CN113837620B - Method for evaluating temperature extremum change - Google Patents
Method for evaluating temperature extremum change Download PDFInfo
- Publication number
- CN113837620B CN113837620B CN202111132485.6A CN202111132485A CN113837620B CN 113837620 B CN113837620 B CN 113837620B CN 202111132485 A CN202111132485 A CN 202111132485A CN 113837620 B CN113837620 B CN 113837620B
- Authority
- CN
- China
- Prior art keywords
- temperature
- extremum
- data
- field
- space
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000008859 change Effects 0.000 title claims abstract description 16
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 20
- 230000003993 interaction Effects 0.000 claims abstract description 15
- 238000004458 analytical method Methods 0.000 claims abstract description 6
- 238000010220 Pearson correlation analysis Methods 0.000 claims abstract description 5
- 238000000556 factor analysis Methods 0.000 claims abstract description 5
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 3
- 230000008520 organization Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000010998 test method Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 6
- 238000012360 testing method Methods 0.000 abstract description 2
- 230000007246 mechanism Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Environmental & Geological Engineering (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Environmental Sciences (AREA)
- Biodiversity & Conservation Biology (AREA)
- Ecology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention provides a method for evaluating temperature extreme value change, firstly, acquiring temperature extreme value index data, circulating field data and large-scale circulating mode index data; then, EOF decomposition is carried out on the data based on different time scales and different seasons, and trend change analysis of the expansion temperature extreme value is further carried out according to least square regression and Mann-Kendall test; then, identifying factors affecting a single circulation field of the temperature extremum and a large-scale circulation mode index through Pearson correlation analysis; finally, factors which significantly contribute to the temperature extremum are identified based on a factor analysis method, and the influence of the interaction effect between the key factors on the temperature extremum is quantified.
Description
Technical Field
The invention belongs to the field of climate research, and particularly relates to a method for evaluating temperature extreme value change.
Background
Over the past few decades, temperature and extreme temperature anomalies have caused extremely serious negative effects on society, economy and people's life. The study of the extreme climate and the influence mechanism thereof at home and abroad is unprecedented, and the knowledge of the change mechanism of the extreme climate is very important for predicting the occurrence of the extreme climate event and taking measures to reduce the related influence. There are intricate interaction effects between elements that affect extreme temperatures (e.g., potential height, wind fields, remote correlation factors, etc.), but these effects are difficult to characterize in terms of traditional functional forms. Currently, most of the research in this field is directed to one or more individual elements, and there is little consideration of the effect of interactions between different elements on temperature extremes.
Object of the Invention
The present invention aims to address the problems faced in the prior art by providing a method for assessing temperature extremum changes, providing advantageous support for future extremum climate change.
Disclosure of Invention
The invention provides a method for evaluating temperature extremum variation, comprising the following steps:
step a, respectively acquiring temperature extremum data, circulating field data and large-scale circulating mode index data; the definition of the temperature extremum data is formulated by a world meteorological organization WMO climate change detection and index expert group ETCCDI, and is obtained by covering a land climate extremum event index data set HadEX3 with the accuracy of 1.875 multiplied by 1.25 longitude and latitude from 1901 to 2018, and specific indexes comprise a daily maximum air temperature value TXx, a daily minimum air temperature value TNx, a daily maximum air temperature value TXn, a daily minimum air temperature value TNn, a cold night TN10p, a warm night TN90p, a cold day TX10p and a hot day TX90p; the circulating field data comprise sea level pressure SLP, 500hPa wind field and 500hPa potential height of ERA5 re-analysis data; the large-scale circulation mode data is an index for measuring a natural variable rate mode, is time series data taking the day as a scale, and comprises index items such as Echinuo, southern Taon, pacific ten year Taon PDO, north Atlantic Taon NAO, north Taon AO and Pacific-North American remote related PNA;
analyzing temperature extreme values from time sequences and space scales according to different time dimensions of each year, years, summer and winter by using an empirical orthogonal function EOF method, and identifying regions with extreme high temperature and low temperature in summer in northern hemisphere regions; the specific treatment process of the EOF method is as follows: setting the number of lattice points of the temperature extreme value as m and the time sequence length as n, firstly, processing the temperature extreme value data into a distance flat form to obtainMatrix X with respect to temperature extremum time and space m×n ＝[x ij ]I=1, 2, …, m, j=1, 2, …, n, wherein the i-th row represents the value of the temperature extremum x at position i and the j-th column represents the value of the temperature extremum x at instant j; next, for matrix X m×n Performing an orthogonal decomposition, wherein the characteristic vector of the orthogonal decomposition corresponds to a space function S capable of reflecting the space distribution characteristics of the variables, the principal component of the decomposition of the orthogonal decomposition corresponds to a time function T capable of reflecting the change of the space mode with time, and the orthogonal decomposition is expressed as X m×n =s×t; the j-th space field of the temperature extremum is expressed as linear superposition of m typical space fields according to different time coefficients, wherein each column of a space function S represents one space typical field, each row of a time function T represents a time coefficient, when a research variable is a summer temperature extremum of 30 years in a northern hemisphere region, a first eigenvector obtained by using the EOF method through orthogonal decomposition is a characteristic field with the most similar temperature extremum of 30 years from a flat field, so that a region with extreme high temperature and low temperature in summer in the northern hemisphere region is identified;
calculating linear trends of the temperature extreme time sequence according to different time dimensions of each year, years, summer and winter through a least squares regression method, and evaluating statistical significance of the calculated linear trends of the temperature extreme time sequence on a significance level of 0.05 through a non-parameter Mann-Kendall test method;
d, determining the relation between the temperature extremum data and the circulating field data and the large-scale circulating mode index data by adopting a Pearson correlation analysis method;
and e, quantifying the statistical significance of the circulating current field data and the large-scale circulating current mode index data based on a factor analysis method, respectively identifying factors contributing to the temperature extremum, and reflecting the interaction relation among the factors, namely quantifying the influence of the interaction between a single circulating current field or a single large-scale circulating current mode and the temperature extremum, a plurality of circulating current fields on the temperature extremum and the influence of the interaction between a plurality of large-scale circulating current modes or positive and negative phases thereof on the temperature extremum.
Drawings
FIG. 1 is a flow chart of a method for assessing temperature extremum variation in accordance with an embodiment of the present invention.
Detailed Description
Specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
The invention provides a method for evaluating temperature extremum variation, comprising the following steps:
step a, respectively acquiring temperature extremum data, circulating field data and large-scale circulating mode index data; the definition of the temperature extremum data is formulated by a world meteorological organization WMO climate change detection and index expert group ETCCDI, and is obtained by covering a land climate extremum event index data set HadEX3 with the accuracy of 1.875 multiplied by 1.25 longitude and latitude from 1901 to 2018, and specific indexes comprise a daily maximum air temperature value TXx, a daily minimum air temperature value TNx, a daily maximum air temperature value TXn, a daily minimum air temperature value TNn, a cold night TN10p, a warm night TN90p, a cold day TX10p and a hot day TX90p; the circulating field data comprise sea level pressure SLP, 500hPa wind field and 500hPa potential height of ERA5 re-analysis data; the large-scale circulation mode data is an index for measuring a natural variable rate mode, is time series data taking the day as a scale, and comprises index items such as Echinuo, southern Taon, pacific ten year Taon PDO, north Atlantic Taon NAO, north Taon AO and Pacific-North American remote related PNA;
