CN107122606A - The Trends of Sea Level Changes computational methods and device counted based on satellite altitude - Google Patents
The Trends of Sea Level Changes computational methods and device counted based on satellite altitude Download PDFInfo
- Publication number
- CN107122606A CN107122606A CN201710287254.XA CN201710287254A CN107122606A CN 107122606 A CN107122606 A CN 107122606A CN 201710287254 A CN201710287254 A CN 201710287254A CN 107122606 A CN107122606 A CN 107122606A
- Authority
- CN
- China
- Prior art keywords
- data
- sea level
- altimeter
- trends
- satellite
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Landscapes
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The embodiment of the present invention provides the Trends of Sea Level Changes computational methods counted based on satellite altitude and device.In one embodiment, methods described includes：Obtain multiple satellites altimeter data group formed by multiple altimeter datas that at the appointed time section is obtained；Data in altimeter data group are screened, valid data are filtered out from multiple altimeter datas, wherein the effective data packets include sea level data；Calculated according to the valid data and obtain corresponding Hai Pu Lin data；The Hai Pu Lin data of different satellites are subjected to gridding processing respectively based on spherical coordinate；Data after each satellite network is formatted are merged；Sea level must be arrived and change with time function by being calculated according to the data after fusion treatment；And the sea level variability result of calculation of following specified time is obtained according to the change function and the result of calculation is exported.
Description
Technical field
The present invention relates to marine environmental monitoring field, in particular to a kind of Hai Ping counted based on satellite altitude
Face variation tendency computational methods and device.
Background technology
General sea level prediction mode is to utilize tidal station data.Tidal station data are to be fixed on the bench mark of land
On the basis of obtain sea level to measure, and because bench mark can be vertically moved up or down with crustal movement, therefore data institute of tidal station
It is RELATIVE SEA LEVEL to measure obtained sea level.Although tidal station data time series is longer, it is only distributed in coastal area
Influenceed near island, and by crustal movement, Regional Distribution and precision all have certain limitation.
The content of the invention
In view of this, the purpose of the embodiment of the present invention is to provide a kind of sea level variability counted based on satellite altitude
Trend computational methods and device.
A kind of Trends of Sea Level Changes computational methods counted based on satellite altitude provided in an embodiment of the present invention, the party
Method includes：
Obtain multiple satellites altimeter data group formed by multiple altimeter datas that at the appointed time section is obtained；
Data in altimeter data group are screened, valid data are filtered out from multiple altimeter datas,
Wherein described effective data packets include sea level data；
Calculated according to the valid data and obtain corresponding Hai Pu Lin data；
The valid data of different satellites are based respectively on spherical coordinate gridding processing is carried out to Hai Pu Lin data；
Data after each satellite network is formatted are merged；
Sea level must be arrived and change with time function by being calculated according to the data after fusion treatment；And
The sea level variability result of calculation of following specified time is obtained according to the change function and the calculating knot is exported
Really.
The embodiment of the present invention also provides a kind of Trends of Sea Level Changes computing device counted based on satellite altitude, the dress
Put including：
Data acquisition module, for obtaining multiple satellites at the appointed time being formed by multiple altimeter datas of obtaining of section
Altimeter data group；
Data screening module, for being screened to the data in altimeter data group, from multiple altimeter datas
In filter out valid data, wherein the effective data packets include sea level data；
Hai Pu Lin computing module, corresponding Hai Pu Lin data are obtained for being calculated according to the valid data；
Data fusion module, is melted for each satellite to be calculated into the obtained corresponding Hai Pu Lin data
Close；
Gridding processing module, for being carried out based on spherical coordinate to the Hai Pu Lin data after fusion at gridding
Reason；
Change function computation module, for being handled according to gridding after data calculate and must be changed with time to sea level
Function；
As a result output module, the sea level variability result of calculation for obtaining the following specified time according to the change function
And export the result of calculation.
Compared with prior art, the Trends of Sea Level Changes computational methods of the invention counted based on satellite altitude and dress
Put, fusion treatment is carried out by the altimeter data of multiple satellites of acquisition, the sea level is calculated according to the data after processing
Variation tendency, the fusion of a variety of data can be such that result of calculation is more nearly with following trend, make result of calculation more
Plus it is accurate.
To enable the above objects, features and advantages of the present invention to become apparent, preferred embodiment cited below particularly, and coordinate
Appended accompanying drawing, is described in detail below.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be attached to what is used required in embodiment
Figure is briefly described, it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, therefore is not construed as pair
The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, can also be according to this
A little accompanying drawings obtain other related accompanying drawings.
The Trends of Sea Level Changes computational methods counted based on satellite altitude that Fig. 1 provides for present pre-ferred embodiments
Flow chart.
Fig. 2 is satellite example operation schematic diagram provided in an embodiment of the present invention.
The Trends of Sea Level Changes computational methods counted based on satellite altitude that Fig. 3 provides for present pre-ferred embodiments
The middle function schematic diagram for calculating obtained change function.
