Multi-mode set sea temperature three-step method sub-seasonal climate prediction method and system

Abstract

The invention relates to the technical field of climate forecasting, in particular to a multi-mode set sea temperature three-step seasonal climate forecasting method and system, and the method comprises the following steps: obtaining sea surface temperature data and sea ice coverage rate data; preprocessing the sea surface temperature data and the sea ice coverage rate data to obtain standard input data; constructing an input field of an atmospheric circulation mode based on the standard input data; inputting the input field into the atmospheric circulation mode, and obtaining atmospheric circulation information and earth surface information based on the atmospheric circulation mode; and according to the atmospheric circulation information and the earth surface information, performing dynamic downscaling based on a regional climate mode to obtain a high-precision sub-seasonal climate prediction result. According to the method, the problem of insufficient precision of the existing prediction technology is effectively solved, the accuracy and stability of the sub-seasonal climate prediction are improved, a new idea is provided for the sub-seasonal climate prediction, the sub-seasonal climate prediction skill is improved, and the prediction product precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of climate prediction, and in particular to a three-step sub-seasonal climate prediction method and system for multi-model ensemble sea surface temperature. Background Art

[0002] The World Weather Research Programme and the World Climate Research Programme of the World Meteorological Organization jointly launched the Sub-seasonal to Seasonal Prediction Project, aiming to support the sub-seasonal prediction business needs in fields such as agriculture, energy, and transportation. Therefore, strengthening the research and application of refined sub-seasonal climate prediction technology has important scientific significance and extensive social application value.

[0003] At present, sub-seasonal to seasonal climate prediction products can be provided in Europe, the United States and other regions. These products have good performance in the low-latitude region, but the prediction level for the mid-high latitude region, especially the summer precipitation in East Asia, is relatively low. With the proposal of the concept of the Chinese climate service framework, meteorological services for major events urgently need to provide sub-seasonal scale high-resolution climate prediction products, while the current international mainstream global climate models cannot meet the prediction requirements of relevant accuracy and accuracy. Therefore, it is urgent to carry out research on sub-seasonal refined prediction technology.

[0004] Sea surface temperature anomaly is an important external forcing factor affecting the sub-seasonal changes of the East Asian summer monsoon and flood season precipitation. However, the lack of understanding of the process of external forcing factors such as sea surface temperature affecting the atmospheric circulation and sub-seasonal changes of precipitation has greatly hindered the understanding of the sub-seasonal predictability of the East Asian summer monsoon and the improvement of the sub-seasonal prediction level.

[0005] In view of this, the present invention discloses a three-step sub-seasonal climate prediction method and system for multi-model ensemble sea surface temperature. First, obtain the sea surface temperature data and sea ice coverage data of multiple advanced global ocean-land-atmosphere-ice coupled climate system models, and after statistical correction, preprocessing, and ensemble averaging, input them into the global atmospheric circulation model for non-ocean-atmosphere coupled prediction, and extract the atmospheric circulation information to drive the regional climate model for dynamic downscaling to obtain high-precision sub-seasonal climate prediction results. It effectively solves the problem of insufficient accuracy of existing prediction technologies, improves the accuracy and stability of sub-seasonal forecasts, provides new ideas for sub-seasonal prediction, can improve the sub-seasonal prediction skills of climate models for China, and enhances the accuracy of prediction products. Summary of the Invention

[0006] Aiming at the defects in the prior art, the present invention provides a three-step sub-seasonal climate prediction method and system for multi-model ensemble sea surface temperature.

[0007] To achieve the above object, in a first aspect, the present invention provides a multi-mode ensemble sea surface temperature three-step sub-seasonal climate prediction method, and the method includes the following steps: obtaining sea surface temperature data and sea ice coverage data, and preprocessing the sea surface temperature data and the sea ice coverage data to obtain standard input data; constructing an input field of a general circulation model based on the standard input data; inputting the input field into the general circulation model, and obtaining general circulation information and surface information based on the general circulation model; and based on the general circulation information and the surface information, performing dynamic downscaling based on a regional climate model to obtain a high-precision sub-seasonal climate prediction result. Through multi-source data fusion and multi-mode collaborative optimization, the present invention significantly improves the spatio-temporal resolution and reliability of the high-precision sub-seasonal climate prediction result; standardized preprocessing ensures the spatio-temporal consistency of sea temperature and sea ice data, providing high-quality input for the model; the general circulation model couples surface information to accurately depict the key processes of sea-air interaction; finally, the dynamic downscaling technology of the regional climate model effectively captures local climate characteristics, breaking through the resolution limitation of traditional global models; achieving high-precision prediction of sub-seasonal climate events such as extreme weather and season transition.

