CN113221385B - Initialization method and system for dating forecast - Google Patents
Initialization method and system for dating forecast Download PDFInfo
- Publication number
- CN113221385B CN113221385B CN202110638200.XA CN202110638200A CN113221385B CN 113221385 B CN113221385 B CN 113221385B CN 202110638200 A CN202110638200 A CN 202110638200A CN 113221385 B CN113221385 B CN 113221385B
- Authority
- CN
- China
- Prior art keywords
- forecast
- field
- initial
- sea ice
- dating
- 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.)
- Active
Links
- 238000011423 initialization method Methods 0.000 title claims abstract description 16
- 238000004088 simulation Methods 0.000 claims abstract description 35
- 230000008878 coupling Effects 0.000 claims abstract description 17
- 238000010168 coupling process Methods 0.000 claims abstract description 17
- 238000005859 coupling reaction Methods 0.000 claims abstract description 17
- 238000012950 reanalysis Methods 0.000 claims abstract description 11
- 238000000034 method Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000002474 experimental method Methods 0.000 claims description 7
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000001932 seasonal effect Effects 0.000 claims description 3
- 230000002708 enhancing effect Effects 0.000 abstract 1
- 238000010792 warming Methods 0.000 description 12
- 238000001228 spectrum Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000004907 flux Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides an initialization method and system for dating forecast, which relates to the technical field of dating climate forecast, and comprises the following steps: obtaining a forecast initial field based on historical simulation of a global coupling mode to obtain a plurality of groups of atmosphere forecast initial fields; based on the ocean sea ice historical simulation driven by the atmosphere reanalysis data, correcting the climate state of the ocean sea ice historical simulation into the climate state of the ocean and sea ice of the global coupling mode historical simulation to obtain an ocean sea ice forecasting initial field; combining the obtained multiple groups of atmospheric forecast initial fields with land, ocean and sea ice initial fields respectively to construct multiple groups of initial fields for adult interpersonal ensemble forecast; initializing a pre-constructed dating forecasting system, and performing historical postforecast or dating forecasting on a future business type set. The invention can ensure that the service type annual prediction is carried out, reduce the influence caused by impact after initialization, and improve the accuracy of the prediction system on the natural variability, thereby enhancing the service prediction level of the annual prediction system.
Description
Technical Field
The invention relates to the technical field of chronologic climate prediction, in particular to an initialization method and system for chronologic forecast.
Background
Short-term climate change prediction has been recognized by the world climate research project organization as one of the major challenges facing the international climate research community.
The Chinese patent with publication number CN111291944A discloses a marine climate prediction method and system based on NPSDV driving factor identification, and the prediction method comprises the following steps: obtaining sea surface salinity SSS analysis parameters; determining a time series of NPSDV (numerical control software development software) and a driving factor index time series of the annual change of the sea surface salinity of the North Pacific ocean according to SSS (satellite navigation satellite System) analysis parameters, and calculating a time series power spectrum of the NPSDV, a driving factor index time series power spectrum, time series lag cross autocorrelation of the NPSDV, time series lag cross autocorrelation of the driving factor index time series, and lag cross correlation; reconstructing space point SSS abnormity by using an autoregressive process model, and determining an SSS abnormity reconstruction result; determining a driving factor according to the time series power spectrum of the NPSDV, the index time series power spectrum of the driving factor, the time series lag cross autocorrelation of the NPSDV, the index time series lag cross autocorrelation of the driving factor, the lag cross autocorrelation and the SSS abnormal reconstruction result; and predicting the marine climate according to the driving factor.
In recent years, the field of short-term climate prediction has developed rapidly, and the initialization of coupling patterns based on observation can significantly improve the prediction capability of one year to ten years. The fifth and sixth coupling mode comparison plans consider the influence of initialization on the inter-year climate prediction. The enormous computational cost of running and analyzing the annual internode forecasting experiments is a significant obstacle to recent climate forecasting progress, making it difficult to systematically assess the sensitivity of the annual forecasting system to parameter selection, such as gather size, gather generation method, start date, number of sets of gathers, and initialization method, the number of initialized earth system components, and the pattern resolution. Every current evaluation is still insufficient, requiring more design and computational resources to be invested.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an initialization method and system for dating forecast.
