KR20230058838A - Method for predicting the probability distribution of ground temperature using a hybrid model, and coumputing apparatus for performing the mehthod - Google Patents

Abstract

The present invention discloses a climate change predicting method and a computing device for predicting the probability distribution of ground temperatures affecting the climate through a hybrid model combining a dynamical model and a phase model. The climate change predicting method can predict the probability distribution of ground temperatures affecting the climate within the Northern or Southern Hemisphere of the entire globe using the hybrid model. The dynamical model constituting the hybrid model predicts the index of the Arctic oscillation (or the index of the Antarctic oscillation) and the index of atmospheric teleconnection patterns using sea-level pressure data and geopotential height data. The phase model constituting the hybrid model predicts the probability distribution of ground temperatures affecting the climate according to the phase of the Arctic oscillation using the indices of the Arctic oscillation and atmospheric teleconnection patterns predicted by the dynamical model as input data.

Description

A method for predicting the probability distribution of ground temperature using a hybrid model and a computing device performing the method

The present invention relates to a climate change prediction method and computing device using a hybrid model, and more specifically, to a probability distribution of ground temperature that affects climate using a hybrid model combining dynamic prediction and statistical prediction according to climate variability It relates to a method and apparatus for predictive technology.

Climate is the overall state of the atmosphere at a particular place on the earth's surface, depending on the natural and anthropogenic environment. As the Earth's average temperature has changed in recent decades. The climate is gradually changing over time. Climate change occurs mainly due to anthropogenic emission and concentration increase of greenhouse gases present in the earth's atmosphere due to climate fluctuations over a long period of time. As such, climate change causes not only material damage, but also human damage, as a countermeasure to respond or adapt to climate change, a predictive technology has been proposed.

In detail, climate forecasting technology predicted climate change by region and season, with mid-range forecasts of up to 10 days and seasonal forecasts of up to 3 months. In addition, in recent decades, the use of ensemble prediction techniques and improved initialization, coupling, and physical parameterization have greatly improved the prediction techniques of dynamic prediction systems. However, even with these improvements, generating proficient forecasts of global surface variables in lead times of weeks or more remains challenging. This may be because the predictive skills of dynamic models quickly deteriorate as initial errors compound over time.

Nonetheless, climate forecasting techniques produce reliable forecasts for major climate modes such as the North Atlantic Oscillation (NAO) and the Madden-Julian Oscillation (MJO) over non-seasonal timescales. However, although this prediction method could increase the prediction technology of the model due to the immediate effect of the remote connection pattern of the climate mode, it has the disadvantage of rapidly weakening due to the relatively short time scale of the remote connection pattern.

The present invention provides a method and apparatus for predicting the probability distribution of the ground temperature (T2m) that affects the climate using a hybrid model in which a numerical/physical dynamics model and a statistical phase model are combined.

The present invention provides a method and apparatus for more reliably predicting predictors for analyzing statistical relationships by predicting Arctic oscillations (or Antarctic oscillations) and atmospheric telecorrelation patterns using a dynamic model representing quantitative changes in sea level air pressure. to provide.

The present invention utilizes the Arctic oscillation (or Antarctic oscillation) and the atmospheric remote correlation pattern predicted through the dynamics model as input data of the phase model, thereby increasing the level of prediction over time during the preceding period of forecasting and the level of analysis reliability due to uncertainty. It provides a method and apparatus for improving.

A climate change prediction method according to an embodiment of the present invention includes the steps of predicting an index of Arctic Oscillation appearing in the Northern Hemisphere by applying sea level pressure data observed through artificial satellites to a dynamics model; Predicting an index of an atmospheric telecorrelation pattern using the height data of the mid-atmospheric layer (500-hPa) predicted by the dynamics model; and predicting a probability distribution of surface air temperature that affects the climate by applying the index of the Arctic oscillation and the index of the atmospheric telecorrelation pattern to a phase model. can include

The step of predicting the index of Arctic vibration according to an embodiment of the present invention includes extracting sea level air pressure in a sea level region from sea level pressure data through the dynamic model; and predicting an index of Arctic oscillation by interpolating the daily sea level air pressure into the Arctic oscillation pattern extracted from the previously observed monthly sea level air pressure; can include

The step of extracting sea level air pressure for each day of the sea level area according to an embodiment of the present invention includes removing noise included in sea level air pressure data by using an ensemble average based on the dynamic model; and performing bias correction on the sea level air pressure data from which the noise has been removed to extract daily sea level air pressure for which the bias correction has been performed. can include

The step of predicting the index of Arctic Oscillation according to an embodiment of the present invention includes determining a weather pattern related to monthly sea level air pressure through an empirical orthogonal function; interpolating daily sea level pressure to the weather pattern for the monthly sea level pressure; and estimating an index of Arctic Oscillation from a change value between the interpolated meteorological pattern and daily sea level air pressure; can include

The index of the Arctic Oscillation according to an embodiment of the present invention may be a value to which an anomaly is applied from which a daily or monthly climate value defined according to an empirical orthogonal function pattern is removed.

Predicting the index of the atmospheric telecorrelation pattern according to an embodiment of the present invention includes filtering the position elevation through an empirical orthogonal function; and determining an index of an atmospheric telecorrelation pattern by applying a varimax rotation to the weather pattern with respect to the filtered station altitude; can include

Predicting the probability distribution of ground air temperature according to an embodiment of the present invention includes determining a phase of the Arctic oscillation from an index of the Arctic oscillation through the phase model; determining a phase of the standby remote correlation pattern from an index of the standby remote correlation pattern through the phase model; and estimating a probability distribution of ground air temperature likely to occur in the northern hemisphere during a specific season according to an advance forecast period from the phase of the determined polar oscillation and the phase of the atmospheric telecorrelation pattern; can include

A climate change prediction method according to another embodiment of the present invention includes predicting an index of Antarctic Oscillation appearing in the Southern Hemisphere by applying sea level pressure data observed through artificial satellites to a dynamics model; Predicting the index of the atmospheric telecorrelation pattern by applying the height data of the mid-atmospheric layer (500-hPa) to the epidemiological model; and predicting a probability distribution of surface air temperature that affects climate by applying the index of the Antarctic Oscillation and the index of the atmospheric telecorrelation pattern to a phase model; can include

The step of predicting the index of the Antarctic Oscillation according to an embodiment of the present invention includes extracting daily sea level pressure in a sea level area from sea level pressure data through the dynamic model; and predicting an anomaly-applied index of the Antarctic Oscillation by interpolating daily sea level pressure with previously observed monthly sea level pressure; can include

Predicting the index of the atmospheric telecorrelation pattern according to an embodiment of the present invention includes filtering the position elevation through an empirical orthogonal function; and determining an index of an atmospheric telecorrelation pattern by applying a varimax rotation to the weather pattern with respect to the filtered station altitude; can include

The step of predicting the probability distribution of ground air temperature according to an embodiment of the present invention includes determining a phase of the Antarctic Oscillation from an index of the Antarctic Oscillation through the phase model; determining a phase of the standby remote correlation pattern from an index of the standby remote correlation pattern through the phase model; and estimating a probability distribution of ground air temperature likely to occur in the southern hemisphere during a specific season according to a forecast advance period from the phase of the determined Antarctic Oscillation and the phase of the atmospheric telecorrelation pattern; can include

The processor included in the computing device according to an embodiment of the present invention applies sea level air pressure data observed through artificial satellites to a dynamics model to predict the index of the Arctic Oscillation appearing in the Northern Hemisphere, and to predict the position of the middle atmosphere (500-hPa). It is possible to predict the index of the atmospheric telecorrelation pattern by applying the altitude data to the dynamics model, and to predict the probability distribution of surface air temperature that affects the climate by applying the index of the arctic oscillation and the index of the atmospheric telecorrelation pattern to the phase model. .

The processor according to an embodiment of the present invention extracts daily sea level air pressure in the sea level region from sea level air pressure data through the dynamic model, and interpolates the daily sea level air pressure to previously observed monthly sea level air pressure to predict the index of Arctic Oscillation. there is.

The processor according to an embodiment of the present invention determines a weather pattern related to sea level air pressure by month through an empirical orthogonal function, interpolates daily sea level air pressure to the weather pattern related to the monthly sea level air pressure, and interpolates the weather pattern based on the interpolation and sea level air pressure by day. The index of the polar oscillation can be predicted from the change in atmospheric pressure.

A processor according to an embodiment of the present invention may extract a weather pattern with respect to position altitude through an empirical orthogonal function, and determine an index of an atmospheric telecorrelation pattern by applying Varimax rotation to the filtered weather pattern with respect to position altitude. .

The processor according to an embodiment of the present invention determines the phase of the Arctic oscillation from the index of the Arctic oscillation through the phase model, determines the phase of the atmospheric telecorrelation pattern from the index of the atmospheric telecorrelation pattern through the phase model, and From the phase of the determined polar oscillation and the phase of the atmospheric telecorrelation pattern, it is possible to predict the probability distribution of surface air temperature that can occur in the northern hemisphere during a specific season according to the forecast advance period.

The processor included in the computing device according to another embodiment of the present invention predicts the index of the Antarctic Oscillation appearing in the Southern Hemisphere by applying sea level pressure data observed through artificial satellites to a dynamics model, and the position of the middle atmosphere (500-hPa) It is possible to predict the index of the atmospheric telecorrelation pattern by applying the altitude data to the dynamics model, and to predict the probability distribution of surface air temperature that affects the climate by applying the index of the Antarctic Oscillation and the index of the atmospheric telecorrelation pattern to the phase model. .

The processor according to an embodiment of the present invention extracts daily sea level air pressure in the sea level region from sea level air pressure data through the dynamic model, and interpolates the daily sea level air pressure to previously observed monthly sea level air pressure to index the anomaly-applied Antarctic Oscillation. can predict

A processor according to an embodiment of the present invention may extract a weather pattern with respect to position altitude through an empirical orthogonal function, and determine an index of an atmospheric telecorrelation pattern by applying Varimax rotation to the filtered weather pattern with respect to position altitude. .

The processor according to an embodiment of the present invention determines the phase of the South Pole vibration from the index of the South Pole vibration through the phase model, determines the phase of the atmospheric remote correlation pattern from the index of the atmospheric remote correlation pattern through the phase model, and From the phase of the determined Antarctic Oscillation and the phase of the atmospheric telecorrelation pattern, it is possible to predict the probability distribution of surface air temperature that can occur in the Southern Hemisphere during a specific season according to the forecast advance period.

According to one embodiment of the present invention, the climate change prediction method predicts the probability distribution of the ground temperature (T2m) that affects the climate using a hybrid model in which a numerical and physical dynamics model and a statistical phase model are combined. can

According to an embodiment of the present invention, the method for predicting climate change predicts Arctic oscillations (or Antarctic oscillations) and atmospheric telecorrelation patterns using a dynamic model representing quantitative changes in sea level air pressure, thereby predicting a statistical relationship. Factors can be predicted more reliably.

According to one embodiment of the present invention, the climate change prediction method utilizes the Arctic oscillation (or Antarctic oscillation) and the atmospheric remote correlation pattern predicted through the dynamics model as input data of the phase model, thereby It is possible to improve the level of prediction level and the level of analysis reliability by uncertainty.

1 is a diagram illustrating a computing device for explaining an overall operation for predicting a probability distribution of ground temperature according to an embodiment of the present invention.
2 is a diagram illustrating a detailed operation through a processor included in a computing device according to an embodiment of the present invention.
FIG. 3 is a diagram illustrating a process of predicting a probability distribution of ground air temperature through an Arctic Oscillation (or Antarctic Oscillation) and an atmospheric telecorrelation pattern according to an embodiment of the present invention.
FIG. 4 is a diagram showing results of evaluating Heidke Skill Score (HSS) for each region using a hybrid model according to an embodiment of the present invention.
5 is a diagram showing simulated results for predicting Arctic oscillations in winter according to an ensemble average based on a dynamics model according to an embodiment of the present invention.
6 is a diagram illustrating a result of predicting a probability distribution of ground air temperature within a forecast lead time using Arctic Oscillation according to an embodiment of the present invention.
FIG. 7 is a diagram illustrating reliability evaluation criteria (calibration functions) for the phase of the north pole oscillation or the south pole oscillation within a forecast preceding period according to an embodiment of the present invention.
8 is a diagram showing a corrected result for a specific region within a forecast preceding period using the phase of the Arctic Oscillation or the Antarctic Oscillation according to an embodiment of the present invention.
9 is a flowchart illustrating a climate change prediction method according to an embodiment of the present invention.
10 is a flowchart illustrating a method for predicting climate change according to another embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating a computing device for explaining an overall operation for predicting a probability distribution of ground temperature according to an embodiment of the present invention.