[0008] Optionally, the obtaining of the sea surface temperature data and the sea ice coverage data includes: obtaining sea surface temperature prediction products and sea ice coverage prediction products of multiple advanced global sea-land-air-ice coupled climate system models; obtaining the sea surface temperature data according to the sea surface temperature prediction products; and obtaining the sea ice coverage data according to the sea ice coverage prediction products. The present invention obtains sea temperature and sea ice prediction products through multi-mode ensemble, makes full use of the advantages of multi-source models, effectively reduces the uncertainty of a single model, and improves the spatio-temporal coverage rate and physical consistency of the data. By fusing key element data of sea-air coupling, the thermal state of the ocean is accurately depicted, providing more reliable data support for subsequent general circulation simulation and a solid data foundation for sub-seasonal climate prediction.

[0009] Optionally, the preprocessing of the sea surface temperature data and the sea ice coverage data to obtain standard input data includes: judging the missing data or null data of the sea surface temperature data and the sea ice coverage data to obtain a judgment result; based on the judgment result, using the neighborhood difference method to circularly fill the missing data or the null data; after circular filling, using linear interpolation to improve the horizontal resolution of the sea surface temperature data and the sea ice coverage data; combining the horizontal resolution, modifying the sea surface temperature data and the sea ice coverage data according to the grid resolution of the input field of the atmospheric circulation model to obtain the standard input data. The present invention significantly improves the data quality and model adaptability through multi-level optimization; the circular filling strategy based on the neighborhood difference method effectively repairs data holes, ensures the continuity of the physical field through spatial correlation constraints, and avoids false mutations during model operation; after linear interpolation improves the resolution, the ability to capture ocean phenomena is enhanced; by dynamically matching the grid resolution of the atmospheric circulation model, the scale matching between the input field and the model architecture is achieved, ensuring the computational efficiency of sub-seasonal climate prediction.

[0010] Optionally, the use of linear interpolation to improve the horizontal resolution of the sea surface temperature data and the sea ice coverage data includes: in the one-dimensional case, the linear interpolation method satisfies the following relationship: ; where is the value of the new grid point, is the distance between the new grid point and the original data grid point , is the value corresponding to the original data grid point , is the distance between the new grid point and the original data grid point , is the value corresponding to the original data grid point . The present invention significantly optimizes the data quality through spatial continuity constraints; the new grid point is calculated by distance weighting based on adjacent grid points, and spatial smooth transition is achieved through weight allocation, effectively eliminating the stepped mutations caused by insufficient resolution of the original data; the key features of the sea temperature gradient are retained, the resolution of the sea ice edge transition zone is improved, a finer sea-air interface thermal driving field is provided for the atmospheric circulation model, and the physical authenticity of the initial field of sub-seasonal prediction is significantly improved.

[0011] Optionally, constructing the input field of the atmospheric circulation model based on the standard input data includes: performing equal-weight averaging on the sea surface temperature data according to the standard input data to obtain the ensemble-averaged sea surface temperature data; performing equal-weight averaging on the sea ice coverage data according to the standard input data to obtain the ensemble-averaged sea ice coverage data; using the ensemble-averaged sea surface temperature data and the ensemble-averaged sea ice coverage data as the input field of the atmospheric circulation model. The present invention significantly improves the physical consistency of the input field through multi-model ensemble averaging. First, equal-weight averaging operations are performed on sea temperature and sea ice data, effectively eliminating the random errors and systematic biases of single-model predictions and making the ocean boundary conditions closer to the real state. Second, the ensemble averaging process smooths the data noise and strengthens the spatial continuity of the ocean thermal signal, providing a more stable driving field for the atmospheric circulation model. The finally constructed input field reduces the overfitting risk and enhances the predictability by retaining the multi-model consensus information, providing a reliable physical basis for sub-seasonal climate prediction.