According to the initialization method and the system for the annual prediction provided by the invention, the scheme is as follows:
in a first aspect, there is provided a method for initializing an dating forecast, the method comprising:
step S1: obtaining a forecast initial field based on historical simulation of a global coupling mode, and performing multiple disturbance on the obtained group of atmospheric initial fields to obtain multiple groups of atmospheric forecast initial fields;
step S2: based on the ocean sea ice historical simulation driven by the atmosphere reanalysis data, the climate state of the ocean sea ice historical simulation is corrected into the climate state of the ocean and the ocean ice historical simulation in the global coupling mode, and therefore an ocean sea ice forecasting initial field is obtained;
step S3: combining the obtained multiple groups of atmospheric forecast initial fields with land, ocean and sea ice initial fields respectively to construct multiple groups of initial fields for adult generation ensemble forecast;
step S4: initializing a pre-constructed inter-generation forecast system, and performing historical postforecast or performing inter-generation forecast on a future business type set.
Preferably, the restart field in step S1, which stores the specified date, has the atmosphere and the land as the forecast initial field.
Preferably, the step S1 further includes superimposing random disturbances of the machine truncation error level on the atmospheric restart field, and obtaining different atmospheric forecast initial fields by using disturbances of different sizes.
Preferably, the step S2 includes:
selecting and acquiring atmospheric reanalysis data;
specific variables need to be converted to the forced field format required for sea and sea ice content;
and performing a multi-wheel driving experiment to obtain a restart field of the ocean and the atmosphere which are fully adjusted in the deep sea.
Preferably, the initial sea ice forecast field in step S2 is obtained by adding the climate state of the sea ice restart field to the abnormal field of the sea ice restart field.
Preferably, the step S3 includes designating the start and end dates of the postcursor/forecast in the initial field of the adult interpersonal ensemble forecast.
In a second aspect, there is provided a system for initializing an dating forecast, the system comprising:
selection unit of forecast initial field: for specifying a starting state of the forecast;
a model construction unit: mode component selection for constructing/specifying a forecast, selection of an external forcing field;
a result analysis unit: for analyzing the return/predicted effect.
Preferably, the selecting unit for forecasting an initial field includes:
an atmospheric component selection module: for selecting which set of atmospheric forecast initial fields;
ocean sea ice weight module: for selecting which round of ocean ice forecasts the initial field.
Preferably, the model building unit includes:
a forecast system parameter setting module: the mesh and source of the external forces for the specified pattern, the length of the run, the settings of the output variables and the file.
Compiling and running module of forecasting system: the system is used for connecting an interface between the forecasting system and the server.
Preferably, the result analysis unit includes:
a data processing module: the system is used for splicing the monthly data/files into seasonal average or annual average data;
a drawing module: the spatial structure is used for plotting the annual change of the global surface temperature and the trend of the surface temperature.
Compared with the prior art, the invention has the following beneficial effects:
combining a plurality of groups of atmospheric forecast initial fields with land, ocean and sea ice initial fields respectively to construct a plurality of groups of initial fields for adult generation ensemble forecast; the method comprises the steps of initializing a pre-constructed inter-year forecasting system, performing historical prediction or future business type collective inter-year forecasting, using a quasi-real-time ocean sea ice initial field driven by atmosphere reanalysis data, and performing an initialization method for correcting the weather state of the field, so that the business type inter-year forecasting can be ensured, the influence caused by impact after initialization is reduced, the accuracy of the forecasting system on natural variability forecasting is improved, and the business forecasting level of the inter-year forecasting system is enhanced.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic overall flow diagram of the present invention;
FIG. 2 is a graph of the global surface temperature of example 1 of the present invention;
FIG. 3 is a spatial distribution diagram of the surface temperature trend in the winter season in example 1 of the present invention;
FIG. 4 is a spatial distribution diagram of the surface temperature trend in the winter season in example 2 of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the concept of the invention. All falling within the scope of the present invention.
The embodiment of the invention provides an initialization method for an annual prediction, which is shown in figure 1 and comprises the following steps:
step S1: and obtaining a forecast initial field based on historical simulation of the global coupling mode, and obtaining forecast initial fields of atmosphere and land from the global coupling mode simulation, wherein the forecast initial fields of atmosphere and land are obtained after random disturbance.
Step S2: and correcting the climate state of the ocean ice based on the ocean sea ice historical simulation driven by the atmosphere reanalysis data into the climate state of the ocean and the ocean ice of the global coupling mode historical simulation, thereby obtaining an ocean sea ice forecasting initial field.
Step S3: and combining the obtained multiple groups of atmospheric forecast initial fields with land, ocean and sea ice initial fields respectively to construct multiple groups of initial fields for adult generation ensemble forecast.
Step S4: initializing a pre-constructed inter-generation forecast system, and performing historical postforecast or performing inter-generation forecast on a future business type set.
In step S1, forecast initial fields of the atmosphere and the land are obtained, a global coupling mode is selected for history simulation, and a restart field with a specified date is saved, wherein the atmosphere and the land are used as forecast initial fields of an inter-year forecast system.
And (3) randomly disturbing the truncation error level of the superposition machine of the atmospheric restart field, and obtaining different atmospheric forecast initial fields by adopting disturbances with different sizes.
In step S2, the construction of the historical simulation of ocean ice includes:
selecting and acquiring atmospheric reanalysis data;
specific variables need to be converted to the forced field format required for sea and sea ice content;
performing a multi-wheel driving experiment to obtain a restart field of the ocean and the atmosphere which are fully adjusted in deep sea;
and adding the climate state of the sea ice restarting field and the abnormal field of the sea ice restarting field to finally obtain the sea ice forecasting initial field.
In step S3, constructing sets of initial fields for adult interpersonal ensemble forecasting includes specifying the start and end dates of postforecasting/forecasting.
The invention also provides an initialization system for the dating forecast, which comprises:
selection unit of forecast initial field: for specifying a starting state of the forecast;
a model construction unit: mode component selection for constructing/specifying a forecast, selection of an external forcing field;
a result analysis unit: for analyzing the return/predicted effect.
Wherein, the selection unit for forecasting the initial field comprises:
an atmospheric component selection module: for selecting which set of atmospheric forecast initial fields;
sea ice weight module: for selecting which round of ocean ice forecasts the initial field.
The model building unit comprises:
a forecast system parameter setting module: setting of grids and external forcing sources for a specified mode, the length of operation, output variables and files;
compiling and running module of forecasting system: the system is used for connecting an interface between the forecasting system and the server.
The result analysis unit specifically comprises:
a data processing module: the system is used for splicing the monthly data/files into seasonal average data or annual average data;
a drawing module: the spatial structure is used for plotting the annual change of the global surface temperature and the trend of the surface temperature.
Next, the present invention will be described in more detail.
Example 1:
predictability of global warming stasis. The global average surface air temperature no longer continues to warm during 2002-. Future trends in global warming stagnation and its impact on climate are hot issues of concern at present, including the scientific and social and economic communities. Although the warming stagnation is the result of global surface temperature averaging, it does not behave uniformly in each area. The formation of these regional modes and the corresponding dative-perennial variations are the basis for predictability of the warming stagnation, however, research in this area is lacking.
Referring to fig. 1, the steps are as follows:
the method comprises the following steps: historical simulation of global coupling mode;
setting historical simulation of global coupling mode, comprising:
the mode and version are, cesm1_1_1_ lrg _ ens; the pattern grid is, f09_ g 16;
the simulation period is, 1980 to 2019;
for 1980-2005, the comp set adopted was B20TRC5 CN;
for 2006-2019, the adopted comp is BRCP85C5 CN;
the output frequency is once a month for analyzing the climate simulation ability;
the restart site for this example is selected as month 1, year 1, i.e., month 1, 1980, month 1, 1981, … …, month 1, 2020; this experiment was used as a control experiment.
Step two: performing multiple disturbances on the group of atmospheric initial fields;
the method for using the machine to intercept the disturbance of the error level is to add pertlim to 1.d-14 in the user _ nl _ cam, that is, the random disturbance has a size of 10 14 ；
By analogy, the pertlim is 2.d-14, the pertlim is 1.d-14, … …, the pertlim is 9.d-14 obtains 9 groups of disturbed atmosphere initial fields, and adds the original atmosphere initial fields to obtain 10 groups of atmosphere initial fields;
step three: obtaining atmosphere reanalysis data and converting the data into a forced field required by an ocean sea ice mode;
in this example, the atmospheric re-analysis data was taken from NCEP2, with time ranges of 1979 and 2019, and the downloaded variables included:
monthly rainfall, day-by-day short wave flux downwards, long wave flux downwards, short wave flux upwards, 6-hour sea mark pressure field, 10 meters latitude and longitude wind, 2 meters specific humidity and air temperature.
The specific humidity and air temperature of 2 meters are converted into 10 meters, and the conversion formula adopts the method published by Large and Yeager on the clinic Dynamics in 2009.
Step four: performing historical simulation based on the ocean sea ice driven by the atmospheric forced field in the third step;
the mode and version are, cesm1_1_1_ lrg _ ens;
the pattern grid is, f09_ g 16;
the simulation period is 1979 to 2019;
the adopted comp is GIAF;
the output frequency is once a month for analyzing the climate simulation ability;
the restart site for this example is selected as month 1, 1979, month 1, 1980, month 1, … …, or month 1 2020;
the first round resulted in a restart site of 42 years, 1980-2020, 1/2020;
taking the restart field of 1/2020 as 1/1979, and bringing the restart field into the mode setting, thereby obtaining a 42-year restart field of the second round;
in the same way, 5 rounds are carried out, so that the ocean state and the atmospheric forced field are basically balanced;
a restart field of 5 th round, 1/month 1 1980-1/month 2020, is reserved.
Step five: obtaining forecast initial fields of oceans and sea ice;
restarting the field in the second step, and calculating the climate states of the ocean and the sea ice;
restarting the field in the fourth step, and calculating the abnormal fields of the ocean and the sea ice;
adding the two to be used as a forecast initial field of the ocean and the sea ice;
step six: annual prediction of global warming stasis;
the schema and version are, cem 1_1_2_ LENS _ n 17; the pattern grid is, f09_ g 16;
the simulation period is from 2002 to 2013;
the adopted comp is B20 TRLENS;
the output frequency is once a month for analyzing the ability to simulate global warming stagnation;
taking the atmospheric and land forecast initial field of 1 month and 1 day 2002 obtained in the step one and the ocean and sea ice forecast initial field of 1 month and 1 day 2002 obtained in the step five as the initial field of the first group of annual forecast;
operating for 11 years by adopting the mode setting to obtain a first group of annual prediction results;
taking the atmospheric and land forecast initial fields of 1 month and 1 day 2002 obtained in the step two and the ocean and sea ice forecast initial fields of 1 month and 1 day 2002 obtained in the step five as the annual forecast initial fields of the second group to the ninth group;
and (5) setting to operate for 11 years by adopting the mode, and obtaining the annual forecasting results of the second group to the ninth group.
Step seven: analyzing the annual prediction result of the global warming stagnation;
the global average surface temperature trend in the re-analysis data NCEP 2002-2013 is close to 0, and the surface temperature trend predicted by 10 groups of generations is close to the control experiment, which are shown as a certain warming trend as shown in fig. 2, wherein the result of the fourth group of simulations is closest to the observation, and the temperature trend is 0.23 ℃/10 years, as shown in table 1:
TABLE 1
Furthermore, the spatial structure of the fourth set of simulated temperature trends is able to capture features in the observations to some extent, as shown with reference to fig. 3, such as the cooling trends of north america, south pacific, australia and south atlantic. But fails to reproduce the tendency of continental europe, the northern atlantic ocean to become cold and the mid-eastern pacific ocean to become cold. In fact, the middle east pacific of the other nine experimental simulations exhibited consistent warming structures and thus also exhibited warming trends in the global average surface temperature.
Example 2:
future climate prediction, in the present embodiment, a prediction method for constructing a future short-term climate is provided, as shown in fig. 1, specifically as follows:
the first to fifth steps are the same as in example 1;
step six: prediction of future short-term climate;
the schema and version are, cem 1_1_2_ LENS _ n 17; the pattern grid is, f09_ g 16;
the simulation time period is 2020 to 2009;
the adopted comp is B20 TRLENS;
the output frequency is once a month, and is used for analyzing the prediction capability of the future short-term climate;
taking the atmospheric and land forecast initial field of 1 month and 1 day 2020 as an initial field for the first group of inter-year forecast by combining the ocean and sea ice forecast initial field of 1 month and 1 day 2020 as an initial field for the fifth group of inter-year forecast;
the mode is adopted to set and operate for 10 years, and a first group of inter-year forecast results are obtained;
taking the atmospheric and land forecast initial fields of 1 month and 1 day 2020 as the second to ninth groups, and combining the ocean and sea ice forecast initial fields of 1 month and 1 day 2020 as the fifth to ninth groups;
setting the operation for 10 years by adopting the mode to obtain the annual forecasting results of the second group to the ninth group;
step seven: analyzing the prediction result of the future short-term climate;
the predicted 2020-2029 global surface temperature trend towards warming is shown with reference to fig. 2, spatially expressed as tropical pacific and european asian continents are the most warming regions and arctic and south oceans are the cooler regions as shown in fig. 4.
The embodiment of the invention provides an initialization method and system for annual prediction, wherein prediction initial fields of atmosphere and land are obtained based on historical simulation of a global coupling mode, and a plurality of disturbance are carried out on the obtained atmospheric initial fields to obtain a plurality of groups of atmospheric prediction initial fields; based on the ocean sea ice historical simulation driven by the atmosphere reanalysis data, correcting the climate state of the ocean sea ice historical simulation into the climate state of the ocean and sea ice historical simulation of the global coupling mode, and thus obtaining a forecast initial field of the ocean and sea ice; combining the obtained multiple groups of atmospheric forecast initial fields with land, ocean and sea ice initial fields respectively to construct multiple groups of initial fields for adult generation ensemble forecast; therefore, a pre-constructed dating forecasting system is initialized, historical postforecast or future business type collective dating forecast is implemented, a quasi-real-time ocean sea ice initial field driven by atmosphere reanalysis data is used, and an initialization method for correcting the weather state of the field is carried out, so that the business type dating forecast can be ensured, the influence caused by impact after initialization is reduced, the accuracy of the forecasting system on natural variability forecast is improved, and the business forecasting level of the dating forecasting system is enhanced.
It is well within the knowledge of a person skilled in the art to implement the system and its various devices, modules, units provided by the present invention in a purely computer readable program code means that the same functionality can be implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the present invention can be regarded as a hardware component, and the devices, modules and units included therein for implementing various functions can also be regarded as structures within the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. An initialization method for an dating forecast, comprising:
step S1: obtaining a group of atmospheric forecast initial fields and a group of land forecast initial fields based on historical simulation of a global coupling mode, and performing multiple disturbance on the group of atmospheric forecast initial fields to obtain multiple groups of atmospheric forecast initial fields;
step S2: based on the ocean sea ice historical simulation driven by the atmosphere reanalysis data, correcting the climate state of the ocean sea ice historical simulation into the climate state of the ocean and the climate state of the sea ice of the global coupling mode historical simulation, and thus obtaining an ocean forecast initial field and a sea ice forecast initial field;
step S3: combining the obtained multiple groups of atmospheric forecast initial fields with a land forecast initial field, an ocean forecast initial field and a sea ice forecast initial field to construct multiple groups of initial fields for adult generation ensemble forecast;
step S4: and (4) pre-constructing an intergeneration forecast system, initializing the intergeneration forecast system by using a plurality of groups of initial fields of the intergeneration collective forecast obtained in S3, and performing historical postforecast or annual intergeneration forecast on a future service type collective.
2. The initialization method of an dating forecast according to claim 1, wherein in step S1, the global coupling mode is selected for historical simulation, and a restart field of an end date specified by the historical simulation is saved as the atmospheric forecast initial field and the terrestrial forecast initial field.
3. The method for initializing an dating forecast according to claim 2, wherein said step S1 further comprises superimposing random perturbations of the machine truncation error level on the atmospheric forecast initial field, and using perturbations of different magnitudes to obtain different atmospheric forecast initial fields.
4. The initialization method of an dating forecast according to claim 1, wherein said step S2 comprises:
selecting and acquiring atmospheric reanalysis data;
the specific variables need to be converted to the forced field format required for the sea component and the sea ice component;
and performing a multi-wheel driving experiment to obtain a fully-adjusted deep sea restart field and a fully-adjusted deep sea ice restart field.
5. The initialization method of an dating forecast according to claim 4, wherein said marine forecast initial field in step S2 is a climate state of the marine restart field added to the abnormal field of the marine restart field, and said sea ice forecast initial field is a climate state of the sea ice restart field added to the abnormal field of the sea ice restart field.
6. The method for initializing an intergeneration forecast as claimed in claim 1, wherein said step S3 of constructing multiple sets of initial fields of an adult intergeneration forecast includes specifying start and end dates of the postforecast/forecast.
7. An initialization system of an dating forecast, based on the initialization method of the dating forecast according to any of claims 1 to 6, comprising:
selection unit of forecast initial field: for specifying a starting state of the forecast;
a model construction unit: mode component selection for constructing/specifying a forecast, selection of an external forcing field;
a result analysis unit: for analyzing the return/predicted effect.
8. The initialization system of an dating forecast according to claim 7, wherein said selection unit of the forecast initial field comprises:
an atmospheric component selection module: for selecting which set of atmospheric forecast initial fields;
ocean sea ice weight module: for selecting which round of the ocean forecast initial field and the sea ice forecast initial field.
9. The initialization system of an dating forecast according to claim 7, wherein said model building unit comprises:
a forecast system parameter setting module: setting of grids and external forces for a specified mode, the length of operation, output variables and files;
compiling and running module of forecasting system: the interface is used for connecting the forecasting system with the server.
10. The initialization system of an dating forecast according to claim 7, wherein said result analyzing unit comprises:
a data processing module: the system is used for splicing the monthly data/files into seasonal average data or annual average data;
a drawing module: the spatial structure is used for plotting the annual change of the global surface temperature and the trend of the surface temperature.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110638200.XA CN113221385B (en) | 2021-06-08 | 2021-06-08 | Initialization method and system for dating forecast |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110638200.XA CN113221385B (en) | 2021-06-08 | 2021-06-08 | Initialization method and system for dating forecast |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113221385A CN113221385A (en) | 2021-08-06 |
| CN113221385B true CN113221385B (en) | 2022-09-23 |
Family
ID=77083221
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110638200.XA Active CN113221385B (en) | 2021-06-08 | 2021-06-08 | Initialization method and system for dating forecast |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113221385B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114462247B (en) * | 2022-02-14 | 2022-10-21 | 中国人民解放军61540部队 | Method and system for identifying annual representative modality of sea surface salinity of North Pacific ocean |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942325A (en) * | 2014-04-29 | 2014-07-23 | 中南大学 | Method for association rule mining of ocean-land climate events with combination of climate subdivision thought |
| CN107103396A (en) * | 2017-06-13 | 2017-08-29 | 南京大学 | The Chinese seasonal climate Forecasting Methodology modeled based on main SVD mode |
| CN108549961A (en) * | 2018-05-02 | 2018-09-18 | 河海大学 | A method of wave significant wave height is estimated based on CMIP5 |
| CN110309901A (en) * | 2019-05-16 | 2019-10-08 | 同济大学 | The improvement artificial bee colony data processing method of solving condition nonlinear optimal perturbation |
| CN110443993A (en) * | 2019-06-12 | 2019-11-12 | 中国科学院大气物理研究所 | A method of suitable for model predictions ENSO |
| CN110532500A (en) * | 2019-08-08 | 2019-12-03 | 河海大学 | The construction method of multi-reservoir Parameterization Scheme in a kind of regional atmospheric hydrological model |
| CN111401634A (en) * | 2020-03-13 | 2020-07-10 | 成都信息工程大学 | Processing method, system and storage medium for acquiring climate information |
| CN112069449A (en) * | 2020-09-04 | 2020-12-11 | 中科三清科技有限公司 | Weather forecasting method and device based on initial value set |
| CN112819254A (en) * | 2021-03-05 | 2021-05-18 | 兰州大学 | Assimilation planet scale and machine learning external forcing climate mode prediction method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10909446B2 (en) * | 2019-05-09 | 2021-02-02 | ClimateAI, Inc. | Systems and methods for selecting global climate simulation models for training neural network climate forecasting models |
| CN110543987A (en) * | 2019-08-28 | 2019-12-06 | 向波 | Intelligent climate prediction system |
| CN111291944B (en) * | 2020-03-16 | 2022-09-23 | 中国人民解放军61540部队 | Marine climate prediction method and system based on NPSDV driving factor identification |
| CN112036617B (en) * | 2020-08-17 | 2021-10-29 | 国能大渡河流域水电开发有限公司 | Dynamic-statistical objective quantitative climate prediction method and system |
-
2021
- 2021-06-08 CN CN202110638200.XA patent/CN113221385B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942325A (en) * | 2014-04-29 | 2014-07-23 | 中南大学 | Method for association rule mining of ocean-land climate events with combination of climate subdivision thought |
