CN110443993A - A method of suitable for model predictions ENSO - Google Patents
A method of suitable for model predictions ENSO Download PDFInfo
- Publication number
- CN110443993A CN110443993A CN201910646457.2A CN201910646457A CN110443993A CN 110443993 A CN110443993 A CN 110443993A CN 201910646457 A CN201910646457 A CN 201910646457A CN 110443993 A CN110443993 A CN 110443993A
- Authority
- CN
- China
- Prior art keywords
- value set
- forecast
- enso
- initiation parameter
- group
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
Landscapes
- Environmental & Geological Engineering (AREA)
- Business, Economics & Management (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Emergency Management (AREA)
- Atmospheric Sciences (AREA)
- Computing Systems (AREA)
- Environmental Sciences (AREA)
- Ecology (AREA)
- Biodiversity & Conservation Biology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of methods suitable for model predictions ENSO, include the following steps, S1, pass through change initiation parameter SST-nudging strength values, to obtain at least five initiation parameter；S2, each initiation parameter generate one group of first first value set being at least made of 10 initial values, obtain five group of first just value set, at least 50 initial values altogether；S3, an initial value, the second first value set that composition one is at least made of 10 initial values are at least chosen respectively from every group first first value set, and obtains the forecast result of the second first value set；S4, the second first corresponding forecast result of value set is counted to get final forecast result is arrived.Advantage is: problem-solving pattern ENSO forecast skill breaks through the forecast bottleneck of previous scheme, helps to improve model predictions skill；Meanwhile the ENSO forecast skill of General Ocean-Atmosphere Coupled Model is improved, facilitate forecast and early warning, the reduction personnel and property loss of climate damage.
Description
Technical field
The present invention relates to the forecast field ENSO more particularly to a kind of methods suitable for model predictions ENSO.
Background technique
El Nio-Southern Oscillation (ENSO) has great influence to global climate, can cause serious flood.Cause
This, raising ENSO forecast skill is conducive to various countries and prevents and reduces natural disasters.In prior art, (sea surface temperature forces SST-nudging
Scheme) intensity is traditionally arranged to be fixed numbers, to generate the initial value for being closer to observation data.However, in initialization procedure
Observation data are generally truthful data by a series of assimilation simulation process generate analyze data again, is not exclusively equal to
Actual conditions.Finally, even if generating the initial value closest to observation data, the unstable of forecast system can also be caused.This makes existing
There is technical solution not and can be further improved the technical bottleneck of ENSO forecast skill.Previous research points out, SST-nudging intensity
It is a crucial initiation parameter in the negative feedback process of Surface heat flux, determines that mode forces SST to tend to actual observation
The speed of data.However, SST-nudging intensity how Effect Mode initial value and ENSO forecast skill, this is one does not have so far
It solves the problems, such as.
Summary of the invention
The purpose of the present invention is to provide a kind of methods suitable for model predictions ENSO, to solve to deposit in the prior art
Foregoing problems.
To achieve the goals above, The technical solution adopted by the invention is as follows:
A method of suitable for model predictions ENSO, include the following steps,
S1, pass through change initiation parameter SST-nudging strength values, to obtain at least five initiation parameter；
S2, each initiation parameter generate one group of first first value set being at least made of 10 initial values, obtain five altogether
Group first first value set, at least 50 initial values；
S3, an initial value is at least chosen respectively from every group first just value set, form one at least by 10 initial value groups
At second just value set, and obtain this second just value set forecast result；
S4, the second first corresponding forecast result of value set is counted to get final forecast result is arrived.
Preferably, initiation parameter SST-nudging strength values in step S1 are able to use wind-stress replacement.
The beneficial effects of the present invention are: 1, this method problem-solving pattern ENSO forecast skill, breaches the forecast of previous scheme
Bottleneck extends to other modes, helps to improve model predictions skill.2, facilitate to prevent and reduce natural disasters, ENSO is to influence the whole world
The important factor of weather Annual variations, this method improve the ENSO forecast skill of General Ocean-Atmosphere Coupled Model, facilitate weather
The forecast and early warning of disaster, facilitate reduction personnel and property loss.
Detailed description of the invention
Fig. 1 is the flow diagram of method in the embodiment of the present invention；
Fig. 2 is the prediction result schematic diagram obtained in the embodiment of the present invention using this method.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with attached drawing, to the present invention into
Row is further described.It should be appreciated that the specific embodiments described herein are only used to explain the present invention, it is not used to
Limit the present invention.
Embodiment one
As shown in Figure 1, include the following steps the present invention provides a kind of method suitable for model predictions ENSO,
S1, pass through change initiation parameter SST-nudging strength values, to obtain at least five initiation parameter；
S2, each initiation parameter generate one group of first first value set being at least made of 10 initial values, obtain five altogether
Group first first value set, at least 50 initial values；
S3, an initial value is at least chosen respectively from every group first just value set, form one at least by 10 initial value groups
At second just value set, and obtain this second just value set forecast result；
S4, the second first corresponding forecast result of value set is counted to get final forecast result is arrived.
In the present embodiment, in step S1, the number of initiation parameter can be configured according to the actual situation, so as to more preferable
Meet actual demand；In step S2, membership in initiation parameter can be configured according to the actual situation, so as to
Better meet actual demand.
In the present embodiment, initiation parameter SST-nudging strength values in step S1 are able to use wind-stress replacement.
It can be made to determine whether to replace initiation parameter SST-nudging intensity number using wind-stress by done forecast or test
Value.
In the present embodiment, existing model predictions ENSO method is generated multiple first by selecting a nudging intensity
Value, and multiple nudging intensity are selected in the present invention, multiple initial values are generated, and provide the selection Initial Schemes of optimization.
Embodiment two
As shown in Fig. 2, providing the use process that a specific example illustrates this method in the present embodiment:
Period: 1981-2010
Forecasting Object: ENSO (Nino3.4 index)
Optimization randomly selects scheme are as follows: from the first first value set of 5 groups of each own 10 initial values, every group of first initial value
Set selects 2 initial values at random, totally 10 initial values, and the forecast knot of the 1-12 month in advance to their corresponding 1981-2010
Fruit is cooked ensemble average, obtains the result of DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM.
For the conspicuousness of proof scheme, we do significance test using Monte Carlo (Monte Carlo) method, have
Gymnastics make it is as follows: value set at the beginning of randomly selecting 20000 with above method, find out corresponding 20000 DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEMs as a result, and
1 to 12 lunate tail phase in advance is sought with the Nino3.4 index of the Nino3.4 index of DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM and Hai Wen observational data HadSST1
Relationship number amounts to and calculates 20000*12 related coefficient.
Calculate five group of first just value set 1 to 12 lunate tail related coefficient, wherein best one group of result is A to note.It is right
The forecast of N (N=1,2,3,4,5,6,7,8,9,10,11, the 12) moon in advance, by the absolute value of corresponding 20000 related coefficients
Compared with the forecast skill of A, the ratio that related coefficient is big in 20000 related coefficient ratio A is found out.
As shown in Fig. 2, being significant in the promotion of the 1-10 month in advance (the especially 1-8 month) ENSO forecast skill.
In the present embodiment, the Monte Carlo significance test is to examine report after us to test in (hindcast)
The significance that nudging scheme promotes forecast skill.
By using above-mentioned technical proposal disclosed by the invention, following beneficial effect has been obtained:
The present invention is broken through by providing a kind of method suitable for model predictions ENSO, problem-solving pattern ENSO forecast skill
The forecast bottleneck of previous scheme, extends to other modes, helps to improve model predictions skill；Meanwhile helping to take precautions against natural calamities
Mitigation, ENSO are the important factors for influencing global climate Annual variations, and this method improves the ENSO of General Ocean-Atmosphere Coupled Model
Forecast skill facilitates the forecast and early warning of climate damage, facilitates reduction personnel and property loss.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
Depending on protection scope of the present invention.
Claims (2)
1. a kind of method suitable for model predictions ENSO, it is characterised in that: include the following steps,
S1, pass through change initiation parameter SST-nudging strength values, to obtain at least five initiation parameter；
S2, each initiation parameter generate one group of first first value set being at least made of 10 initial values, obtain five groups the altogether
One first value set, at least 50 initial values；
S3, an initial value is at least chosen respectively from every group first just value set, what composition one was at least made of 10 initial values
Second first value set, and obtain the forecast result of the second first value set；
S4, the second first corresponding forecast result of value set is counted to get final forecast result is arrived.
2. the method according to claim 1 suitable for model predictions ENSO, it is characterised in that: initialize ginseng in step S1
Number SST-nudging strength values are able to use wind-stress replacement.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2019105066794 | 2019-06-12 | ||
| CN201910506679 | 2019-06-12 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110443993A true CN110443993A (en) | 2019-11-12 |
| CN110443993B CN110443993B (en) | 2021-04-02 |
Family
ID=68430634
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910646457.2A Active CN110443993B (en) | 2019-06-12 | 2019-07-17 | Method suitable for sea air coupling mode ENSO forecasting |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110443993B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221385A (en) * | 2021-06-08 | 2021-08-06 | 上海交通大学 | Initialization method and system for dating forecast |
| CN114707708A (en) * | 2022-03-21 | 2022-07-05 | 国家海洋环境预报中心 | ENSO prediction method, device and computer readable storage medium |
| CN114722593A (en) * | 2022-03-25 | 2022-07-08 | 中国人民解放军61540部队 | Method and system for generating ocean field mode value based on ocean gas coupling mode |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110084617A (en) * | 2010-01-18 | 2011-07-26 | 한국해양연구원 | Ensemble climate prediction method using empirical singular vector |
| CN102855662A (en) * | 2012-07-25 | 2013-01-02 | 中国科学院对地观测与数字地球科学中心 | Ocean environment visualization method |
| CN106203686A (en) * | 2016-06-30 | 2016-12-07 | 上海海洋大学 | The raw colony in northwest Pacific squid Winter-Spring based on gray system abundance Forecasting Methodology |
| CN106250699A (en) * | 2016-08-04 | 2016-12-21 | 中国南方电网有限责任公司 | ENSO aggregative indicator and Runoff Forecast method thereof |
| JP2017123809A (en) * | 2016-01-14 | 2017-07-20 | 有限会社フソウ精工 | Apparatus for lowering water temperature of surface of sea to suppress occurrence and power of typhoon and generation of el nino phenomenon |
| CN107403237A (en) * | 2017-07-03 | 2017-11-28 | 上海海洋大学 | West and central Pacific ocean stripped tuna fishing ground forecasting procedure based on Different climate condition |
| US10197706B1 (en) * | 2016-03-04 | 2019-02-05 | The Florida State University Research Foundation, Inc. | System and method for sub-seasonal forecasts of extreme weather events in winter |
| CN109655941A (en) * | 2018-12-09 | 2019-04-19 | 中国人民解放军国防科技大学 | Tropical cyclone ensemble forecasting method based on target system disturbance |
-
2019
- 2019-07-17 CN CN201910646457.2A patent/CN110443993B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110084617A (en) * | 2010-01-18 | 2011-07-26 | 한국해양연구원 | Ensemble climate prediction method using empirical singular vector |
| CN102855662A (en) * | 2012-07-25 | 2013-01-02 | 中国科学院对地观测与数字地球科学中心 | Ocean environment visualization method |
| JP2017123809A (en) * | 2016-01-14 | 2017-07-20 | 有限会社フソウ精工 | Apparatus for lowering water temperature of surface of sea to suppress occurrence and power of typhoon and generation of el nino phenomenon |
| US10197706B1 (en) * | 2016-03-04 | 2019-02-05 | The Florida State University Research Foundation, Inc. | System and method for sub-seasonal forecasts of extreme weather events in winter |
| CN106203686A (en) * | 2016-06-30 | 2016-12-07 | 上海海洋大学 | The raw colony in northwest Pacific squid Winter-Spring based on gray system abundance Forecasting Methodology |
| CN106250699A (en) * | 2016-08-04 | 2016-12-21 | 中国南方电网有限责任公司 | ENSO aggregative indicator and Runoff Forecast method thereof |
| CN107403237A (en) * | 2017-07-03 | 2017-11-28 | 上海海洋大学 | West and central Pacific ocean stripped tuna fishing ground forecasting procedure based on Different climate condition |
| CN109655941A (en) * | 2018-12-09 | 2019-04-19 | 中国人民解放军国防科技大学 | Tropical cyclone ensemble forecasting method based on target system disturbance |
Non-Patent Citations (3)
| Title |
|---|
| CHEN DAKE ET AL.: "An improved procedure for el-nino forecasting-implications for predictability", 《SCIENCE》 * |
| LUO JING-JIA ET AL.: "Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts", 《JOURNAL OF CLIMATE》 * |
| 黄平 等: "ElNi no事件对其衰减阶段夏季中国降水季节内演变的影响及其机理", 《大气科学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113221385A (en) * | 2021-06-08 | 2021-08-06 | 上海交通大学 | Initialization method and system for dating forecast |
| CN113221385B (en) * | 2021-06-08 | 2022-09-23 | 上海交通大学 | Initialization method and system for dating forecast |
| CN114707708A (en) * | 2022-03-21 | 2022-07-05 | 国家海洋环境预报中心 | ENSO prediction method, device and computer readable storage medium |
| CN114707708B (en) * | 2022-03-21 | 2023-03-14 | 国家海洋环境预报中心 | ENSO prediction method, apparatus and computer readable storage medium |
| CN114722593A (en) * | 2022-03-25 | 2022-07-08 | 中国人民解放军61540部队 | Method and system for generating ocean field mode value based on ocean gas coupling mode |
| CN114722593B (en) * | 2022-03-25 | 2022-11-15 | 中国人民解放军61540部队 | Method and system for generating ocean field mode value based on ocean gas coupling mode |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110443993B (en) | 2021-04-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110443993A (en) | A method of suitable for model predictions ENSO | |
| CN108509389B (en) | Spare part demand calculation method for Weibull serial parts | |
| CN110147610B (en) | Tracing method for river sudden water pollution event | |
| CN109472345A (en) | A kind of weight update method, device, computer equipment and storage medium | |
| CN111679952A (en) | Alarm threshold generation method and device | |
| CN108776879A (en) | A kind of pile shape planing method based on weight study | |
| CN108416535A (en) | The method of patent valve estimating based on deep learning | |
| Yeates et al. | Initiation of coronal mass ejections in a global evolution model | |
| CN109946424A (en) | Demarcate Gas classification method and system based on artificial bee colony and neural network | |
| Shackelford et al. | Isolation predicts compositional change after discrete disturbances in a global meta‐study | |
| CN106685749B (en) | The method of inspection and device of network flow | |
| Song et al. | Balancing fish-energy-cost tradeoffs through strategic basin-wide dam management | |
| Russell | Fine-grained decision-theoretic search control | |
| He et al. | Quantitative characterization of magnetic flux rope properties for two solar eruption events | |
| Cox et al. | Framework for the assessment of Atlantic halibut stocks on Scotian Shelf and Southern Grand Banks | |
| CN102374936A (en) | Mechanical failure diagnostic method based on complex immune network algorithm | |
| CN110441820A (en) | Architectonic intelligent interpretation method | |
| Wiryani et al. | Profitability, feminism of board of directors and corporate sustainability performance: Role of independent board as a moderating variable | |
| Rubec et al. | Spatial modeling of fish habitat suitability in Florida estuaries | |
| Shin et al. | Weighted penalty model for content balancing in CATs | |
| CN106384002A (en) | Back-fitting algorithm-based flood forecast real-time correction method | |
| Alonso et al. | Suppression of scroll wave turbulence by noise | |
| CN109711097A (en) | A kind of Design of Cantilever Beam method based on sine and cosine mechanism chaos grasshopper algorithm | |
| CN110807251A (en) | Network public opinion polarization method and system integrating individual heterogeneity and dynamic dependency | |
| CN109816099A (en) | A kind of initialization of deep neural network and training method |
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 |