[0012] Optionally, performing equal-weight averaging on the sea surface temperature data to obtain the ensemble-averaged sea surface temperature data includes: the ensemble-averaged sea surface temperature data satisfies the following relationship: ; where is the ensemble-averaged sea surface temperature data, is the total number of models, is the index variable of the model, is the th sea surface temperature data of the

[0013] The present invention significantly improves the reliability of the sea temperature field through multi-model fusion. The ensemble-averaged sea surface temperature data is obtained by equal-weight averaging the results of multiple models, effectively suppressing the outliers caused by the initial field errors or parameterization defects of a single model; retaining the independent climatological characteristics of each model, and eliminating the high-frequency noise through statistical averaging, significantly enhancing the credibility of sub-seasonal climate prediction. ; where is the ensemble-averaged sea ice coverage data, is the total number of models, is the index variable of the model, is the Sea ice coverage data of multiple models. The present invention significantly improves the physical consistency of the sea ice field through multi-model fusion. Equal-weight averaging effectively eliminates the discrete deviation caused by differences in sea ice parameterization schemes in a single model, retains the independent expression of each model for the thermodynamic-dynamic processes of sea ice, and suppresses spurious oscillations through statistical averaging, effectively enhancing the simulation ability of the ocean-atmosphere coupling process in sub-seasonal climate prediction.

[0014] Optionally, the step of inputting the input field into the general circulation model and obtaining general circulation information and surface information based on the general circulation model includes: inputting the input field into the general circulation model and obtaining a non-ocean-atmosphere coupling prediction result based on the general circulation model; extracting the general circulation information and the surface information based on the non-ocean-atmosphere coupling prediction result. The present invention realizes efficient extraction of key variables through the operation of a non-ocean-atmosphere coupling model. First, the one-way driving model greatly reduces the computational complexity, quickly generates general circulation information and surface parameters, and significantly shortens the prediction time. Second, the modular design effectively isolates the ocean forcing signal from the atmospheric response process, facilitating quantification of the independent influence of ocean boundary conditions on the general circulation. Finally, the extracted high-precision initial field provides spatio-temporally consistent data input for the regional climate model.

[0015] Optionally, the step of obtaining a high-precision sub-seasonal climate prediction result through dynamic downscaling based on the general circulation information and the surface information includes: obtaining the initial field and lateral boundary conditions of the regional climate model based on the general circulation information and the surface information; driving the regional climate model based on the initial field and the lateral boundary conditions to achieve dynamic downscaling to obtain the high-precision sub-seasonal climate prediction result. The present invention significantly improves the refinement level of sub-seasonal prediction through dynamic downscaling. Using the general circulation field to construct a high-resolution initial field and lateral boundary conditions effectively transmits large-scale circulation signals to the regional climate model, ensuring the physical consistency of the ocean-atmosphere interaction process. Furthermore, through dynamic downscaling by the regional climate model, the accuracy of the high-precision sub-seasonal climate prediction result is effectively improved, providing more accurate climate guidance for industry decision-making.

[0016] Second aspect, the present invention provides a multi-model ensemble sea surface temperature three-step sub-seasonal climate prediction system. The system executes the multi-model ensemble sea surface temperature three-step sub-seasonal climate prediction method provided by the present invention. The system includes an input device, an output device, a processor, and a memory. The advantage lies in that: the hardware facilities integrated in the present invention have excellent performance. The input device, the output device, the processor, and the memory are interconnected with each other, and the information transfer between each component is smooth. Through the interaction of multiple hardware facilities, an efficient information processing system is constructed. By integrating high-performance hardware facilities, the present invention significantly improves the efficiency of sub-seasonal climate prediction; optimizes the resource utilization rate of regional climate model dynamic downscaling, improves the overall effectiveness of the prediction system, and provides stable and reliable technical support for sub-seasonal climate prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of a multi-model ensemble sea surface temperature three-step sub-seasonal climate prediction method according to an embodiment of the present invention;

[0018] Figure 2 It is a comparison chart of the climatological distribution of the rain belt position according to an embodiment of the present invention;

[0019] Figure 3 It is a framework diagram of a multi-model ensemble sea surface temperature three-step sub-seasonal climate prediction system according to an embodiment of the present invention;