| CN107103396A (en) * | 2017-06-13 | 2017-08-29 | 南京大学 | The Chinese seasonal climate Forecasting Methodology modeled based on main SVD mode |
| CN108549961A (en) * | 2018-05-02 | 2018-09-18 | 河海大学 | A method of wave significant wave height is estimated based on CMIP5 |
| CN110309901A (en) * | 2019-05-16 | 2019-10-08 | 同济大学 | The improvement artificial bee colony data processing method of solving condition nonlinear optimal perturbation |
| CN110443993A (en) * | 2019-06-12 | 2019-11-12 | 中国科学院大气物理研究所 | A method of suitable for model predictions ENSO |
| CN110532500A (en) * | 2019-08-08 | 2019-12-03 | 河海大学 | The construction method of multi-reservoir Parameterization Scheme in a kind of regional atmospheric hydrological model |
| CN111401634A (en) * | 2020-03-13 | 2020-07-10 | 成都信息工程大学 | Processing method, system and storage medium for acquiring climate information |
| CN112069449A (en) * | 2020-09-04 | 2020-12-11 | 中科三清科技有限公司 | Weather forecasting method and device based on initial value set |
| CN112819254A (en) * | 2021-03-05 | 2021-05-18 | 兰州大学 | Assimilation planet scale and machine learning external forcing climate mode prediction method |
Non-Patent Citations (7)
| Title |
|---|
| Christian Katlein.Platelet Ice Under Arctic Pack Ice in Winter.《Geophysical Research Letters》.2020, * |
| RAFI ULLAH KHAN.Risk Assessment and Decision Support for Sustainable Traffic Safety in Hong Kong Waters.《IEEE Access》.2020, * |
| Yidan Xu.Contribution of SST change to multidecadal global and continental surface air temperature trends between 1910 and 2013.《Climate Dynamics》.2019, * |
| 万修全.地球***模式CESM及其在高性能计算机上的配置应用实例.《地球科学进展》.2014, * |
| 周天军.年代际气候预测问题: 科学前沿与挑战.《地球科学进展》.2017, * |
| 应美佳.南大洋混合层的时空变化特征.《海洋与湖沼》.2019, * |
| 林霄沛.全球变暖"停滞"现象辨识与机理研究.《地球科学进展》.2016, * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113221385A (en) | 2021-08-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hashem et al. | Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar | |
| Alter et al. | Rainfall consistently enhanced around the Gezira Scheme in East Africa due to irrigation | |
| Dhanapala Arachchige et al. | Least square support vector and multi-linear regression for statistically downscaling general circulation model outputs to catchment streamflows | |
| Miyoshi et al. | The local ensemble transform Kalman filter with the Weather Research and Forecasting model: Experiments with real observations | |
| Wang et al. | Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions | |
| Wartenburger et al. | Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets | |
| Zheng et al. | Projection of future global offshore wind energy resources using CMIP data | |
| Koster et al. | A mechanism for land–atmosphere feedback involving planetary wave structures | |
| Ozbek et al. | Deep learning approach for one-hour ahead forecasting of energy production in a solar-PV plant | |
| Katzfey et al. | High-resolution simulations for Vietnam-methodology and evaluation of current climate | |
| Lavers et al. | European precipitation connections with large-scale mean sea-level pressure (MSLP) fields | |
| Hu et al. | Relation of the South China Sea precipitation variability to tropical Indo-Pacific SST anomalies during spring-to-summer transition | |
| CN113221385B (en) | Initialization method and system for dating forecast | |
| Misra et al. | Characterizing the rainy season of Peninsular Florida | |
| Shang et al. | Simulation of the dipole pattern of summer precipitation over the Tibetan Plateau by CMIP6 models | |
| Data | CLIMATE DATA 3 | |
| CN114139760A (en) | Method, system, storage medium and equipment for predicting typhoon path | |
| US9536021B1 (en) | System and method for providing a renewable energy network optimization tool | |
| Yang et al. | Will the arid and semi-arid regions of Northwest China become warmer and wetter based on CMIP6 models? | |
| Zhang et al. | Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia | |
| Raut et al. | Trends in CMIP5 rainfall patterns over southwestern Australia | |
| Zheng et al. | Characterization of model spread in PMIP2 mid-Holocene simulations of the African monsoon | |
| Gusev et al. | Weather noise impact on the uncertainty of simulated water balance components of river basins | |
| Sun et al. | Mapping water vulnerability of the Yangtze River Basin: 1994–2013 | |
| Rahman et al. | Identifying and ranking of CMIP6-global climate models for projected changes in temperature over Indian subcontinent |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |