CN115081314A - Method and device for correcting climate prediction model - Google Patents
Method and device for correcting climate prediction model Download PDFInfo
- Publication number
- CN115081314A CN115081314A CN202210619056.XA CN202210619056A CN115081314A CN 115081314 A CN115081314 A CN 115081314A CN 202210619056 A CN202210619056 A CN 202210619056A CN 115081314 A CN115081314 A CN 115081314A
- Authority
- CN
- China
- Prior art keywords
- prediction model
- prediction
- statistical
- climate
- dynamic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention provides a method and a device for correcting a climate prediction model. The method comprises the following steps: establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction; acquiring a preset target value and a predicted value corresponding to a target forecast element according to a statistical prediction model and the dynamic prediction model respectively; and adopting a data assimilation mode to approach the predicted value to the preset target value. The method can correct the prediction deviation of the power prediction model at the initial reporting stage, thereby improving the prediction precision of the power prediction model. Due to the continuity of the evolution of the climate system, the improvement of the prediction precision at the initial stage of the forecast also contributes to the improvement of the prediction precision at the later stage.
Description
Technical Field
The invention relates to the technical field of climate prediction in atmospheric science, in particular to a method and a device for correcting a climate prediction model.
Background
The climate system is a unified physical system which comprises an air space, a water space, a land surface, an ice and snow space and a biosphere and can determine climate formation, climate distribution and climate change. Due to the large space scale and long time scale of the climate system, it is more difficult to predict climate anomalies.
Climate prediction can be currently divided into two main categories: statistical model prediction and dynamic model prediction. The statistical model prediction is based on the analysis of the change rules and characteristics of the relations between the interior or other variables of the climate system by using a large amount of past data. The dynamic model prediction is based on numerical patterns. Although the dynamic model prediction fully considers the physical process of the climate system, the prediction effect (or prediction skill) is low within a few months in the prediction process, even obviously lower than the prediction result of simple statistical model prediction, which undoubtedly influences the prediction effect of the dynamic model prediction.
Therefore, the existing climate prediction process by using dynamic model prediction has the problem of low accuracy of prediction results.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for correcting a climate prediction model, which can improve the accuracy and stability of the power model for predicting the climate.
In a first aspect, the present invention provides a method for correcting a climate prediction model, the method comprising:
establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction;
acquiring a preset target value and a predicted value corresponding to a target forecast element according to a statistical prediction model and the dynamic prediction model respectively;
and adopting a data assimilation mode to approach the predicted value to the preset target value.
The statistical prediction model is constructed by adopting any one of a unitary linear regression mode, a multiple linear regression mode, a nonlinear regression mode and a deep learning mode.
The target forecast elements comprise at least one of sea surface temperature, three-dimensional sea temperature, sea salinity, atmospheric sea level air pressure, air temperature of each layer height, wind field of each layer height and potential altitude field of each isobaric surface.
Further, the approximating the predicted value to the preset target value by using a data assimilation method includes:
and carrying out real-time constraint on the predicted temperature value based on the preset target value by adopting a relaxation approximation method in a data assimilation mode.
Optionally, the strength coefficient of the relaxation approximation used in the relaxation approximation method is a globally identical coefficient, a coefficient of a region difference, or a coefficient of a depth difference.
Optionally, the intensity coefficient of the relaxation approximation used in the relaxation approximation method is in a time-varying form.
Optionally, the intensity factor is inversely proportional to time.
In a second aspect, the present invention provides an apparatus for correcting a climate prediction model, the apparatus comprising:
the model unit is used for establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction;
the prediction unit is used for acquiring a preset target value and a predicted value corresponding to a target prediction element according to a statistical prediction model and the dynamic prediction model respectively;
and the correcting unit is used for approaching the predicted value to the preset target value in a data assimilation mode.
In a third aspect, the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and a communication bus; wherein the content of the first and second substances,
the processor, the communication interface and the memory complete mutual communication through a communication bus;
the processor is configured to invoke computer instructions in the memory to perform the steps of the method for correcting a climate prediction model described above.
In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions that, when executed, cause the computer to perform the steps of the method for correcting a climate prediction model as described above.