The Trends of Sea Level Changes computing device counted based on satellite altitude that Fig. 4 provides for present pre-ferred embodiments
High-level schematic functional block diagram.
Embodiment
Below in conjunction with accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Ground is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.Generally exist
The component of the embodiment of the present invention described and illustrated in accompanying drawing can be arranged and designed with a variety of configurations herein.Cause
This, the detailed description of the embodiments of the invention to providing in the accompanying drawings is not intended to limit claimed invention below
Scope, but it is merely representative of the selected embodiment of the present invention.Based on embodiments of the invention, those skilled in the art are not doing
The every other embodiment obtained on the premise of going out creative work, belongs to the scope of protection of the invention.
It should be noted that：Similar label and letter represents similar terms in following accompanying drawing, therefore, once a certain Xiang Yi
It is defined in individual accompanying drawing, then it further need not be defined and explained in subsequent accompanying drawing.Meanwhile, the present invention's
In description, term " first ", " second " etc. are only used for distinguishing description, and it is not intended that indicating or implying relative importance.
Referring to Fig. 1, being the Trends of Sea Level Changes counted based on satellite altitude that present pre-ferred embodiments are provided
The flow chart of computational methods.The idiographic flow shown in Fig. 1 will be described in detail below.
Step S101, obtains multiple satellites height formed by multiple altimeter datas that at the appointed time section is obtained and counts
According to group.
The satellite includes：At least one of T/P, Jason-1, Jason-2 and HY-2.Wherein, TOPEX/
Poseidon satellites are that, by US National Aeronautics and Space Administration NASA and French NASA CNES cooperative research and development, abbreviation T/P is defended
Star.Jason-1 satellites are the subsequent satellites of T/P satellites, by US National Aeronautics and Space Administration with French NASA in 2001 12
Moon joint transmitting, main target is to be not less than the Continuous Observation that the measurement accuracy of T/P satellites provides sea level, so as to measure
When the research work such as sea level variability, the change of research ocean current, improvement climatic prediction level and improvement tidal model provide longer
Between sequence observation data.The Jason-2 satellites detect tissue (Eumetsat), U.S. by French CNES, European Meteorological Satellite
State's NASA and NOAA joint research and development, in successful launch in June, 2008, so far still in orbit.It is T/P and Jason-1 tasks
It is follow-up.HY-2 satellites are that the satellite of ocean two is first ocean dynamical environment satellite of China, are mainly used in obtaining global ocean
Dynamic environment parameter." ocean two " satellite was succeeded in sending up on the 16th in August in 2011, input business test run on March 2nd, 2012
OK.
As shown in Fig. 2 Fig. 2 is satellite example operation schematic diagram provided in an embodiment of the present invention.As shown in Fig. 2, satellite leads to
Cross and measure the altimeter data around the earth.It can include in the altimeter data：Average sea apparent height (Mean
Sea surface, abbreviation MSS), geoid (Geoid), reference ellipsoid (reference ellipsoid), extra large surface
Highly (Sea surface High, abbreviation SSH), extra large surface and the distance (range) of the satellite.As shown in Fig. 2 wherein institute
It is the distance between average extra large surface and described reference ellipsoid to state MSS.The SSH be extra large surface with it is described
The distance between reference ellipsoid.
In the present embodiment, the mark for flag data type can also be included in the altimeter data.For example, described
Mark can be digital " 0 " or " 1 ".For example, " 1 " is labeled as if the altimeter data is sea level data, if the height
" 0 " is labeled as when degree is counted as other data such as land, ice faces.
It is described to can be applied to calculate based on the Trends of Sea Level Changes computational methods that satellite altitude is counted in the present embodiment
Machine terminal, the terminal by receive user input data with obtain multiple satellites at the appointed time section obtain by
The altimeter data group of multiple altimeter data formation；Can also load data in the internal memory of terminal to obtain.
Data in altimeter data group are screened by step S102, are filtered out from multiple altimeter datas
Valid data.
In the present embodiment, the valid data are sea level datas.
In the present embodiment, the altimeter data includes sea level data, on ice data and the land that the satellite is measured
Data；Step S102 can include：The sea level data is filtered out from the altimeter data.
In the present embodiment, step S102 includes：The standard figures model with prestoring is carried out to the sea level data screened
Enclose and be compared, be described by sea level data screening if the sea level data is in the standard scale
Valid data.
In the present embodiment, step S102 includes：The sea level is filtered out according to the mark that the altimeter data is carried
Data.For example, the mark can be digital " 0 " or " 1 ".For example, being marked if the altimeter data is sea level data
For " 1 ", if being labeled as " 0 " when the altimeter data is other data such as land, ice face.The terminal is from the height
Degree filters out the altimeter data labeled as " 1 " in counting.
In an example, during data screening, selection first is located at open or half exposed waters altimeter data；Separately
Outside, Ku wave band datas quality is good, can choose what Ku wave bands were obtained in described open or half exposed waters altimeter data
Altimeter data；For another example the altimeter data that rain, snow or ice Weather Phase are obtained can also be eliminated；Word can also be removed
Section is the data record of null value.
