CN105975763A - Fusion method and device of multisource sea surface wind field - Google Patents

Abstract

The invention provides a fusion method and device of a multisource sea surface wind field. The fusion method comprises the following steps: obtaining multisource sea surface wind field data which comprises sea surface wind field data and/ or multiple pieces of reanalysis meteorological sea surface wind field data collected by a plurality of satellite borne microwave remote sensors; according to a preset temporal-spatial resolution, independently carrying out meshing processing on the sea surface wind field data obtained by each satellite borne microwave remote sensor to obtain multiple pieces of corresponding sea surface wind field data with the equal longitude and latitude; and utilizing a temporal-spatial interpolation algorithm to carry out interpolation calculation on all sea surface wind field data with the equal longitude and latitude to obtain fusion sea surface wind field data. The fusion method can perform the advantage of the cooperative observation of a multisource satellite, can effectively improve the coverage range and the temporal-spatial resolution of the sea surface wind field data through the fusion sea surface wind field data constructed by the fusion of satellite remote sensing wind field data and/ or reanalysis meteorological sea surface wind field data on a premise that meso-and micro-scale characteristic information can be kept, and can better meet the requirements of numerical weather prediction, marine forecasting research and marine meso-and micro-scale system research.

Description

The fusion method of a kind of multi-source Ocean Wind-field and device

Technical field

The present invention relates to ocean microwave remote sensing technical field, in particular to the fusion side of a kind of multi-source Ocean Wind-field Method and device.

Background technology

Ocean surface wind vector is to affect the basic parameter enlivening the factor and ocean dynamics of wave, ocean current, water body, its Important value is had in the accuracy etc. improving global atmosphere, ocean dynamics Forecast Mode is studied.Meanwhile, sea wind arrow Amount be also principal element affect navigation, operation on the sea, fish production etc., grasp Ocean Wind-field be optimize course line, air route guarantee, Avoiding the key of typhoon, search and rescue work, therefore, the observation of sea surface wind vector is significant.

Satellite remote sensing with large-area synchro measure, acquisition speed is fast, coverage is big, spatial and temporal resolution is high, can be continuous The advantages such as observation and become the Main Means of at present whole world wind vector observation.Wherein, satellite-borne microwave scatterometer can be fine with it Empty and under the conditions of having cloud the feature such as round-the-clock offer sea surface wind vector (including wind speed and direction) observation data become so far Obtain the topmost microwave remote sensor of global ocean wind vector.Transmit first lift-launch businessization from the U.S. in 1978 and run scattered Since penetrating the satellite Seasat of meter, satellite remote sensing Ocean Wind-field observation technology obtains tremendous development, has included ERS-1/SCAT (1991.07.17～1996.06), ERS-2 (1995.04.21～2001.01), ADEOS-I/NSCAT (1996.08.17～ 1997.06.30), QuikSCAT (1999.06.19～2009.11.23), ADEOS-II/SeaWinds (2002.12.14～ 2003.10.23), MetOp-A/ASCAT (2006.10.19), HY-2/SCAT etc. are at interior multiple satellite-borne microwave scatterometers and star Borne microwave radiometer had already put into businessization and had run, and greatly improved Global ocean wind field observing capacity.

Mode based on above-mentioned satellite remote sensing, is the most all to use single star to carry out sea surface wind vector (or ocean surface wind speed) Observation, and along with the continuous progress of science and technology, further appreciate that air coupled system and development numerical weather forecast and ocean are pre- The accuracy of observation to Ocean Wind-field observation data and spatial and temporal resolution is reported to propose the highest requirement, when some application requires Between resolution and spatial resolution respectively reach 6h and 50km, now, the observation data relying solely on single satellite have been difficult to full Foot the demand.As a example by HY-2 satellite scatterometer data product, although its L2B level Wind Products can provide Small and Medium Sized ocean With the feature that becomes more meticulous of air, but this Ocean Wind-field data product presses track storage, wind vector unitary space skewness, every It observation area is not fixed, in the number of times that passes by of low latitude region be usually one day twice, covering the whole world the most about need 3 days, its Single sing data is difficult to meet the Ocean Wind-field observation requirements of high-spatial and temporal resolution.

Inventor finds under study for action, relies on the observation data of single satellite cannot meet high observation essence in prior art Degree and the high request of high-spatial and temporal resolution.

Summary of the invention

In view of this, the purpose of the embodiment of the present invention is to provide fusion method and the device of a kind of multi-source Ocean Wind-field, To be effectively improved coverage and the spatial and temporal resolution of Ocean Wind-field data.

First aspect, embodiments provides the fusion method of a kind of multi-source Ocean Wind-field, and described method includes:

Obtain multi-source Ocean Wind-field data；Described multi-source Ocean Wind-field data include: multiple satellite-borne microwave remote sensors gather Ocean Wind-field data and/or multiple analyze meteorological Ocean Wind-field data again；Wherein, described satellite-borne microwave remote sensor includes spaceborne Microwave scatterometer and/or satellite-borne microwave radiometer；Described Ocean Wind-field data include: air speed data and/or wind direction data；Described Analyzing meteorological Ocean Wind-field data again is to wait the Ocean Wind-field data of longitude and latitude；

The described sea surface wind number of fields respectively satellite-borne microwave remote sensor each described obtained according to default spatial and temporal resolution According to carrying out gridding process, obtain corresponding respectively to the Ocean Wind-field data waiting longitude and latitude of each satellite-borne microwave remote sensor；

Utilize temporal-spatial interpolating algorithm that all of described Ocean Wind-field data waiting longitude and latitude are carried out interpolation calculation, melted Close Ocean Wind-field data；Wherein, described fusion Ocean Wind-field data include: wind speed fused data and wind direction fused data.

In conjunction with first aspect, embodiments provide the first possible embodiment of first aspect, wherein, institute State before utilizing temporal-spatial interpolating algorithm that all of described Ocean Wind-field data waiting longitude and latitude are carried out interpolation calculation, also include:

Utilize linear interpolation method that the Ocean Wind-field data of analysis meteorology again described in each are carried out interpolation processing, obtain institute State the Ocean Wind-field data of default spatial and temporal resolution.

In conjunction with the first possible embodiment of first aspect, embodiments provide the second of first aspect Possible embodiment, wherein, described utilize temporal-spatial interpolating algorithm to all of described wait longitude and latitude Ocean Wind-field data enter Row interpolation calculates, and obtains merging Ocean Wind-field data, including:

Utilize the adaptive sliding dynamic mesh window look-up table made in advance, calculate each described sea waiting longitude and latitude described The hunting zone of face wind field data；

Search each described Ocean Wind-field data described search in its correspondence waiting longitude and latitude of described acquisition respectively In the range of effectively observe data；

Utilize temporal-spatial interpolating algorithm that all effective observation data are carried out interpolation calculation, obtain merging Ocean Wind-field data.

In conjunction with the embodiment that the second of first aspect is possible, embodiments provide the third of first aspect Possible embodiment, wherein, the method making adaptive sliding dynamic mesh window look-up table in advance, including:

The first dimension that traversal standard grid middle latitude is corresponding, calculates the dependent adaptive Sliding mesh that each latitude is corresponding Window size；

According to the dependent adaptive Sliding mesh window size that latitudes all in standard grid are corresponding, make described in each Adaptive sliding dynamic mesh window look-up table Deng the Ocean Wind-field data of longitude and latitude.

In conjunction with the third possible embodiment of first aspect, embodiments provide the 4th kind of first aspect Possible embodiment, wherein, the first dimension that described traversal standard grid middle latitude is corresponding, calculate the phase that each latitude is corresponding Close adaptive sliding dynamic mesh window size, including:

The latitude that in described standard grid, each grid cell is corresponding: lat_real=i_lat/ is calculated according to below equation 4-90.125；Wherein, lat_real represents the latitude that grid cell is corresponding；I_lat represents the line number that grid cell is corresponding, described Line number be save mesh data preset group in the first dimension lower label；

Calculate according to below equation between the center of multiple grid cells adjacent with the line number of described grid cell Spherical distance naber_distance (i_lat)；

The correlation radius corresponding with the latitude of the line number of each grid cell association: eff_ is calculated according to below equation Win (i_lat)=25/naber_distance (i_lat)；Wherein, eff_win (i_lat) represents the line number with grid cell The corresponding correlation radius of latitude of association；

The adaptive sliding dynamic mesh corresponding with the latitude of the line number of each grid cell association is calculated according to below equation Window size: eff_win (i_lat)=round (1/eff_win (i_lat))；Wherein, eff_grid (i_lat) represents and net The corresponding adaptive sliding dynamic mesh window size of latitude of the line number association of lattice unit.

In conjunction with the third possible embodiment of first aspect, embodiments provide the 5th kind of first aspect Possible embodiment, wherein, when described Ocean Wind-field includes air speed data, utilizes temporal-spatial interpolating algorithm to all ocean surface wind speed Effectively observation data carry out interpolation calculation, obtain merging ocean surface wind speed data, including:

Calculate all ocean surface wind speed according to below equation and effectively observe the weighted value of data:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};$

Calculate all ocean surface wind speed according to below equation and effectively observe the fusion ocean surface wind speed data of data:

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point space-time Coordinate, u_estimateRepresent the interpolating estimation result of interpolation point；Subscript k represents observation data point, (x_k,y_k,t_k) represent k point Spacetime coordinate, w_kRepresent the weight at k point, u_kRepresenting the observation at k point, R and T is correlation radius on room and time respectively Size, N is the quantity at significant figure strong point in correlation radius；w_kBy temporal and spatial correlations radius and interpolation point to observation station Time-space matrix together decides on.

In conjunction with the third possible embodiment of first aspect, embodiments provide the 6th kind of first aspect Possible embodiment, wherein, when described Ocean Wind-field includes air speed data and wind direction data, described utilization make in advance from Adapt to Sliding mesh window look-up table, calculate the hunting zone of each described Ocean Wind-field data waiting longitude and latitude described, bag Include:

Each gridding is processed the sea surface wind number of fields including air speed data and wind direction data waiting longitude and latitude obtained According to being converted into U, V data；

Utilize the adaptive sliding dynamic mesh window look-up table made in advance, calculate searching of U, V data described in each group respectively Rope scope.

In conjunction with the 6th kind of possible embodiment of first aspect, embodiments provide the 7th kind of first aspect Possible embodiment, wherein, utilize temporal-spatial interpolating algorithm to all include air speed data and wind direction data effectively observe number According to carrying out interpolation calculation, obtain merging wind direction of ocean surface data, including:

According to all weighted values effectively observing data including air speed data and wind direction data of below equation calculating:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};$

According to all U, V data effectively observing data including air speed data and wind direction data of below equation calculating:

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point space-time Coordinate, u_estimateRepresent the interpolating estimation result of interpolation point；Subscript k represents observation data point, (x_k,y_k,t_k) represent k point Spacetime coordinate, w_kRepresent the weight at k point, u_kRepresenting the observation at k point, R and T is correlation radius on room and time respectively Size, N is the quantity at significant figure strong point in correlation radius；w_kBy temporal and spatial correlations radius and interpolation point to observation station Time-space matrix together decides on；

According to below equation, described U, V data are calculated, obtain merging wind direction of ocean surface data: blend_wind_dir (i, j)=90 atan2 (V (I, j), U (I, j))/pi*180；Wherein, (i j) represents: line number is i, row blend_wind_dir Number fusion wind direction of ocean surface data included for the grid cell of j；J represents the row number that grid cell is corresponding, and described row number are storage Second dimension lower label in the preset group of gridded data.

Second aspect, the embodiment of the present invention additionally provides the fusing device of a kind of multi-source Ocean Wind-field, and described device includes:

Acquisition module, is used for obtaining multi-source Ocean Wind-field data；Described multi-source Ocean Wind-field data include: multiple spaceborne micro- The Ocean Wind-field data of ripple remote sensor collection and/or multiple meteorological Ocean Wind-field data of analysis again；Wherein, described satellite-borne microwave is distant Sensor includes satellite-borne microwave scatterometer and/or satellite-borne microwave radiometer；Described Ocean Wind-field data include: air speed data and/or Wind direction data；Described meteorology Ocean Wind-field data of analyzing again are to wait the Ocean Wind-field data of longitude and latitude；

Gridding processing module, for obtaining satellite-borne microwave remote sensor each described respectively according to default spatial and temporal resolution The described Ocean Wind-field data taken carry out gridding process, obtain corresponding respectively to the longitude and latitude such as grade of each satellite-borne microwave remote sensor Ocean Wind-field data；

First interpolation calculation module, is used for utilizing temporal-spatial interpolating algorithm to all of described sea surface wind number of fields waiting longitude and latitude According to carrying out interpolation calculation, obtain merging Ocean Wind-field data；Wherein, described fusion Ocean Wind-field data include: wind speed merges number According to wind direction fused data.

In conjunction with second aspect, embodiments provide the first possible embodiment of second aspect, wherein, institute State device also to include:

Second interpolation calculation module, is used for utilizing linear interpolation method to analyzing meteorological sea surface wind number of fields described in each again According to carrying out interpolation processing, obtain the Ocean Wind-field data of described default spatial and temporal resolution.

The fusion method of a kind of multi-source Ocean Wind-field that the embodiment of the present invention provides and device, including: obtain multi-source sea Wind field data；Net is carried out according to the Ocean Wind-field data that each satellite-borne microwave remote sensor is obtained by default spatial and temporal resolution respectively Format process, obtain corresponding respectively to the Ocean Wind-field data waiting longitude and latitude of each satellite-borne microwave remote sensor；Space-time is utilized to insert Value-based algorithm carries out interpolation calculation to all of Ocean Wind-field data waiting longitude and latitude, obtains merging Ocean Wind-field data；With existing Technology relies on the observation data of single satellite cannot meet high accuracy of observation to compare with the high request of high-spatial and temporal resolution, its The advantage of the synergistic observation of multi-source satellite can be played, by multi-source satellite remote sensing Ocean Wind-field data and/or the sea of analysis meteorology again The fusion Ocean Wind-field data that face wind field data fusion builds effectively can carry on the premise of retaining Small and Medium Sized characteristic information The coverage of high Ocean Wind-field data and spatial and temporal resolution, can preferably meet numerical weather forecast, marine forecasting research and The demand of ocean mesoscale and small scale systems research.

For making the above-mentioned purpose of the present invention, feature and advantage to become apparent, preferred embodiment cited below particularly, and coordinate Appended accompanying drawing, is described in detail below.

Accompanying drawing explanation

In order to be illustrated more clearly that the technical scheme of the embodiment of the present invention, below by embodiment required use attached Figure is briefly described, it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, and it is right to be therefore not construed as The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to according to this A little accompanying drawings obtain other relevant accompanying drawings.

Fig. 1 shows that the observation geometry of Ocean Wind-field data observed by a kind of HY2-scatterometer that the embodiment of the present invention is provided Schematic diagram；

Fig. 2 shows a kind of wind vector unit and the signal of wind vector unit coordinate space that the embodiment of the present invention provided Figure；

Fig. 3 shows the flow chart of the fusion method of a kind of multi-source Ocean Wind-field that the embodiment of the present invention provided；

Fig. 4 shows the flow chart of the fusion method of the another kind of multi-source Ocean Wind-field that the embodiment of the present invention provided；

Fig. 5 shows the flow chart of the fusion method of the another kind of multi-source Ocean Wind-field that the embodiment of the present invention provided；

Fig. 6 shows the structural representation of the fusing device of a kind of multi-source Ocean Wind-field that the embodiment of the present invention provided；

Fig. 7 shows the structural representation of the fusing device of the another kind of multi-source Ocean Wind-field that the embodiment of the present invention provided Figure.

Detailed description of the invention

For making the purpose of the embodiment of the present invention, technical scheme and advantage clearer, below in conjunction with the embodiment of the present invention Middle accompanying drawing, is clearly and completely described the technical scheme in the embodiment of the present invention, it is clear that described embodiment is only It is a part of embodiment of the present invention rather than whole embodiments.Generally real with the present invention illustrated described in accompanying drawing herein The assembly executing example can be arranged with various different configurations and design.Therefore, below to the present invention's provided in the accompanying drawings The detailed description of embodiment is not intended to limit the scope of claimed invention, but is merely representative of the selected reality of the present invention Execute example.Based on embodiments of the invention, the institute that those skilled in the art are obtained on the premise of not making creative work There are other embodiments, broadly fall into the scope of protection of the invention.

Single star is used to carry out the observation of sea surface wind vector (or ocean surface wind speed) in view of mode based on satellite remote sensing, and Along with the continuous progress of science and technology, further appreciate that air coupled system and development numerical weather forecast and marine forecasting are to sea Accuracy of observation and the spatial and temporal resolution of wind field observation data propose the highest requirement, and some application requires temporal resolution Respectively reaching 6h and 50km with spatial resolution, now, the observation data relying solely on single satellite are difficult to meet above-mentioned need Ask.

As a example by HY-2 satellite scatterometer data product, although its L2B level Wind Products can provide Small and Medium Sized ocean and The feature that becomes more meticulous of air, but this Ocean Wind-field data product presses track storage, wind vector unitary space skewness, every day Observation area is not fixed, in the number of times that passes by of low latitude region be usually one day twice, covering the whole world the most about need 3 days, it is single Sing data is difficult to meet the Ocean Wind-field observation requirements of high-spatial and temporal resolution.

Based on this, embodiments providing fusion method and the device of a kind of multi-source Ocean Wind-field, it is to not simultaneous interpretation Sensor, different moonscope platform, different observation time, different spatial and temporal resolution, the multi-source satellite remote sensing of different error characteristics Observation Ocean Wind-field observation data are reprocessed, and play the advantage of the synergistic observation of multi-source satellite, form the standard of gridding Data product, convenient diagnostic analysis and the application doing satellite monitoring key element further.Data fusion in the embodiment of the present invention is Processing one of effective ways of the problems referred to above, the fusion wind field built by multi-source satellite remote sensing Ocean Wind-field data fusion can On the premise of retaining Small and Medium Sized characteristic information, it is effectively improved coverage and the spatial and temporal resolution of Ocean Wind-field data, can Preferably meet numerical weather forecast, marine forecasting research and the demand of ocean mesoscale and small scale systems research；Below by enforcement Example is described.

The most first the embodiment of the present invention can be related to HY-2 scatterometer, HY-2 scatterometer data product and wind vector element mesh Lattice coordinate system illustrates:

1, HY-2 scatterometer brief introduction

In August, 2012 launch ocean two, be equipped with first, China can businessization run microwave scatterometer HY2- SCAT.HY2-SCAT be mainly used in Global ocean wind field observation, survey wind wind speed range be 4～24m/s, wind speed precision be 2m/s or 10%；It is 0～360 ° that wind direction measures scope, and wind direction precision is ± 20 °.HY2-SCAT operating frequency is 13.256GHz, uses pen Shape wave beam conical scanning mode, is rotated around nadir direction with fixed elevation by pencil beam, in satellite platform straight rail direction Motion in form certain covered ground swath (as shown in Fig. 1 .a)；Scatterometer system includes two polarization modes of VV and HH, It is observed with different incidence angles respectively, same resolution cell can be obtained different polarization mode by the motor process of platform, The repetitive measurement result (as shown in Fig. 1 .b) of different incidence angles degree, to overcome the many-valued fuzziness problem of Ocean Wind-field direction inverting. Interior wave beam uses HH polarization mode, and angle of incidence is 41 °, and corresponding ground footmark size is about 23km × 31km, swath width For 1400km.Outer wave beam uses VV polarization mode, and angle of incidence is 48 °, and corresponding ground footmark size is about 25km × 38km, swath Width is 1700km.

2, HY-2 scatterometer data product brief introduction

The ocean two current available data product of satellite scatterometer is divided into L1B level product data product, L2A DBMS Product, L2B DBMS product and L3 DBMS product.Relevant to the embodiment of the present invention for L1B DBMS and L2A DBMS. The problem that the embodiment of the present invention solves is that the sea ice mark from L1B DBMS file to L2A DBMS process of producing product is asked Topic.

Wherein, L1B data are that data observed by the scatterometer carrying out storing for order with the time of telemetry frame.Each telemetry frame Measuring pulses including 96 scatterometers, each measurement pulse includes backscattering coefficient, the geographical position of each pulse footprint, with And it is used for describing the parameter of the information such as the quality of measurement data and uncertainty, this data file also comprises simultaneously and pass through GPS The latitude and longitude information of the sub-satellite track of data acquisition.

L2A product documentation includes that each radar raster-displaying sigma0 that satellite platform obtains in a space orbit surveys Value.Additionally, L2A product also comprises the assistance data element that some are corresponding with each sigma0 measured value.These supplementary numbers The relevant informations such as the position, quality and the uncertainty that list each sigma0 measured value according to element.In L2A product Sigma0 is grouped with wind vector unit.Each wind vector cell row corresponding ground measures a cross rail cutting of swath. Each L2A wind vector unit is the square of a 25km.Accordingly, it would be desirable to 1624 wind vector cell row complete the earth The most completely cover.

L2B product data file, organizes in units of track, and the wind vector measurement data of the most each track constitutes one Individual L2B file.Each data element in L2B product can be indexed by the row, column number of wind vector unit.L2B wind The bearing of trend of vector units row and star roll off the production line perpendicular, and the bearing of trend of row direction of rolling off the production line with star is consistent.L2B processes software The auxiliary information such as the sigma0 measured value in each wind vector unit and azimuth, angle of incidence, polarization are utilized to be obtained by inverting One group of wind vector may solve, and these wind vectors solution may be referred to as fuzzy solution, and then recycling ambiguity removal algorithm determines only The wind vector solution of one, the wind vector solution finally utilizing DIR algorithm to select ambiguity removal algorithm is made further optimization and is processed.L2B Product gives up to 4 wind speed, wind direction fuzzy solution, and presses likelihood value order arrangement from high to low.

Wherein, above-mentioned each L2B file comprises 1624 × 76 wind vector unit, records the position of each wind vector unit Put (longitude and latitude), wind speed, wind direction and other relevant auxiliary information.L2B DBMS stores in units of wind vector unit, Each L2B file comprises all wind that a complete track (satellite encloses, and cover width is 1750km) observation obtains around the earth one Vector units.

HY-2A scatterometer Level3 data provide the Global Sea-level wind of every day with the grid configuration of 0.25 ° × 0.25 ° of size Field data.

Each data element in L3 product stores with a single SDS object.Each SDS is to liking one three Dimension group.Wherein, the line number of one-dimensional representation grid cell, the row number of two-dimensional representation grid cell, the third dimension represents rail lift With fall rail.The row and column of grid cell corresponding latitude and longitudinal respectively.When grid cell size is 0.25 ° × 0.25 °, The sum of row and column is respectively 720 and 1440.Grid cell line number is respectively from south orientation north with from west with the chronological order of row number Eastwards, starting point is respectively-90 ° of latitude and longitude 0 °.

Above-mentioned L3 DBMS product is Global Grid wind speed and direction product, each grid cell size is 0.25 ° × 0.25 °, this data product all L2B DBMS products of Dan Tian from wind vector cell projection to wait longitude and latitude grid.

3, wind vector unit grid coordinate system

Require to simplify calculating along the bin matching process of ground trace coordinate to sigma0 measuring unit, therefore, sea Ocean No. two microwave scatterometers have employed one conveniently grid model, centered by sub-satellite track, with straight rail to and hand over Rail represents appointment position to coordinate, uses the resolution of 25km × 25km to carry out resampling.The initial point of this coordinate system is set to The corresponding point of the border of HY2-SCAT (i.e. ocean two scatterometer) track, i.e. latitude substar southernmost.Due to HY2-SCAT Swath sufficiently narrow, therefore, negligible " longitude " compression at swath edge.This model can be considered a strict rectangular mesh, So process also can simplify calculating.For convenience of description, this mesh coordinate system is designated as wind vector unitary space, with specific reference to figure Shown in 2.

Embodiments providing the fusion method of a kind of multi-source Ocean Wind-field, with reference to Fig. 3, described method includes as follows Step:

S101, acquisition multi-source Ocean Wind-field data；Described multi-source Ocean Wind-field data include: multiple satellite-borne microwave remote sensors The Ocean Wind-field data gathered and/or multiple meteorological Ocean Wind-field data of analysis again；Wherein, described satellite-borne microwave remote sensor includes Satellite-borne microwave scatterometer and/or satellite-borne microwave radiometer；Described Ocean Wind-field data include: air speed data and/or wind direction data； Described meteorology Ocean Wind-field data of analyzing again are to wait the Ocean Wind-field data of longitude and latitude.