The invention provides a correction method and a correction device of a climate prediction model, which are characterized in that a statistical prediction model and a dynamic prediction model are established; the statistical prediction model and the dynamic prediction model are used for weather prediction; acquiring a preset target value and a predicted value corresponding to a target forecast element according to a statistical prediction model and the dynamic prediction model respectively; and adopting a data assimilation mode to approach the predicted value to the preset target value. The prediction deviation of the power prediction model at the initial reporting stage can be corrected, so that the prediction precision of the power prediction model is improved. Due to the continuity of the evolution of the climate system, the improvement of the prediction precision at the initial stage of the forecast also contributes to the improvement of the prediction precision at the later stage.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate embodiments of the present invention or solutions in the prior art, the drawings that are needed in the embodiments or solutions in the prior art will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and are therefore not to be considered limiting of scope, and that other relevant drawings can be derived from those drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for calibrating a climate prediction model according to the present invention;
FIG. 2 is a schematic structural diagram of a calibration apparatus for a climate prediction model according to the present invention;
fig. 3 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for correcting a climate prediction model, which is shown in fig. 1 and specifically comprises the following contents:
s101: establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction;
in this step, a statistical forecasting model for weather prediction is constructed using the existing data. The statistical prediction model can be constructed by a simple statistical method (such as unary linear regression), a more complex statistical method (such as multiple linear regression and nonlinear regression), and an artificial intelligence method (such as deep learning).
It should be noted that, according to the prediction variables that need to be constrained, a statistical prediction model and a dynamic prediction model are respectively constructed according to the statistical prediction model construction method.
Furthermore, the constructed statistical prediction model and dynamic prediction model are used for carrying out prediction back calculation (namely, one time of prediction again) on the climate elements in the past of the last 30 years (or 20 years and 40 years), and the prediction skills of the predicted climate elements are statistically analyzed, so that the predicted climate elements with the prediction skills of the statistical prediction model higher than that of the dynamic prediction model are selected. Corresponding constraints can be carried out on the specific forecasted climate elements which are counted.
S102: acquiring a preset target value and a predicted value corresponding to a target forecast element according to a statistical prediction model and the dynamic prediction model respectively;
in this step, the target forecast elements to be constrained are selected from the plurality of climate elements. If the prediction of the past climate elements is performed in step S101, the target forecast elements to be subjected to the constraint can be selected from the counted climate elements for the specific forecast. In this embodiment, the target forecast element includes at least one of a sea surface temperature, a three-dimensional sea temperature, an ocean salinity, an atmospheric sea level air pressure, an air temperature at each layer height, an air field at each layer height, and a potential altitude field at each isobaric surface.
And (4) constraining the target forecast elements, and freely forecasting the other forecast elements except the target forecast elements in the dynamic prediction model. It can be understood that the rest of the forecast elements are freely forecasted in the dynamic prediction model, but are influenced by the "constrained forecast elements", and the forecast elements are not directly "constrained", but the forecast skills of the forecast elements are still improved, so that the forecast skills of the dynamic prediction mode are improved as a whole, and are not limited to only the "constrained forecast elements". The target forecast element is a predictive variable that needs to be constrained. And respectively constructing a statistical prediction model and a dynamic prediction model according to the prediction variables needing to be constrained and the statistical prediction model construction method.
S103: and adopting a data assimilation mode to approximate the predicted value to the target value.
In this step, a relaxation approximation method in a data assimilation mode is specifically adopted to carry out real-time constraint on the predicted value based on a preset target value.
It should be noted that, in the process of constraining the predicted value of the dynamic prediction model by using the preset target value of the statistical prediction model, a relaxation approximation technique may be used for constraint, and other assimilation methods may also be used for constraint.
In the process of constraining the target forecast element, the constraint is not carried out in the whole process, but only carried out in the time window. The length of this time window depends on the time the statistical predictive model skills are higher than the dynamic predictive model skills. That is, in the process of constraining the power prediction model by the statistical prediction model, only the region in which the accuracy of the output result of the statistical prediction model is higher than the accuracy of the output result of the power prediction model is constrained. For example: the statistical predictive model skills are higher than the dynamic predictive model skills within 2 months from the start of prediction (the dynamic predictive model skills are higher than the statistical predictive model skills after 3 months), then the time window is set to 2 months.
