impact of SST anomalies in other regions of interest, demonstrating strong portability and universal applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and other features of the present disclosure will become more apparent through a detailed description of the embodiments shown in conjunction with the accompanying drawings. The same reference numerals in the drawings of the present disclosure represent the same or similar elements. Obviously, the drawings described below are only some embodiments of the present disclosure. It is possible for a person skilled in the art to derive other drawings based on these drawings without inventive effort. In the drawings:

[0042] Figure 1 Shown is a flow chart of the method for analyzing the impact of sea temperature anomalies on precipitation in the maritime continental region according to the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other unless there is a conflict. The same reference numerals used throughout the drawings indicate the same or similar parts.

[0044] Example 1, with reference to Figure 1 The present invention proposes a method for analyzing the impact of sea temperature anomalies on precipitation in maritime continental regions, including the following:

[0045] Step 110: Acquire multi-source climate grid historical data of the target area;

[0046] Step 120: Analyze the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly based on the multi-source climate grid historical data to obtain a first result; this result is the impact of the overall climate and changes of the maritime continent on precipitation in the target area, including the impact of climate variation signals in the Pacific and Indian Oceans.

[0047] Step 130: Based on the multi-source climate grid historical data, the impact of ENSO-independent summer sea temperature anomalies in the Maritime Continental region on precipitation in the target region is analyzed to obtain a second result. Because ENSO is the strongest interannual climate variability signal in the tropical Pacific, sea temperature anomalies in the Pacific side of the Maritime Continental region, through vertical circulation and equatorial waves, are also strongly influenced by the ENSO signal in areas surrounding the Indonesian archipelago and the tropical western Pacific. The second result represents the characteristics and impact of sea temperature anomalies in the Maritime Continental region after filtering out the influence of ENSO. The impact of sea temperature anomalies in this region on precipitation in the target region is further analyzed after eliminating the influence of ENSO.

[0048] Step 140: Analyze the impact of abnormal sea temperature changes in the maritime continental region on the precipitation in the target area based on the multi-source climate grid historical data, filtering out ENSO and IOD, to obtain a third result; since the Indian Ocean side of the maritime continental region will be affected by the Indian Ocean air-sea interaction process, IOD, as an important signal of tropical Indian Ocean air-sea interaction, its impact on the maritime continental region cannot be ignored. Therefore, on the basis of excluding ENSO, further filter out the IOD signal, and analyze the impact of the maritime continental region on the precipitation in the target area after filtering out the dual effects of ENSO and IOD. This result can better reflect the connection between the local climate change in the maritime continental region itself and the precipitation in the target area;

[0049] The three results progressively analyze the impact of sea surface temperature in the maritime continental region on precipitation in the target area when it is affected by the Pacific and Indian Ocean climate variability signals and when it is independent of the two signals.

[0050] Step 150: Output the first result, the second result, and the third result to complete the analysis of the impact of the sea temperature anomaly in the maritime continental region on the precipitation in the target area.

[0051] In this Example 1, a multi-dimensional analysis was conducted on the impact mechanism of SST anomalies on precipitation in the target area under two scenarios: SST anomalies and East Asian summer monsoon anomalies, filtering out ENSO signals, and filtering out both ENSO and IOD signals. This effectively revealed the impact of SST anomalies in the maritime continental region on the climate of a specific region.

[0052] As a preferred embodiment of the present invention, specifically, the multi-source climate grid historical data includes:

[0053] The OISST V2 data from the National Oceanic and Atmospheric Administration of the United States includes daily sea surface temperature data with a grid resolution of 0.25°×0.25° and monthly mean SST data with a grid resolution of 1.0°×1.0°; the GODAS data includes monthly mean sea surface height, subsurface sea temperature, and ocean current data with a grid resolution of 0.33°×1.0°; the CMAP data includes monthly mean precipitation data and monthly mean outward longwave radiation data with a grid resolution of 2.5°×2.5°; the NCEP / NCAR reanalysis data includes variable The data include monthly mean data of wind field, wind stress, temperature, height field and humidity field at 10m, lower troposphere (850hPa), middle troposphere (500hPa) and upper troposphere (200hPa), monthly mean data of sea level pressure, net longwave radiation, net shortwave radiation, net sensible heat flux, net latent heat flux and total cloud cover, with a grid resolution of 2.5°×2.5°. The precipitation data in the target area are derived from the grid data of CN07, with a grid resolution of 0.25°×0.25°.

