Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Classification of El Niño/La Niña and Runoff Prediction Method Using Enso Comprehensive Index

A comprehensive indicator, El Niño technology, used in forecasting, data processing applications, instruments, etc., can solve problems such as not being able to give true evaluations

Active Publication Date: 2019-02-19
CHINA SOUTHERN POWER GRID COMPANY
View PDF5 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although it expresses the comprehensive status of marine elements in each month, it cannot give a true evaluation of each El Niño (La Niña) event, which has comprehensive characteristics such as persistence, regional expansion, and intensity characteristics of the El Niño (La Niña) event. closely related

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Classification of El Niño/La Niña and Runoff Prediction Method Using Enso Comprehensive Index
  • Classification of El Niño/La Niña and Runoff Prediction Method Using Enso Comprehensive Index
  • Classification of El Niño/La Niña and Runoff Prediction Method Using Enso Comprehensive Index

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The present invention will be further described below in conjunction with specific examples.

[0029] A composite ENSO index and its runoff prediction method, re-studied and divided the multivariate ENSO (MEI). Taking ±0.5 as the standard value, the frequency of positive MEI≥0.5 is about 30.94%, the frequency of negative MEI≤-0.5 is 29.01%, and the remaining 40.05% is the frequency of normal years.

[0030] The intensity and duration of any ENSO event vary greatly. We defined five characteristic values, including extreme value, mean value and duration of months, to describe each event. With ±0.5 as the critical value, extract every 5 consecutive times to reach ±0.5 procedure to calculate the extreme intensity (MEI max ), mean intensity (MEI mean ), the number of months after reaching the standard (MEI month ), Spatial Expansion (MEI spa ) and extreme anomaly area expansion (MEI exspa ). Combining these five characteristic parameters with different weights can ge...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an ENSO (El Nino-Southern Oscillation) aggregative indicator and a runoff prediction method thereof. Duration time (MEImonth) is used for reflecting the persistence of an El Nino / La Nina event; average intensity (MEImean) and extreme intensity (MEImax) are used for expressing the intensity characteristics of the El Nino / La Nina event; a range that the temperature increase of equatorial pacific sea temperature is greater than or equal to 0.5DEG C(or less than or equal to 0.5) is used for representing the space expansibility (MEIspa) of a warm water region; a region of which the temperature change achieves or exceeds + / -2DEG C represents the expansion range (MEIexspa) of an extreme warming area; and the five parameters are used for constructing the ENSO aggregative indicator (MEIZ). The method comprehensively inspects the influence of the El Nino / La Nina event on atmosphere, the function of the method is that every event is scientifically distinguished, and the influence degree of the El Nino / La Nina event on the atmosphere is objectively evaluated.

Description

technical field [0001] The invention relates to a method for classifying El Niño / La Niña and predicting runoff by using ENSO comprehensive index, and belongs to the technical field of environmental protection. Background technique [0002] The multivariate ENSO (MEI, Wolter and Timlin, 1993, 1998) index is obtained through principal component analysis based on six tropical ocean observation elements. These six elements are sea level air pressure, zonal wind, meridional wind, sea surface temperature, air temperature above the sea, and total cloud cover in the sky. At present, the multivariate ENSO index is just a statistical data, which is the result of principal component analysis of six meteorological elements on the ocean. Although it expresses the comprehensive status of marine elements in each month, it cannot give a true evaluation of each El Niño (La Niña) event, which has comprehensive characteristics such as persistence, regional expansion, and intensity characteris...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04
CPCG16Z99/00
Inventor 李树山唐红兵李崇浩荣艳淑徐亚男魏佳胡玉恒张亮贺晓婧朱坚葛朝霞
Owner CHINA SOUTHERN POWER GRID COMPANY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com