In the process of constraining the predicted value of the dynamic prediction model by using the preset target value of the statistical prediction model, the constraint time may be within 1 month after the report, or within 2 months. The time is determined based on the prediction skills of the dynamic prediction model and the prediction skills of the statistical prediction model. Because the prediction skill of the prediction result of the power prediction model is usually low within 1-2 months from the beginning of the mode report, even obviously lower than the prediction result of a simple statistical prediction model, the prediction variable of the power prediction model is constrained and corrected by using the advantages predicted by the statistical prediction model within 1-2 months from the beginning of the mode report, so that the prediction accuracy of the power prediction model is improved.
And selecting an area needing to be restricted by taking the prediction of the statistical prediction model and the prediction skill of the statistical prediction model higher than that of the dynamic prediction model as standards, only restricting target forecast elements of the selected area, and performing free integration on the dynamic prediction models of other areas.
It should be noted that the constraint time windows for different forecast elements are different. The forecast elements and constraint time windows that need to be constrained may be different for different reporting months.
In the process that the statistical prediction model is accompanied with the constraint power prediction model, only the region of which the prediction skill of the statistical prediction model is higher than that of the power prediction model is constrained, and the power prediction models in other regions are integrated freely, so that the advantages of the statistical prediction model are utilized to the maximum extent, and the advantages of the power prediction model are also kept, thereby being beneficial to improving the prediction skill of the power prediction model.
In this embodiment, a relaxation approximation method is adopted and a preset target value of the statistical prediction model is used to constrain the prediction value of the dynamic prediction model. In the process of constraint, the intensity coefficient of the relaxation approximation adopted in the relaxation approximation method is the same coefficient in the whole world, the coefficient of regional difference or the coefficient of depth difference.
Optionally, the intensity coefficient of the relaxation approximation used in the relaxation approximation method is in a form varying with time, and the specific intensity coefficient is in an inversely proportional relation with time. For example: in the process of constraining the predicted value of the dynamic prediction model by using the preset target value of the statistical prediction model, the strength coefficient of the relaxation approximation can be set as a coefficient which gradually attenuates along with time, and the attenuation is minimum at the end of 2 months, so that seamless connection with the subsequent mode free integration is realized. Therefore, the prediction result of the power prediction model is subjected to constraint correction by utilizing the prediction advantages of the statistical prediction model in 1-2 months at the initial stage of the start of the power prediction model, so that the prediction precision of the power prediction model is improved.
It should be noted that the strength factor of the relaxation approximation technique varies from month to month of the presentation.
As can be seen from the above description, according to the method for correcting the climate prediction model provided in this embodiment, the prediction deviation of the power prediction model at the initial reporting stage can be corrected by constraining the power prediction model with the statistical prediction model, so as to improve the prediction accuracy of the power prediction model. Due to the continuity of the evolution of the climate system, the improvement of the prediction precision in the initial stage of the forecast is also beneficial to the improvement of the later stage forecast precision.
The embodiment also provides a method for constructing a statistical prediction model, which specifically comprises the following steps:
constructing a statistical prediction model of the sea surface temperature based on a statistical method, predicting the sea surface temperature in the next 1-2 months by using actual observation data such as the sea surface temperature in the current month and the like, and taking the predicted sea surface temperature as a target sea temperature value for next assimilation;
sea temperature observation values of all grid points (such as 1 DEG) around the world can be obtained through observation (usually, the sea temperature observation values are developed by other professional organizations and can be directly downloaded from the internet for use), and therefore, the sea temperature observation data of the month and the previous months can be obtained.
A statistical model is first constructed. For each lattice point, by using the historical observed data of 4 months, 3 months, 2 months and the like in 1980-2021 as independent variables (x1, x2 and x3 …) and the historical sea temperature of 5 months as dependent variable (y), a statistical equation y is constructed as a · x1+ b · x2+ c · x3 …, and the coefficients (a, b, c …) corresponding to the respective variables are obtained through statistics, and then statistical prediction can be carried out. For each grid point, the statistically predicted sea temperature forecast value of the month 5 can be obtained according to the statistical equation by utilizing the existing observation data of the month 4 at the current and the month (month 3 and month 2 …) at the previous time.
In the above construction of the statistical prediction model, the sea temperature in the current month may be used as an input variable (independent variable), and the sea temperatures in the current month and the first few months may be used as an input variable (independent variable).
In the above construction of the statistical prediction model, the sea temperature may be used as an input variable, the atmospheric sea level air pressure, the air temperature at each floor height, the wind field at each floor height, and the potential height field at each floor isopotential surface may be used as an input variable (independent variable), various combinations of the above variables may be used as an input variable (independent variable), and various combinations of different variables for different months may be used as an input variable (independent variable).
There are many options for the variables that are constrained, and the steps are described herein using a sea-surface temperature variable as an example.
The embodiment further provides a specific way of performing real-time constraint on the predicted value based on the preset target value by using a relaxation approximation method, including:
and in 1-2 months after the prediction of the power prediction model is started, approximating the sea surface sea temperature variable predicted by the power prediction model to a preset target sea temperature value obtained by the statistical prediction model by a relaxation approximation method, and realizing the real-time constraint of the result of the statistical prediction model on the prediction variable of the power prediction model.
The method comprises the following specific steps:
in the integral forecasting process of the power forecasting model, the sea temperature variable (T) of the power forecasting model is controlled by the evolution process of the power forecasting model, the sea temperature value at the next moment is constantly equal to the sea temperature value at the current moment plus the sea temperature change tendency (the change tendency is comprehensively determined by a plurality of processes in the power forecasting model, if the change tendency is not existed, the sea temperature variable is constant all the time, and is a constant), and therefore the continuous change along with the time is formed by integrating step by step.
On the basis, a relaxation approximation term, namely a Nudging term, is additionally added to the integral of the dynamic prediction model. The Nudging term is obtained by subtracting the current sea temperature value of the power prediction model from the preset target sea temperature value predicted by the statistical prediction model and multiplying a coefficient (that is, the Nudging strength, i.e., the relaxation approximation strength coefficient in the above embodiment) by the current sea temperature value of the power prediction model.
In the process of constraining the dynamic force by using the statistical result, the constrained region may be a global region or an individual region.
Furthermore, the statistical result constraint power prediction mode in the above embodiment may be developed based on a coupled marine coupling mode, and may also be developed based on a global system mode. The method can be developed for the short-term climate prediction of seasonal scales, the climate prediction of sub-seasonal scales, and the prediction of the time of year and the time of the year. The statistical constraint power prediction technology can be applied to short-term climate prediction of seasonal scales, can be applied to climate prediction of sub-seasonal scales, and can also be applied to climate prediction of the annual age.
The embodiment of the present invention provides a specific implementation manner of a correction apparatus for a climate prediction model, which can implement all contents in the correction method for the climate prediction model, and referring to fig. 2, the correction apparatus for the climate prediction model specifically includes the following contents:
the model unit 10 is used for establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction;
the prediction unit 20 is used for acquiring a preset target value and a predicted value corresponding to a target forecast element according to a statistical prediction model and the dynamic prediction model respectively;
and the correcting unit 30 is configured to approach the predicted value to the preset target value by means of data assimilation.
The statistical prediction model is constructed by adopting any one of a unitary linear regression mode, a multiple linear regression mode, a nonlinear regression mode and a deep learning mode.
The target forecast elements comprise at least one of sea surface temperature, three-dimensional sea temperature, sea salinity, atmospheric sea level air pressure, air temperature of each layer height, wind field of each layer height and potential altitude field of each isobaric surface.
Wherein, the correction unit 30 includes:
and the syndrome unit is used for carrying out real-time constraint on the predicted value on the basis of the preset target value by adopting a relaxation approximation method in a data assimilation mode.
Optionally, the intensity coefficient of the relaxation approximation used in the relaxation approximation method is a globally same coefficient, a coefficient of regional difference, or a coefficient of depth difference.
Optionally, the intensity coefficient of the relaxation approximation used in the relaxation approximation method is in a time-varying form. Wherein the intensity coefficient is inversely proportional to time.
The embodiment of the correction apparatus for a climate prediction model provided in the present invention may be specifically used for executing the processing procedure of the embodiment of the correction method for a climate prediction model in the foregoing embodiment, and the functions thereof are not described herein again, and reference may be made to the detailed description of the embodiment of the method.
As can be seen from the above description, the correction device for the climate prediction model provided in the embodiment of the present invention establishes a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction; acquiring a preset target value and a predicted value corresponding to a forecast element according to a statistical prediction model and the dynamic prediction model respectively; and adopting a data assimilation mode to approach the predicted value to the preset target value. The prediction deviation of the power prediction model at the initial reporting stage can be corrected, so that the prediction precision of the power prediction model is improved. Due to the continuity of the evolution of the climate system, the improvement of the prediction precision at the initial stage of the forecast also contributes to the improvement of the prediction precision at the later stage.