Step S103, calculates according to the valid data and obtains corresponding Hai Pu Lin data.
In the present embodiment, the Hai Pu Lin data (SEA LEVEL ANOMALY, abbreviation SLA), as shown in Fig. 2
The SLA is the distance on extra large surface and average extra large surface, it is understood that be, the SLA can be SSH and MSS difference.
Because the data that satellite is measured may be influenceed by extraneous factor, the SLA and SSH and MSS difference has one
Fixed error.In an example, it can be calculated by below equation and obtain the SLA.
For example, Ku frequencies satellite after SLA=satellite orbital altitudes (alt)-witness to sea distance (range_ku)-
Altimeter Ku frequencies ionosphere corrections (iono_corr_alt_ku)-dry tropospheric correction (model_dry_tropo_corr)-wet
Tropospheric correction (rad_wet_tropo_corr)-Ku frequencies sea situation corrects (sea_state_bias_ku)-earth tide correction
(solid_earth_tide)-the earth's core tide calibration model 1 (ocean_tide_soll)-extremely damp (pole_tide)-inverse air pressure
Correct (inv_bar_corr)-sea high frequency sea situation correction (hf_fluctuations_corr)-mean sea level height
(mean_sea_surface)。
The valid data of different satellites are based respectively on spherical coordinate and carry out grid to Hai Pu Lin data by step S104
Change is handled.
In the present embodiment, spherical coordinate Shepard algorithms can be based on, by the discrete sea level height data by screening
Target gridding is interpolated into, forms gridding Hai Pu Lin number to realize that Hai Pu Lin data carry out gridding processing.It is described
Shepard algorithms are referred to as the weighting method being inversely proportional with distance, and its basic thought is to be defined as interpolating function F (x, y)
Each data point functional value fkWeighted average.
For large scale scattered data fitting problems, Shepard proposes following rational approach method.In an example
In, if (xi,xj), i=1,2..., n is interpolation point, fiFor interpolation point (xi,xj) place is worth accordingly, interpolation curved surface can be write as
Lower form：
Wherein,
ri=0 represents interpolated point and sampled point (xi,xj) overlap, interpolated value is equal to the sampled value of the point.Obvious F (x, y)
Meet min1≤i≤Nfi≤F(x,y)≤max1≤i≤Nfi, weight factor [ρ (ri)]μIn μ be typically greater than 1 constant, if choosing
Obtain too big, then make the power of the sampled point away from interpolation point too small.For example, μ=2 can be chosen.Certainly, those skilled in the art
μ value is determined by experiment for concrete application scene.
In another example, can also use Shepard propose another partial approximation model, by radius be R (with
Interpolation point (x, y)) fitting divide into two annulus, and define weight function ρ (r) respectively:
In formula,The weight function continuously differentiable.In actual applications
Need suitably to choose R, fitting circle is had an appropriate number of sampled value point.In the present embodiment, M can be selected<10.
In the present embodiment, (the xi,xj), i=1,2..., n can be insertion point, it is understood that into target gridding
Coordinate, the fiFor interpolation point (xi,xj) place is worth accordingly, described value can be understood as the value of the Hai Pu Lin.This implementation
In example, the sea level calculates obtained Hai Pu Lin data to that should have positional information on earth.In an example,
The positional information can be with coordinate of the Hai Pu Lin data based on the spherical coordinate.
Step S105, the data after each satellite network is formatted are merged.
In the present embodiment, the data by different satellites in same time period are merged.For example, satellite A obtains 2001
The sea level altitude in year to 2008 is counted, and satellite B obtains the altimeter data on the sea level of 2005 to 2011, then
Satellite A and satellite B was counted in the sea level altitude of 2005 to 2008 and merged.
Method in the present embodiment, methods described also includes：Obtain station data；Step S105 may include：Defended each
Data after StarNet formats are merged with the station data.
In the present embodiment, it is described each satellite network is formatted after data and the station data merge can also be
The altimeter data of the satellite of same time period is merged with the station data.
In the present embodiment, make the data after the gridding of different satellites by carrying out fusion to different satellites and station data
One group of gridded data is fused at identical point or phase near point.
The station data observe data for the sea level of tidal station.Wherein, the tidal station refers in selected place,
Set automatic tide gage or water gauge to record the change of water level, and then understand the observation station of the tidal fluctuations rule in sea area.
In one embodiment, it is possible to use the data after best interpolation method is formatted to each satellite network are melted
Close.Station data and radar altimeter sea level height data are merged using best interpolation method, Satellite Product has been played
The characteristics of broad covered area and high station data precision, ambient field is set up first with satellite data, station data as observation,
And it is related to assume that the error between observation station and ambient field exists with other observation stations around the observation station with the error of ambient field
Property.
In the present embodiment, fusion method first looks for around point of interest satisfactory observation station control information and assigned not
With a weight, weight then is solved with least square method, and from corrected value of the point of interest relative to ambient field is obtained, is obtained
Fused data：
Wherein, k is analysis lattice point, and i is effective lattice point, WiFor weighting function, the inclined of observation and first guess on i points is represented
The weight that difference is distributed in estimation.The radius of an analyst coverage is chosen, the effective lattice point searched in the radius, then from
It is middle to choose the several effective lattice points participation best interpolations nearest away from analysis lattice point, wherein the data of effective lattice point are no more than
Specified quantity, for example, the effective lattice point chosen is no more than 9.
