CN113221385A - Initialization method and system for dating forecast - Google Patents
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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 generation 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 rapidly developed, and the initialization of an observation-based coupling mode can significantly improve the prediction capability of one 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 forecasting experiments is a significant obstacle to recent climate forecast 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 and initialization method, number of initialized earth system components and 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 prediction.
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 disturbances 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, correcting the climate state of the ocean sea ice historical simulation into the climate state of the ocean and the ocean ice of the global coupling mode historical simulation, and thus obtaining an ocean sea ice forecasting initial field;
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 dating forecasting system, and performing historical postforecast or dating forecasting 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 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.
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; 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.
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 it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit 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: 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 comprise a plurality of groups of atmosphere forecast initial fields 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 dating forecasting system, and performing historical postforecast or dating forecasting 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;
ocean 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 forces 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 1014；
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;
the 5 th round restart site was reserved from 1/1980 to 1/2020.
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) operating for 11 years by adopting the mode setting to obtain the dating forecast 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;
setting the operation for 10 years by adopting the mode to obtain the annual prediction result of the first group;
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 dating prediction 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.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully 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 invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in 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 forecast initial field based on historical simulation of a global coupling mode, and performing multiple disturbances 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, correcting the climate state of the ocean sea ice historical simulation into the climate state of the ocean and the ocean ice of the global coupling mode historical simulation, and thus obtaining an ocean sea ice forecasting initial field;
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 dating forecasting system, and performing historical postforecast or dating forecasting on a future business type set.
2. The initialization method of an dating forecast according to claim 1, wherein said restart field holding the specified date in step S1 uses the atmosphere and the land as forecast start 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 restart 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;
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.
5. The initialization method of an dating forecast according to claim 4, wherein said initial field of ocean ice forecast in step S2 is the addition of the climate state of the ocean ice restart field and 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 6, 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 ocean ice forecasts the initial field.
9. The initialization system of an dating forecast according to claim 6, wherein said model building unit comprises:
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.
10. The initialization system of an dating forecast according to claim 6, 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.
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