analyzing temperature extreme values from time sequences and space scales according to different time dimensions of each year, years, summer and winter by using an empirical orthogonal function EOF method, and identifying regions with extreme high temperature and low temperature in summer in northern hemisphere regions; the specific treatment process of the EOF method is as follows: setting the number of lattice points of the temperature extreme value as m and the time sequence length as n, firstly, processing the temperature extreme value data into a distance flat form to obtain a matrix X related to the time and space of the temperature extreme value m×n ＝[x ij ]I=1, 2, …, m, j=1, 2, …, n, wherein the i-th row represents the value of the temperature extremum x at position i and the j-th column represents the value of the temperature extremum x at instant j; next, for matrix X m×n Performing an orthogonal decomposition, wherein the characteristic vector of the orthogonal decomposition corresponds to a space function S capable of reflecting the space distribution characteristics of the variables, the principal component of the decomposition of the orthogonal decomposition corresponds to a time function T capable of reflecting the change of the space mode with time, and the orthogonal decomposition is expressed as X m×n =s×t; the j-th space field of the temperature extremum is expressed as linear superposition of m typical space fields according to different time coefficients, wherein each column of a space function S represents one space typical field, each row of a time function T represents a time coefficient, when a research variable is a summer temperature extremum of 30 years in a northern hemisphere region, a first eigenvector obtained by using the EOF method through orthogonal decomposition is a characteristic field with the most similar temperature extremum of 30 years from a flat field, so that a region with extreme high temperature and low temperature in summer in the northern hemisphere region is identified;
calculating linear trends of the temperature extreme time sequence according to different time dimensions of each year, years, summer and winter through a least squares regression method, and evaluating statistical significance of the calculated linear trends of the temperature extreme time sequence on a significance level of 0.05 through a non-parameter Mann-Kendall test method;
d, determining the relation between the temperature extremum data and the circulating field data and the large-scale circulating mode index data by adopting a Pearson correlation analysis method;
and e, quantifying the statistical significance of the circulating current field data and the large-scale circulating current mode index data based on a factor analysis method, respectively identifying factors contributing to the temperature extremum, and reflecting the interaction relation among the factors, namely quantifying the influence of the interaction between a single circulating current field or a single large-scale circulating current mode and the temperature extremum, a plurality of circulating current fields on the temperature extremum and the influence of the interaction between a plurality of large-scale circulating current modes or positive and negative phases thereof on the temperature extremum.
The present invention will be described in further detail with reference to the drawings and examples, so that the objects, features and advantages of the present invention can be more clearly understood.
Examples: as shown in fig. 1, the present invention provides a method for assessing temperature extremum changes. Firstly, acquiring extreme value index data related to temperature through a high-precision grid point land-surface climate extreme event index data set of HadEX 3; re-analyzing the data through ERA5 to obtain circulating field data such as 500hPa wind field, 500hPa potential height, sea level pressure and the like; and acquiring ENSO, PDO, PNA, AO, NAO and other large-scale circulation mode index data through the national weather service. Secondly, carrying out EOF decomposition on the data, and further carrying out trend change analysis on the expansion temperature extreme value according to the minimum two-component regression and Mann-Kendall test based on different time scales and different seasons; extracting corresponding circulating flow field and large-scale circulating flow mode data according to the occurrence period of the temperature extremum, and identifying factors affecting single circulating flow field and large-scale circulating flow mode indexes of the temperature extremum through Pearson correlation analysis; further, factors that significantly contribute to the temperature extremum are identified based on a factor analysis method, and the impact of the interaction effect between these key factors on the temperature extremum is quantified. The invention can simultaneously consider single and multiple circulation fields and large-scale circulation mode indexes, and quantify the significance of the influence of the interaction between the single circulation field and the multiple circulation fields on the temperature extreme value change, thereby revealing the mechanism of the change of the trend of the temperature extreme value.
Compared with the prior art, the invention has the beneficial effects that: an analysis method of temperature extremum and its influence mechanism of different time scales is provided, and the influence of interaction of multiple influence factors on the temperature extremum is quantified at the same time.
Claims (1)
1. A method for assessing temperature extremum changes, comprising the steps of:
step a, respectively acquiring temperature extremum data, circulating field data and large-scale circulating mode index data; the definition of the temperature extremum data is formulated by a world meteorological organization WMO climate change detection and index expert group ETCCDI, and is obtained by covering a land climate extremum event index data set HadEX3 with the accuracy of 1.875 multiplied by 1.25 longitude and latitude from 1901 to 2018, and specific indexes comprise a daily maximum air temperature value TXx, a daily minimum air temperature value TNx, a daily maximum air temperature value TXn, a daily minimum air temperature value TNn, a cold night TN10p, a warm night TN90p, a cold day TX10p and a hot day TX90p; the circulating field data comprise sea level pressure SLP, 500hPa wind field and 500hPa potential height of ERA5 re-analysis data; the large-scale circulation mode data is an index for measuring a natural variable rate mode, is time series data taking the day as a scale, and comprises Echinono and southern Taon movement ENSO, pacific ten year Taon movement PDO, north Atlantic Taon movement NAO, north America Taon movement AO and Pacific-North America remote related PNA;
analyzing temperature extreme values from time sequences and space scales according to different time dimensions of each year, years, summer and winter by using an empirical orthogonal function EOF method, and identifying regions with extreme high temperature and low temperature in summer in northern hemisphere regions; the specific treatment process of the EOF method is as follows: setting the number of lattice points of the temperature extreme value as m and the time sequence length as n, firstly, processing the temperature extreme value data into a distance flat form to obtain a matrix X related to the time and space of the temperature extreme value m×n ＝[x ij ]I=1, 2, …, m, j=1, 2, …, n, wherein the i-th row represents the value of the temperature extremum x at position i and the j-th column represents the value of the temperature extremum x at instant j; next, for matrix X m×n Performing an orthogonal decomposition, wherein the characteristic vector of the orthogonal decomposition corresponds to a space function S capable of reflecting the space distribution characteristics of the variables, the principal component of the decomposition of the orthogonal decomposition corresponds to a time function T capable of reflecting the change of the space mode with time, and the orthogonal decomposition is expressed as X m×n =s×t; the j-th space field of the temperature extremum is expressed as linear superposition of m typical space fields according to different time coefficients, wherein each column of a space function S represents one space typical field, each row of a time function T represents a time coefficient, when a research variable is a summer temperature extremum of 30 years in a northern hemisphere region, a first eigenvector obtained by using the EOF method through orthogonal decomposition is a characteristic field with the most similar temperature extremum of 30 years from a flat field, so that a region with extreme high temperature and low temperature in summer in the northern hemisphere region is identified;
calculating linear trends of the temperature extreme time sequence according to different time dimensions of each year, years, summer and winter through a least squares regression method, and evaluating statistical significance of the calculated linear trends of the temperature extreme time sequence on a significance level of 0.05 through a non-parameter Mann-Kendall test method;
d, determining the relation between the temperature extremum data and the circulating field data and the large-scale circulating mode index data by adopting a Pearson correlation analysis method;
and e, quantifying the statistical significance of the circulating current field data and the large-scale circulating current mode index data based on a factor analysis method, respectively identifying factors contributing to the temperature extremum, and reflecting the interaction relation among the factors, namely quantifying the influence of the interaction between a single circulating current field or a single large-scale circulating current mode and the temperature extremum, a plurality of circulating current fields on the temperature extremum and the influence of the interaction between a plurality of large-scale circulating current modes or positive and negative phases thereof on the temperature extremum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111132485.6A CN113837620B (en) | 2021-09-26 | 2021-09-26 | Method for evaluating temperature extremum change |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111132485.6A CN113837620B (en) | 2021-09-26 | 2021-09-26 | Method for evaluating temperature extremum change |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113837620A CN113837620A (en) | 2021-12-24 |
| CN113837620B true CN113837620B (en) | 2024-02-02 |
Family
ID=78970317
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111132485.6A Active CN113837620B (en) | 2021-09-26 | 2021-09-26 | Method for evaluating temperature extremum change |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113837620B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114462247B (en) * | 2022-02-14 | 2022-10-21 | 中国人民解放军61540部队 | Method and system for identifying annual representative modality of sea surface salinity of North Pacific ocean |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011088473A2 (en) * | 2010-01-18 | 2011-07-21 | The Regents Of The University Of California | System and method for identifying patterns in and/or predicting extreme climate events |
| WO2018081559A1 (en) * | 2016-10-27 | 2018-05-03 | Ohio University | Air data probe |
| CN110058328A (en) * | 2019-01-30 | 2019-07-26 | 沈阳区域气候中心 | Summer Precipitation in Northeast China multi-mode combines NO emissions reduction prediction technique |
| CN111856621A (en) * | 2020-07-17 | 2020-10-30 | 中国气象科学研究院 | Integrated evolution SVD conversion method based on mode and observation data fusion |
-
2021
- 2021-09-26 CN CN202111132485.6A patent/CN113837620B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011088473A2 (en) * | 2010-01-18 | 2011-07-21 | The Regents Of The University Of California | System and method for identifying patterns in and/or predicting extreme climate events |
| WO2018081559A1 (en) * | 2016-10-27 | 2018-05-03 | Ohio University | Air data probe |
| CN110058328A (en) * | 2019-01-30 | 2019-07-26 | 沈阳区域气候中心 | Summer Precipitation in Northeast China multi-mode combines NO emissions reduction prediction technique |
| CN111856621A (en) * | 2020-07-17 | 2020-10-30 | 中国气象科学研究院 | Integrated evolution SVD conversion method based on mode and observation data fusion |
Non-Patent Citations (3)
| Title |
|---|
| Evaluation of Extreme Cold and Drought over the Mongolian Plateau;Zhaofei Liu等;《water》;第11卷(第74期);第1-17页 * |
| Spatiotemporal Variations of Extreme Precipitation Events in the Jinsha River Basin, Southwestern China;Dan Zhang等;《Advances in Meteorology》;第2020卷;第1-13页 * |
| 基于ETCCDI 指数2017 年中国极端温度和降水特征分析;尹红等;《气候变化研究进展》;第15卷(第4期);第363-373页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113837620A (en) | 2021-12-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109919353B (en) | Distributed photovoltaic prediction method of ARIMA model based on spatial correlation | |
| CN112949945B (en) | Wind power ultra-short-term prediction method for improving bidirectional long-term and short-term memory network | |
| Shi et al. | Urbanization in China from the end of 1980s until 2010–spatial dynamics and patterns of growth using EO-data | |
| CN111665575B (en) | Medium-and-long-term rainfall grading coupling forecasting method and system based on statistical power | |
| CN113837620B (en) | Method for evaluating temperature extremum change | |
| CN113505923A (en) | Regional power grid short-term load prediction method and system | |
| Xu et al. | Determinants and identification of the northern boundary of China’s tropical zone | |
| CN117909888B (en) | Intelligent artificial intelligence climate prediction method | |
| CN117891005A (en) | Ground wire icing prediction method and system based on microclimate factor time sequence | |
| Zhao et al. | Regulation factors driving vegetation changes in China during the past 20 years | |
| CN107895203B (en) | Building subentry cold load acquisition method based on signal sparse representation | |
| CN113610436B (en) | Method and system for evaluating dynamic vulnerability of disaster-bearing body | |
| Zhang et al. | Simulation of daily precipitation from CMIP5 in the Qinghai-Tibet Plateau | |
| CN115292667A (en) | Space probability analysis method and system based on rainfall forecast and remote correlation corresponding relation | |
| Ren et al. | Research on Hourly Precipitation Preprocessing Method Based on Deep Learning | |
| Mares et al. | Improvement of long-range forecasting by EEOF extrapolation using an AR-MEM model | |
| CN118378664B (en) | Multi-stage diffusion sea surface temperature prediction method for signal decomposition | |
| CN117973703B (en) | Hierarchical damage assessment method and system for forest ecological environment | |
| Niu et al. | Research on the Impact of Urban Land Use Structure Based on Big Data on Space Performance in Kunming | |
| Marshall et al. | A comparison of the CCM1-simulated climates for pre-industrial and present-day CO2 levels | |
| Feng et al. | Decreased response contrast of Hadley circulation to the equatorially asymmetric and symmetric tropical SST structures during the recent hiatus | |
| CN113837288A (en) | Regional climate feature classification and representative lattice selection method | |
| Leetmaa et al. | Ocean data assimilation as a component of a climate forecast system | |
| Lei et al. | Spatio-temporal variation of temperature for the recent 40 years in Lhasa | |
| Flores‐Tolentino et al. | The use of ecological niche models improves biogeographic regionalization of the Balsas Depression, Mexico |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |