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Dynamic statistics combined sub-season prediction method based on low-frequency increment space-time coupling

A prediction method and incremental technology, applied in the field of atmospheric science, can solve problems such as low forecasting skills, errors in forecast results, and difficult forecasting skills, and achieve clear physical relationships, improved forecasting capabilities, and improved forecasting capabilities

Active Publication Date: 2021-12-17
NANJING UNIV OF INFORMATION SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Because the previous forecast factors may change during the evolution process, they may not be able to accurately reflect the subsequent changes in the forecast field, resulting in errors in the forecast results
At the same time, if the statistical relationship between the previous predictors and the predictors is relatively weak, the forecasting skills of statistical modeling will also be very low; secondly, the models considering the purely statistical relationship often fail to fully clarify the physical mechanism between the predictors and the predictors , and existing studies have shown that the use of predictors with physical meaning can effectively improve forecasting skills
Third, although similar methods have a certain ability to predict extreme temperatures and heavy precipitation, this purely statistical model is less effective in forecasting the intensity of disaster weather, and it is also more difficult for process forecasting
However, previous studies have found that the S2S model has limited ability to directly forecast meteorological elements (temperature, precipitation, etc.) at the subseasonal scale, but has better forecasting skills for large-scale subseasonal atmospheric circulation modes [7-8] How to effectively improve the subseasonal forecasting skills of meteorological elements (temperature, precipitation) and extreme events is still a difficult point in today's business

Method used

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  • Dynamic statistics combined sub-season prediction method based on low-frequency increment space-time coupling
  • Dynamic statistics combined sub-season prediction method based on low-frequency increment space-time coupling
  • Dynamic statistics combined sub-season prediction method based on low-frequency increment space-time coupling

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0068] A subseasonal forecasting method based on low-frequency incremental spatio-temporal coupling dynamic-statistical integration, such as figure 1 shown, including the following specific steps:

[0069] 1) Select the geopotential height field at middle and high latitudes 500hPa outside the tropics and the OLR field in the tropics at the same time as the forecast data from the observation data as the predictor variable, and calculate the predictor variable and the incremental anomaly of the forecast quantity respectively, where the forecast quantity includes the air temperature Forecast quantity or precipitation forecast quantity; the increment of forecast factor variable and forecast quantity can be pentad, ten days, 15 days, 20 days and 25 days, corresponding to the forecast time limit of one wai, one ten days, 15 days, 20 days and 25 days in advance .

[0070] 2) Using the SVD method, extract the first j high-coupling modes between the incremental anomalies of the left-f...

Embodiment 2

[0076] The optional further design of this embodiment is: in step 1) in this example, the incremental anomaly of the predictor variable and the predictor is calculated according to the following formula:

[0077] δOLR(x,t)=OLR(x,t)-OLR(x,t-2)

[0078] δH500(x, t) = H500(x, t)-H500(x, t-2)

[0079] δP(x,t)=P(x,t)-P(x,t-2)

[0080]

[0081]

[0082]

[0083] Among them, OLR, H500, P are the observed original values ​​of OLR field, 500hPa geopotential height field and forecast quantity, respectively;

[0084] δOLR, δH500, δP are the increments of OLR field, 500hPa geopotential height field and forecast quantity, respectively;

[0085] ΔOLR, ΔH500, and ΔP are the incremental anomalies of the OLR field, the 500hPa geopotential height field, and the forecast quantity, respectively;

[0086] is the average value; x is the spatial dimension of the data, x∈[1,n], n is the total number of spatial dimensions of the data; t is the return time period.

Embodiment 3

[0088] The optional further design of this embodiment is: the specific steps of step 2) in this example are as follows:

[0089]2.1) Divide the reward period t equally to form n subsets, select one of the subsets as the test period, and the remaining n-1 subsets as the training period;

[0090] 2.2) SVD decomposes the incremental anomaly of the right field forecast in the observation data training period, the incremental anomaly of the 500hPa geopotential height field in the left field, and the incremental anomaly of the OLR field, respectively, to obtain the corresponding forecast in the training period The first j coupled modes of the incremental anomaly of 500hPa geopotential height field and the incremental anomaly of the 500hPa geopotential height field, and the first j coupled modes of the incremental anomaly of the forecast quantity and the incremental anomaly of the OLR field; the first j Each coupled mode includes paired left and right singular vectors and paired left...

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Abstract

The invention discloses a dynamic statistics combined sub-seasonal prediction method based on low-frequency increment space-time coupling, and the method comprises the steps: selecting tropical and tropical outside atmosphere abnormal signals as a prediction factor variable, taking the low-frequency increment of the variable as a prediction object and a prediction factor, and eliminating the interference of a weather change rate and a seasonal change rate. On one hand, a synchronous physical relationship between a forecast factor and a forecast quantity increment is considered, a singular value decomposition statistical method is utilized to find a high coupling mode of a synchronous forecast factor increment and a forecast quantity increment, and a multiple linear regression method is adopted to establish a sub-season prediction model based on a physical mechanism. On the other hand, by means of the advantage that the dynamic mode has a good forecasting effect on the sub-season tropical and extra-tropical atmosphere abnormal modes, the time coefficient (namely, the forecasting factor) of the tropical and extra-tropical atmosphere abnormal high coupling modes predicted by the dynamic mode is substituted into the forecasting model, and a power-statistics combined sub-season prediction model is further constructed to predict meteorological elements.

Description

Technical field: [0001] The invention relates to a subseasonal prediction method based on low-frequency incremental time-space coupling combined with dynamic statistics, belonging to the field of atmospheric science. Background technique: [0002] Sub-season forecast refers to the forecast of weather anomalies several weeks in advance. It is the gap between weather (less than 10 days)-climate (more than three months) forecast, and is an important part of seamless forecast. It is very important for the formulation of disaster prevention and mitigation policies. play an important role. However, even with the continuous improvement of short- and medium-term weather and climate forecasting capabilities, subseasonal forecasting skills are still low, which has become a difficulty in scientific research and operational applications in recent years. In response to this difficulty, Xu Bangqi, Zhu Zhiwei and others have developed a statistical forecasting model STPM (Spatial Temporal...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06F16/2458G06Q50/26
CPCG06Q10/04G06F16/2462G06Q50/26Y02A90/10
Inventor 李娟朱志伟徐邦琪张可越
Owner NANJING UNIV OF INFORMATION SCI & TECH
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