Multi-source Ocean Wind-field data in the embodiment of the present invention, can be the sea surface winds gathered by satellite-borne microwave remote sensor Field data, it is also possible to the sea surface wind number of fields of analysis meteorology more directly or indirectly obtained from third-party platform (such as weather bureau) etc. According to；Wherein, above-mentioned satellite-borne microwave remote sensor can individually include satellite-borne microwave scatterometer (i.e. active remote sensing device), it is also possible to individually Including satellite-borne microwave radiometer (i.e. passive remote sensing device), it is also possible to both included satellite-borne microwave scatterometer, and included again satellite-borne microwave spoke Penetrate meter.

Wherein, gather data by satellite-borne microwave remote sensor and want to participate in subsequent calculations, need first to carry out gridding process； And Ocean Wind-field data (such as NCEP numerical forecasting product) the inherently gridding of analysis meteorology again obtained from third-party platform Data, therefore when it is carried out subsequent treatment, it is not necessary to it is carried out gridding process.

When multi-source Ocean Wind-field data only include the Ocean Wind-field data gathered by satellite-borne microwave radiometer, due to spoke Penetrate meter and can not record wind direction of ocean surface, therefore, corresponding, Ocean Wind-field data only include air speed data.If it is spaceborne micro-including The Ocean Wind-field data that scattering of wave meter gathers, and when analyzing meteorological Ocean Wind-field data again, Ocean Wind-field data the most both included Air speed data, includes again wind direction data.

And above-mentioned multi-source Ocean Wind-field data, the sea surface wind number of fields gathered by satellite-borne microwave remote sensor can be only included According to, it is also possible to both included the Ocean Wind-field data gathered by satellite-borne microwave remote sensor, also include directly from third-party platform (as Weather bureau) etc. acquisition the Ocean Wind-field data of analysis meteorology again.

Further, concrete acquisition channel and the concrete quantity of above-mentioned multi-source Ocean Wind-field data can be the most any Selecting, this is not particularly limited by the embodiment of the present invention.

S102, the described sea surface wind respectively satellite-borne microwave remote sensor each described obtained according to default spatial and temporal resolution Field data carries out gridding process, obtains corresponding respectively to the sea surface wind number of fields waiting longitude and latitude of each satellite-borne microwave remote sensor According to.

Concrete, in order to make to gather, by satellite-borne microwave remote sensor, the calculating process that data participation is follow-up, need these Data carry out gridding process；And above-mentioned default spatial and temporal resolution can be set as required；The embodiment of the present invention sets Resolution of fixing time is 24h, and spatial resolution is 25km × 25km.And set array blend_wind_speed (720,1440, 2) store standard network format merge Wind Products wind speed (i.e. gridding process after Ocean Wind-field data in air speed data), Setting array blend_wind_dir (720,1440,2) storage standard network is formatted, and (i.e. gridding processes fusion Wind Products wind direction After Ocean Wind-field data in wind direction data).Wherein, in above-mentioned array, the first dimension corresponding latitude, the second dimension correspondence warp Degree, the lift rail of third dimension correspondence satellite remote sensor.Corresponding, line direction is latitude direction, and column direction is longitudinal, entirely The grid on surface, ball sea comprises 720 row, 1440 row.

The longitude and latitude that each grid is corresponding can be calculated by following formula:

Latitude=-89.875+grid_cell_row/4；

Longitude=0.125+grid_cell_column/4.Wherein, atitude represents the latitude that grid cell is corresponding Degree；Longitude represents the longitude that grid cell is corresponding；

In units of sky, to the HY-2A satellite scatterometer L2B DBMS product stored along rail and F15/SSMI microwave radiation Counting product to carry out standard network and format process, generating spatial resolution is 0.25 ° × 0.25 °, and temporal resolution is the mark of 24h The longitude and latitude mesh products such as standard.To each satellite remote sensor, arrange array grid_wind_speed grid wind speed (720,1440, 2) and grid_wind_dir grid wind direction (720,1440,2), and to compose initial value be 0, and the satellite being respectively used to record gridding is distant Sense wind speed and direction.Wherein array the first dimension corresponding latitude, the second latitude correspondence longitude, third dimension correspondence lift rail.By Originally it is exactly gridded data product in NCEP numerical forecasting product, again it need not be done gridding operation the most here.

Wherein, array grid_wind_speed is the array of the longitude and latitude gridding wind speed such as the record whole world, grid_ Wind_dir is the array of the longitude and latitude gridding wind directions such as the record whole world；Wherein, the first corresponding latitude of dimension of above-mentioned two array Degree, the second corresponding longitude of dimension, the corresponding grid cell of each element of array, third dimension correspondence rail lift and fall rail.

And the Ocean Wind-field data of the analysis meteorology again (such as NCEP numerical forecasting product) that third-party platform obtains is exactly originally Gridded data product, itself then has fixing spatial and temporal resolution, itself is i.e. i.e. the longitude and latitude such as grade after gridding processes Ocean Wind-field data.

S103, utilize temporal-spatial interpolating algorithm that the Ocean Wind-field data of all of longitude and latitude such as described are carried out interpolation calculation, Obtain merging Ocean Wind-field data；Wherein, described fusion Ocean Wind-field data include: wind speed fused data and wind direction merge number According to.

Concrete, all Ocean Wind-field data waiting longitude and latitude include: the sea waiting longitude and latitude after gridding processes Face wind field data, and/or, the Ocean Wind-field data of analysis meteorology again of gridding.

Then utilize temporal-spatial interpolating algorithm that all Ocean Wind-field data waiting longitude and latitude are carried out interpolation calculation, merged Ocean Wind-field data；In the embodiment of the present invention, the air speed data utilizing space-time Weighted Fusion algorithm to include wind field data uses Scalar merges, the mode of the wind direction data acquisition Vector Fusion including wind field data, it is achieved that to active remote sensing device with passive The ocean surface wind speed of remote sensor offer and wind direction, and the fusion of the ocean surface wind speed data of passive remote sensing device offer.Concrete, for Only include the wind field data of air speed data；Utilize temporal-spatial interpolating algorithm that air speed data is carried out interpolation calculation, obtain wind speed and merge Data；For including the wind field data of air speed data and wind direction data, first these wind field data are converted to U, V data, then Utilize temporal-spatial interpolating algorithm interpolation respectively to go out U, V component, then utilize and merge wind field U, V component calculating acquisition wind direction fusion product (i.e. wind direction fused data).

The fusion method of a kind of multi-source Ocean Wind-field that the embodiment of the present invention provides, with prior art rely on single satellite Observation data cannot meet high accuracy of observation and compared with the high request of high-spatial and temporal resolution, its can by different sensors, no With moonscope platform, different observation time, different spatial and temporal resolution, the multi-source satellite remote sensing observation sea of different error characteristics The meteorological Ocean Wind-field data of wind field data and/or again analysis carry out fusion treatment, play the advantage of the synergistic observation of multi-source satellite, The wind field that merges built by multi-source satellite remote sensing Ocean Wind-field data fusion can be before retaining Small and Medium Sized characteristic information Put, be effectively improved coverage and the spatial and temporal resolution of Ocean Wind-field data, can preferably meet numerical weather forecast, ocean Prediction research and the demand of ocean mesoscale and small scale systems research.

It is i.e. gridded data in view of the Ocean Wind-field data itself of analysis meteorology again obtained, and this gridded data Resolution may be different from default spatial and temporal resolution, at this time, it may be necessary to become to preset by conversion of resolution corresponding for gridded data Spatial and temporal resolution；Corresponding, after getting the meteorological Ocean Wind-field data of analysis again, and, utilize temporal-spatial interpolating algorithm pair Before all of described Ocean Wind-field data waiting longitude and latitude carry out interpolation calculation, also include: utilize linear interpolation method to often Analyze meteorological Ocean Wind-field data described in one again and carry out interpolation processing, obtain the sea surface wind number of fields of described default spatial and temporal resolution According to.

Single star collection observation data, significant figure strong point in realizing relevant space-time radius is used in view of prior art During search, it usually needs first calculate interpolation point to the distance between each observation station, and judge whether this distance is being correlated with Within space-time radius, but the method is computationally intensive, and the time of consuming is long, therefore for improving computational efficiency in the embodiment of the present invention, First the single star satellite remote sensing Ocean Wind-field data stored along rail are carried out waiting graticules to format process by the present invention, and realize right In relevant space-time radius during the search at significant figure strong point, when will calculate space-time weight coefficient, say that the correlation radius needed is converted into " oneself Adapt to Sliding mesh window ", thus it is effectively improved computational efficiency.Concrete, with reference to Fig. 4, above-mentioned steps 103, utilize space-time to insert Value-based algorithm carries out interpolation calculation to all of described Ocean Wind-field data waiting longitude and latitude, obtains merging Ocean Wind-field data, tool Body, including:

The adaptive sliding dynamic mesh window look-up table that S201, utilization make in advance, calculates each described longitude and latitude such as grade described The hunting zone of the Ocean Wind-field data of degree.