An embodiment of the present invention provides an embodiment of an electronic device for implementing all or part of contents in an embodiment of a correction method for a climate prediction model, and referring to fig. 3, the electronic device specifically includes the following contents:
a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the computer instructions in the memory 830 to perform the following method:
establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction;
acquiring a preset target value and a predicted value corresponding to a target forecast element according to a statistical prediction model and the dynamic prediction model respectively;
and adopting a data assimilation mode to approach the predicted value to the preset target value.
An embodiment of the present invention provides a computer-readable storage medium for implementing all or part of the contents of the embodiment of the method for correcting a climate prediction model, where the computer-readable storage medium has stored thereon computer instructions, where the computer instructions, when executed, cause the computer to perform all the steps of the method for correcting a climate prediction model in the above-mentioned embodiment, for example, when the processor executes the computer instructions, the following steps are implemented:
establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction;
acquiring a preset target value and a predicted value corresponding to a target forecast element according to a statistical prediction model and the dynamic prediction model respectively;
and adopting a data assimilation mode to approach the predicted value to the preset target value.
Although the present invention provides method steps as described in the examples or flowcharts, more or fewer steps may be included based on routine or non-inventive labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus (system) embodiment, since it is substantially similar to the method embodiment, the description is relatively simple and reference may be made to the partial description of the method embodiment for relevant points.
In this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention is not limited to any single aspect, nor is it limited to any single embodiment, nor is it limited to any combination and/or permutation of these aspects and/or embodiments. Moreover, each aspect and/or embodiment of the present invention may be utilized alone or in combination with one or more other aspects and/or embodiments thereof.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present invention in its spirit and scope. Are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for calibrating a climate prediction model, comprising:
establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction;
acquiring a preset target value and a predicted value corresponding to a target forecast element according to a statistical prediction model and the dynamic prediction model respectively;
and adopting a data assimilation mode to approximate the predicted value to the preset target value.
2. The method for correcting the climate prediction model according to claim 1, wherein the statistical prediction model is constructed by any one of unary linear regression, multiple linear regression, nonlinear regression, and deep learning.
3. The method of calibrating a climate prediction model according to claim 1, wherein the target forecast element comprises at least one of a sea surface temperature, a three-dimensional sea temperature, an ocean salinity, an atmospheric sea level air pressure, an air temperature at each level, a wind field at each level, and a potential altitude field at each level isobaric surface.
4. The method for correcting the climate prediction model according to claim 1, wherein the approximating the predicted value to the preset target value by data assimilation comprises:
and carrying out real-time accompanying constraint on the predicted value on the basis of the preset target value by adopting a relaxation approximation method in a data assimilation mode.
5. The method for correcting the climate prediction model according to claim 4, wherein the intensity coefficient of the relaxation approximation used in the relaxation approximation method is a globally identical coefficient, a region difference coefficient or a depth difference coefficient.
6. The method for correcting a climate prediction model according to claim 4, wherein the intensity coefficient of the relaxation approximation used in the relaxation approximation method is in a time-varying form.
7. The method of calibrating a climate prediction model according to claim 6, wherein the intensity factor is inversely proportional to time.
8. An apparatus for correcting a climate prediction model, comprising:
the model unit is used for establishing a statistical prediction model and a dynamic prediction model; the statistical prediction model and the dynamic prediction model are used for weather prediction;
the prediction unit is used for acquiring a preset target value and a predicted value corresponding to a target prediction element according to a statistical prediction model and the dynamic prediction model respectively;
and the correcting unit is used for approaching the predicted value to the preset target value in a data assimilation mode.
9. An electronic device, comprising: a processor, a memory, a communication interface, and a communication bus; wherein the content of the first and second substances,
the processor, the communication interface and the memory complete mutual communication through a communication bus;
the processor is adapted to invoke computer instructions in the memory to perform the steps of the method of correcting a climate prediction model according to any of claims 1-7.
10. A computer-readable storage medium storing computer instructions that, when executed, cause the computer to perform the steps of the method of correcting a climate prediction model according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210619056.XA CN115081314A (en) | 2022-06-01 | 2022-06-01 | Method and device for correcting climate prediction model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210619056.XA CN115081314A (en) | 2022-06-01 | 2022-06-01 | Method and device for correcting climate prediction model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115081314A true CN115081314A (en) | 2022-09-20 |
Family
ID=83248711
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210619056.XA Pending CN115081314A (en) | 2022-06-01 | 2022-06-01 | Method and device for correcting climate prediction model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115081314A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102221389A (en) * | 2011-04-11 | 2011-10-19 | 国家海洋信息中心 | Method for predicting tide-bound water level by combining statistical model and power model |
| US20140278314A1 (en) * | 2013-03-13 | 2014-09-18 | The Government Of 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 |
| CN109426886A (en) * | 2017-08-29 | 2019-03-05 | 北京思湃德信息技术有限公司 | A kind of climatic prediction system |
| CN110837902A (en) * | 2018-08-15 | 2020-02-25 | 中国电力科学研究院有限公司 | Relaxation approximation data assimilation method and system |
| CN113159714A (en) * | 2021-04-01 | 2021-07-23 | 国网河南省电力公司电力科学研究院 | Meteorological data correction method for power grid |
-
2022
- 2022-06-01 CN CN202210619056.XA patent/CN115081314A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102221389A (en) * | 2011-04-11 | 2011-10-19 | 国家海洋信息中心 | Method for predicting tide-bound water level by combining statistical model and power model |
| US20140278314A1 (en) * | 2013-03-13 | 2014-09-18 | The Government Of 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 |
| CN109426886A (en) * | 2017-08-29 | 2019-03-05 | 北京思湃德信息技术有限公司 | A kind of climatic prediction system |
| CN110837902A (en) * | 2018-08-15 | 2020-02-25 | 中国电力科学研究院有限公司 | Relaxation approximation data assimilation method and system |
| CN113159714A (en) * | 2021-04-01 | 2021-07-23 | 国网河南省电力公司电力科学研究院 | Meteorological data correction method for power grid |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Beven et al. | Concepts of information content and likelihood in parameter calibration for hydrological simulation models | |
| Parker | Computer simulation, measurement, and data assimilation | |
| Balmaseda et al. | Evaluation of the ECMWF ocean reanalysis system ORAS4 | |
| Huerta et al. | Time-varying models for extreme values | |
| Agilan et al. | Is the covariate based non-stationary rainfall IDF curve capable of encompassing future rainfall changes? | |
| Fuentes et al. | Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models | |
| Göttl et al. | Mass-related excitation of polar motion: an assessment of the new RL06 GRACE gravity field models | |
| CN109379240B (en) | Internet of vehicles flow prediction model construction method and device and electronic equipment | |
| CN113204061B (en) | Method and device for constructing lattice point wind speed correction model | |
| Pompa-Garcia et al. | Temporal variation of wood density and carbon in two elevational sites of Pinus cooperi in relation to climate response in northern Mexico | |
| Robert et al. | A reduced-order strategy for 4D-Var data assimilation | |
| CN112418498A (en) | Temperature prediction method and system for intelligent greenhouse | |
| Blažica et al. | Rotational and divergent kinetic energy in the mesoscale model ALADIN | |
| US20180196892A1 (en) | Massively accelerated bayesian machine | |
| Lang et al. | A systematic method of parameterisation estimation using data assimilation | |
| Lei et al. | A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: application in the Lorenz system | |
| KR101409316B1 (en) | Multi-model probabilistic prediction System and Method thereof | |
| CN115081314A (en) | Method and device for correcting climate prediction model | |
| Ailliot et al. | Long term object drift forecast in the ocean with tide and wind | |
| CN110110448B (en) | Weather simulation method and system based on WRF and readable storage medium | |
| Durai et al. | Location specific forecasting of maximum and minimum temperatures over India by using the statistical bias corrected output of global forecasting system | |
| Rodríguez Genó et al. | Parameterization of the collision–coalescence process using series of basis functions: COLNETv1. 0.0 model development using a machine learning approach | |
| Moroni et al. | Understanding the Various Perspectives of Earth Science Observational Data Uncertainty | |
| CN116757490A (en) | Vegetation carbon sink evaluation method, system, electronic equipment and storage medium | |
| Bertolacci et al. | Climate inference on daily rainfall across the Australian continent, 1876–2015 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |