CN109884734A - A kind of ocean temperature Similarity Method for Forecasting based on similar disparity - Google Patents
A kind of ocean temperature Similarity Method for Forecasting based on similar disparity Download PDFInfo
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Abstract
The present invention is to provide a kind of ocean temperature Similarity Method for Forecasting based on similar disparity.Step 1, data are analyzed ocean temperature again to pre-process；Step 2, the similitude of ocean temperature time series is calculated；Step 3, the similar time is chosen and deviation is corrected；Step 4, the set weighted average of seawater temperature forecast value is calculated.The ocean temperature Similarity Method for Forecasting of kind of the invention based on similar disparity can forecast that calculation amount is small to the ocean temperature of single lattice point, and forecast result is better than Climatological data and inertia forecast result.
Description
Technical field
The present invention relates to a kind of ocean temperature prediction techniques.
Background technique
In scientific research of seas field, ocean temperature is a key parameter, and seawater temperature forecast is marine forecasting composition
In an important ring, the variation of temperature affects the variation of density, and temperature field is the prerequisite for understanding aerodynamic field in ocean, together
When it be understanding and research marine organisms and the earth physical and chemical process a key, be atmosphere and ocean prediction
The input parameter of system.Global unusual weather conditions and ocean temperature have indivisible relationship, and the variation of ocean temperature is
Important physical process on atmosphere and Marine stratocumulus, and have profound influence to climate change.In certain specific sea areas,
The situation of sea surface temperature can directly affect selection of the navigating officer to course line, while sea fishery is also by the distribution of seawater temperature field
With the influence of variation, so carry out precise and high efficiency ocean temperature analysis and prediction have a very important significance.
Countries in the world are made a general survey of, many countries have carried out ocean temperature analysis and the works such as the research of forecast aspect and service support
Make, the work such as early sixties seawater temperature forecast research is gradually spread out in China.Early start is China coastal seas and northwest
Pacific waters ten days, the seawater temperature forecast of scale analyzed work with live, and the part based on single station observation finally is per day
Ocean temperature and the forecast of coastal outdoor bathing place water temperature, while with the continuous development of marine economy, also there is higher new forecast
Demand, forecasting procedure also become diversification.Conclusion learns that seawater temperature forecast method can briefly be divided into three classes: (1) experience
Forecasting procedure；(2) statistical method；(3) Numerical Prediction Method.In three of the above method, there is meter in Numerical Prediction Models
Evaluation time is long, and resources requirement is big, the not sufficiently effective disadvantage of long-term forecasting, and the subjective factor of experimental forecast method and people have with big
Relationship, statistical method can make up for it the poor disadvantage of long-term forecasting effect of numerical model, and be better than experimental forecast method, similar
Forecasting procedure is both one kind of statistical method.Chen Jing " dynamic similarity method Upper Yangtze River Daily rainfall forecast in
Using " devise circulation evolution dynamics Similarity Method for Forecasting in a text, she 850 bold and unconstrained bar temperature fields, 500 bold and unconstrained bar height fields and
1000 person of outstanding talent's bar fields of pressure extract the good similar factors of 3 effects, using second level similar standard come Precipitation forecast, the value of forecasting compared with
It is good.The selection of criterion of similarity is the key that similar forecasting, and common criterion method mainly has: Hamming distances, Euclidean distance, analog quantity
With similar disparity etc..Similar disparity is at present using most methods.Liu Aimei etc. is " similarity leave measuring is in Daily rainfall probability
Application in forecast " the Daily rainfall probability that similar disparity principle carries out Jinan City and its surrounding area is applied in a text.Lee
It is rich to wait in " comprehensive multiple-stage analog prediction technology is in the inspection in heavy rain short-period forecast " text using based on the more of similar disparity
Grade Similarity Method for Forecasting is used for heavy rain short-period forecast, and Li Long etc. makes in " Test for analog prediction of winter precipitation in Taipei " text
Winter precipitation in Taipei forecast experiments have been carried out with similarity leave measuring.
Summary of the invention
The purpose of the present invention is to provide a kind of calculation amount is small, forecast result is accurately based on the ocean temperature of similar disparity
Similarity Method for Forecasting.
The object of the present invention is achieved like this:
Step 1, data are analyzed ocean temperature again to pre-process；
Step 2, the similitude of ocean temperature time series is calculated；
Step 3, the similar time is chosen and deviation is corrected；
Step 4, the set weighted average of seawater temperature forecast value is calculated.
The present invention may also include:
Described in 1. ocean temperature is analyzed again data carry out pretreatment specifically include: the ocean temperature analyzes number again
According to being to be adjusted according to the field data of ship and buoy to satellite radiometer data deviation, the finally obtained daily whole world
Sea surface temperature data；The pretreatment is L days temperature data sequences time and the N history according to weather report for each lattice point
Data carry out similar comparison, to forecast following one month temperature changing trend, wherein L >=10.
It is a fixed length by each Grid data processing of data file each in all historical datas in pretreatment described in 2.
The time series of degree, all temperature data processing are anomaly value, i.e., include that forecast time data subtract day year after year by all data
Average temperature value needs to forecast the ocean temperature of N+1, single lattice point if existing N number of time analyzes data information again
The anomaly value calculation formula of data are as follows:
Wherein formulaFor day average formula year after year, whereinIndicate m n-th day day average year after year,Indicate n-th day the 1st year the sum of all temperature value to N,Indicate m n-th day temperature anomaly value,Table
Show m n-th day true temperature value, m=1 ..., N represent which in all times in year, and n=1 ..., 365 represents one
Which day in year, is finally calculated daily anomaly value.
3. the similitude of calculating ocean temperature time series described in specifically includes: a sample set, H=are indicated with H
(H1,H2,...Hn), wherein HiIndicate i-th of sample of sample set, Hi=(hi1,hi2,...hik), hikIt indicates in i-th of sample
K-th of numerical value, then the similar disparity expression formula between two samples are as follows:
Wherein:
hijk=hik-hjk
eijIndicate the average value of the sum of difference between sample i and sample j；DijFor value coefficient, indicate between two samples
The average value of the sum of absolute difference；SijFor shape coefficient, the dispersion degree of the difference between two samples is reflected；
Similar disparity algorithm calculates similitude: sample i is N+1 forecast time data with existing comData [L], sample
This j indicates a certain annual data in historical data hisData [N] [L], then forecasts that the similar disparity of time and historical years calculates
As follows,
hijk=comData [k]-hisData [j] [k]
Forecast time data comData [L] and historical years data hisData [N] are calculated by similar disparity algorithm
Similar disparity value C between [L] two timesij, finally obtain N number of similar disparity value Cij[N], shape coefficient Sij[N], value coefficient
Dij[N] and deviation mean value eij[N], CijValue more hour shows more similar between two groups of data.
4. the similar time described in chooses and deviation is corrected and specifically included:
Historical years are obtained by Similarity measures and forecast the similar disparity value C between the timeij[N], according to similar disparity
The similar disparity array finalCij [F] that F most like time data are newly arranged before value selects, 10≤F≤N are counted at this time
It is obtained according to be arranged from small to large by similar disparity value, the choosing from array preHisData [N] [D] by its corresponding time data
SelcYearData [F] [D] equally is obtained by similitude arrangement storage out, according to formulaCalculated deviation,
F time corresponding deviation E [F] is obtained, the deviation for the F time data selected is removed respectively, obtains data by F >=10
FinalData [F] [D], its calculation formula is: finalData [m] [d]=selcYeardata [m] [d]-E [m], i.e., it will be every
Year daily historical data removes a deviation, is forecast according to obtained data,
Wherein finalData [m] [d] indicates the d days historical datas of m of removal deviation, selcYeardata [m]
[d] indicates the d days historical datas of m for not removing deviation, and E [m] is the deviation of m.
5. in the set weighted average calculation of the seawater temperature forecast value, the daily hygrometric formula of forecasting period:
Wherein, TdIndicate the temperature of forecast in the d days of a certain lattice point in region.
It is an object of the invention to realize the seawater temperature forecast based on similar method, data branch is provided for offshore activities
It holds, while temperature initial fields can also be provided for systems such as fact analyses.The ocean temperature phase of kind of the invention based on similar disparity
Like forecasting procedure, it can forecast that calculation amount is small to the ocean temperature of single lattice point, forecast result is better than Climatological data and is used to
Property forecast result.
Detailed description of the invention
Fig. 1 is the flow chart of the ocean temperature Similarity Method for Forecasting of the invention based on similarity leave measuring.
Fig. 2 is that ocean temperature analyzes data prediction schematic diagram again.
Fig. 3 is the flow chart of similar disparity algorithm.
Fig. 4 is that the similar time is chosen and deviation corrects flow chart.
Fig. 5 a- Fig. 5 c is area forecast result figure compared with actual result.
Specific embodiment
It illustrates below and the present invention is described in more detail.
The present invention is a kind of seawater temperature forecast method based on similar disparity, analyzes the pre- of data again including ocean temperature
Processing, Similarity measures, the similar time is chosen and deviation is corrected, the contents such as set weighted average forecast.Detailed process such as Fig. 1,
Specific step is as follows:
Step 1. ocean temperature analyzes the pretreatment of data again
The data that the present embodiment uses are NOAA (National Oceanic and Atmospheric
Administration, U.S.National Oceanic and Atmospheric Administration) analyze sea surface temperature data again.The data according to ship and
The field data of buoy adjusts satellite radiometer data deviation, finally obtains daily Global SST data,
Wherein nineteen eighty-two, totally 13149 data files, data spatial resolution were 0.25 ° * 0.25 ° by 2017.The side of the present embodiment
Method be for each lattice point L (L >=10) day temperature data sequence according to weather report time and nineteen eighty-two to 36 years 2017 history
Data carry out similar comparison, to forecast following one month temperature changing trend, therefore need to be by number every in all historical datas
It is the time series of certain length according to each Grid data processing of file, because forecast of the invention is directed to the expansion of anomaly value,
Therefore all temperature datas, which should be handled, subtracts balance samming year after year comprising forecast time data that is, by all data for anomaly value
Angle value, then, it is assumed that existing N number of time analyzes data information again, needs to forecast the ocean temperature of N+1, single lattice
The anomaly value calculation formula of point data is as follows:
Formula (1) is day average formula year after year, whereinIndicate m n-th day day average year after year,Indicate the
N days the 1st year the sum of all temperature values to N,Indicate m n-th day temperature anomaly value,Indicate m n-th day
True temperature value, m=1 ..., N represent which in all times in year, and n=1 ..., 365 represents which day in 1 year,
Daily anomaly value is finally calculated.
It can be calculated daily anomaly value of all times and year after year day average by above-mentioned, define one-dimensional floating-point later
Type array comData [L] is used to store the temperature anomaly data in L (L >=10) day before N+1 forecasting period, two-dimentional floating-point
Type array hisData [N] [L] store L (L >=10) the day temperature of the 1st to year and N and comData [L] same time period away from
Flat data, for calculating similitude with comData [L], two-dimentional floating point type array preHisData [N] [D] is for storing the 1st
Nian Zhi N forecasts the history anomaly data in period D (D >=10) day, and the anomaly temperature of future time instance is calculated based on this data,
One-dimensional floating point type array staData [D] is used to store forecasting period D days day averages year after year, for calculating final reality
Forecast temperature.
Such as forecast that certain puts in December, 2017 daily temperature, on November 21st, 2017 to 30 Time of Day in March need to be utilized
Sequence data is similar compared with the identical period data of history, and the temperature data in December for choosing 5 similar times afterwards is gathered
Weighted average forecast, then the data processing of the point be comData [10] store 10 days on the 30th November 21 to November in 2017 away from
Flat temperature, hisData [35] [10] storage nineteen eighty-two to temperature anomaly data 10 days on the 30th November 21 to November in 2016, the
One expression year, second indicate number of days.It is warm daily in December, 2016 that preHisData [35] [31] stores nineteen eighty-two
Anomaly data are spent, staData [31] stores daily day average temperature value year after year in December.It can be carried out after the completion of data processing
Similarity system design, detailed process are as shown in Figure 2.
The Similarity measures of step 2. ocean temperature time series compare
Using similar disparity method as similarity criteria in the present invention, it is chiefly used in the forecast such as precipitation in weather, does not have at present
There is the precedent used in extra large temperature forecast.Similar disparity can reflect out the gap of " shape " and " value " of sample, such as be indicated with H
One sample set, H=(H1,H2,...Hn), HiIndicate i-th of sample of sample set, Hi=(hi1,hi2,...hid), hikTable
Show k-th of numerical value in i-th of sample, then the similar disparity expression formula between two samples are as follows:
Wherein:
hijk=hik-hjk (6)
eijIndicate the average value of the sum of difference between sample i and sample j.DijIt is referred to as value coefficient, table under normal circumstances
Show the average value of the sum of absolute difference between two samples, it reflects the difference between two samples in overall average numerically
Off course degree.SijIt is then called shape coefficient, reflects the dispersion degree of the difference between two samples, value is smaller, then two samples
The shape of curve is more similar, characterizes the shape similarity degree between Factor Fields.Under normal circumstances, default similar between two samples
From the average value that degree is value coefficient and shape coefficient.
According to definition above, in the present embodiment, the use of similar disparity algorithm is as follows, and sample i is N+1
Year forecast time data with existing comData [L], sample j indicate a certain annual data in historical data hisData [N] [L].Then
Forecast that the similar disparity of time and historical years calculates as follows.
hijk=comData [k]-hisData [j] [k] (8)
Forecast time data comData [L] and historical years data hisData can be calculated by similar disparity algorithm
Similar disparity value C between [N] [L] two timesij, finally obtain N number of similar disparity value Cij[N], shape coefficient Sij[N], value system
Number Dij[N] and deviation mean value eij[N], CijValue more hour shows more similar between two groups of data.Similar disparity algorithm flow is as schemed
Shown in 3.
The step 3. similar time chooses and deviation is corrected
Historical years are obtained by the Similarity measures of step 2 and forecast the similar disparity value C between the timeij[N], according to phase
Like the similar disparity array finalCij [F] for selecting preceding a most like time data of F (10≤F≤N) newly to be arranged from angle value
(at this time data obtain for being arranged from small to large by similar disparity value), by its corresponding time data from array preHisData
It is selected in [N] [D] and equally obtains selcYearData [F] [D] by similitude arrangement storage, it is calculated partially according to formula (9)
Difference obtains F (F >=10) a time corresponding deviation E [F], the deviation for the F time data selected is removed respectively, is obtained
Data finalData [F] [D], calculation formula is as follows, i.e., annual daily historical data is removed a deviation,
It is forecast according to obtained data, it is as shown in Figure 4 that the similar time chooses process.
FinalData [m] [d]=selcYeardata [m] [d]-E [m] (13)
Wherein finalData [m] [d] indicates the d days historical datas of m of removal deviation, selcYeardata [m]
[d] indicates the d days historical datas of m for not removing deviation, and E [m] is the deviation of m.
The set weighted average calculation of step 4. seawater temperature forecast value
According to the thought of DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM, for forecasting in December, 2017 daily temperature data, by final data
FinalData [5] [31] carries out set weighted average forecast, can be with using similar disparity value finalCij [F] as weighted value
Obtain the daily hygrometric formula of forecasting period:
Wherein, TdThe temperature for indicating forecast in the d days of a certain lattice point in region, since similar disparity is smaller more similar, because
This selects biggish similar disparity value as the weight in similitude higher time using finalCij [F-m].Other points in region
Temperature the forecast temperature value of future time instance also can be obtained after the same method.
Claims (6)
1. a kind of ocean temperature Similarity Method for Forecasting based on similar disparity, it is characterized in that:
Step 1, data are analyzed ocean temperature again to pre-process；
Step 2, the similitude of ocean temperature time series is calculated；
Step 3, the similar time is chosen and deviation is corrected；
Step 4, the set weighted average of seawater temperature forecast value is calculated.
2. the ocean temperature Similarity Method for Forecasting according to claim 1 based on similar disparity, it is characterized in that pair
Ocean temperature analyze again data carry out pretreatment specifically include: it is according to ship and buoy that the ocean temperature analyzes data again
Field data satellite radiometer data deviation is adjusted, finally obtained daily Global SST data；It is described
Pretreatment be for each lattice point L days temperature data sequences according to weather report the time with N historical data progress it is similar compared with,
Forecast following one month temperature changing trend, wherein L >=10.
3. being wanted according to right: the ocean temperature Similarity Method for Forecasting described in 2 based on similar disparity, it is characterized in that: described is pre-
In processing, each Grid data of data file each in all historical datas is handled into the time series for certain length, is owned
Temperature data processing is anomaly value, i.e., includes that forecast time data subtract day average temperature value year after year by all data, if existing N
A time analyzes data information again, needs to forecast that the ocean temperature of N+1, the anomaly value of single Grid data calculate public
Formula are as follows:
Wherein formulaFor day average formula year after year, whereinIndicate m n-th day day average year after year,
Indicate n-th day the 1st year the sum of all temperature value to N,Indicate m n-th day temperature anomaly value,Indicate m
Year n-th day true temperature value, m=1 ..., N represent which in all times in year, and n=1 ..., 365 was represented in 1 year
Daily anomaly value is finally calculated in which day.
4. the ocean temperature Similarity Method for Forecasting according to claim 3 based on similar disparity, it is characterized in that the meter
The similitude for calculating ocean temperature time series specifically includes: a sample set, H=(H are indicated with H1,H2,...Hn), wherein Hi
Indicate i-th of sample of sample set, Hi=(hi1,hi2,...hik), hikIndicate k-th of numerical value in i-th of sample, then two
Similar disparity expression formula between sample are as follows:
Wherein:
hijk=hik-hjk
eijIndicate the average value of the sum of difference between sample i and sample j；DijFor value coefficient, the difference between two samples is indicated
The average value of the sum of absolute value；SijFor shape coefficient, the dispersion degree of the difference between two samples is reflected；
Similar disparity algorithm calculates similitude: sample i is N+1 forecast time data with existing comData [L], sample j table
Show a certain annual data in historical data hisData [N] [L], then forecasts that the similar disparity calculating of time and historical years is as follows
It is shown,
hijk=comData [k]-hisData [j] [k]
Forecast time data comData [L] and historical years data hisData [N] [L] two are calculated by similar disparity algorithm
Similar disparity value C between a timeij, finally obtain N number of similar disparity value Cij[N], shape coefficient Sij[N], value coefficient Dij[N]
And deviation mean value eij[N], CijValue more hour shows more similar between two groups of data.
5. the ocean temperature Similarity Method for Forecasting according to claim 4 based on similar disparity, it is characterized in that the phase
It is chosen like the time and deviation is corrected and specifically included:
Historical years are obtained by Similarity measures and forecast the similar disparity value C between the timeij[N] is selected according to similar disparity value
Similar disparity array finalCij [F], 10≤F≤N that preceding F most like time data are newly arranged are selected, data are at this time
It arranges and obtains from small to large by similar disparity value, its corresponding time data is selected from array preHisData [N] [D] same
Sample obtains selcYearData [F] [D] by similitude arrangement storage, according to formulaCalculated deviation, obtains
F time corresponding deviation E [F], the deviation for the F time data selected is removed respectively, obtains data by F >=10
FinalData [F] [D], its calculation formula is: finalData [m] [d]=selcYeardata [m] [d]-E [m], i.e., it will be every
Year daily historical data removes a deviation, is forecast according to obtained data,
Wherein finalData [m] [d] indicates the d days historical datas of m of removal deviation, selcYeardata [m] [d]
Indicate the d days historical datas of m for not removing deviation, E [m] is the deviation of m.
6. the ocean temperature Similarity Method for Forecasting according to claim 5 based on similar disparity, it is characterized in that the seawater
In the set weighted average calculation of temperature forecast value, the daily hygrometric formula of forecasting period:
Wherein, TdIndicate the temperature of forecast in the d days of a certain lattice point in region.
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Application publication date: 20190614 |