[0054] In this preferred trial method, these data can reflect the atmospheric circulation characteristics of each altitude layer, the temperature, pressure, altitude, radiation and other characteristics of the sea surface and subsurface, as well as the precipitation characteristics of the target area.

[0055] As a preferred embodiment of the present invention, specifically, the first result obtained by analyzing the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly based on the multi-source climate grid historical data includes:

[0056] The sea surface temperature anomaly data in the region [90°E–150°E, 10°S–20°N] and the sea level pressure anomaly data in the region [105°E–150°E, 20°N-45°N] are selected. The singular value decomposition (SVD) method is used to calculate the main singular value decomposition modes SVD1 and SVD2 between the sea surface temperature anomaly (SSTA) in the maritime continental region and the sea level pressure anomaly in East Asia. The correlation index I is constructed based on the first two modes of the main modes. svd1 and I svd2 , and then analyze the relationship between the target area and the SSTA in the tropical region in the previous and same period, and then obtain the first result;

[0057] According to the time series of the left and right fields of SVD1 and SVD2, the time series of the left and right fields are normalized and their average values ​​are taken, which are defined as the SVD joint index, which are I svd1 and I svd2 :

[0058]

[0059]

[0060] PC1 L 、PC1 R are the time series of the left and right fields of SVD1, PC2 L 、PC2 R are the time series of the left and right fields of SVD2, respectively.

[0061] In this preferred embodiment, the singular value decomposition (SVD) method can effectively correlate and analyze the coordinated changes in climate anomalies between the maritime continental region and the target region. The SVD joint index can more intuitively and conveniently reflect the degree of coordinated changes between the two regions.

[0062] As a preferred embodiment of the present invention, specifically, the second result is obtained by analyzing the impact of abnormal changes in sea temperature in the maritime continental region in summer independent of ENSO on precipitation in the target area based on the multi-source climate grid historical data, including:

[0063] Using a simple linear regression to filter out the ENSO signal, which is subtracted from all other time series data, such as sea surface temperature anomalies, EOF analysis was performed on the summer sea surface temperature anomalies over the maritime continent after filtering out the ENSO signal, yielding the first two modes. The spatial morphology of the main mode of sea surface temperature anomalies over the maritime continent before and after filtering out the ENSO signal was then compared. By calculating the composite difference field of precipitation and outward longwave radiation over the target region, the possible impact of the main mode of sea surface temperature anomalies over the maritime continent, independent of ENSO, on precipitation in China was analyzed, yielding the second result.

[0064] Let Y′ represent the time series of SST anomaly, then the SST anomaly after filtering out ENSO signal is Y′ r It can be expressed as:

[0065] Y′ r =Y′-αI′ N3JJA -βI′ N3DJF ,

[0066] where I′ N3JJA and I′ N3DJF The Nino3 index is calculated by screening the sea surface temperature data in the range of 150°W-90°W, 5°S-5°N. α and β are the regression coefficients of the Nino3 index and sea surface temperature anomaly in summer and previous winter, respectively.

[0067] In this preferred embodiment, since ENSO's impact on the Maritime Continent is clear, a simple linear regression filtering method is used to intuitively and easily filter out ENSO's impact on the Maritime Continent. Since the effects of ENSO during the same summer and the preceding winter are independent of each other, when filtering out ENSO's impact, the effects of both periods are filtered out together to minimize the impact of ENSO. Furthermore, calculating the composite difference field of precipitation and outward longwave radiation in the target region can amplify and highlight precipitation anomalies, thereby more clearly analyzing the impact of sea temperature anomalies in the Maritime Continent on precipitation in the target region.

[0068] As a preferred embodiment of the present invention, a third result is obtained by analyzing the multi-source climate grid historical data and filtering out the impact of abnormal changes in sea temperature in the maritime continental region caused by ENSO and IOD on precipitation in the target area. Specifically,

[0069] After filtering out the ENSO signal, a simple linear regression was used to further filter out the IOD signal. EOF analysis was performed on the SST anomalies over the maritime continental region after further filtering out the IOD signal to obtain its main mode. By calculating the synthetic difference field of precipitation and outward longwave radiation over the target region, the possible impact of the main mode of SST anomalies over the maritime continental region, independent of ENSO and IOD, on the precipitation in the target region was analyzed.

[0070] When filtering out the IOD signal of the JJA in the same summer, the IOD index of the JJA in the same summer is first filtered out, which is the time coefficient of the difference between the seasonal mean sea surface temperature anomalies in the region [50°E-70°W, 10°S-10°N] and the region [90°E-110°E, 10°S-EQ]. N3JJA and I′ N3DJF , and obtain the IOD index I′ independent of ENSO influence IODJJAr :

[0071] I′ IODJJAr =I′ IODJJA -λI′ N3JJA -τI′ N3DJF

[0072] The SST anomaly after filtering out ENSO and IOD signals is defined as Y′ r :

[0073] Y′ r =Y′-αI′ N3JJA -βI′ N3DJF -ηI′ IoDJJAr

[0074] where I′ N3JJA and I′ N3DJFThe Nino3 index is calculated by screening the sea surface temperature data in the range of 150°W-90°W, 5°S-5°N. α and β are the regression coefficients of the Nino3 index and sea surface temperature anomaly in summer and previous winter, respectively; I′ IODJJAr represents the IOD index independent of the influence of ENSO, and η is the regression coefficient of the IOD index independent of the influence of ENSO in summer and the SST anomaly.

[0075] In this preferred embodiment, since the IOD has a clear impact on the maritime continental region, the use of a univariate linear regression filtering method can intuitively and easily filter out the impact of the IOD on the maritime continental region. Since the impact of the IOD is very weak in winter, after filtering out the comprehensive impact of ENSO, only the IOD signal from the summer of the same period is filtered out. There is also a certain connection between the IOD and ENSO in the summer of the same period. To avoid repeatedly filtering out the mutually related portions between the IOD and ENSO, when calculating the IOD signal for the summer of the same period, the univariate linear regression filtering method is first used to remove the ENSO-related portion of the IOD signal from the summer of the same period, resulting in an IOD signal completely unrelated to ENSO. On this basis, the ENSO comprehensive signal and the independent IOD signal are then filtered out of the sea temperature in the maritime continental region, thereby obtaining the anomaly characteristics of the maritime continental sea temperature that are independent of ENSO and IOD.

[0076] The present invention also proposes an analysis device for the impact of sea temperature anomalies on precipitation in a maritime continental region, comprising the following:

[0077] Data acquisition module, used to obtain multi-source climate grid historical data of the target area;

[0078] A first analysis module is configured to analyze the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly based on the multi-source climate grid historical data to obtain a first result;

[0079] The second analysis module is used to analyze the impact of the abnormal change of sea surface temperature in the maritime continental region in summer, which is independent of ENSO, on the precipitation in the target area based on the multi-source climate grid historical data to obtain a second result;

[0080] A third analysis module is used to analyze the multi-source climate grid historical data and filter out the impact of the abnormal changes in sea temperature in the maritime continental region caused by ENSO and IOD on the precipitation in the target area to obtain a third result;

[0081] The result output module is used to output the first result, the second result and the third result to complete the analysis of the impact of the sea temperature anomaly in the maritime continental region on the precipitation in the target area.

[0082] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0083] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or system that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0084] Although the present invention has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but rather should be construed as providing a broad possible interpretation of these claims in view of the prior art by reference to the appended claims, thereby effectively encompassing the intended scope of the invention. In addition, the invention has been described above in terms of embodiments foreseen by the inventors for the purpose of providing a useful description, and those insubstantial modifications of the invention that are not currently foreseen may still represent equivalent modifications of the invention.

