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An Atmospheric Forecasting Method Based on Similarity Set Algorithm Based on Time Weight

A technology of time weighting and aggregation, applied in the field of atmospheric science, can solve problems such as large amount of calculation, many combinations of variable weights, and huge amount of calculation.

Active Publication Date: 2021-11-16
京津冀环境气象预报预警中心
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Problems solved by technology

[0010] 1. There are too many combinations of variable weights, and the amount of calculation is huge. For example, assuming that the minimum weight factor is 0.1 and the predictor variable is 5, there are as many as 252 weight combinations, and the amount of calculation is huge;

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  • An Atmospheric Forecasting Method Based on Similarity Set Algorithm Based on Time Weight
  • An Atmospheric Forecasting Method Based on Similarity Set Algorithm Based on Time Weight
  • An Atmospheric Forecasting Method Based on Similarity Set Algorithm Based on Time Weight

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[0022] best practice

[0023] In order to make those skilled in the art understand more clearly an atmospheric forecasting method using a similarity set algorithm based on time weight provided by the present invention, it will be described in detail below with reference to the accompanying drawings.

[0024] Such as figure 1 with figure 2 As shown, the embodiment of the present invention provides an atmospheric forecasting method using a similarity set algorithm based on time weight, which specifically includes the following steps:

[0025] S 1 : Carry out similarity judgment according to the distance between the current numerical model forecast result and the historical forecast result;

[0026] S 2 : giving corresponding weights for different numerical model forecast times in the determination process;

[0027] S 3 : Judging according to the similarity degree, the actual observation results most similar to the current numerical model prediction results in the historic...

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Abstract

The invention belongs to the field of atmospheric science, and provides an atmospheric forecasting method using a similarity set algorithm based on time weight, which judges the similarity according to the distance between the current numerical model forecast result and the historical forecast, and considers different forecasts during the determination process. According to the importance of times, different weights are assigned to different forecast times; finally, based on selecting the most similar live observation results in history as similar ensemble forecast results. After the present invention increases the weights of different forecast times, the importance of the current forecast time can be highlighted, and the similarity judgment between the current forecast and the historical forecast is more accurate, thereby obtaining a more accurate similarity set and improving the forecast of atmospheric variables. accuracy.

Description

technical field [0001] The invention relates to the field of atmospheric science, in particular to an atmospheric forecast method using a time weight-based similarity set algorithm. Background technique [0002] Similar Ensemble Forecast (AnEn) was first proposed by Professor Luca of the US National Environmental Atmospheric Administration in 2013 (Luca et al., 2013). It is based on the assumption that the weather system is relatively stable, and it is believed that similar historical numerical model forecast errors , which can be used to correct the current numerical model forecast results. Using the historical forecast results of the numerical model as the training data to find similar ones can greatly increase the sample size of the training data, and on the other hand, it can avoid the systematic error of the model that needs to be considered when looking for the observation samples directly. In addition, considering the time continuity of predictors and the similarity ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/26G06K9/62
CPCG06Q10/04G06Q50/26G06F18/22
Inventor 李梓铭王垚唐宜西张楠楠刘湘雪朱晓婉李颖若尹晓梅吴进韩婷婷乔林马志强熊亚军赵秀娟邱雨露郭恒徐敬权维俊张自银蒲维维
Owner 京津冀环境气象预报预警中心
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