In the embodiment of the present invention, using the adaptive sliding dynamic mesh window look-up table made in advance, this look-up table is applicable to The Ocean Wind-field data waiting longitude and latitude after the gridding process that all of remote sensor is corresponding, and, the sea of analysis again of gridding Face wind field data；

Then calculate according to this look-up table each etc. the hunting zone of Ocean Wind-field data of longitude and latitude, in order to follow-up Search for valid data in this hunting zone, and participate in the calculating of follow-up fusion Ocean Wind-field data.

S202, search the Ocean Wind-field data of the longitudes and latitudes such as each of described acquisition is described respectively described in its correspondence Data are effectively observed in hunting zone.

Concrete, follow-up fusion Ocean Wind-field calculating is participated in for each and waits the Ocean Wind-field data of longitude and latitude, first In the hunting zone that these data are corresponding, first search for its valid data participating in calculating, and make these valid data participate in follow-up meter In calculation.

S203, utilize temporal-spatial interpolating algorithm that all effective observation data are carried out interpolation calculation, obtain merging Ocean Wind-field Data.

Concrete, calculate all Ocean Wind-field according to below equation and effectively observe the weighted value of data:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};$

Fusion ocean surface wind speed data according to the below equation all Ocean Wind-field of calculating:

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point space-time Coordinate, u_estimateRepresent the interpolating estimation result of interpolation point；Subscript k represents observation data point, (x_k,y_k,t_k) represent k point Spacetime coordinate, w_kRepresent the weight at k point, u_kRepresenting the observation at k point, R and T is correlation radius on room and time respectively Size, N is the quantity at significant figure strong point in correlation radius；w_kBy temporal and spatial correlations radius and interpolation point to observation station Time-space matrix together decides on.

With reference to Fig. 5, corresponding to above-mentioned steps 104, firstly the need of with the adaptive sliding made in advance in the embodiment of the present invention Dynamic mesh window look-up table, concrete manufacture method, including:

S301, the first dimension that traversal standard grid middle latitude is corresponding, calculate dependent adaptive corresponding to each latitude sliding Dynamic mesh window size.

Concrete, arranging time correlation radius in the embodiment of the present invention is 12h, and space correlation radius is 75km.

First dimension (i.e. the dimension of corresponding latitude) of pair warp and weft net is circulated, and calculates corresponding being correlated with of each latitude Adaptive sliding dynamic mesh window size, and make relevant grid interpolation table.Computational methods are as follows:

Concrete, the dependent adaptive Sliding mesh window size calculating each latitude corresponding includes:

The latitude that in described standard grid, each grid cell is corresponding: lat_real=i_lat/ is calculated according to below equation 4-90.125；Wherein, lat_real represents the latitude that grid cell is corresponding；I_lat represents the line number that grid cell is corresponding, described Line number be save mesh data preset group in the first dimension lower label；

Calculate according to below equation between the center of multiple grid cells adjacent with the line number of described grid cell Spherical distance naber_distance (i_lat)；

The correlation radius corresponding with the latitude of the line number of each grid cell association is calculated according to below equation；eff_ Win (i_lat)=25/naber_distance (i_lat)；Wherein, eff_win (i_lat) represents the line number with grid cell The corresponding correlation radius of latitude of association；

The adaptive sliding dynamic mesh corresponding with the latitude of the line number of each grid cell association is calculated according to below equation Window size: eff_win (i_lat)=round (1/eff_win (i_lat))；Wherein, eff_grid (i_lat) represents and net The corresponding adaptive sliding dynamic mesh window size of latitude of the line number association of lattice unit.

Concrete, the line number (the i.e. first dimension lower label) of note current grid unit is i_lat, calculates firm grid cell pair The latitude answered can be calculated by following formula: lat_real=i_lat/4-90.125；

Calculate the spherical distance between the center of adjacent the grid cell that line number is i_lat, and be designated as naber_ distance(i_lat)；Wherein, the unit of naber_distance (i_lat) is km；

Calculate with line number be latitude corresponding for i_lat to corresponding correlation radius, be recorded as eff_win (i_lat): eff_ Win (i_lat)=25/naber_distance (i_lat)；

Calculate with line number be latitude corresponding for i_lat to corresponding adaptive sliding dynamic mesh window size, be recorded as eff_ Grid (i_lat): eff_win (i_lat)=round (1/eff_win (i_lat)).

S302, according to dependent adaptive Sliding mesh window size corresponding to latitudes all in standard grid, make each The adaptive sliding dynamic mesh window look-up table of the individual described Ocean Wind-field data waiting longitude and latitude.

In the embodiment of the present invention, when the data obtained only include multiple satellite-borne microwave radiometer, above-mentioned sea surface wind number of fields According to only including air speed data；When the data obtained include multiple satellite-borne microwave scatterometer and/or be to analyze meteorological Ocean Wind-field again During data, above-mentioned Ocean Wind-field data include simultaneously: air speed data and wind direction data.Include according to above-mentioned Ocean Wind-field data Data are different, and the corresponding interpolation calculation in above-mentioned steps 202 includes two kinds of different modes:

First, when described Ocean Wind-field includes air speed data, above-mentioned steps 202 is then for utilizing temporal-spatial interpolating algorithm to institute Having ocean surface wind speed effectively to observe data and carry out interpolation calculation, obtain merging ocean surface wind speed data, circular is as follows:

Calculate all ocean surface wind speed according to below equation and effectively observe the weighted value of data:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};\mathrm{...}\left(1\right)$

Calculate all ocean surface wind speed according to below equation and effectively observe the fusion ocean surface wind speed data of data:

$u_{estimate}=\frac{{\Σ}_{k=1}^N{w}_k{u}_k}{{\Σ}_{k=1}^N{w}_k};\mathrm{...}\left(2\right)$

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point spacetime coordinate, u_estimateRepresent to be inserted The interpolating estimation result of value point；Subscript k represents observation data point, (x_k,y_k,t_k) represent k point spacetime coordinate, w_kRepresent at k point Weight, u_kRepresenting that R and T is correlation radius size on room and time respectively in the observation of k point, N is to have in correlation radius The quantity of effect data point；w_kTogether decided on to the time-space matrix observation station by temporal and spatial correlations radius and interpolation point.

Utilize three-dimensional space-time interpolation algorithm, use the method that scalar merges, make standard network ocean surface wind speed of formatting and merge and produce Product.To being designated as under blend_wind_speed (mixing wind speed) (i, mesh point j), search respectively HY-2A satellite scatterometer, It is marked on scope (i-eff_window (i): i+eff_ under F15/SSMI microwave radiometer gridded data grid_wind_speed Window (i), j-2:j+2) in effectively observe data, it is thus achieved that for calculate this mesh point merge wind speed calculate correlation radius Interior Wind observation data.On this basis, utilizing formula for interpolation (1), (2), interpolation obtains the wind corresponding with this grid cell Speed；Wherein, above-mentioned scope (i-eff_window (i): i+eff_window (i), j-2:j+2) is i.e. for calculate according to step 201 The hunting zone of air speed data.

Second, when Ocean Wind-field includes air speed data and wind direction data simultaneously, above-mentioned steps 201 specifically includes:

Each gridding is processed the sea surface wind number of fields including air speed data and wind direction data waiting longitude and latitude obtained According to being converted into U, V data；

Utilize the adaptive sliding dynamic mesh window look-up table made in advance, calculate searching of U, V data described in each group respectively Rope scope.

Now, corresponding, above-mentioned steps 202 is then for utilizing temporal-spatial interpolating algorithm to include air speed data and wind direction number to all According to effectively observation data carry out interpolation calculation, obtain merging wind direction of ocean surface data, circular includes:

According to all weighted values effectively observing data including air speed data and wind direction data of below equation calculating:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};\mathrm{...}\left(1\right)$

According to all U, V data effectively observing data including air speed data and wind direction data of below equation calculating:

$u_{estimate}=\frac{{\Σ}_{k=1}^N{w}_k{u}_k}{{\Σ}_{k=1}^N{w}_k};\mathrm{...}\left(2\right)$

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point spacetime coordinate, u_estimateRepresent to be inserted The interpolating estimation result of value point；Subscript k represents observation data point, (x_k,y_k,t_k) represent k point spacetime coordinate, w_kRepresent at k point Weight, u_kRepresenting that R and T is correlation radius size on room and time respectively in the observation of k point, N is to have in correlation radius The quantity of effect data point；w_kTogether decided on to the time-space matrix observation station by temporal and spatial correlations radius and interpolation point；

After U, V data having obtained effective observation data, in addition it is also necessary to utilize and merge wind field U, V component calculating acquisition wind To fusion product, it may be assumed that

According to below equation, described U, V data are calculated, obtain merging wind direction of ocean surface data: blend_wind_dir (i, j)=90 atan2 (V (I, j), U (I, j))/pi*180；Wherein, (i j) represents: line number is i, row blend_wind_dir Number fusion wind direction of ocean surface data included for the grid cell of j；J represents the row number that grid cell is corresponding, and described row number are storage Second dimension lower label in the preset group of gridded data.

Concrete, the air speed data that the embodiment of the present invention carries out gridding with HY-2A satellite scatterometer with wind direction data is Example illustrates:

(1) air speed data that HY-2A satellite scatterometer carries out gridding is converted into U, V data with wind direction data, conversion Formula is as follows:

Grid_v (i, j)=grid_wind_speed (i, j) * sin (grid_wind_dir (i, j))

Grid_u (i, j)=grid_wind_speed (i, j) * cos (grid_wind_dir (i, j))；Wherein, Grid_v (i, j) represents the U data after conversion, and (i j) represents the V data after conversion to grid_u.

(2) meteorological Ocean Wind-field data (NCEP gridding U, V data product) will then be analyzed again by linear interpolation side Method, is interpolated into the resolution (the most above-mentioned default spatial and temporal resolution) identical with HY-2A satellite scatterometer gridded data, and is designated as ncep_v,ncep_u。

(3) array blend_wind_u (720,1440) (both (mixing wind) arrays) is set, blend_wind_v (720, 1440) respectively storage standard network format fusion wind field U, V component.(i, mesh point difference j) it is designated as under blend_wind_v Search (1), (2) step calculates grid_v and the ncep_v array index of acquisition at Adaptive windowing mouth (i-eff_ Window (i): i+eff_window (i), j-2:j+2) in effectively observe data, it is thus achieved that be used for calculating this mesh point and merge V Observation data in the correlation radius that component calculates.On this basis, utilize above-mentioned formula for interpolation (1), (2), interpolation obtain with The V component that this grid cell is corresponding.

In like manner, it is designated as under blend_wind_u that (i, mesh point j) searches (1) respectively, calculates and obtain in (2) step The effective sight in (i-eff_window (i): i+eff_window (i), j-2:j+2) of grid_u and the ncep_u array index Survey data, it is thus achieved that merge the observation data in the correlation radius that U component calculates for calculating this mesh point.On this basis, profit With above-mentioned formula for interpolation (1), (2), interpolation obtains the U component corresponding with this grid cell.Wherein, above-mentioned scope (i-eff_ Window (i): i+eff_window (i), j-2:j+2) i.e. include air speed data and wind direction data for calculate according to step 201 The hunting zone of wind field data.

Then utilize and merge wind field U, V component calculating acquisition wind direction fusion product: be concrete, take the arc tangent of V/U, and will The polar angle that U, V vector is corresponding is transformed into the angle of ocean wind direction definition, and specific formula for calculation is as follows: blend_wind_ Dir (i, j)=90 atan2 (V (I, j), U (I, j))/pi*180.

The fusion method of a kind of multi-source Ocean Wind-field that the embodiment of the present invention provides:

1, using scalar to merge wind speed, wind direction uses the mode of Vector Fusion, it is achieved that provide active remote sensing device Ocean surface wind speed and wind direction, and the fusion of the ocean surface wind speed data of passive remote sensing device offer, it is possible to retaining Small and Medium Sized feature On the premise of information, it is effectively improved coverage and the spatial and temporal resolution of Ocean Wind-field data.

2, in realizing relevant space-time radius during the search at significant figure strong point, when the present invention will calculate space-time weight coefficient Say that the correlation radius needed is converted into " adaptive sliding dynamic mesh window ", simplify calculating process, improve computational efficiency and calculate essence Exactness.

The fusion method of a kind of multi-source Ocean Wind-field that the embodiment of the present invention provides, with prior art rely on single satellite Observation data cannot meet high accuracy of observation and compared with the high request of high-spatial and temporal resolution, it can play the association of multi-source satellite With the advantage of observation, built by multi-source satellite remote sensing Ocean Wind-field data and/or the Ocean Wind-field data fusion of analysis meteorology again Fusion Ocean Wind-field data can retain on the premise of Small and Medium Sized characteristic information, be effectively improved covering of Ocean Wind-field data Lid scope and spatial and temporal resolution, can preferably meet numerical weather forecast, marine forecasting research and ocean mesoscale and small scale systems and grind The demand studied carefully.

The embodiment of the present invention additionally provides the fusing device of a kind of multi-source Ocean Wind-field, and described device is used for performing above-mentioned many The fusion method of source Ocean Wind-field, with reference to Fig. 6, described device includes:

Acquisition module 11, is used for obtaining multi-source Ocean Wind-field data；Described multi-source Ocean Wind-field data include: multiple spaceborne The Ocean Wind-field data of microwave remote sensor collection and/or multiple meteorological Ocean Wind-field data of analysis again；Wherein, described satellite-borne microwave Remote sensor includes satellite-borne microwave scatterometer and/or satellite-borne microwave radiometer；Described Ocean Wind-field data include: air speed data and/ Or wind direction data；Described meteorology Ocean Wind-field data of analyzing again are to wait the Ocean Wind-field data of longitude and latitude；

Gridding processing module 12, for according to presetting spatial and temporal resolution respectively to satellite-borne microwave remote sensor each described The described Ocean Wind-field data obtained carry out gridding process, obtain corresponding respectively to the longitude and latitude such as grade of each satellite-borne microwave remote sensor The Ocean Wind-field data of degree；

First interpolation calculation module 13, is used for utilizing temporal-spatial interpolating algorithm to all of described Ocean Wind-field waiting longitude and latitude Data carry out interpolation calculation, obtain merging Ocean Wind-field data；Wherein, described fusion Ocean Wind-field data include: wind speed merges Data and wind direction fused data.

The fusing device of a kind of multi-source Ocean Wind-field that the embodiment of the present invention provides, with prior art rely on single satellite Observation data cannot meet high accuracy of observation and compared with the high request of high-spatial and temporal resolution, it can play the association of multi-source satellite With the advantage of observation, built by multi-source satellite remote sensing Ocean Wind-field data and/or the Ocean Wind-field data fusion of analysis meteorology again Fusion Ocean Wind-field data can retain on the premise of Small and Medium Sized characteristic information, be effectively improved covering of Ocean Wind-field data Lid scope and spatial and temporal resolution, can preferably meet numerical weather forecast, marine forecasting research and ocean mesoscale and small scale systems and grind The demand studied carefully.

Single star collection observation data, significant figure strong point in realizing relevant space-time radius is used in view of prior art During search, it usually needs first calculate interpolation point to the distance between each observation station, and judge whether this distance is being correlated with Within space-time radius, but the method is computationally intensive, and the time of consuming is long, therefore for improving computational efficiency in the embodiment of the present invention, First the single star satellite remote sensing Ocean Wind-field data stored along rail are carried out waiting graticules to format process by the present invention, and realize right In relevant space-time radius during the search at significant figure strong point, when will calculate space-time weight coefficient, say that the correlation radius needed is converted into " oneself Adapt to Sliding mesh window ", thus it is effectively improved computational efficiency.With reference to Fig. 7, described device also includes:

Second interpolation calculation module 14, is used for utilizing linear interpolation method to analyzing meteorological Ocean Wind-field described in each again Data carry out interpolation processing, obtain the Ocean Wind-field data of described default spatial and temporal resolution.

Further, in the fusing device of multi-source Ocean Wind-field, the first interpolation calculation module 13, including:

First calculating sub module, for utilizing the adaptive sliding dynamic mesh window look-up table made in advance, calculates each Hunting zone Deng the Ocean Wind-field data of longitude and latitude；

Search submodule, for searching Ocean Wind-field data the searching in its correspondence of each longitude and latitude such as grade of acquisition respectively Data are effectively observed in the range of rope；

Second calculating sub module, is used for utilizing temporal-spatial interpolating algorithm that all effective observation data are carried out interpolation calculation, To merging Ocean Wind-field data.

Further, the fusing device of multi-source Ocean Wind-field also includes:

Spider module, for traveling through the first dimension that standard grid middle latitude is corresponding；

Computing module, for calculating the dependent adaptive Sliding mesh window size that each latitude is corresponding；

Make module, for the dependent adaptive Sliding mesh window size corresponding according to latitudes all in standard grid, Make the adaptive sliding dynamic mesh window look-up table of each Ocean Wind-field data waiting longitude and latitude.

Further, in the fusing device of multi-source Ocean Wind-field, spider module, including:

First calculating sub module, the latitude for corresponding according to each grid cell in below equation calculating standard grid: Lat_real=i_lat/4-90.125；Wherein, lat_real represents the latitude that grid cell is corresponding；I_lat represents grid list The line number that unit is corresponding, line number be save mesh data preset group in the first dimension lower label；

Second calculating sub module, for calculating the multiple grid cells adjacent with the line number of grid cell according to below equation Center between spherical distance naber_distance (i_lat)；

3rd calculating sub module, relative with the latitude of the line number of each grid cell association for calculating according to below equation The correlation radius answered；Eff_win (i_lat)=25/naber_distance (i_lat)；Wherein, eff_win (i_lat) represents The correlation radius corresponding with the latitude of the line number of grid cell association；

4th calculating sub module, relative with the latitude of the line number of each grid cell association for calculating according to below equation The adaptive sliding dynamic mesh window size answered: eff_win (i_lat)=round (1/eff_win (i_lat))；Wherein, eff_ Grid (i_lat) represents the adaptive sliding dynamic mesh window size corresponding with the latitude of the line number of grid cell association.

Further, in the fusing device of multi-source Ocean Wind-field, the second calculating sub module, including:

First computing unit, for effectively observing the weighted value of data according to the below equation all ocean surface wind speed of calculating:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};$

Second computing unit, effectively observes the fusion sea surface wind of data for calculating all ocean surface wind speed according to below equation Speed data:

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point space-time Coordinate, u_estimateRepresent the interpolating estimation result of interpolation point；Subscript k represents observation data point, (x_k,y_k,t_k) represent k point Spacetime coordinate, w_kRepresent the weight at k point, u_kRepresenting the observation at k point, R and T is correlation radius on room and time respectively Size, N is the quantity at significant figure strong point in correlation radius；w_kBy temporal and spatial correlations radius and interpolation point to observation station Time-space matrix together decides on.

Further, in the fusing device of multi-source Ocean Wind-field, the first calculating sub module, including:

Converting unit, for by each gridding process obtain wait longitude and latitude include air speed data and wind direction data Ocean Wind-field data be converted into U, V data；

3rd computing unit, for utilizing the adaptive sliding dynamic mesh window look-up table made in advance, calculates each respectively The hunting zone of group U, V data.

Further, in the fusing device of multi-source Ocean Wind-field, the second calculating sub module, including:

4th computing unit, for calculating all effective observation including air speed data and wind direction data according to below equation The weighted value of data:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};$

5th computing unit, for calculating all effective observation including air speed data and wind direction data according to below equation U, V data of data:

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point space-time Coordinate, u_estimateRepresent the interpolating estimation result of interpolation point；Subscript k represents observation data point, (x_k,y_k,t_k) represent k point Spacetime coordinate, w_kRepresent the weight at k point, u_kRepresenting the observation at k point, R and T is correlation radius on room and time respectively Size, N is the quantity at significant figure strong point in correlation radius；w_kBy temporal and spatial correlations radius and interpolation point to observation station Time-space matrix together decides on；

6th computing unit, for calculating U, V data according to below equation, obtains merging wind direction of ocean surface data: Blend_wind_dir (i, j)=90 atan2 (V (I, j), U (I, j))/pi*180；Wherein, and blend_wind_dir (i, j) Represent: the fusion wind direction of ocean surface data that the grid cell that line number is i, row number are j includes；J represents the row number that grid cell is corresponding, Arrange the second dimension lower label in number preset group being save mesh data.

The fusing device of a kind of multi-source Ocean Wind-field that the embodiment of the present invention provides, with prior art rely on single satellite Observation data cannot meet high accuracy of observation and compared with the high request of high-spatial and temporal resolution, it can play the association of multi-source satellite With the advantage of observation, built by multi-source satellite remote sensing Ocean Wind-field data and/or the Ocean Wind-field data fusion of analysis meteorology again Fusion Ocean Wind-field data can retain on the premise of Small and Medium Sized characteristic information, be effectively improved covering of Ocean Wind-field data Lid scope and spatial and temporal resolution, can preferably meet numerical weather forecast, marine forecasting research and ocean mesoscale and small scale systems and grind The demand studied carefully.

The fusing device of the multi-source Ocean Wind-field that the embodiment of the present invention is provided can be the specific hardware on equipment or The software being installed on equipment or firmware etc..The device that the embodiment of the present invention is provided, it realizes the technology effect of principle and generation Fruit is identical with preceding method embodiment, for briefly describing, and the not mentioned part of device embodiment part, refer to preceding method and implement Corresponding contents in example.Those skilled in the art is it can be understood that arrive, for convenience and simplicity of description, described above The specific works process of system, device and unit, is all referred to the corresponding process in said method embodiment, the most superfluous at this State.

In embodiment provided by the present invention, it should be understood that disclosed apparatus and method, can be by other side Formula realizes.Device embodiment described above is only that schematically such as, the division of described unit, the most only one are patrolled Volume function divides, and actual can have other dividing mode when realizing, the most such as, multiple unit or assembly can in conjunction with or can To be integrated into another system, or some features can be ignored, or does not performs.Another point, shown or discussed each other Coupling direct-coupling or communication connection can be the INDIRECT COUPLING by some communication interfaces, device or unit or communication link Connect, can be electrical, machinery or other form.

The described unit illustrated as separating component can be or may not be physically separate, shows as unit The parts shown can be or may not be physical location, i.e. may be located at a place, or can also be distributed to multiple On NE.Some or all of unit therein can be selected according to the actual needs to realize the mesh of the present embodiment scheme 's.

It addition, each functional unit in the embodiment that the present invention provides can be integrated in a processing unit, it is possible to Being that unit is individually physically present, it is also possible to two or more unit are integrated in a unit.

If described function is using the form realization of SFU software functional unit and as independent production marketing or use, permissible It is stored in a computer read/write memory medium.Based on such understanding, technical scheme is the most in other words The part contributing prior art or the part of this technical scheme can embody with the form of software product, this meter Calculation machine software product is stored in a storage medium, including some instructions with so that a computer equipment (can be individual People's computer, server, or the network equipment etc.) perform all or part of step of method described in each embodiment of the present invention. And aforesaid storage medium includes: USB flash disk, portable hard drive, read only memory (ROM, Read-Only Memory), random access memory are deposited The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic disc or CD.

It should also be noted that similar label and letter represent similar terms, therefore, the most a certain Xiang Yi in following accompanying drawing Individual accompanying drawing is defined, then need not it be defined further and explains in accompanying drawing subsequently, additionally, term " the One ", " second ", " the 3rd " etc. are only used for distinguishing and describe, and it is not intended that instruction or hint relative importance.

It is last it is noted that the detailed description of the invention of embodiment described above, the only present invention, in order to the present invention to be described Technical scheme, be not intended to limit, protection scope of the present invention is not limited thereto, although with reference to previous embodiment to this Bright it is described in detail, it will be understood by those within the art that: any those familiar with the art In the technical scope that the invention discloses, the technical scheme described in previous embodiment still can be modified or can be light by it It is readily conceivable that change, or wherein portion of techniques feature is carried out equivalent；And these are revised, change or replace, do not make The essence of appropriate technical solution departs from the spirit and scope of embodiment of the present invention technical scheme.All should contain the protection in the present invention Within the scope of.Therefore, protection scope of the present invention should described be as the criterion with scope of the claims.