[0020] Figure 4 It is an execution flowchart of a multi-model ensemble sea surface temperature three-step sub-seasonal climate prediction system according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described here are only for illustrative purposes and are not used to limit the present invention. In the following description, in order to provide a thorough understanding of the present invention, a large number of specific details are elaborated. However, it is obvious to those of ordinary skill in the art that: it is not necessary to adopt these specific details to implement the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0022] Throughout the specification, references to "one embodiment", "an embodiment", "one example" or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Thus, the phrases "in one embodiment", "in an embodiment", "one example" or "an example" appearing throughout the specification do not necessarily all refer to the same embodiment or example. Additionally, the particular features, structures, or characteristics may be combined in any suitable combination and / or sub-combination in one or more embodiments or examples. Further, those of ordinary skill in the art should understand that the diagrams provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0023] Please refer to Figure 1 , an embodiment of the present invention provides a multi-mode integrated sea surface temperature three-step sub-seasonal climate prediction method, which includes the following steps:

[0024] S1. Obtain sea surface temperature data and sea ice coverage data, and preprocess the sea surface temperature data and the sea ice coverage data to obtain standard input data.

[0025] In this embodiment, sea surface temperature prediction products and sea ice coverage prediction products of multiple advanced global ocean-land-atmosphere-ice coupled climate system models in sub-seasonal prediction operations are obtained as sub-seasonal model products.

[0026] Specifically, considering data quality and timeliness, relevant prediction ability analysis literature is investigated, the required advanced global ocean-land-atmosphere-ice coupled climate system model products are identified, and sub-seasonal model products are obtained. The global ocean-land-atmosphere-ice coupled climate system models selected are the Beijing Climate Centre Sub-seasonal to Seasonal and Interannual Prediction System Version 3 (abbreviated as CPSv3) model of the China Meteorological Administration, the Integrated Forecasting System (abbreviated as IFS) model of the European Centre for Medium-Range Weather Forecasts, and the Climate Forecast System Version 2 (abbreviated as CFSv2) model of the National Centers for Environmental Prediction of the United States.

[0027] Further, based on the CPSv3 model, IFS model, and CFSv2 model as a multi-model ensemble, sea surface temperature prediction products and sea ice concentration prediction products of the CPSv3 model, IFS model, and CFSv2 model are obtained respectively. Sea surface temperature data is obtained based on the sea surface temperature prediction products, and sea ice concentration data is obtained according to the sea ice concentration prediction products.

[0028] In this embodiment, the sea surface temperature data and sea ice concentration data under the above multi-model ensemble are preprocessed so that they can be used as the input fields of the general circulation model, including: determining whether the land areas in the sea surface temperature data and sea ice concentration data are missing data or null data; if the land areas are missing data or null data, the missing data or null data in the land areas are circularly filled using the neighborhood interpolation method according to the sea surface temperature data or sea ice concentration data in the land-sea interface zone; the horizontal resolution of the filled sea surface temperature data and sea ice concentration data is enhanced by means of linear interpolation so that it is higher than the spatial resolution of the input fields of the general circulation model; the high-precision sea surface temperature data and sea ice concentration data are modified according to the grid scale of the input fields of the general circulation model to obtain standard input data.

[0029] Specifically, preprocessing the sea surface temperature data and sea ice concentration data includes the following steps:

[0030] First, determine whether the land areas in the sea surface temperature data and sea ice concentration data are missing data or null data.

[0031] Second, if the land areas are missing data or null data, the missing data or null data in the land areas are circularly filled using the neighborhood interpolation method according to the sea surface temperature data or sea ice concentration data in the land-sea interface zone, specifically as follows:

[0032] The first step is to count the missing data and record the coordinates. The missing data grid points in the sea surface temperature data or sea ice concentration data are counted to obtain the number of missing data grid points, and the number of missing data grid points is represented by miss num At the same time, two arrays are created, which are represented by x grid and y grid respectively, and the two arrays are used to accurately record the coordinate positions of each missing data grid point in the data matrix.

[0033] The second step is to determine the neighborhood range of the missing points and adjust the boundaries. As long as the number of missing data grid points is greater than zero (i.e., miss num> 0), for each missing point, the following operations are performed: Determine the range of a grid point around the missing point, that is, the neighborhood formed by one grid point above, below, to the left, and to the right of it. If the coordinates of some points in the neighborhood exceed the range of the data itself, for example, exceed the number of rows or columns of the data matrix, then the coordinates that exceed the range are adjusted accordingly to ensure that the neighborhood range is within the data range.