The error variance of assay value is on analysis site：Wherein, TkFor the true value of k points.
It is assumed that observation error is uncorrelated with field error is just estimated, i.e.,
It can be analyzed according to several formula above and obtain error variance and be：
Wherein,Just to estimate field error variance,Respectively
Just to estimate field error covariance and observation error covariance.
In this example, for formulaUtilize a most young waiter in a wineshop or an inn
Multiplication builds linear equation：
Wherein,Represent just to estimate field error association correlation,Represent that observation error association is related, λiFor observation error mark on i points
Quasi- deviationWith just estimate error standard deviationRatio.
In the present embodiment, formula can also be passed throughDetermine weight Wi,
Finally obtain the minimum variance estimate of analytical error：
Step S106, sea level must be arrived and change with time function by being calculated according to the data after fusion treatment.
In the present embodiment, by linear process a linear function can be obtained according to the data after fusion.In an example
In, as shown in figure 3, Fig. 3 is the time series in CHINESE OFFSHORE face in January, 1993 in December, 2016, based on stochastic and dynamic analysis
Model, the linear trend of CHINESE OFFSHORE sea level variability is calculated using linear regression method.Wherein, point discrete in figure is represented not
With the value of the Hai Pu Lin in time CHINESE OFFSHORE face, for example, the Hai Pu Lin value of in January, 1993 CHINESE OFFSHORE be about-
5cm.Wherein, the linear letter for the CHINESE OFFSHORE sea level variability that the straight line extended along coordinate system lower-left upper right obtains for calculating
Number.Wherein, slope k=0.39cm/yr of the linear function.In this example, according to the slope k and it is any known to the time,
The Hai Pu Lin value for the CHINESE OFFSHORE for obtaining the following specified time can be calculated.
Step S106 may include：The calculating of data after the fusion treatment is handled by linear regression method and obtains the sea
The linear function of sea level changes trend.
In the present embodiment, step S106 may include：The data of different waters are respectively calculated and obtain different waters
Change with time function on sea level.
In the present embodiment, step S106 may include：The data of Various Seasonal are respectively calculated and obtain different waters
Change with time function on the sea level of Various Seasonal.
Step S107, obtains the sea level variability result of calculation of following specified time according to the change function and exports institute
State result of calculation.
The step S107 may include：The sea level variability without marine site is obtained according to the corresponding change function of different waters
Result of calculation and the result of calculation for exporting different waters.
The step S107 may also include：The following specified time is obtained not according to the change function of the data of Various Seasonal
With season sea level variability result of calculation and export the result of calculation.
In the present embodiment, sea level interannual relationship can be analyzed according to the data after fusion.One
Plant in embodiment, wavelet transformation analysis method can be used to the data after the sea level fusion treatment of multiple satellites and the station
Analyze the sea level interannual relationship feature of different zones scope.In an example, method is as follows：Wavelet transformation is
The method of signal Analysis in time-frequency domain.If f (t) is a function of time, wavelet transformation can be defined as：
Wherein, τ is local time's index, and s is wavelet scale, and ψ represents wavelet basis function, and asterisk represents the common strategic point of plural number.
In an example, the basic function that Mexico's small cap is wavelet transformation can be selected, then basic function is represented by：
Wherein, η is nondimensional time parameter, ω0For dimensionless frequency.Because the basic function is by real part and imaginary part group
Into plural number, so the analysis result on time-frequency domain has three kinds of method for expressing：W (τ, real part, mould and position phase s).
In an example, (τ, real part s) characterizes transformation results to selection W on Time And Frequency domain.|W|2It is defined as Wavelet Spectrum.
To global Wavelet Spectrum, the measuring for local spectra of the terrace cut slice on small echo figure, all specific frequency Wavelet Spectrums or all can be made
The time average out to of local Wavelet Spectrum：
Wherein, T is the number of samples of time series.The time of Wavelet Spectrum is averagely global Wavelet Spectrum.
According to foregoing description can to multiple satellites of acquisition at the appointed time section obtain by multiple altimeter datas and
Station data are analyzed, it will be appreciated that sea level interannual relationship, further, are the meter of Trends of Sea Level Changes
Calculate and support is provided.
In the present embodiment, sea level spatial-temporal characteristics can be analyzed according to the data after fusion.A kind of real
Apply in mode, the data after the sea level fusion treatment of multiple satellites and the station can be used with Empirical Orthogonal Function (EOF) side
Method analyzes nearly 30 time changes and sky for coming the full marine site of coastal area of china, each sea area and each province (autonomous region, municipality directly under the Central Government) sea level
Between distribution characteristics.In an example, analysis method is as follows：
First the data on the sea level of variable are provided with a matrix type after the processing of anomaly value：
Wherein, m is observation frequency in formula, and n is observation website (m ＞ n).xijRepresent that the ith on j-th of survey station point is seen
Survey the anomaly value of field.