Referring to FIG. 1 , the computing device 101 may predict climate differently depending on regions and seasons in consideration of dynamically moving atmospheric variability. The computing device 101 uses a hybrid model (Hybrid Model) 102 in which a dynamic model (103) and a phase model (104) are combined, and a probability distribution (107) of ground temperature that affects climate can predict

Here, the dynamics model 103 is a numerical model for predicting Arctic Oscillation (AO), Antarctic Oscillation (AAO), and atmospheric remote correlation patterns that affect climate by considering continuous atmospheric flow. can For example, the dynamics model 103 may predict an index of the Arctic Oscillation or Antarctic Oscillation using sea level pressure data 105 (Sea-Level Pressure Data) observed through artificial satellites. The dynamics model 103 can predict the index of the atmospheric telecorrelation pattern using 500-hPa geopotential height data (106, Geopotential Height Data).

The dynamics model 103 may continuously predict Arctic oscillation or Antarctic oscillation on a sub-seasonal time scale based on a linear relationship according to a regression equation between a predictor and a predictor variable. In the present invention, an ensemble average may be applied to compensate for a numerical error of the dynamics model 103. At this time, the present invention may utilize a dynamics model 103 having a numerical prediction model used by institutions that predict weather. The present invention can be repeatedly performed a plurality of times to predict future weather, and an ensemble average can be applied using all ensemble members acceptable from the repeated performance results. In addition, the present invention can perform prediction on the ensemble average by including a random error in the initial field, which is the input value of the dynamics model 103.

As an example, the dynamics model 103 may predict the index of arctic oscillations occurring innumerably within a specific section from the sea level air pressure data 105 . A specific section may mean one cycle, and one cycle may mean one day or 24 hours. The present invention may collect a plurality of indices of the Arctic oscillations predicted in a specific section as an ensemble, and generate an ensemble average for the collected ensemble. The present invention can compensate for numerical errors by removing noise included in sea level pressure data using the ensemble average.

The phase model 104 may be a probability model or a statistical model that applies a statistical relationship to the north pole oscillation or the south pole oscillation numerically predicted through the dynamics model 103 . The phase model 104 may predict a probability of ground temperature for predicting climate based on a non-linear relationship between a predictor and a predictor variable.

Here, the predictor is an element that enables the result to be predicted through each model. For example, the predictor of the dynamics model 103 is sea level air pressure information for predicting the Arctic Oscillation or Antarctic Oscillation, or atmospheric remote correlation pattern. It may be position altitude data for prediction. The predictor of the phase model 104 may be the output data output from the dynamics model 103, that is, the index of the predicted north pole oscillation or the index of the south pole oscillation. The predictor variable is a factor used to predict the climate. For example, the predictor variable of the dynamics model 103 may be sea level air pressure and geographic altitude, and the predictor variable of the phase model 104 may be T2m.

The hybrid model 102 utilizes the predictor output from the epidemiological model 103 as input data of the phase model 104, and forecasts climate within a forecast lead time of a certain period through the phase model 104. It can be a hybrid model that improves performance. As an example, the hybrid model 102 uses the phase of the Arctic phase from 1 week to 3 weeks predicted by the epidemiological model 103 for a forecast advance period of 1 to 6 weeks through the statistical model 104 of the northern hemisphere. A probability distribution 107 of ground temperature in winter can be predicted.

As a result, the hybrid model 102 is a phase model 104 based on the empirical relationship between the phase of the north pole vibration (or south pole vibration) of the dynamic model 103 and the phase of the atmospheric remote correlation pattern on the ground can be built in conjunction with Thus, the hybrid model 102 can improve the technical scores and reliability for all preceding forecasting periods to be predicted, and can be a model for predicting climate in non-seasonal conditions.

Accordingly, the computing device 101 may predict the probability distribution 107 of land temperature appearing in the Northern Hemisphere or the Southern Hemisphere by using the hybrid model 102 . The computing device 101 may analyze the probability distribution 107 of each predicted ground temperature to more accurately predict the climate of the Northern Hemisphere or the Southern Hemisphere by season.

2 is a diagram illustrating a detailed operation through a processor included in a computing device according to an embodiment of the present invention.

Referring to FIG. 2 , the computing device may perform a climate change prediction method. The computing device may include a processor 201 to perform the climate change prediction method. The processor 201 may predict a probability distribution of ground temperature that affects the climate using the hybrid model 102 by performing the following operation. Here, the hybrid model 102 may be a hybrid model in which the dynamics model 103 and the phase model 104 are combined.

① Mechanics model

In order to increase the accuracy of climate prediction, the processor 201 determines the initial phase of the Arctic Oscillation (or Antarctic Oscillation: hereinafter, the invention will be described centering on the Arctic Oscillation of the Northern Hemisphere) through the dynamics model 103. value can be set.

In detail, the processor 201 may use the dynamics model 103 to predict the index of the Arctic oscillation or the index 205 of the Antarctic oscillation and the index 206 of the atmospheric telecorrelation pattern. The processor 201 may predict the index 205 of the Arctic Oscillation appearing in the Northern Hemisphere by applying sea level air pressure data 105 observed through artificial satellites to the dynamics model 103 of the computing device 101 . Here, the sea level air pressure data 105 may be data representing a daily 2-meter temperature (T2m) at a horizontal resolution for predicting air temperature. As an example, seawater temperature data (105) was obtained using daily 2-meter temperatures (T2m) from the European Center for Medium Range Weather Forecasts (ECMWF) at a horizontal resolution of 2.5° latitude Х 2.5° longitude from December to February (DJF). may be material.