[0034] Thirdly, calculate the neighborhood average value and fill the missing point. After determining the effective neighborhood range of the missing point, extract the sea surface temperature data and sea ice concentration data within this neighborhood. Process the sea surface temperature data and sea ice concentration data, calculate their average values, and assign the obtained average values to the corresponding missing points to complete one filling operation.

[0035] Fourthly, count the missing data again and update the coordinate records. After completing one round of filling operation, re-count the number of missing data grid points in the data. If there are still missing data at this time, that is, the number of missing data grid points is not zero, then re-create x grid and y grid arrays. Store the coordinates of the grid points that still have missing data in these two newly created arrays to prepare for the next round of filling operation.

[0036] Fifthly, loop to execute the filling operation until there are no missing data grid points. Continuously loop to execute the first step to the fourth step. Each round of loop processes the missing data until there is no more missing data in the data, that is, the number of missing data grid points becomes zero (miss num = 0), at this time, stop the loop and complete the filling of the missing data in the land area.

[0037] Then, use linear interpolation to improve the horizontal resolution of the filled sea surface temperature data and sea ice concentration data so that it is higher than the spatial resolution of the atmospheric circulation model input field, specifically as follows:

[0038] First step, clarify the input and output requirements. Determine the filled sea surface temperature data and sea ice concentration data for which the resolution needs to be improved, and the target that is expected to be higher than the spatial resolution of the atmospheric circulation model input field; at the same time, clarify the relevant parameters for linear interpolation, such as the interpolation direction and the interpolation accuracy requirements, etc.

[0039] Second step, set the target resolution. Based on the spatial resolution of the atmospheric circulation model input field, set a higher target resolution, which will determine the grid point density of the newly generated data. For example, if the original data resolution is one grid point per 1.5 degrees, and the spatial resolution of the atmospheric circulation model input field is one grid point per 1 degree, the target resolution can be set to one grid point per 0.5 degrees.

[0040] Step 3: Perform linear interpolation. For each grid point in the new data framework, use the linear interpolation algorithm to calculate the value of the grid point by leveraging the values of adjacent grid points in the original filled data; the linear interpolation algorithm will perform a weighted average of the values of adjacent grid points in the original data based on the relative position of the new grid point among the original data grid points.

[0041] In the one-dimensional case, if the new grid point is located between the original data grid points x1 and x2, the distance between the new grid point and the original data grid point x1 is d1, and the distance between the new grid point and the original data grid point x2 is d2, then the value of the new grid point satisfies the following calculation formula:

[0042]

[0043] where, is the value of the new grid point, is the distance between the new grid point and the original data grid point , is the original data grid point corresponding value, is the distance between the new grid point and the original data grid point , is the original data grid point corresponding value.

[0044] In two-dimensional data, bilinear interpolation needs to be performed, taking into account the values of adjacent grid points in two directions.

[0045] Step 4: Check and adjust. After completing the calculation of all new grid point values, check the generated high-resolution data. Check for outliers, such as values outside a reasonable range or values that do not conform to the data trend. If outliers exist, analyze the reasons, which may be improper setting of interpolation algorithm parameters or problems with the original data, and make corresponding adjustments according to the analysis results, such as resetting the interpolation parameters or preprocessing the original data.

[0046] Among them, the reasonable range is set as follows: for sea surface temperature data, values below -10 or above 100 appear; for sea ice coverage data, values less than 0 or greater than 1 appear.

[0047] Finally, according to the grid resolution of the input field of the general circulation model, use the method of linear interpolation to process the above high-resolution sea surface temperature data and sea ice coverage data into the grid resolution of the input field of the general circulation model to obtain the standard input data.

[0048] S2. Construct an input field of the general circulation model based on the standard input data.

[0049] In this embodiment, the preprocessed multi-mode sea surface temperature data and sea ice coverage data are subjected to equal-weight averaging operations to obtain the ensemble-averaged sea surface temperature data and the ensemble-averaged sea ice coverage data.

[0050] Specifically, the equal-weight averaging operation is performed on the sea surface temperature data to obtain the ensemble-averaged sea surface temperature data, and the ensemble-averaged sea surface temperature data satisfies the following relationship:

[0051]

[0052] Where, is the ensemble-averaged sea surface temperature data, is the total number of models, is the index variable of the model, is the th sea surface temperature data of the model.