The function of time that above-mentioned matrix decomposition is orthogonal and the orthogonal spatial function sum of products, can be write as：
Wherein, l in formulahjValue of the serial number h typical field at j-th point is represented, it only depends on spatial point and changed, no
Change over time, referred to as space factor；thiWeight coefficient of the serial number h typical fields i-th moment is represented, it is only with the time
And change, referred to as time coefficient, wherein above formulaIt can be write as Xm×n=Tm×mLm×n
Wherein,
, can be spatial function l in this examplehjIt is considered as typical field, function of time thiIt is considered as the weight system of typical field
Number.Therefore,The sea level data not observed in the same time is represented, respectively different weights are pressed by a series of typical fields
Linear superposition is formed, and the difference between each is the difference of each typical field coefficient.
In the present embodiment, can by using the methods such as harmonic analysis model, stochastic dynamic model to above-mentioned fusion after
Data progress processing calculates the sea level variability of following specified time.
In an example, the data after fusion treatment are analyzed using stochastic dynamic model.Sea level time sequence
Arrange Yi(t) [it is designated as Y (t)], can be analyzed to following stacking pattern：
Y (t)=T (t)+P (t)+X (t)+α (t)；
Wherein, Y (t) is sea level；T (t) is Deterministic Trends；P (t) is deterministic periodic term；X (t) is one
Remaining random sequence；α (t) is white noise sequence.In an example, calculating obtains certainty part in sequence and embodies shape
Formula and coefficient, wherein randomness part embody form and coefficient and can at random generated when calculating, at the fusion
Data after reason are fitted calculating and obtain the result of calculation.
Method in above-described embodiment, by the altimeter data and station data fusion for obtaining different satellites,
Follow-up calculating is done using the data after fusion to handle, and by calculating obtained change function, is calculated according to the change function
The possible change in elevation in sea level of future time, the fusion of a variety of data can use result of calculation and following trend
It is more nearly, makes result of calculation more accurate.
Referring to Fig. 4, being being counted applied to terminal based on satellite altitude for present pre-ferred embodiments offer
According to Trends of Sea Level Changes computing device high-level schematic functional block diagram.The sea level variability counted based on satellite altitude
Trend computing device 100 includes data acquisition module 110, data screening module 120, Hai Pu Lin computing module 130, grid
Change processing module 150, data fusion module 150, change function computation module 160 and result output module 170.
The data acquisition module 110, for obtain multiple satellites at the appointed time section obtain counted by multiple height
According to the altimeter data group of formation.In the present embodiment, the data acquisition module 110 is used to perform the step S101, on
The detailed description of the data acquisition module 110, can join the description of the step S101.
The data screening module 120, for being screened to the data in altimeter data group, from multiple height
Valid data are filtered out in counting, wherein the effective data packets include sea level data.In the present embodiment, the data screening
Module 120 is used to perform the step S102, on the detailed description of the data screening module 120, can join the step S102
Description.
The Hai Pu Lin computing module 130, it is different for obtaining corresponding sea level according to valid data calculating
Regular data.In the present embodiment, the Hai Pu Lin computing module 130 is used to perform the step S103, on the sea level
The detailed description of abnormal computing module 130, can join the description of the step S103.
The gridding processing module 140, for the Hai Pu Lin data of different satellites to be distinguished based on spherical coordinate
Carry out gridding processing.In the present embodiment, the gridding processing module 140 is used to perform the step S104, on the net
The detailed description of processing module of formatting 140, can join the description of the step S102.
The data fusion module 150, for the data after each satellite network is formatted to be merged.The present embodiment
In, the data fusion module 150 is used to perform the step S105, can on the detailed description of the data fusion module 150
Join the description of the step S105.
It is described change function computation module 160, for being handled according to gridding after data calculate obtain sea level at any time
Between change function.In the present embodiment, the change function computation module 160 is used to perform the step S106, on the change
Change the detailed description of function computation module 160, the description of the step S106 can be joined.
The result output module 170, the sea level variability for obtaining the following specified time according to the change function
Result of calculation simultaneously exports the result of calculation.In the present embodiment, the result output module 170 is used to perform the step
S107, on the detailed description of the result output module 170, can join the description of the step S107.
In other embodiments, it is described also based on the Trends of Sea Level Changes computing device 110 that satellite altitude is counted
With including station data acquisition module 180, for obtaining station data；The data fusion module 150 is additionally operable to defend each
Data after StarNet formats are merged with the station data.
Other details on the present embodiment can further refer to the description in above method embodiment, herein no longer
Repeat.
Device in above-described embodiment, by the altimeter data and station data fusion for obtaining different satellites,
Follow-up calculating is done using the data after fusion to handle, and by calculating obtained change function, is calculated according to the change function
The possible change in elevation in sea level of future time, the fusion of a variety of data can use result of calculation and following trend
It is more nearly, makes result of calculation more accurate.
In several embodiments provided herein, it should be understood that disclosed apparatus and method, it can also pass through
Other modes are realized.Device embodiment described above is only schematical, for example, flow chart and block diagram in accompanying drawing
Show according to the device of multiple embodiments of the present invention, the architectural framework in the cards of method and computer program product,
Function and operation.At this point, each square frame in flow chart or block diagram can represent the one of a module, program segment or code
Part a, part for the module, program segment or code is used to realize holding for defined logic function comprising one or more
Row instruction.It should also be noted that in some implementations as replacement, the function of being marked in square frame can also with different from
The order marked in accompanying drawing occurs.For example, two continuous square frames can essentially be performed substantially in parallel, they are sometimes
It can perform in the opposite order, this is depending on involved function.It is also noted that every in block diagram and/or flow chart
The combination of individual square frame and block diagram and/or the square frame in flow chart, can use the special base for performing defined function or action
Realize, or can be realized with the combination of specialized hardware and computer instruction in the system of hardware.
In addition, each functional module in each embodiment of the invention can integrate to form an independent portion
Point or modules individualism, can also two or more modules be integrated to form an independent part.
If the function is realized using in the form of software function module and is used as independent production marketing or in use, can be with
It is stored in a computer read/write memory medium.Understood based on such, technical scheme is substantially in other words
The part contributed to prior art or the part of the technical scheme can be embodied in the form of software product, the meter
Calculation machine software product is stored in a storage medium, including some instructions are to cause a computer equipment (can be individual
People's computer, server, or network equipment etc.) perform all or part of step of each of the invention embodiment methods described.
And foregoing storage medium includes：USB flash disk, mobile hard disk, read-only storage (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), magnetic disc or CD etc. are various can be with the medium of store program codes.Need
Illustrate, herein, such as first and second or the like relational terms be used merely to by an entity or operation with
Another entity or operation make a distinction, and not necessarily require or imply between these entities or operation there is any this reality
The relation or order on border.Moreover, term " comprising ", "comprising" or its any other variant are intended to the bag of nonexcludability
Contain, so that process, method, article or equipment including a series of key elements are not only including those key elements, but also including
Other key elements being not expressly set out, or also include for this process, method, article or the intrinsic key element of equipment.
In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that including the key element
Process, method, article or equipment in also there is other identical element.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.It should be noted that：Similar label and letter exists
Similar terms is represented in following accompanying drawing, therefore, once being defined in a certain Xiang Yi accompanying drawing, is then not required in subsequent accompanying drawing
It is further defined and explained.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained
Cover within protection scope of the present invention.Therefore, protection scope of the present invention described should be defined by scope of the claims.
Claims (10)
1. a kind of Trends of Sea Level Changes computational methods counted based on satellite altitude, it is characterised in that this method includes：
Obtain multiple satellites altimeter data group formed by multiple altimeter datas that at the appointed time section is obtained；
Data in altimeter data group are screened, valid data are filtered out from multiple altimeter datas, wherein
The valid data include sea level data；
Calculated according to the valid data and obtain corresponding Hai Pu Lin data；
The Hai Pu Lin data of different satellites are subjected to gridding processing respectively based on spherical coordinate；
Data after each satellite network is formatted are merged；
Sea level must be arrived and change with time function by being calculated according to the data after fusion treatment；And
The sea level variability result of calculation of following specified time is obtained according to the change function and the result of calculation is exported.
2. the Trends of Sea Level Changes computational methods as claimed in claim 1 counted based on satellite altitude, it is characterised in that
The data according to after fusion treatment calculate must arrive sea level change with time function the step of include：
The calculating of data after the fusion treatment is handled by linear regression method and obtains the linear of the Trends of Sea Level Changes
Function.
3. the Trends of Sea Level Changes computational methods as claimed in claim 1 counted based on satellite altitude, it is characterised in that
Methods described also includes：
Obtain station data；
It is described each satellite network is formatted after data include the step of merged：
Data after each satellite network is formatted are merged with the station data.
4. the Trends of Sea Level Changes computational methods counted based on satellite altitude as described in claim 1-3 any one,
Characterized in that, calculated according to the data after fusion treatment must arrive sea level change with time function the step of include：
The data of different waters are respectively calculated and obtains the sea level of different waters and changes with time function；
It is described that the sea level variability result of calculation of following specified time is obtained according to the change function and the calculating knot is exported
The step of fruit, includes：
The sea level variability result of calculation of different waters is obtained according to the corresponding change function of different waters and different waters are exported
Result of calculation.
5. the Trends of Sea Level Changes computational methods as claimed in claim 4 counted based on satellite altitude, it is characterised in that
The data according to after fusion treatment calculate must arrive sea level change with time function the step of include：
Change with time letter on the sea level that the data of Various Seasonal are respectively calculated with the Various Seasonal for obtaining different waters
Number；
It is described that the sea level variability result of calculation of following specified time is obtained according to the change function and the calculating knot is exported
The step of fruit, includes：
The sea level variability that the Various Seasonal of following specified time is obtained according to the change function of the data of Various Seasonal calculates knot
Fruit simultaneously exports the result of calculation.
6. the Trends of Sea Level Changes computational methods as claimed in claim 5 counted based on satellite altitude, it is characterised in that
The altimeter data includes sea level data, on ice data and the land data that the satellite is measured；
Data in the group to altimeter data are screened, and valid data are filtered out from multiple altimeter datas
Step includes：
The sea level data is filtered out from the altimeter data.
7. the Trends of Sea Level Changes computational methods as claimed in claim 6 counted based on satellite altitude, it is characterised in that
Data in the group to altimeter data are screened, the step of filtering out valid data from multiple altimeter datas
Including：
The standard scale that the sea level data screened is carried out with prestoring is compared, if the sea level data exists
Then it is the valid data by sea level data screening in the standard scale.
8. the Trends of Sea Level Changes computational methods as claimed in claim 6 counted based on satellite altitude, it is characterised in that
Each altimeter data carries corresponding mark, described that the sea level data is filtered out from the altimeter data
The step of include：
The sea level data is filtered out according to the mark that the altimeter data is carried.
9. a kind of Trends of Sea Level Changes computing device counted based on satellite altitude, it is characterised in that the device includes：
Data acquisition module, for obtaining multiple satellites height formed by multiple altimeter datas that at the appointed time section is obtained
Count group；
Data screening module, for being screened to the data in altimeter data group, is sieved from multiple altimeter datas
Valid data are selected, wherein the effective data packets include sea level data；
Hai Pu Lin computing module, corresponding Hai Pu Lin data are obtained for being calculated according to the valid data；
Gridding processing module, for the Hai Pu Lin data of different satellites to be carried out at gridding respectively based on spherical coordinate
Reason；
Data fusion module, for the data after each satellite network is formatted to be merged；
Change function computation module, for being handled according to gridding after data calculate and must arrive sea level and change with time letter
Number；
As a result output module, for obtaining the sea level variability result of calculation of following specified time according to the change function and defeated
Go out the result of calculation.
10. the Trends of Sea Level Changes computing device as claimed in claim 9 counted based on satellite altitude, its feature is existed
In the device also includes：
Station data acquisition module, for obtaining station data；
The data fusion module is additionally operable to the data after each satellite network is formatted and merged with the station data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710287254.XA CN107122606A (en) | 2017-04-26 | 2017-04-26 | The Trends of Sea Level Changes computational methods and device counted based on satellite altitude |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710287254.XA CN107122606A (en) | 2017-04-26 | 2017-04-26 | The Trends of Sea Level Changes computational methods and device counted based on satellite altitude |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107122606A true CN107122606A (en) | 2017-09-01 |
Family
ID=59726521
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710287254.XA Pending CN107122606A (en) | 2017-04-26 | 2017-04-26 | The Trends of Sea Level Changes computational methods and device counted based on satellite altitude |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107122606A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107945446A (en) * | 2017-11-20 | 2018-04-20 | 北京中科锐景科技有限公司 | The method and apparatus that forest hot spot is identified in monitoring based on multi-source satellite |
| CN108319772A (en) * | 2018-01-26 | 2018-07-24 | 中国科学院海洋研究所 | A kind of analysis method again of wave long term data |
| CN109191408A (en) * | 2018-04-19 | 2019-01-11 | 中国气象局公共气象服务中心 | Rapid Circulation Ground Meteorological fusion method, device and server |
| CN111505619A (en) * | 2020-03-03 | 2020-08-07 | 自然资源部第一海洋研究所 | Gridding processing method for height measurement data of satellite altimeter with irregular and uneven space-time distribution |
| CN112697232A (en) * | 2020-12-15 | 2021-04-23 | 自然资源部国土卫星遥感应用中心 | Water level measurement and change monitoring method and device based on multi-source satellite height measurement data |
| CN113507280A (en) * | 2021-06-22 | 2021-10-15 | 中国海洋大学 | Ocean first mode Rosbee wave signal separation and extraction method |
| CN113945202A (en) * | 2021-10-25 | 2022-01-18 | 海南长光卫星信息技术有限公司 | Sea level height prediction method and system and readable storage medium |
| CN114756640A (en) * | 2022-04-27 | 2022-07-15 | 国家卫星海洋应用中心 | Sea surface height data evaluation method and device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101451821B1 (en) * | 2014-03-12 | 2014-10-17 | 우승범 | Extraction method of pressure jump |
| CN105975763A (en) * | 2016-04-29 | 2016-09-28 | 国家卫星海洋应用中心 | Fusion method and device of multisource sea surface wind field |
-
2017
- 2017-04-26 CN CN201710287254.XA patent/CN107122606A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101451821B1 (en) * | 2014-03-12 | 2014-10-17 | 우승범 | Extraction method of pressure jump |
| CN105975763A (en) * | 2016-04-29 | 2016-09-28 | 国家卫星海洋应用中心 | Fusion method and device of multisource sea surface wind field |
Non-Patent Citations (2)
| Title |
|---|
| 刘桂凤等: "卫星测高推求的中国近海与全球海平面变化的趋势及小波相关分析", 《测绘科学》 * |
| 团文征: "越南沿海海平面特征及其变化趁势的研究", 《中国博士学位论文全文数据库（基础科学辑）》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107945446A (en) * | 2017-11-20 | 2018-04-20 | 北京中科锐景科技有限公司 | The method and apparatus that forest hot spot is identified in monitoring based on multi-source satellite |
| CN108319772A (en) * | 2018-01-26 | 2018-07-24 | 中国科学院海洋研究所 | A kind of analysis method again of wave long term data |
| CN108319772B (en) * | 2018-01-26 | 2021-05-04 | 中国科学院海洋研究所 | Wave long-term data reanalysis method |
| CN109191408A (en) * | 2018-04-19 | 2019-01-11 | 中国气象局公共气象服务中心 | Rapid Circulation Ground Meteorological fusion method, device and server |
| CN109191408B (en) * | 2018-04-19 | 2022-03-01 | 中国气象局公共气象服务中心 | Rapid circulation ground weather fusion method and device and server |
| CN111505619A (en) * | 2020-03-03 | 2020-08-07 | 自然资源部第一海洋研究所 | Gridding processing method for height measurement data of satellite altimeter with irregular and uneven space-time distribution |
| CN112697232A (en) * | 2020-12-15 | 2021-04-23 | 自然资源部国土卫星遥感应用中心 | Water level measurement and change monitoring method and device based on multi-source satellite height measurement data |
| CN113507280A (en) * | 2021-06-22 | 2021-10-15 | 中国海洋大学 | Ocean first mode Rosbee wave signal separation and extraction method |
| CN113945202A (en) * | 2021-10-25 | 2022-01-18 | 海南长光卫星信息技术有限公司 | Sea level height prediction method and system and readable storage medium |
| CN114756640A (en) * | 2022-04-27 | 2022-07-15 | 国家卫星海洋应用中心 | Sea surface height data evaluation method and device |
| CN114756640B (en) * | 2022-04-27 | 2022-10-21 | 国家卫星海洋应用中心 | Sea surface height data evaluation method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107122606A (en) | The Trends of Sea Level Changes computational methods and device counted based on satellite altitude | |
| Wagle et al. | Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum | |
| Rozante et al. | Combining TRMM and surface observations of precipitation: technique and validation over South America | |
| Forristall | Wave crest distributions: Observations and second-order theory | |
| Rodrigues et al. | Spatial and temporal assessment of the extreme and daily precipitation of the Tropical Rainfall Measuring Mission satellite in Northeast Brazil | |
| CN106021864B (en) | The method of inspection and device of space scatterometer backscattering coefficient | |
| CN107918165A (en) | More satellites fusion Prediction of Precipitation method and system based on space interpolation | |
| Kiptala et al. | Land use and land cover classification using phenological variability from MODIS vegetation in the Upper Pangani River Basin, Eastern Africa | |
| Thompson et al. | Modeling hurricane-caused urban forest debris in Houston, Texas | |
| Whigham et al. | Combining HGM and EMAP procedures to assess wetlands at the watershed scale—status of flats and non-tidal riverine wetlands in the Nanticoke River watershed, Delaware and Maryland (USA) | |
| Valdes et al. | Estimation of multidimensional precipitation parameters by areal estimates of oceanic rainfall | |
| da Silva et al. | Fisher Shannon analysis of drought/wetness episodes along a rainfall gradient in Northeast Brazil | |
| Hutley et al. | Evaluating the effect of data-richness and model complexity in the prediction of coastal sediment loading in Solomon Islands | |
| Malamos et al. | Field survey and modelling of irrigation water quality indices in a Mediterranean island catchment: A comparison between spatial interpolation methods | |
| Tripathi et al. | Site-specific nitrogen management in rice using remote sensing and geostatistics | |
| Güler et al. | Comparison of different interpolation techniques for modelling temperatures in Middle Black Sea Region. | |
| Al-Nasrawi et al. | Geoinformatic analysis of vegetation and climate change on intertidal sedimentary landforms in southeastern Australian estuaries from 1975-2015 | |
| Chadwick et al. | Bias adjustment to preserve changes in variability: the unbiased mapping of GCM changes | |
| CN115390160A (en) | Typhoon center automatic positioning method and device | |
| Vishwakarma et al. | Assessment of CMIP5 and CORDEX-SA experiments in representing multiscale temperature climatology over central India | |
| Ozelkan et al. | Land surface temperature-Based spatial interpolation using a modified Inverse Distance Weighting method | |
| Sharan | Analyzing the effects of tropical cyclone on critical frequency of the F 2-region ionosphere in South Pacific Region | |
| Borzì et al. | Integration of microseism, wavemeter buoy, HF radar and hindcast data to analyze the Mediterranean cyclone Helios | |
| CN112380984B (en) | Remote sensing-based salt-biogas vegetation slow-flow capacity space evaluation method | |
| Mongkolnithithada et al. | Rice Yield Estimation Based on Machine Learning Approaches using MODIS 250 m Data |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170901 |