[0085] The above description is merely a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. As long as the technical effects of the present invention are achieved by the same means, they shall fall within the scope of protection of the present invention. Within the scope of protection of the present invention, various modifications and variations of the technical solutions and / or implementation methods may be made.

Claims

1. The method for analyzing the impact of sea temperature anomalies on precipitation in maritime continental regions is characterized by: These include: Obtain multi-source historical climate grid data for the target area; The first result is obtained by analyzing the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly based on the multi-source climate grid historical data; The second result is obtained by analyzing the impact of the abnormal changes in sea surface temperature in the maritime continental region in summer, which is independent of ENSO, on the precipitation in the target area based on the multi-source climate grid historical data. The third result is obtained by analyzing the historical data of multi-source climate grids and filtering out the impact of ENSO and IOD sea temperature anomalies in the maritime continental region on the precipitation in the target area. Outputting the first result, the second result, and the third result to complete the analysis of the impact of the sea temperature anomaly in the maritime continental region on the precipitation in the target area; Specifically, based on the multi-source climate grid historical data, the second result is obtained by analyzing the impact of the abnormal changes in sea temperature in the summer maritime continental region independent of ENSO on the precipitation in the target area, including: A simple linear regression was used to filter out the ENSO signal, which was subtracted from all other time series of SST anomalies. EOF analysis was performed on the summer SST anomalies over the maritime continent after ENSO removal, yielding the first two modes. A comparative analysis of the spatial morphology of the main modes of SST anomalies over the maritime continent before and after ENSO removal was performed. By calculating the composite difference field of precipitation and outward longwave radiation over the target region, the possible impact of the main mode of SST anomalies over the maritime continent, independent of ENSO, on precipitation in China was analyzed, yielding the second result. use represents the time series of sea temperature anomalies, then the sea temperature anomaly after filtering out the ENSO signal is It can be expressed as: , in and The Nino3 index represents the Nino3 index of the summer and the previous winter respectively. The Nino3 index is calculated by screening the sea surface temperature data within the range of 150°W-90°W, 5°S-5°N. and are the regression coefficients of the Nino3 index and SST anomalies in summer and previous winter, respectively.

2. The method for analyzing the impact of sea temperature anomalies on precipitation in the maritime continental region according to claim 1, characterized in that: Specifically, the multi-source climate grid historical data includes: The OISST V2 data from the National Oceanic and Atmospheric Administration (NOAA) provide daily sea surface temperature data with a grid resolution of 0.25°×0.25°, and monthly mean SST data with a grid resolution of 1.0°×1.0°; the GODAS data provide monthly mean sea surface height, subsurface sea temperature, and ocean current data with a grid resolution of 0.33°×1.0°; the CMAP data provide monthly mean precipitation data and monthly mean outward longwave radiation data with a grid resolution of 2.5°×2.5°; and the NCEP / NCAR reanalysis data, with variables including the 10m, lower troposphere (850 hPa), middle troposphere (500 hPa), and upper troposphere (200 hPa) monthly mean data on wind field, wind stress, temperature, altitude field, and humidity field; monthly mean data on sea level pressure; monthly mean data on net longwave radiation, net shortwave radiation, net sensible heat flux, net latent heat flux, and total cloud cover; all with a grid resolution of 2.5° × 2.5°; precipitation data in the target area are derived from the CN07 grid data with a grid resolution of 0.25° × 0.25°.

3. The method for analyzing the impact of sea temperature anomalies on precipitation in maritime continental regions according to claim 1, characterized in that: Specifically, based on the multi-source climate grid historical data, the first results of the analysis of the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly are obtained, including: The sea surface temperature anomaly data in the region [90°E–150°E, 10°S–20°N] and the sea level pressure anomaly data in the region [105°E–150°E, 20°N–45°N] were selected. The singular value decomposition (SVD) method was used to calculate the main singular value decomposition modes SVD1 and SVD2 between the sea surface temperature anomaly (SSTA) in the maritime continental region and the sea level pressure anomaly in East Asia. The correlation index was constructed based on the first two modes of the main modes. and , and then analyze the relationship between the target area and the SSTA in the tropical region in the previous and same period, and then obtain the first result; According to the time series of the left and right fields of SVD1 and SVD2, the time series of the left and right fields are normalized and their average values ​​are taken, which are defined as the SVD joint index, respectively. and : , , in 、 are the time series of the left and right fields of SVD1, 、 are the time series of the left and right fields of SVD2, respectively.

4. The method for analyzing the impact of sea temperature anomalies on precipitation in maritime continental regions according to claim 1, characterized in that: The third result is obtained based on the analysis of the multi-source climate grid historical data, while filtering out the impact of ENSO and IOD on the precipitation in the maritime continental region. Specifically, After filtering out the ENSO signal, a simple linear regression was used to further filter out the IOD signal. EOF analysis was performed on the SST anomalies over the maritime continental region after further filtering out the IOD signal to obtain its main mode. By calculating the synthetic difference field of precipitation and outward longwave radiation over the target region, the possible impact of the main mode of SST anomalies over the maritime continental region, independent of ENSO and IOD, on the precipitation in the target region was analyzed. When filtering out the IOD signal of JJA in the same summer, we first filter out the time coefficient of the IOD index of JJA in the same summer, which is the seasonal mean difference between the sea surface temperature anomalies in the region [50°E–70°W, 10°S–10°N] and the region [90°E–110°E, 10°S-EQ]. and , and obtain the IOD index independent of ENSO influence : , The SST anomaly after filtering out ENSO and IOD signals is defined as : , in and The Nino3 index represents the Nino3 index of the summer and the previous winter respectively. The Nino3 index is calculated by screening the sea surface temperature data within the range of 150°W-90°W, 5°S-5°N. and are the regression coefficients of the Nino3 index and SST anomalies in summer and previous winter, respectively; represents the IOD index independent of ENSO influence, is the regression coefficient of the IOD index and SST anomaly in summer, which is independent of ENSO.

5. An analysis device for the impact of sea temperature anomalies on precipitation in maritime continental regions, characterized in that: These include: Data acquisition module, used to obtain multi-source climate grid historical data of the target area; A first analysis module is configured to analyze the relationship between the sea surface temperature anomaly in the maritime continental region and the East Asian summer monsoon anomaly based on the multi-source climate grid historical data to obtain a first result; The second analysis module is used to analyze the impact of the abnormal change of sea surface temperature in the maritime continental region in summer, which is independent of ENSO, on the precipitation in the target area based on the multi-source climate grid historical data to obtain a second result; A third analysis module is used to analyze the multi-source climate grid historical data and filter out the impact of the abnormal changes in sea temperature in the maritime continental region caused by ENSO and IOD on the precipitation in the target area to obtain a third result; A result output module is used to output the first result, the second result, and the third result to complete the analysis of the impact of the sea temperature anomaly in the maritime continental region on the precipitation in the target area; Specifically, based on the multi-source climate grid historical data, the second result is obtained by analyzing the impact of the abnormal changes in sea temperature in the summer maritime continental region independent of ENSO on the precipitation in the target area, including: A simple linear regression was used to filter out the ENSO signal, which was subtracted from all other time series of SST anomalies. EOF analysis was performed on the summer SST anomalies over the maritime continent after ENSO removal, yielding the first two modes. A comparative analysis of the spatial morphology of the main modes of SST anomalies over the maritime continent before and after ENSO removal was performed. By calculating the composite difference field of precipitation and outward longwave radiation over the target region, the possible impact of the main mode of SST anomalies over the maritime continent, independent of ENSO, on precipitation in China was analyzed, yielding the second result. use represents the time series of sea temperature anomalies, then the sea temperature anomaly after filtering out the ENSO signal is It can be expressed as: , in and The Nino3 index represents the Nino3 index of the summer and the previous winter respectively. The Nino3 index is calculated by screening the sea surface temperature data within the range of 150°W-90°W, 5°S-5°N. and are the regression coefficients of the Nino3 index and SST anomalies in summer and previous winter, respectively.

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