[0053] Specifically, the equal-weight averaging operation is performed on the sea ice coverage data to obtain the ensemble-averaged sea ice coverage data, and the ensemble-averaged sea ice coverage data satisfies the following relationship:

[0054]

[0055] Where, is the ensemble-averaged sea ice coverage data, is the total number of models, is the index variable of the model, is the th sea ice coverage data of the model.

[0056] Furthermore, the obtained ensemble-averaged sea surface temperature data and the ensemble-averaged sea ice coverage data are used as the input fields of the general circulation model.

[0057] S3. Input the input fields into the general circulation model, and obtain the general circulation information and surface information based on the general circulation model.

[0058] In this embodiment, the input fields are input into the general circulation model, and the non-sea-air coupled prediction results are obtained based on the general circulation model; the general circulation information and surface information are extracted based on the non-sea-air coupled prediction results.

[0059] Specifically, the above-mentioned corrected ensemble-averaged sea surface temperature data and ensemble-averaged sea ice coverage data are input into the general circulation model, and the general circulation model is freely run to obtain the non-sea-air coupled prediction results for the next 0 - 60 days, and the required general circulation information and surface information are extracted from the non-sea-air coupled prediction results, and the general circulation information and surface information are used to drive the regional climate model.

[0060] It should be noted that since the atmospheric circulation model is adopted, the simulation during the operation of the atmospheric circulation model is a non-ocean-atmosphere coupled simulation.

[0061] The atmospheric circulation information and surface information include, but are not limited to, the 6-hour average surface air pressure, sea surface air pressure, surface air temperature, 10-meter meridional wind, 10-meter zonal wind, integrated absolute humidity, integrated air temperature, integrated meridional wind, integrated zonal wind, geopotential height, surface temperature, soil moisture, snow cover, and sea surface temperature.

[0062] S4. Based on the atmospheric circulation information and the surface information, perform dynamic downscaling based on a regional climate model to obtain a high-precision sub-seasonal climate prediction result.

[0063] In this embodiment, the initial field and lateral boundary conditions of the regional climate model are obtained based on the atmospheric circulation information and the surface information; the regional climate model is driven based on the initial field and the lateral boundary conditions to achieve dynamic downscaling, so as to obtain a high-precision sub-seasonal climate prediction result.

[0064] Specifically, the atmospheric circulation information and surface information generated by the atmospheric circulation model are made into the initial field and lateral boundary conditions of the regional climate model by means of linear interpolation.

[0065] Furthermore, the above initial field and lateral boundary conditions are used to drive the regional climate model to achieve dynamic downscaling, and the regional climate model is run to generate high-precision prediction results for the next 0 - 60 days as the high-precision sub-seasonal climate prediction result.

[0066] S5. Verify and evaluate the high-precision sub-seasonal climate prediction result.

[0067] In an optional embodiment, the multi-model ensemble sea surface temperature three-step sub-seasonal climate prediction method (represented by Tier-3) constructed by the present invention is significantly superior to the traditional two-step sub-seasonal climate prediction method (represented by Tier-2) in terms of sea surface temperature prediction performance.

[0068] Please refer to Figure 2 , which is a comparison diagram of the climatological distribution of the rain belt position; it shows a comparison diagram of the climatological distribution of the rain belt position (observed results and predicted results) from June to August on average from 2008 to 2023. Left figure: Climatological distribution map of the observed rain belt position; upper right: Schematic diagram of the simulated results of the rain belt position of Tier-2; lower right: Schematic diagram of the simulated results of the rain belt position of Tier-3.

[0069] Based on Figure 2It can be seen that the Tier-3 method shows significantly better performance than the Tier-2 method in sub-seasonal precipitation forecasting, especially in the simulation of the dynamic evolution of regional precipitation and rain belt position. The Tier-2 method has a significant overestimation phenomenon: the simulated regional average precipitation intensity reaches 8mm / d, which is about 70% higher than the observed value (4.5mm / d). The Tier-3 method effectively corrects this deviation by optimizing the coupling mechanism between the initial sea temperature field and the atmospheric model: the error of the predicted regional average precipitation intensity is reduced to about 25%. Rain belt position distribution map based on the 2008-2023 climate state average ( Figure 2 ) shows that the northward movement of the rain belt predicted by the traditional Tier-2 method is weak, and it failed to accurately predict the position of the rain belt in the southern part of Northeast China in mid-to-late August; in contrast, the Tier-3 method successfully captured the trend of the rain belt advancing from the Yangtze River Basin to North China and Northeast China in the prediction 2-3 pentads in advance, and showed a significant advantage in the prediction of the northward movement of the rain belt from late July to August (10-18 pentads). The improvement of the prediction ability of the Tier-3 method provides scientific support for responding to extreme weather in the flood season in the north.