Claims (10)

1. the fusion method of a multi-source Ocean Wind-field, it is characterised in that described method includes:

Obtain multi-source Ocean Wind-field data；Described multi-source Ocean Wind-field data include: the sea that multiple satellite-borne microwave remote sensors gather Face wind field data and/or multiple meteorological Ocean Wind-field data of analysis again；Wherein, described satellite-borne microwave remote sensor includes satellite-borne microwave Scatterometer and/or satellite-borne microwave radiometer；Described Ocean Wind-field data include: air speed data and/or wind direction data；Described divide again Gassing is to wait the Ocean Wind-field data of longitude and latitude as Ocean Wind-field data；

Enter according to the described Ocean Wind-field data that satellite-borne microwave remote sensor each described is obtained by default spatial and temporal resolution respectively Row gridding processes, and obtains corresponding respectively to the Ocean Wind-field data waiting longitude and latitude of each satellite-borne microwave remote sensor；

Utilize temporal-spatial interpolating algorithm that all of described Ocean Wind-field data waiting longitude and latitude are carried out interpolation calculation, obtain merging sea Face wind field data；Wherein, described fusion Ocean Wind-field data include: wind speed fused data and wind direction fused data.

The fusion method of multi-source Ocean Wind-field the most according to claim 1, it is characterised in that described utilize temporal-spatial interpolating to calculate Before method carries out interpolation calculation to all of described Ocean Wind-field data waiting longitude and latitude, also include:

Utilize linear interpolation method that the Ocean Wind-field data of analysis meteorology again described in each are carried out interpolation processing, obtain described pre- If the Ocean Wind-field data of spatial and temporal resolution.

The fusion method of multi-source Ocean Wind-field the most according to claim 2, it is characterised in that described utilize temporal-spatial interpolating to calculate Method carries out interpolation calculation to all of described Ocean Wind-field data waiting longitude and latitude, obtains merging Ocean Wind-field data, including:

Utilize the adaptive sliding dynamic mesh window look-up table made in advance, calculate each described sea surface wind waiting longitude and latitude described The hunting zone of field data；

Search the Ocean Wind-field data of each described longitude and latitude such as grade of described acquisition respectively in the described hunting zone of its correspondence Interior effectively observes data；

Utilize temporal-spatial interpolating algorithm that all effective observation data are carried out interpolation calculation, obtain merging Ocean Wind-field data.

The fusion method of multi-source Ocean Wind-field the most according to claim 3, it is characterised in that make self adaptation in advance and slide The method of grid window look-up table, including:

The first dimension that traversal standard grid middle latitude is corresponding, calculates the dependent adaptive Sliding mesh window that each latitude is corresponding Size；

According to the dependent adaptive Sliding mesh window size that latitudes all in standard grid are corresponding, make each described warp such as grade The adaptive sliding dynamic mesh window look-up table of the Ocean Wind-field data of latitude.

The fusion method of multi-source Ocean Wind-field the most according to claim 4, it is characterised in that in described traversal standard grid The first dimension that latitude is corresponding, calculates the dependent adaptive Sliding mesh window size that each latitude is corresponding, including:

The latitude that in described standard grid, each grid cell is corresponding: lat_real=i_lat/4-is calculated according to below equation 90.125；Wherein, lat_real represents the latitude that grid cell is corresponding；I_lat represents the line number that grid cell is corresponding, described row It number it is the first dimension lower label in the preset group of save mesh data；

The ball between the center of multiple grid cells adjacent with the line number of described grid cell is calculated according to below equation Identity distance is from naber_distance (i_lat)；

According to the correlation radius that the latitude that below equation calculates with the line number of each grid cell associates is corresponding: eff_win (i_ Lat)=25/naber_distance (i_lat)；Wherein, eff_win (i_lat) expression associates with the line number of grid cell The correlation radius that latitude is corresponding；

The adaptive sliding dynamic mesh window corresponding with the latitude of the line number of each grid cell association is calculated according to below equation Size: eff_win (i_lat)=round (1/eff_win (i_lat))；Wherein, eff_grid (i_lat) represents and grid list The corresponding adaptive sliding dynamic mesh window size of latitude of the line number association of unit.

The fusion method of multi-source Ocean Wind-field the most according to claim 4, it is characterised in that described Ocean Wind-field includes wind During speed data, utilize temporal-spatial interpolating algorithm that all ocean surface wind speed are effectively observed data and carry out interpolation calculation, obtain merging sea Air speed data, including:

Calculate all ocean surface wind speed according to below equation and effectively observe the weighted value of data:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};$

Calculate all ocean surface wind speed according to below equation and effectively observe the fusion ocean surface wind speed data of data:

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point spacetime coordinate, u_estimateRepresent the interpolating estimation result of interpolation point；Subscript k represents observation data point, (x_k,y_k,t_k) represent that the space-time of k point is sat Mark, w_kRepresent the weight at k point, u_kRepresenting the observation at k point, R and T is correlation radius size on room and time respectively, N For the quantity at significant figure strong point in correlation radius；w_kBy the time Ullage temporal and spatial correlations radius and interpolation point to observation station From together deciding on.

The fusion method of multi-source Ocean Wind-field the most according to claim 4, it is characterised in that described Ocean Wind-field includes wind Speed data and during wind direction data, the adaptive sliding dynamic mesh window look-up table that described utilization makes in advance, calculate described each The hunting zone of the described Ocean Wind-field data waiting longitude and latitude, including:

The Ocean Wind-field data including air speed data and wind direction data that each gridding processes the longitude and latitude such as grade obtained are equal Be converted to U, V data；

Utilize the adaptive sliding dynamic mesh window look-up table made in advance, calculate the search model of U, V data described in each group respectively Enclose.

The fusion method of multi-source Ocean Wind-field the most according to claim 7, it is characterised in that utilize temporal-spatial interpolating algorithm pair All effectively observation data including air speed data and wind direction data carry out interpolation calculation, obtain merging wind direction of ocean surface data, bag Include:

According to all weighted values effectively observing data including air speed data and wind direction data of below equation calculating:

$w_k=\frac{2-\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}}{2+\[\frac{{(x_k-x_0)}^2+{(y_k-y_0)}^2}{R^2}\]+\frac{{(t_k-t_0)}^2}{T^2}};$

According to all U, V data effectively observing data including air speed data and wind direction data of below equation calculating:

In formula, subscript 0 represents interpolation point, (x₀,y₀,t₀) represent interpolation point spacetime coordinate, u_estimateRepresent the interpolating estimation result of interpolation point；Subscript k represents observation data point, (x_k,y_k,t_k) represent that the space-time of k point is sat Mark, w_kRepresent the weight at k point, u_kRepresenting the observation at k point, R and T is correlation radius size on room and time respectively, N For the quantity at significant figure strong point in correlation radius；w_kBy the time Ullage temporal and spatial correlations radius and interpolation point to observation station From together deciding on；

According to below equation, described U, V data are calculated, obtain merge wind direction of ocean surface data: blend_wind_dir (i, J)=90 atan2 (V (I, j), U (I, j))/pi*180；Wherein, (i, j) represents blend_wind_dir: line number is i, row number The fusion wind direction of ocean surface data included for the grid cell of j；J represents the row number that grid cell is corresponding, and described row number are storage net Second dimension lower label in the preset group of data of formatting.

9. the fusing device of a multi-source Ocean Wind-field, it is characterised in that described device includes:

Acquisition module, is used for obtaining multi-source Ocean Wind-field data；Described multi-source Ocean Wind-field data include: multiple satellite-borne microwaves are distant The Ocean Wind-field data of sensor collection and/or multiple meteorological Ocean Wind-field data of analysis again；Wherein, described satellite-borne microwave remote sensor Including satellite-borne microwave scatterometer and/or satellite-borne microwave radiometer；Described Ocean Wind-field data include: air speed data and/or wind direction Data；Described meteorology Ocean Wind-field data of analyzing again are to wait the Ocean Wind-field data of longitude and latitude；

Gridding processing module, for according to presetting what satellite-borne microwave remote sensor each described was obtained by spatial and temporal resolution respectively Described Ocean Wind-field data carry out gridding process, obtain corresponding respectively to the sea waiting longitude and latitude of each satellite-borne microwave remote sensor Face wind field data；

First interpolation calculation module, is used for utilizing temporal-spatial interpolating algorithm to enter all of described Ocean Wind-field data waiting longitude and latitude Row interpolation calculates, and obtains merging Ocean Wind-field data；Wherein, described fusion Ocean Wind-field data include: wind speed fused data and Wind direction fused data.

The fusing device of multi-source Ocean Wind-field the most according to claim 9, it is characterised in that described device also includes:

Second interpolation calculation module, is used for utilizing linear interpolation method to enter the Ocean Wind-field data of analysis meteorology again described in each Row interpolation processes, and obtains the Ocean Wind-field data of described default spatial and temporal resolution.