The processor 201 may extract daily sea level pressure 204 of the sea level area from sea level pressure data through a dynamic model. Here, the processor 201 may remove noise included in sea level pressure data by using an ensemble average based on a dynamic model. For example, the processor 201 may correct numerical errors by horizontally interpolating sea level pressure data on a 2.5 ° Х 2.5 ° grid. In the present invention, the bias according to the ensemble average can be corrected through Equations 1 and 2 below.

Here, H and O represent hindcast and observation data sets, respectively, at each grid point, primes represent bias-corrected threshold values, and over bars may represent monthly climates.

는 편이 보정된 과거 재현 자료를 의미하며,

는 예측된 월별 기후값을 의미하고,

는 관측된 월별 기후값을 의미하며,

는 주별 예측 선행 시간

을 의미할 수 있다.here,

Means the past reproduction data with side correction,

is the predicted monthly climate value,

is the observed monthly climate value,

is the predicted lead time by week

can mean

The processor 201 may perform bias correction on sea level air pressure data from which noise has been removed to extract the sea level air pressure 204 for each day for which the bias correction has been performed.

Thereafter, the processor 201 may interpolate the daily sea level pressure 204 to the monthly sea level pressure 203 to predict the index of the Arctic Oscillation. The monthly sea level pressure 203 is previously observed and collected information, and the processor 201 may use the monthly sea level pressure 203 stored in the database 202 .

The processor 201 may determine a weather pattern related to the monthly sea level pressure 203 through an empirical orthogonal function (EOF). The processor 201 interpolates the daily sea level pressure 204 to the weather pattern for the monthly sea level pressure 203, and predicts the Arctic Oscillation Index 205 from the change between the interpolated weather pattern and the daily sea level pressure. . As an example, the processor 201 interpolates the bias-corrected anomaly of the daily sea level pressure 204 to the first empirical orthogonal function pattern of the anomaly of the monthly sea level pressure 203 observed in an area of 20 degrees north latitude or higher, and interpolates the North Pole. The index of vibration 205 can be predicted.

이상일 때를 의미하고, 반대로, 음성(Negative Phase)은 -0.43

이하일 때를 의미할 수 있다.Here, the index 205 of the polar oscillation may be a value to which an anomaly is applied, from which a climate value defined per day or month according to an empirical orthogonal function pattern is removed. In this case, the anomaly may be a value obtained by removing climate values defined by month or day. For example, the polar vibration index 205 is applied with an anomaly in the SLP of ECMWF hindcast, and the vibration intensity can be expressed as a numerical value between -5 and +5. Positive Phase has an index of Arctic Oscillation of 0.43

It means when it is abnormal, and on the contrary, Negative Phase is -0.43

It can mean when below.

In predicting the index of the Antarctic oscillation, the present invention is the same as the method of predicting the index of the Arctic oscillation described above, but the first empirical orthogonal function pattern of the anomaly for the monthly sea level pressure observed in the region below 20 degrees south latitude The index of the Antarctic Oscillation can be predicted by interpolating a bias-corrected daily sea level pressure anomaly.

The processor 201 may predict the index 206 of the atmospheric telecorrelation pattern by applying the status altitude data 106 of the mid-atmospheric layer (500-hPa) to the dynamics model 103 for weekly prediction performance evaluation. The processor 201 may filter the status elevation through an empirical orthogonal function. The processor 201 may determine the index 206 of the atmospheric telecorrelation pattern by applying Varimax Rotation to the weather pattern with respect to the filtered position altitude. Varimax rotation can be a way to reduce the number of predictors that are highly loaded on one factor, targeting the filtered status elevation. As a result, the processor 201 filters the position height with an empirical orthogonal function, and then redefines it with a rotated empirical orthogonal function (REOF) using varimax rotation, so that the index of the atmospheric remote correlation pattern (206 ) can be predicted.

As an example, the present invention is defined in the mid-tropospheric North Atlantic Oscillation (NAO), Pacific / North American Pattern (PNA), East Atlantic (EA), East Atlantic / West Russia ( Indices can be predicted for northern hemisphere atmospheric telecorrelation patterns, including EAWR, East Atlantic/Western Russia), (SCAND, Scandinavia), (PE, Polar/Eurasia), and (WP, West Pacific).

② Phase model

The processor 201 may predict the probability distribution 107 of ground air temperature according to the index 205 of the polar vibration and the index 206 of the atmospheric telecorrelation pattern by applying an empirical relationship through the phase model 104 .

In detail, the processor 201 may use the output data of the dynamics model 103, that is, the index 205 of polar oscillation and the index 206 of the atmospheric telecorrelation pattern, as input data of the phase model 104. The processor 201 may predict a probability distribution 107 of surface air temperature affecting the climate by applying the index 205 of the polar vibration and the index 206 of the atmospheric telecorrelation pattern to the phase model.

The processor 201 may determine the phase 207 of the Arctic oscillation from the index 205 of the Arctic oscillation through the phase model, and determine the phase 208 of the atmospheric telecorrelation pattern from the index of the atmospheric telecorrelation pattern. The processor 201 may predict a probability distribution of ground air temperature that may occur in the northern hemisphere during a specific season according to the forecast advance period from the phase 207 of the polar oscillation and the phase 208 of the atmospheric telecorrelation pattern.

FIG. 3 is a diagram illustrating a process of predicting a probability distribution of ground air temperature through an Arctic Oscillation (or Antarctic Oscillation) and an atmospheric telecorrelation pattern according to an embodiment of the present invention.

Referring to FIG. 3 , the hybrid model 102 is a mixed model in which the dynamics model 104 and the phase model 103 are combined, and the probability distribution of the ground temperature can be predicted through perfect forecast determination.

In S1 301 , the computing device may extract daily sea level pressure in the sea level area from sea level pressure data through the dynamics model 104 .

In step S2 302, the computing device may remove noise included in the sea level pressure data by using the ensemble average of the specific section. The computing device may perform bias correction on sea level air pressure data from which noise has been removed to extract daily sea level air pressure for which the bias correction has been performed.

In S3 303 , the computing device may determine a weather pattern related to sea level pressure by month through an empirical orthogonal function. The computing device may interpolate the daily sea level pressure into a weather pattern related to the monthly sea level pressure.

In S4 304 , the computing device may predict the index of the Arctic Oscillation from the interpolated meteorological pattern and the daily sea level air pressure change value. The computing device may predict the index of the atmospheric remote correlation pattern of the ground from the status quo data through the dynamics model 104 . Thereafter, the computing device may use output data (an index of polar vibration and an index of an atmospheric telecorrelation pattern) output through the dynamics model 104 as input data of the phase model 103 .

The computing device may determine the phase of the Arctic oscillation and the phase of the atmospheric telecorrelation pattern, respectively, corresponding to the index of the Arctic oscillation and the index of the atmospheric telecorrelation pattern. The computing device may be built in conjunction with the phase model 104 based on the empirical relationship between the phase of the polar oscillation of the dynamics model 103 and the phase of the atmospheric telecorrelation pattern on the ground. The phase model can be observed by the composite field between the predictor (AO) and the predictor variable (T2m).

In this case, the computing device may predict the probability distribution of the ground temperature through perfect prognosis. The perfect prediction combination can be expressed as Equation 3 below.

The present invention can calculate a relational expression between a predictor and a predictor variable based on observational data, and the result calculated from the epidemiological model can be used as a predictor under the assumption that the prediction result of the epidemiological model is perfect. Then, the computing device combines the relationship between the phase (initial) of the Arctic oscillation and the forecast advance period predicted through the dynamic model according to the complete forecast determination, thereby delaying the decrease in forecast performance according to the forecast advance period, and obtaining more accurate ground temperature. can predict the probability distribution of

FIG. 4 is a diagram showing results of evaluating Heidke Skill Score (HSS) for each region using a hybrid model according to an embodiment of the present invention.

The graph of FIG. 4 is a graph showing the probability distribution of the predicted ground air temperature according to the preceding forecasting period for each region. The present invention can predict the probability distribution of ground air temperature that changes according to the preceding period of forecasting by analyzing the relationship of the abnormal distribution appearing between the phase of the polar oscillation and the phase of the atmospheric telecorrelation pattern for each region. The present invention can analyze different relationships between the phase of the Arctic oscillation and the phase of the atmospheric telecorrelation pattern for each forecast preceding period. Here, one week may be defined as 7 days, that is, an average of 7 days centered on days 4-10. In addition, it may be defined as 7 days centered on 14 to 42 days from week 2 to week 6, respectively. This can be represented as shown in Table 1 below.

In the category of FIG. 4 , AO ₇₉₁₈ may mean the Arctic oscillation observed through NOAA/CPC, and AO ₉₈₁₇ may mean the Arctic oscillation predicted through the method proposed in the present invention. In addition, H1 to H4 denote each predictable step in the hybrid model, and H1 may be a version (1 week dyn. fcst) in which the phase of the north pole oscillation predicted for 1 week is combined as a predictor factor. This suggests that the high initial prediction performance of the dynamics model may contribute to improving probability prediction. This is similar to H1 in Week 2, but shows a lower prediction accuracy than H1, and in Weeks 3-4, improved prediction performance compared to H1 can be improved by limiting the Eurasia region. H3 and H4 can improve the predictive performance of Week 4.

These, each version can be represented as shown in Table 2 below.

ⓐ Prediction period (1 week) When predicting the first week of climate, the processor can dynamically predict the probability distribution of surface air temperature through the simultaneous relationship between the polar oscillation phase and the anomaly distribution between the phases of the atmospheric telecorrelation pattern.

ⓑ Pre-forecast period (more than 2 weeks)

In case of predicting the climate of the second week or more, the processor may dynamically predict the probability distribution of the surface air temperature through a delay relationship between the phase of the Arctic oscillation and the abnormal distribution between the phases of the atmospheric telecorrelation pattern.

In the present invention, the probability distribution of the ground temperature for each forecast preceding period can be predicted using the Heidke Skill Score (HSS) defined as in Equation 4 below.

Here, T means the total number of predictions, C means the number of successful predictions, and E means the number of successful predictions expected by chance. It can be adopted as T / 3, which can be expressed as in Equation 5.

The HSS ranges from -50% to 100%, with 0 representing the baseline of the model demonstrating a better technique than random prediction.

The graph (a) of FIG. 4 is the result of evaluating the forecast accuracy proficiency in the Northern Hemisphere region, and the graph (b) of FIG. 4 is the result of evaluating the forecast accuracy proficiency of the Eurasia region. Graph (c) may be a result of evaluating proficiency in forecasting in North America.

Looking at the results of evaluating the forecast hit proficiency evaluated according to the forecast lead period centered on each region, in the graphs (a) to (c) of FIG. It can be seen that the skill level is lowered, which may be due to low accuracy in predicting climate as the past Arctic oscillation is used based on a single phase model. On the other hand, the method using the hybrid model proposed in the present invention appears to remain relatively flat even if the forecast lead period becomes longer. It can be seen that it has high accuracy for climate prediction in , and thus the prediction performance is improved.

In addition, among the graphs (a) to (c) of FIG. 4, the forecast hit skill in the Eurasia region corresponding to graph (b) is slightly different from the single phase model compared to other graphs (a) and (c). appears low. This indicates that the initial phase of the Arctic Oscillation, which is relatively high in Eurasia, has high predictive performance, and it can be confirmed that there is a strong correlation between the Arctic Oscillation and the temperature in Eurasia.

5 is a diagram showing simulated results for predicting Arctic oscillations in winter according to an ensemble average based on a dynamics model according to an embodiment of the present invention.

The graph of FIG. 5 is a result of predicting the phase of the Arctic oscillation in winter, and shows the skill level of forecast accuracy for the Arctic oscillation of past reproduction data for each forecast preceding period. In this graph, it is possible to determine when the phase of the polar oscillation becomes an amplitude (Active Phases).

In detail, the present invention can predict up to 32 days with a more skilled prediction than any prediction (i.e., HSS > 0%) by evaluating all stages (All) of re-predicting past reproduction data. At this time, the initial skill on the first day of prediction starts at a forecast hit skill level close to 85%, which may decrease to about 35% on the 14th day of prediction. Forecast hit proficiency is reduced by 15% at Prediction 32, but can be maintained higher than Neutral (Neu).