[0070] The multi-model ensemble three-step sub-seasonal prediction method for sea temperature constructed by the present invention makes up for the incoordination problem among the atmosphere, ocean and land surface information in the current two-step sub-seasonal climate prediction, effectively improves the sub-seasonal prediction skill of the climate model for China, and can enhance the accuracy of the current sub-seasonal climate prediction products.

[0071] See also Figure 3 In an optional embodiment, the present invention provides a multi-model ensemble sea temperature three-step sub-seasonal climate prediction system, the system includes an input device, an output device, a processor and a memory, the hardware facilities are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the specific steps of the embodiment of the multi-model ensemble sea temperature three-step sub-seasonal climate prediction method provided by the present invention. The multi-model ensemble sea temperature three-step sub-seasonal climate prediction system provided by the present invention has a complete structure, objective and stable, and improves the overall applicability and practical application ability of the present invention.

[0072] See also Figure 4 The figure shows the execution flow chart of the multi-model ensemble sea temperature three-step sub-seasonal climate prediction system; it shows the operation process of the sub-seasonal climate prediction system in detail and describes the specific sub-seasonal climate prediction processing process of the system processor.

[0073] In an optional embodiment, the multi-model ensemble sea temperature three-step sub-seasonal climate prediction system includes a data acquisition module, a data processing module, an ensemble average module, a global sub-seasonal prediction module and a regional dynamic downscaling module.

[0074] Specifically, a data acquisition module is used to acquire sea surface temperature data and sea ice coverage data of an advanced global ocean-land-atmosphere-ice coupled climate system model; a data processing module is used to statistically correct the sea surface temperature data and sea ice coverage data; an ensemble mean module is used to perform ensemble averaging on the statistically corrected sea surface temperature data and sea ice coverage data and then input them into a global atmospheric circulation model; a global sub-seasonal prediction module is used to generate atmospheric circulation information and surface information for driving a regional model; and a regional dynamical downscaling module is used to obtain a high-precision sub-seasonal prediction result for a predicted region.

[0075] In summary, a multi-model ensemble sea temperature three-step sub-seasonal climate prediction method and system provided by the method of the present invention, by acquiring sea surface temperature data and sea ice coverage data of an advanced global ocean-land-atmosphere-ice coupled climate system model; correcting and ensemble averaging the sea surface temperature data and sea ice coverage data and then inputting them into a global atmospheric circulation model for non-ocean-atmosphere coupled simulation operation; and then using the non-ocean-atmosphere coupled prediction result of the atmospheric circulation model as the initial field and boundary conditions to input into a regional model for dynamical downscaling to obtain a high-precision regional climate prediction result for the next 0-60 days; improving the current sub-seasonal prediction skill, providing a new idea for sub-seasonal prediction, and effectively improving the accuracy and precision of sub-seasonal prediction. The method of the present invention is easy to understand, simple in calculation, with less workload, convenient for engineering application, and provides a theoretical basis and technical support for the further development of climate prediction technology.

[0076] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A three-step sub-seasonal climate prediction method for multi-mode ensemble sea surface temperature, characterized in that It includes the following steps: Obtain sea surface temperature data and sea ice coverage data, and preprocess the sea surface temperature data and the sea ice coverage data to obtain standard input data; Construct an input field of the atmospheric circulation model based on the standard input data; Input the input field into the atmospheric circulation model, and obtain atmospheric circulation information and surface information based on the atmospheric circulation model; According to the atmospheric circulation information and the surface information, perform dynamic downscaling based on the regional climate model to obtain a high-precision sub-seasonal climate prediction result.

2. The multi-mode ensemble SST three-step sub-seasonal climate prediction method according to claim 1, wherein The obtaining of the sea surface temperature data and the sea ice coverage data includes: Obtain sea surface temperature prediction products and sea ice coverage prediction products of multiple advanced global ocean-land-atmosphere-ice coupled climate system models; Obtain the sea surface temperature data based on the sea surface temperature prediction products; Obtain the sea ice coverage data according to the sea ice coverage prediction products.

3. The multi-mode ensemble SST three-step sub-seasonal climate prediction method according to claim 1, characterized in that, The preprocessing of the sea surface temperature data and the sea ice coverage data to obtain standard input data includes: Judge the missing data or null data of the sea surface temperature data and the sea ice coverage data to obtain a judgment result; Based on the judgment result, use the neighborhood difference method to circularly fill the missing data or the null data; After circular filling, use the linear interpolation method to improve the horizontal resolution of the sea surface temperature data and the sea ice coverage data; Combined with the horizontal resolution, modify the sea surface temperature data and the sea ice coverage data according to the grid resolution of the input field of the atmospheric circulation model to obtain the standard input data.

4. The multi - mode ensemble SST three - step sub - seasonal climate prediction method according to claim 3, wherein, The use of the linear interpolation method to improve the horizontal resolution of the sea surface temperature data and the sea ice coverage data includes: In the one-dimensional case, the linear interpolation method satisfies the following relationship: ； Among them, is the value of the new lattice point, is the distance between the new lattice point and the original data lattice point and, is the value corresponding to the original data lattice point . is the distance between the new lattice point and the original data lattice point and, is the original data lattice point corresponding value.

5. The multi-mode ensemble SST three-step sub-seasonal climate prediction method according to claim 1, wherein The construction of an input field of the atmospheric circulation model based on the standard input data includes: According to the standard input data, perform equal-weight averaging operation on the sea surface temperature data to obtain the ensemble-averaged sea surface temperature data; According to the standard input data, perform equal-weight averaging operation on the sea ice coverage data to obtain the ensemble-averaged sea ice coverage data; Use the ensemble-averaged sea surface temperature data and the ensemble-averaged sea ice coverage data as the input field of the atmospheric circulation model.

6. The multi-mode ensemble SST three-step sub-seasonal climate prediction method according to claim 5, characterized in that, The performing of the equal-weight averaging operation on the sea surface temperature data to obtain the ensemble-averaged sea surface temperature data includes: The ensemble-averaged sea surface temperature data satisfies the following relationship: ； Among them, is the ensemble-mean sea surface temperature data, is the total amount of the model, is the index variable of the model, is the sea surface temperature data of the 7. The multi - mode ensemble SST three - step sub - seasonal climate prediction method according to claim 5, characterized in that, The performing of the equal-weight averaging operation on the sea ice coverage data to obtain the ensemble-averaged sea ice coverage data includes: The ensemble-averaged sea ice coverage data satisfies the following relationship: ； Among them, is the ensemble-averaged sea ice concentration data, is the total amount of the model, is the index variable of the model, is the th sea ice concentration data of the model.

8. The multi-mode ensemble SST three-step sub-seasonal climate prediction method according to claim 1, characterized in that The inputting of the input field into the atmospheric circulation model and obtaining atmospheric circulation information and surface information based on the atmospheric circulation model includes: Input the input field into the atmospheric circulation model, and obtain a non-ocean-atmosphere coupling prediction result based on the atmospheric circulation model; Extract the atmospheric circulation information and the surface information based on the non-ocean-atmosphere coupling prediction result.

9. The multi-mode ensemble SST three-step sub-seasonal climate prediction method according to claim 1, wherein Performing dynamic downscaling based on the regional climate model according to the atmospheric circulation information and the surface information to obtain a high-precision sub-seasonal climate prediction result, including: Obtaining the initial field and lateral boundary conditions of the regional climate model according to the atmospheric circulation information and the surface information; Driving the regional climate model based on the initial field and the lateral boundary conditions to achieve dynamic downscaling, so as to obtain the high-precision sub-seasonal climate prediction result.

10. A multi-mode integrated SST three-step sub-seasonal climate prediction system, characterized in that, The system includes an input device, an output device, a processor, and a memory. The input device, the output device, the processor, and the memory are interconnected. Among them, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the multi-model ensemble SST three-step sub-seasonal climate prediction method according to any one of claims 1-9.

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