In the present invention, measuring skills for active phases (Pos: Positive & Neg: Negative) can increase predictive skills by 5-20% for all lead times compared to all phases (All). However, the technical difference between the positive phase (Pos) and the negative phase (Neg) constituting the active phase may be insignificant. However, forecast hit proficiency is shown to fall below 0% on forecast day 11 for the neutral phase (Neu).

Therefore, reliable prediction due to the amplification of the polar oscillation through the dynamic model proposed in the present invention is possible in the positive phase (Pos) and negative phase (Neg) of the dynamic model, and more skilled prediction is possible in the neutral phase (Neu) may be limited to two weeks in the case of

6 is a diagram illustrating a result of predicting a probability distribution of ground air temperature within a forecast lead time using Arctic Oscillation according to an embodiment of the present invention.

The graph of FIG. 5 is a result of predicting T _2m of past reproduction data of ECMWF, and shows the skill level of predicting the temperature in winter in the forecast preceding period. In the present invention, T _2m can be evaluated at the positive stage (Pos) and the negative stage (Neg) disclosed in FIG. 5 based on the hybrid model.

o-Southern Oscillation)의 대규모 변동성이 낮기 때문일 수 있다. 반면, 예보 적중 숙련도는 중 ·고위 지역에서 초기의 높은 예측 성능이 빠르게 감소하는 것으로 나타나며, 이는 중위도의 강한 온도 경도 ~ 경합이 불안정하며, 대륙을 중심으로 더욱 급격하게 예측성이 감소하기 때문일 수 있다. 이러한 결과는, 아래의 표 3과 같이 나타낼 수 있다.Looking at the graph of FIG. 5, the forecast hit proficiency shows consistently high accuracy in the tropical region, which is ENSO (El Ni

This may be due to the low large-scale volatility of the o-Southern Oscillation. On the other hand, forecast hit proficiency shows that the initial high forecasting performance rapidly decreases in the middle and high latitudes, which may be because the strong temperature gradient in the mid-latitude ~ competition is unstable, and the predictability decreases more rapidly around the continent. . These results can be shown in Table 3 below.

FIG. 7 is a diagram illustrating reliability evaluation criteria (calibration functions) for the phase of the north pole oscillation or the south pole oscillation within a forecast preceding period according to an embodiment of the present invention.

Referring to FIG. 7 , the closer the reliability evaluation is to '1', the more it can be determined that the climate is measured stably and consistently through a minimum error. This can be represented as a graph 701.

In the graph 702 shown on the left side of the graph 701, a slight error occurred on the x-axis based on '1', but this is a result of appropriate prediction in response to the condition of the predictor, and has high accuracy. can In the graph 703 shown on the right side of the graph 701, a large error occurred on the x-axis and y-axis based on '1', which has low accuracy as a result of prediction meeting the condition of the predictor. can

In addition, the graph 704 shown above the graph 701 is in the form of a low slope in the lower region based on '1', which is a result of excessive prediction compared to the condition of the predictor, and the wet bias (wet bias). In addition, the graph 705 shown above the graph 701 is in the form of a high slope in the upper region based on '0', which is a result of underprediction compared to the condition of the predictor, dry bias (dry bias).

8 is a diagram illustrating a result of improving reliability of a hybrid model according to an embodiment of the present invention.

Referring to FIG. 8 , the computing device may improve the reliability of the hybrid model with respect to the 4-week forecast lead period. The graphs of FIG. 8 may represent the relationship between predicted probability and relative frequency according to the present invention. Figure 8 (a) is a graph with low accuracy of the polar oscillation of the positive stage, and Figure 8 (b) is a graph with high accuracy of the polar oscillation of the positive stage. In addition, (c) of FIG. 8 is a graph with low accuracy due to negative polarity oscillation, and FIG. 8 (d) is a graph with high accuracy due to negative polarity oscillation.

The computing device may calibrate each stage of the hybrid model represented by each graph, and through this, each stage may be calibrated as follows.

- The polar oscillation using the single-phase model has an overconfidence smaller than 1 and can be biasedly predicted as a positive phase polar oscillation.

- H1 can be predicted in a form similar to the single phase model.

- H2 can be predicted with improved reliability compared to H1.

- H3 and H4 belong to the lower tertile and can be over-predicted, or belong to the upper tertile and can be under-predicted. This represents the form closest to the 1:1 baseline, and reliability can be improved accordingly.

9 is a flowchart illustrating a climate change prediction method according to an embodiment of the present invention.

In step 901, the computing device may predict the index of the Arctic Oscillation appearing in the Northern Hemisphere by applying sea level pressure data observed through artificial satellites to a dynamics model.

The computing device may extract daily sea level pressure in the sea level area from sea level pressure data through a dynamic model. In this case, the computing device may remove noise included in the sea level pressure data by using an ensemble average based on the dynamic model. The computing device may perform bias correction on sea level air pressure data from which noise has been removed to extract daily sea level air pressure for which the bias correction has been performed.

Thereafter, the computing device may predict the index of the Arctic Oscillation by interpolating the daily sea level pressure with previously observed monthly sea level pressure. In this case, the computing device may determine a weather pattern related to sea level pressure by month through an empirical orthogonal function. The computing device may interpolate the daily sea level pressure into a weather pattern related to the monthly sea level pressure. The computing device may predict the index of Arctic oscillation from the interpolated meteorological pattern and the daily sea level air pressure change value.

The index of the Arctic Oscillation predicted through the above-described process may be a value to which an anomaly is applied from which a daily or monthly climate value defined according to an empirical orthogonal function pattern is removed. The anomaly may be a difference value that changes from an average value of climate observed for a predetermined period centered on a specific region.

In step 902, the computing device may predict the index of the atmospheric telecorrelation pattern by applying the position altitude data of the atmospheric middle layer (500-hPa) to the dynamics model. The computing device may extract a weather pattern related to position altitude through an empirical orthogonal function. The computing device may determine an index of an atmospheric telecorrelation pattern by applying a varimax rotation to the weather pattern with respect to the filtered status altitude.

In step 903, the computing device may predict a probability distribution of surface air temperature affecting the climate by applying the index of the polar oscillation and the index of the atmospheric telecorrelation pattern to the phase model. The computing device may determine the phase of the Arctic oscillation from the index of the Arctic oscillation and the phase of the atmospheric telecorrelation pattern from the index of the atmospheric telecorrelation pattern through the phase model. The computing device may predict a probability distribution of surface air temperatures that may occur in the northern hemisphere during a specific season according to a forecast advance period from the phase of the polar oscillation and the phase of the atmospheric telecorrelation pattern. The computing device can predict climate change in winter in the northern hemisphere through a probability distribution of surface temperature.

10 is a flowchart illustrating a method for predicting climate change according to another embodiment of the present invention.

In step 1001, the computing device may predict the index of the Antarctic Oscillation appearing in the Southern Hemisphere by applying sea level pressure data observed through artificial satellites to a dynamics model. The computing device may extract daily sea level pressure in the sea level area from sea level pressure data through a dynamic model. The computing device may predict the anomaly-applied index of the Antarctic Oscillation by interpolating the daily sea level pressure with the previously observed monthly sea level pressure.

In step 1002, the computing device may predict an index of an atmospheric telecorrelation pattern by applying position altitude data of the atmospheric middle layer (500-hPa) to a dynamics model. The computing device may extract a weather pattern related to position altitude through an empirical orthogonal function. The computing device may determine an index of the atmospheric telecorrelation pattern by applying a Varimax rotation to the weather pattern with respect to station altitude.

In step 1003, the computing device may apply the index of the Antarctic oscillation and the index of the atmospheric telecorrelation pattern to the phase model to predict the probability distribution of surface air temperature affecting the climate. The computing device may determine the phase of the Antarctic Oscillation from the index of the South Pole Oscillation and the phase of the atmospheric telecorrelation pattern from the index of the atmospheric telecorrelation pattern through the phase model. The computing device can predict a probability distribution of surface air temperature that can occur in the Southern Hemisphere during a specific season according to the forecast advance period from the phase of the Antarctic Oscillation and the phase of the atmospheric telecorrelation pattern. The computing device can predict climate change in winter in the southern hemisphere through a probability distribution of surface temperature.

Meanwhile, the method according to the present invention is written as a program that can be executed on a computer and can be implemented in various recording media such as magnetic storage media, optical reading media, and digital storage media.

Implementations of the various techniques described herein may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or combinations thereof. Implementations may be a computer program product, i.e., an information carrier, e.g., a machine-readable storage, for processing by, or for controlling, the operation of a data processing apparatus, e.g., a programmable processor, computer, or plurality of computers. It can be implemented as a computer program tangibly embodied in a device (computer readable medium) or a radio signal. A computer program, such as the computer program(s) described above, may be written in any form of programming language, including compiled or interpreted languages, and may be written as a stand-alone program or in a module, component, subroutine, or computing environment. It can be deployed in any form, including as other units suitable for the use of. A computer program can be deployed to be processed on one computer or multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

Processors suitable for processing a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from read only memory or random access memory or both. Elements of a computer may include at least one processor that executes instructions and one or more memory devices that store instructions and data. In general, a computer may include, receive data from, send data to, or both, one or more mass storage devices that store data, such as magnetic, magneto-optical disks, or optical disks. It can also be combined to become. Information carriers suitable for embodying computer program instructions and data include, for example, semiconductor memory devices, for example, magnetic media such as hard disks, floppy disks and magnetic tapes, compact disk read only memory (CD-ROM) ), optical media such as DVD (Digital Video Disk), magneto-optical media such as floptical disks, ROM (Read Only Memory), RAM (RAM) , Random Access Memory), flash memory, EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), and the like. The processor and memory may be supplemented by, or included in, special purpose logic circuitry.

In addition, computer readable media may be any available media that can be accessed by a computer, and may include both computer storage media and transmission media.

Although this specification contains many specific implementation details, they should not be construed as limiting on the scope of any invention or what is claimed, but rather as a description of features that may be unique to a particular embodiment of a particular invention. It should be understood. Certain features that are described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable subcombination. Further, while features may operate in particular combinations and are initially depicted as such claimed, one or more features from a claimed combination may in some cases be excluded from that combination, and the claimed combination is a subcombination. or sub-combination variations.

Similarly, while actions are depicted in the drawings in a particular order, it should not be construed as requiring that those actions be performed in the specific order shown or in the sequential order, or that all depicted actions must be performed to obtain desired results. In certain cases, multitasking and parallel processing can be advantageous. Further, the separation of various device components in the embodiments described above should not be understood as requiring such separation in all embodiments, and the program components and devices described may generally be integrated together into a single software product or packaged into multiple software products. You have to understand that you can.

On the other hand, the embodiments of the present invention disclosed in this specification and drawings are only presented as specific examples to aid understanding, and are not intended to limit the scope of the present invention. In addition to the embodiments disclosed herein, it is obvious to those skilled in the art that other modified examples based on the technical idea of the present invention can be implemented.

101: computing device
102: hybrid model
103: dynamics model
104: phase model
105: Sea level pressure data
106: position altitude data
107: Probability distribution of ground temperature

Claims (20)

Predicting an index of Arctic Oscillation (AO) in the Northern Hemisphere by applying sea level pressure data observed through satellites to a dynamics model;
Predicting an index of an atmospheric telecorrelation pattern by applying geopotential height data (500-hPa) to a dynamic model; and
predicting a probability distribution of surface air temperature that affects the climate by applying the polar vibration index and the atmospheric telecorrelation pattern index to a phase model;
A climate change prediction method comprising a. According to claim 1,
Predicting the index of the polar vibration,
extracting daily sea level pressure (SLP) in the sea level area from sea level pressure data through the dynamic model; and
predicting an index of Arctic oscillation by interpolating the daily sea level pressure with previously observed monthly sea level pressure;
A climate change prediction method comprising a. According to claim 2,
The step of extracting the daily sea level air pressure of the sea level area,
removing noise included in sea level pressure data using an ensemble average based on the dynamic model; and
extracting sea level air pressure for each day for which the bias correction has been performed by performing bias correction on the sea level air pressure data from which the noise has been removed;
A climate change prediction method comprising a. According to claim 2,
Predicting the index of the polar vibration,
Determining a weather pattern related to monthly sea level air pressure through an empirical orthogonal function (EOF);
interpolating daily sea level pressure to the weather pattern for the monthly sea level pressure; and
predicting an index of Arctic Oscillation from a change value between the interpolated weather pattern and daily sea level air pressure;
A climate change prediction method comprising a. According to claim 1,
The index of the polar oscillation is,
A method of predicting climate change, which is a value to which an anomaly is applied from which the climate value defined by day or month according to the empirical orthogonal function pattern is removed. According to claim 1,
Predicting the index of the standby remote correlation pattern,
filtering position elevations through an empirical orthogonal function; and
determining an index of an atmospheric telecorrelation pattern by applying Varimax rotation to the weather pattern related to the filtered position altitude;
A climate change prediction method comprising a. According to claim 1,
The step of predicting the probability distribution of the ground temperature,
determining a phase of the Arctic oscillation from an index of the Arctic oscillation through the phase model;
determining a phase of the standby remote correlation pattern from an index of the standby remote correlation pattern through the phase model; and
estimating a probability distribution of ground air temperature likely to occur in the northern hemisphere during a specific season according to an advance forecast period from the phase of the determined polar oscillation and the phase of the atmospheric telecorrelation pattern;
A climate change prediction method comprising a. Predicting an index of Antarctic Oscillation (AAO) in the Southern Hemisphere by applying sea level pressure data observed through satellites to a dynamics model;
Predicting the index of the atmospheric telecorrelation pattern by applying the height data of the mid-atmospheric layer (500-hPa) to the epidemiological model; and
predicting a probability distribution of surface air temperature that affects climate by applying the index of the Antarctic Oscillation and the index of the atmospheric telecorrelation pattern to a phase model;
A climate change prediction method comprising a. According to claim 8,
The step of predicting the index of the Antarctic Oscillation,
extracting daily sea level air pressure in a sea level region from sea level air pressure data through the dynamic model; and
predicting an index of Antarctic Oscillation to which an anomaly is applied by interpolating daily sea level pressure to previously observed monthly sea level pressure;
A climate change prediction method comprising a. According to claim 8,
Predicting the index of the standby remote correlation pattern,
filtering position elevations through an empirical orthogonal function; and
determining an index of an atmospheric telecorrelation pattern by applying a varimax rotation to the weather pattern with respect to the filtered position altitude;
A climate change prediction method comprising a. According to claim 8,
The step of predicting the probability distribution of the ground temperature,
determining a phase of the Antarctic Oscillation from an index of the South Pole Oscillation through the phase model;
determining a phase of the standby remote correlation pattern from an index of the standby remote correlation pattern through the phase model; and
estimating a probability distribution of ground air temperature that may occur in the southern hemisphere during a specific season according to a forecast advance period from the phase of the determined Antarctic Oscillation and the phase of the atmospheric telecorrelation pattern;
A climate change prediction method comprising a. In a computing device that performs a climate change prediction method,
The computing device includes a processor,
the processor,
Predict the index of the Arctic Oscillation in the Northern Hemisphere by applying sea level pressure data observed through satellites to a dynamics model,
Predict the index of the atmospheric telecorrelation pattern by applying the position altitude data of the middle atmosphere (500-hPa) to the epidemiological model,
A computing device for predicting a probability distribution of ground temperature that affects climate by applying the index of the polar vibration and the index of the atmospheric telecorrelation pattern to a phase model. According to claim 12,
the processor,
Daily sea level pressure in the sea level area is extracted from the sea level pressure data through the dynamic model,
A computing device for predicting an index of Arctic oscillation by interpolating the daily sea level pressure to previously observed monthly sea level pressure. According to claim 13,
the processor,
Determining weather patterns for monthly sea level pressure through an empirical orthogonal function;
Interpolating daily sea level pressure to the weather pattern related to the monthly sea level pressure,
A computing device for predicting an index of Arctic oscillation from a change value between the interpolated weather pattern and daily sea level air pressure. According to claim 12,
the processor,
Extract weather patterns related to position elevations through empirical orthogonal functions,
A computing device for determining an index of an atmospheric telecorrelation pattern by applying a varimax rotation to the weather pattern with respect to the filtered status altitude. According to claim 12,
the processor,
determining a phase of the Arctic oscillation from an index of the Arctic oscillation through the phase model;
Determining a phase of a standby remote correlation pattern from an index of the standby remote correlation pattern through the phase model;
A computing device for predicting a probability distribution of ground air temperature that may occur in the northern hemisphere during a specific season according to a forecast advance period from the phase of the determined polar oscillation. In a computing device that performs a climate change prediction method,
The computing device includes a processor,
the processor,
Predict the index of the Antarctic Oscillation in the Southern Hemisphere by applying sea level pressure data observed through satellites to a dynamics model,
Predict the index of the atmospheric telecorrelation pattern by applying the position altitude data of the middle atmosphere (500-hPa) to the epidemiological model,
A computing device for predicting a probability distribution of ground temperature that affects climate by applying the index of the Antarctic Oscillation and the index of the atmospheric telecorrelation pattern to a phase model. According to claim 17,
the processor,
Daily sea level pressure in the sea level area is extracted from the sea level pressure data through the dynamic model,
A computing device that predicts the index of an anomaly applied Antarctic Oscillation by interpolating daily sea level pressure to previously observed monthly sea level pressure. According to claim 17,
the processor,
Extract weather patterns related to position elevations through empirical orthogonal functions,
A computing device for determining an index of an atmospheric telecorrelation pattern by applying a varimax rotation to the weather pattern with respect to the filtered status altitude. According to claim 17,
the processor,
Determine the phase of the Arctic oscillation from the index of the Arctic oscillation through the phase model;
Determining a phase of a standby remote correlation pattern from an index of the standby remote correlation pattern through the phase model;
A computing device for predicting a probability distribution of surface air temperature that may occur in the southern hemisphere during a specific season according to a forecast advance period from the phase of the determined Antarctic Oscillation and the phase of the atmospheric telecorrelation pattern.

Images