CN107918165B - More satellites based on space interpolation merge Prediction of Precipitation method and system - Google Patents

More satellites based on space interpolation merge Prediction of Precipitation method and system Download PDF

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CN107918165B
CN107918165B CN201610880735.7A CN201610880735A CN107918165B CN 107918165 B CN107918165 B CN 107918165B CN 201610880735 A CN201610880735 A CN 201610880735A CN 107918165 B CN107918165 B CN 107918165B
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precipitation
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satellites
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CN107918165A (en
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马颖钊
杨媛
洪阳
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Tsinghua University
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Abstract

The present invention provides a kind of more satellites fusion Prediction of Precipitation method and system based on space interpolation, and the method includes being known grid and grid to be predicted by region division to be predicted；According to the precipitation data of the surface precipitation data of the known grid and at least two satellites, the more satellites for calculating the known grid merge precipitation parameter；Using spatial interpolation algorithm, precipitation parameter is merged according to more satellites of the known grid, the more satellites for calculating the grid to be predicted merge precipitation parameter；The satellite precipitation data that precipitation parameter, more satellites fusion precipitation parameter of the grid to be predicted, the grid to be predicted and known grid are merged according to more satellites of the known grid, calculates the fusion precipitation value in the region to be predicted.According to the difference that the difference of sampling periods and the ground station select, dynamic characteristic is presented in more satellite fusion precipitation parameters on time and space, so that the result of more satellites fusion Prediction of Precipitation is more accurate.

Description

More satellites based on space interpolation merge Prediction of Precipitation method and system
Technical field
The present invention relates to satellites to merge precipitation technical field, merges precipitation more particularly to more satellites based on space interpolation Prediction technique and system.
Background technique
Precipitation is one of most critical input parameter of atmosphere hydrology model uncertainty, and global moisture and energy circulation Core component.In traditional technology, obtaining precipitation data, there are mainly three types of modes.First is that ground station is observed, using office The result of portion's point sampling observation represents the true precipitation within the scope of even several hundred square kilometres of periphery tens.It is now recognized that earth station The precipitation data of point is most reliable precipitation measurement data, but the observation data of ground station exist obviously in spatial and temporal distributions Discontinuity, it is difficult to the significant Spatial-Temporal Variability of the practical precipitation of reflection, and the distribution Dong Mixi of the ground station of China is thin, Observational data spatio-temporal distribution is also unequal, and the precipitation data quality observed based on ground station is caused to be affected, because This, although ground station observation can precise measurement surface precipitation, be affected by network density and its spatial distribution, especially It is the mountain area relative complex for landform, and existing station net layout is not able to satisfy application demand.Second is that weather radar, weather radar By physical quantity related with precipitation in atmospheric sounding, the continuous precipitation information in space is obtained indirectly, is made up to a certain extent The deficiency of ground station spatial distribution, but weather radar is easy by multifactor impacts such as electronic signal and running environment, such as Terrain shading, radar ray lifting and uncertainty of Z-R (radar reflectivity Z and rainfall intensity R) relationship etc., with a varied topography Area has biggish uncertainty, and it is poor that precision is observed compared with the rainfall gauge of ground station.Third is that meteorological satellite, satellite monitoring it is anti- Precipitation data is drilled with observation scope is wide, time interval is short, the spatial and temporal distributions for independent, discrete ground station observation It is more continuous, it has been increasingly becoming the important tool that rainfall monitoring and hazard forecasting early warning are carried out on global and local scale, The hydrologic research for also lacking survey data basin simultaneously for ground provides the rainfall observation information of great application value.But with weather thunder Up to the same, satellite remote sensing technology is also the indirect operation means to rainfall, by factors such as remote sensing instrument, inversion algorithms It influences, the precision of product is relatively low, has significant uncertain.The retrieving precipitation data estimation error and research of each satellite The factors such as region, type of precipitation, rainy season, surface cover situation, landform are related, have in different space-time uniques respective Advantage and disadvantage.In order to more truly describe actual changes and precipitation, need to merge the satellite of preferably capture precipitation field spatial and temporal distributions Retrieving precipitation data comprehensively consider the error characteristics and observation advantage of different satellites.
More satellites fusion precipitation method more traditional at present has: simple arithmetic mean, removal maximum deviation etc..Wherein, Simple arithmetic mean is easily understood, and is one kind quickly by the method for the retrieving precipitation data fusion of each satellite, but this method is recognized For each Satellite Product data weight having the same at different times, the ability for capturing precipitation is identical, this and actual conditions It is not consistent.Maximum deviation method is removed first to reject the maximum satellite Retrieval precipitation data of deviation, it is then anti-to remaining satellite It drills precipitation data and carries out arithmetic average, consider differing for the performance of different satellite Retrieval precipitation to a certain extent, but to surplus When remaining satellite Retrieval precipitation data carries out arithmetic average, the problem of equally existing arithmetic average method.
Further, more satellites fusion Prediction of Precipitation method based on space interpolation more traditional at present is to utilize tradition Satellite merge precipitation method, calculated satellite fusion precipitation data is directly used in region to be predicted, but due to traditional Satellite merges precipitation method, and there are biggish errors, and can not embody the space-time expending of precipitation, lead to prediction result accuracy rate It is too low.
How to make the prediction result of more satellite fusion precipitation more accurate, obtains high-precision and high-spatial and temporal resolution defend more Star fusion Prediction of Precipitation is as a result, be satellite fusion Prediction of Precipitation field technical problem urgently to be resolved.
Summary of the invention
Based on this, it is necessary to for the forecasting problem of more satellites fusion precipitation, provide a kind of based on space interpolation more and defend Star merges Prediction of Precipitation method, which comprises
It is known grid and grid to be predicted by region division to be predicted, the known grid is to include surface precipitation data Grid, the grid to be predicted be the grid not comprising surface precipitation data；
According to the precipitation data of the surface precipitation data of the known grid and at least two satellites, the Hownet is calculated More satellites of lattice merge precipitation parameter, and more satellites fusion precipitation parameter of the known grid includes the fusion weight system of each satellite Number；
Using spatial interpolation algorithm, precipitation parameter is merged according to more satellites of the known grid, is calculated described to be predicted More satellites of grid merge precipitation parameter；
More satellites fusion precipitation ginseng of precipitation parameter, the grid to be predicted is merged according to more satellites of the known grid The satellite precipitation data of several, the described grid to be predicted and known grid calculates the fusion precipitation value in the region to be predicted.
It is described in one of the embodiments, to utilize spatial interpolation algorithm, it is merged according to more satellites of the known grid Precipitation parameter, the more satellites for calculating the grid to be predicted merge precipitation parameter, comprising:
Using spatial interpolation algorithm golden in common gram, precipitation parameter is merged according to more satellites of the known grid, is calculated The interpolation weights coefficient of the grid to be predicted；
The interpolation weights coefficient of precipitation parameter and the grid to be predicted, meter are merged according to more satellites of the known grid Calculate more satellites fusion precipitation parameter of the grid to be predicted.
It is described using spatial interpolation algorithm golden in common gram in one of the embodiments, according to the known grid More satellites merge precipitation parameter, calculate the interpolation weights coefficient of the grid to be predicted, comprising:
Keep the error between the actual value and estimated value of more satellites fusion precipitation parameter of each known grid minimum, and The difference of desired value is equal to 0.
The surface precipitation data according to the known grid and at least two satellites in one of the embodiments, Precipitation data, the more satellites for calculating the known grid merge precipitation parameter, and more satellite fusion precipitation parameters include respectively defending The fusion weight coefficient of star, comprising:
Sampling periods are obtained, the sampling periods include each year in N preset period of time, each preset period of time Duration was less than 1 year, and wherein N is positive integer；
The precipitation data of the surface precipitation data and at least two satellites in the sampling periods is obtained respectively；
Determine the prior probability of each satellite；
According to the prior probability of each satellite, the precipitation data of the surface precipitation data and each satellite, utilize Probability statistics model calculates more satellites and merges precipitation parameter.
It is described according to the prior probability of each satellite, the surface precipitation data and institute in one of the embodiments, The precipitation data for stating each satellite calculates more satellites using probability statistics model and merges precipitation parameter, comprising:
According to the Posterior distrbutionp of the posterior probability of the precipitation data of each satellite and more satellites fusion precipitation, determine defend more The probability density function of star fusion precipitation, wherein the posterior probability of the precipitation data of each satellite is the precipitation number of each satellite According to the posterior probability under the surface precipitation data qualification, the Posterior distrbutionp of more satellite fusion precipitation is based on described each The Posterior distrbutionp of more satellites fusion precipitation under the precipitation data of satellite and the surface precipitation data qualification；
The probability density function of more satellite fusion precipitation is iterated calculating, determines more satellite fusion precipitation Parameter.
The probability density function by more satellite fusion precipitation is iterated meter in one of the embodiments, It calculates, determines more satellite fusion precipitation parameters, comprising:
The precipitation data of the surface precipitation data and each satellite is subjected to normal distribution conversion respectively, obtains ground The normal distribution precipitation data of normal distribution precipitation data and each satellite；
According to the ground normal distribution precipitation data, the normal distribution precipitation data of each satellite, defended described more The probability density function of star fusion precipitation is iterated calculating using EM algorithm, the precipitation of each satellite after determining optimization The posterior probability of data；
The posterior probability of the precipitation data of each satellite after the optimization is determined as to the fusion weight system of each satellite Number.
The normal state according to the ground normal distribution precipitation data, each satellite in one of the embodiments, It is distributed precipitation data, the probability density function of more satellite fusion precipitation is iterated calculating using EM algorithm, The posterior probability of the precipitation data of each satellite after determining optimization, comprising:
According to the precipitation data of each satellite and the surface precipitation data, the precipitation data of each satellite is calculated Error；
By the error of the precipitation data of each satellite, the posterior probability of the precipitation data of each satellite, it is determined as institute State the initial parameter sets to be asked in the probability density function of more satellite fusion precipitation；
According to the set of the parameter initially to be asked, the normal state of the ground normal distribution precipitation data and each satellite It is distributed precipitation data, determines the log-likelihood function of the parameter sets initially to be asked；
Iterate to calculate the log-likelihood function and determine the maximum likelihood value of the log-likelihood function, according to it is described most Maximum-likelihood value determines the parameter sets to be asked after optimization, and after the optimization includes each satellite after optimizing wait seek parameter sets The posterior probability of precipitation data.
In one of the embodiments, the method also includes:
More satellites merge precipitation parameter, further include the control information of more satellite fusion precipitation；
According to the parameter sets to be asked after the optimization, the side of the probability density function of more satellite fusion precipitation is calculated Difference；
The variance of the probability density function of more satellite fusion precipitation is determined as to the mistake of more satellite fusion precipitation Poor information.
More satellites provided by the present invention based on space interpolation merge Prediction of Precipitation method, by more satellites of known grid Precipitation parameter is merged, using space arithmetic, more satellites fusion precipitation parameter of grid to be predicted is calculated, further according to grid to be predicted More satellites fusion precipitation parameter calculate the fusion precipitation value in region to be predicted, can be to avoid traditional based on the more of space interpolation Satellite merges in Prediction of Precipitation method, and the fusion precipitation value of known grid is directly carried out prediction result caused by space interpolation and is missed The excessive problem of difference improves the accuracy of more satellite fusion Prediction of Precipitation.
In one embodiment of the invention, the difference selected according to the difference of sampling periods and the ground station, is obtained The more satellites fusion precipitation parameter taken is not also identical, and more satellite fusion precipitation parameters present dynamic special on time and space Property, every satellite error as caused by monitoring time or the difference in space is excluded to the greatest extent, but also more satellites melt The result for closing Prediction of Precipitation is more accurate.
The present invention also provides a kind of, and more satellites based on space interpolation merge Prediction of Precipitation system, comprising:
Grid dividing module, for being known grid and grid to be predicted, the known grid by region division to be predicted For the grid comprising surface precipitation data, the grid to be predicted is the grid not comprising surface precipitation data；
Known grid computing module, for according to the surface precipitation data of the known grid and the drop of at least two satellites Water number evidence, the more satellites for calculating the known grid merge precipitation parameter, and more satellites of the known grid merge precipitation parameter Fusion weight coefficient including each satellite；
Spatial interpolation module merges precipitation ginseng according to more satellites of the known grid for utilizing spatial interpolation algorithm Number, the more satellites for calculating the grid to be predicted merge precipitation parameter；
Prediction of Precipitation module, for merging precipitation parameter, the grid to be predicted according to more satellites of the known grid More satellites fusion precipitation parameter, the grid to be predicted and known grid satellite precipitation data, calculate the area to be predicted The fusion precipitation value in domain.
The spatial interpolation module in one of the embodiments, comprising:
Interpolation weights coefficient submodule, for utilizing golden spatial interpolation algorithm in common gram, according to the known grid More satellites merge precipitation parameter, calculate the interpolation weights coefficient of the grid to be predicted；
Grid submodule to be predicted, for merging precipitation parameter and described to be predicted according to more satellites of the known grid The interpolation weights coefficient of grid, the more satellites for calculating the grid to be predicted merge precipitation parameter.
The interpolation weights coefficient submodule in one of the embodiments, is specifically used for:
The interpolation weights coefficient is calculated, the interpolation weights coefficient meets the more satellites fusion for making each known grid Error between the actual value and estimated value of precipitation parameter is minimum, and the difference of desired value is equal to 0.
The known grid computing module in one of the embodiments, comprising:
Period samples submodule, for obtaining sampling periods, when the sampling periods include each year default in N Section, the duration of each preset period of time was less than 1 year, and wherein N is positive integer；
Precipitation data acquisition submodule, for obtaining surface precipitation data in the sampling periods and at least two respectively The precipitation data of satellite；
Prior probability determines submodule, for determining the prior probability of each satellite；
Precipitation parameter computational submodule, for according to the prior probability of each satellite, the surface precipitation data and institute The precipitation data for stating each satellite calculates more satellites using probability statistics model and merges precipitation parameter.
The precipitation parameter computational submodule in one of the embodiments, comprising:
Probability density function determination unit, for being melted according to the posterior probability and more satellites of the precipitation data of each satellite The Posterior distrbutionp for closing precipitation determines the probability density function of more satellite fusion precipitation, wherein the precipitation data of each satellite Posterior probability is posterior probability of the precipitation data of each satellite under the surface precipitation data qualification, more satellite fusion drops The Posterior distrbutionp of water is that more satellites under the precipitation data based on each satellite and the surface precipitation data qualification merge drop The Posterior distrbutionp of water；
Precipitation parameter computing unit is merged, based on being iterated the probability density function of more satellite fusion precipitation It calculates, determines more satellite fusion precipitation parameters.
The fusion precipitation parameter computing unit in one of the embodiments, comprising:
Normal distribution conversion subunit, for by the precipitation data of the surface precipitation data and each satellite respectively into Row normal distribution conversion, obtains the normal distribution precipitation data of ground normal distribution precipitation data and each satellite；
Greatest hope computation subunit, for the normal state according to the ground normal distribution precipitation data, each satellite It is distributed precipitation data, the probability density function of more satellite fusion precipitation is iterated calculating using EM algorithm, The posterior probability of the precipitation data of each satellite after determining optimization；And by the posteriority of the precipitation data of each satellite after the optimization Determine the probability is the fusion weight coefficient of each satellite.
The greatest hope computation subunit in one of the embodiments, comprising:
Error computation component, for according to each satellite precipitation data and the surface precipitation data, described in calculating The error of the precipitation data of each satellite；
Initially parameter obtaining component to be asked, for by the drop of the error of the precipitation data of each satellite, each satellite The posterior probability of water number evidence, the initial parameter sets to be asked being determined as in the probability density function of more satellite fusion precipitation；
Likelihood function securing component, for the set according to the parameter initially to be asked, the ground normal distribution precipitation The normal distribution precipitation data of data and each satellite determines the log-likelihood function of the parameter sets initially to be asked；
Maximum likelihood value securing component, for iterating to calculate the log-likelihood function and determining the log-likelihood function Maximum likelihood value, the parameter sets to be asked after optimization, parameter to be asked after the optimization are determined according to the maximum likelihood value Set includes the posterior probability of the precipitation data of each satellite after optimization.
In one of the embodiments, further include:
The known grid computing module is also used to calculate more satellites fusion precipitation parameter of the known grid, described More satellites fusion precipitation parameter of known grid further includes the control information of more satellite fusion precipitation；
The greatest hope computation subunit, further includes:
Variance computation module, for calculating more satellites and merging precipitation according to the parameter sets to be asked after the optimization Probability density function variance；
Control information determines component, for the variance of the probability density function of more satellite fusion precipitation to be determined as institute State the control information of more satellite fusion precipitation.
More satellites provided by the present invention based on space interpolation merge Prediction of Precipitation system, by more satellites of known grid Precipitation parameter is merged, using space arithmetic, more satellites fusion precipitation parameter of grid to be predicted is calculated, further according to grid to be predicted More satellites fusion precipitation parameter calculate the fusion precipitation value in region to be predicted, can be to avoid traditional based on the more of space interpolation Satellite merges in Prediction of Precipitation method, and the fusion precipitation value of known grid is directly carried out prediction result caused by space interpolation and is missed The excessive problem of difference improves the accuracy of more satellite fusion Prediction of Precipitation.
In one embodiment of the invention, the difference selected according to the difference of sampling periods and the ground station, is obtained The more satellites fusion precipitation parameter taken is not also identical, and more satellite fusion precipitation parameters present dynamic special on time and space Property, every satellite error as caused by monitoring time or the difference in space is excluded to the greatest extent, but also more satellites melt The result for closing Prediction of Precipitation is more accurate.
Detailed description of the invention
Fig. 1 is the flow diagram of more satellites fusion Prediction of Precipitation method based on space interpolation in one embodiment；
Fig. 2 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in another embodiment Figure；
Fig. 3 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in another embodiment Figure；
Fig. 4 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in further embodiment Figure；
Fig. 5 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in further embodiment Figure；
Fig. 6 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in further embodiment Figure；
Fig. 7 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in further embodiment Figure；
Fig. 8 is the structural schematic diagram of more satellites fusion Prediction of Precipitation system based on space interpolation in one embodiment；
Fig. 9 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in another embodiment Figure；
Figure 10 is the structural representation that Prediction of Precipitation system is merged by more satellites based on space interpolation in one embodiment Figure；
Figure 11 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in further embodiment Figure；
Figure 12 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in further embodiment Figure；
Figure 13 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in further embodiment Figure；
Figure 14 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in further embodiment Figure.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, right with reference to the accompanying drawings and embodiments The present invention is based on more satellites of space interpolation fusion Prediction of Precipitation method and system to be further elaborated.It should be appreciated that Described herein specific examples are only used to explain the present invention, is not intended to limit the present invention.
Fig. 1 is the flow diagram of more satellites fusion Prediction of Precipitation method based on space interpolation in one embodiment, More satellites based on space interpolation as shown in Figure 1 merge Prediction of Precipitation method, comprising:
Region division to be predicted is known grid and grid to be predicted by step S10000, the known grid be comprising The grid of surface precipitation data, the grid to be predicted are the grid not comprising surface precipitation data.
Specifically, the region to be predicted is the region comprising ground station, and the quantity for the ground station for being included is got over More, the result of prediction is more accurate.
Step S20000, according to the precipitation data of the surface precipitation data of the known grid and at least two satellites, meter More satellites fusion precipitation parameter of the known grid is calculated, more satellites fusion precipitation parameter of the known grid includes each satellite Fusion weight coefficient.
Specifically, according to the satellite precipitation data of the surface precipitation data of the known grid and at least two satellites, benefit With probability statistics algorithm, more satellite fusion precipitation parameters, such as the fusion weight coefficient of each satellite, the fusion weight are calculated Coefficient is the optimal weights coefficient by iterative calculation.
It is described to defend more satellite fusion precipitation parameters in one embodiment provided by the present invention, it further include that more satellites melt Close the control information of precipitation.The control information can be further improved the accuracy of more satellite fusion Prediction of Precipitation.
Step S30000 merges precipitation parameter according to more satellites of the known grid, calculates using spatial interpolation algorithm More satellites of the grid to be predicted merge precipitation parameter.
Step S40000 merges precipitation parameter according to more satellites of the known grid, grid to be predicted defend more Star merges the satellite precipitation data of precipitation parameter, the grid to be predicted and known grid, calculates melting for the region to be predicted Close precipitation value.
Specifically, the weight space distribution situation of all known grids and grid to be predicted using region to be predicted, it will More satellites of the known grid and grid to be predicted fusion precipitation parameter is weighted summation process, obtains after further calculating The fusion precipitation value in region to be predicted.
The satellite fusion Prediction of Precipitation method of space interpolation is based on provided by the present embodiment, it will be by by known grid More satellites fusion precipitation parameter be interpolated into grid to be predicted, obtain region to be predicted more satellites fusion precipitation parameter weight Spatial distribution further calculates the fusion precipitation value in region to be predicted, avoids the fusion precipitation value directly by known grid It is interpolated into the problem of error brought by region to be predicted increases, improves the accuracy rate of more satellite fusion Prediction of Precipitation.
Fig. 2 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in another embodiment Figure, more satellites based on space interpolation as shown in Figure 2 merge Prediction of Precipitation method, comprising:
Step S31000 is merged according to more satellites of the known grid and is dropped using spatial interpolation algorithm golden in common gram Water parameter calculates the interpolation weights coefficient of the grid to be predicted.
Specifically, the assumed condition of ordinary kriging interpolation is, space attribute be it is uniform, for any in space Point has same desired value and variance.
Precipitation parameter is merged according to more satellites of the known grid, calculates interpolation weights coefficient using formula (1).
Wherein,The estimated value of precipitation parameter, Z are merged for the more satellites of known gridiFor the more satellite fusion drops of known grid The actual value of water parameter, n are the number of known grid, λiFor interpolation weights coefficient.
The interpolation weights coefficient lambda being calculated according to formula (1)iBe one include distance and known grid between sky Between relationship functional form.
The interpolation weights coefficient lambdaiMake the actual value and estimation of more satellites fusion precipitation parameter of each known grid Error between value is minimum, and the difference of desired value is equal to 0, that is, meets formula (2) and formula (3).
Step S32000 merges the interpolation of precipitation parameter and the grid to be predicted according to more satellites of the known grid Weight coefficient, the more satellites for calculating the grid to be predicted merge precipitation parameter.
Specifically, likewise, merging precipitation parameter and the known interpolation weight according to more satellites of the known grid Weight coefficient lambdai, can use more satellites fusion precipitation parameter that the formula (1) calculates grid to be predicted.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation method, empty by gold in common gram Value-based algorithm is interleave, merges precipitation parameter using more satellites of known grid, calculates more satellites fusion precipitation ginseng of grid to be predicted Number, the mutual spatial relationship between the distance between grid known to majority and grid to be predicted and the known grid is equal It is considered, the accuracy of more satellites fusion precipitation parameter of the grid to be predicted after ensure that interpolation calculation.
Fig. 3 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in another embodiment Figure, more satellites based on space interpolation as shown in Figure 3 merge Prediction of Precipitation method, comprising:
Step S21000 obtains sampling periods, and the sampling periods include each year in N preset period of time, Mei Gesuo The duration of preset period of time is stated less than 1 year, wherein N is positive integer.
Specifically, carrying out the prediction of precipitation using the method for more satellites fusion precipitation.Provided by the present invention more Satellite merges in precipitation method, according to the different time precisions of the satellite precipitation data, can be used for predicting hour precipitation, The precipitation of the different periods such as intra day ward, monthly total precipitation, it is subsequent specifically to be illustrated using intra day ward.Due to drop Water has the characteristic of Time Continuous, and the general antecedent precipitation amount using prediction day predicts the precipitation of prediction day.Drop Periodic characteristic is presented according to year in water, of the present invention default if the precipitation in 1 year is distributed in spring or summer more Period refers to the preset period using year as the fixation in period, such as annual March 1 on March 31 this period, or annual March 1 before continuous 40 days etc., it is further all fixed in selected n-th year after having selected 1 year preset period of time Select this preset period of time.
Again due to the uncertainty of precipitation, it will appear abnormal fluctuation in the precipitation in certain times, to precipitation Daily prediction in, can to the data in the Abnormal Precipitation time not calculate, when selecting n-th year, according to need It asks, can continuously select, can also discontinuously select.
In one embodiment of the invention, for selected prediction day as October 1 was predicted within 2000 when, selection It is continuous before continuous 40 days before on October 1st, 2000, continuous 40 days before on October 1st, 1999 and on October 1st, 1998 It 40 days, is used as sampling periods within totally 120 days, also may be selected before on October 1st, 1999 continuous 40 days, before on October 1st, 1998 It continuous 40 days and continuous 40 days before on October 1st, 1996, need to be depending on actual conditions.
That is, for needs predict t day 1 year, selection t-x days 1 year to t-1 days 1 year, (n-1)th year the T-x days to t-1 days (n-1)th year, t-1 days t-x days to the n-th -2 years the n-th -2 years (x is the sample length chosen every year), Amount to 3x sampling day；Or, choosing t-x days to t-1 days 1 year 1 year, t-x days to the n-th -2 years the n-th -2 years t-1 Day, t-1 days t-x days to the n-th -4 years the n-th -4 years (x is the sample length chosen every year) amount to 3x sampling day.
Step S22000 obtains the precipitation number of the surface precipitation data and at least two satellites in the sampling periods respectively According to.
Specifically, needing to obtain the drop of surface precipitation data and at least two satellites respectively after sampling periods have been determined Water number evidence, to carry out satellite fusion precipitation.
The surface precipitation data, are generally from ground station, can be straight as needed have ground station in the region predicted Connect use, such as do not have ground station, can also be used the ground station of surrounding precipitation data handled after use.Describedly Face precipitation data is the true value of precipitation.
The selection of the ground station can generate large effect to the calculating of subsequent more satellite fusion precipitation, such as one In the region that piece needs to predict, according to the geographic properties in region, the precipitation data of representative ground station is selected, or to the greatest extent Amount selection is predicted comprising the region of ground station, can all make fused precipitation result more accurate.
The precipitation data of the satellite is the retrieving precipitation data of the satellite after primary Calculation.Since difference is defended The inversion algorithm of star, satellite precision are different, and the error of the retrieving precipitation data of satellite is also by institute estimation range, type of precipitation, drop The influence of water period, vegetation cover, landform etc. and it is different, need the exclusion as far as possible in subsequent calculating so that fusion Precipitation result afterwards is more accurate.
Step S23000 determines the prior probability of each satellite.
Specifically, the prior probability of determination each satellite, including according to surface precipitation data and described each The precipitation data of satellite calculates the prior probability of each satellite, or presets the prior probability of each satellite.In general, described each The sum of prior probability of satellite is equal to 1.
In actual use, for convenience of calculating, equal prior probability can be preset for each satellite, such as shares 4 and defends The prior probability of star, every satellite is equal to 1/4=0.25.
The priori of each satellite can also be calculated according to the precipitation data of the surface precipitation data and each satellite Probability, such as prior probability=(difference of each satellite and surface precipitation data)/each satellite of each satellite and ground drop The sum of the difference of water number evidence).
Step S24000, according to the prior probability of each satellite, the drop of the surface precipitation data and each satellite Water number evidence calculates more satellites using probability statistics model and merges precipitation parameter.
Specifically, the probability statistics model, generally calculates melting for each satellite by iterative calculation using Bayesian model Weight coefficient is closed, the fusion weight coefficient is the optimal weights coefficient by iterative calculation.It is true with surface precipitation data It is worth, after the fusion weight coefficient for determining each satellite, the precipitation data of more satellites is subjected to fusion calculation, is obtained closest The fusion precipitation result of true value.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation method, not according to sampling periods Together and the ground station selection difference, each satellite fusion weight coefficient calculated result it is also not identical, each satellite Fusion weight coefficient dynamic characteristic is presented on time and space, exclude to the greatest extent every satellite due to monitoring when Between or the difference in space caused by error so that more satellites fusion Prediction of Precipitation result it is more accurate.
Fig. 4 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in further embodiment Figure, more satellites fusion Prediction of Precipitation method based on space interpolation as shown in Figure 4 include:
Step S24100, according to the posteriority point of the posterior probability of the precipitation data of each satellite and more satellites fusion precipitation Cloth determines the probability density function of more satellite fusion precipitation, wherein the posterior probability of the precipitation data of each satellite is respectively to defend Posterior probability of the precipitation data of star under the surface precipitation data qualification, the Posterior distrbutionp that more satellites merge precipitation are The Posterior distrbutionp of more satellites fusion precipitation under precipitation data and the surface precipitation data qualification based on each satellite.
Specifically, the probability density function of the satellite fusion precipitation determined is expressed as follows with formula (4):
wk=p (fk| D) formula (5)
Wherein, y is fused precipitation, and D=[y1obs, y2obs ..., yTobs] is the ground precipitation number of sampling periods T It is the precipitation data of different satellites (or radar) according to, fk=[f1, f2 ..., fK], K is the number of satellite, p (fk | D) it is respectively to defend Posterior probability of the precipitation data of star under the surface precipitation data qualification, and P (y | fk, D) it is the drop based on each satellite The Posterior distrbutionp of more satellites fusion precipitation under water number evidence and the surface precipitation data qualification, and p (y | D) it is based on the ground The probability of more satellites fusion precipitation under the conditions of precipitation data.
Specifically, formula (4) need to meet the condition of formula (5) and formula (6) setting.
The probability density function of more satellite fusion precipitation is iterated calculating, determined described more by step S24200 Satellite merges precipitation parameter.
Specifically, relevant parameter information (such as weight coefficient), the fusion results due to Bayes's multiple-model integration cannot It immediately arrives at, the probability density function of above-mentioned more satellite fusion precipitation in actual operation, needs to acquire most by continuous iteration Excellent solution.P (the f obtained after iterative calculationk| D), i.e., the precipitation data of each satellite is under the surface precipitation data qualification Posterior probability, the fusion weight coefficient of as described each satellite.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation method, are given using Bayesian model The formula of probability density function of satellite fusion precipitation out determines the precipitation data of each satellite in surface precipitation number by iterative calculation Posterior probability under the conditions of, so that it is determined that the fusion weight coefficient of each satellite.The present embodiment is true with surface precipitation data Value, comprehensively considers the internal association between data, at any time the difference of sampling periods and surface precipitation data, each satellite provided It is also different to merge weight coefficient, makes fused precipitation to the greatest extent close to true value, it is pre- to improve the more satellite fusion precipitation The accuracy of survey.
Fig. 5 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in further embodiment Figure, more satellites fusion Prediction of Precipitation method based on space interpolation as shown in Figure 5 include:
The precipitation data of the surface precipitation data and each satellite is carried out normal distribution respectively and turned by step S24210 It changes, obtains the normal distribution precipitation data of ground normal distribution precipitation data and each satellite.
Specifically, when being iterated calculating to formula (4), being mostly used most since precipitation data does not meet normal distribution Big Expectation Algorithm, and using the premise of EM algorithm is its data fit normal distribution.Therefore, it is necessary to by surface precipitation number Normal distribution conversion is carried out respectively according to the precipitation data with each satellite.
The present embodiment uses Box-Cox conversion regime, using formula (7) to the precipitation number of ground precipitation data and each satellite According to progress normal distribution conversion.
Wherein, τ is constant, and y is fused precipitation, ZtFused precipitation after turning for normal state.
Step S24220, according to the ground normal distribution precipitation data, the normal distribution precipitation data of each satellite, The probability density function of more satellite fusion precipitation is iterated calculating using EM algorithm, it is each after determining optimization The posterior probability of the precipitation data of satellite.
Specifically, the precipitation data of surface precipitation data and each satellite after normal distribution is converted is utilized maximum Expectation Algorithm is iterated calculating.The calculating Bayes that the algorithm is built upon the hypothesis of the equal Normal Distribution of K model is more The most effectual way of model integrated.
The posterior probability of the precipitation data of each satellite after the optimization is determined as each satellite by step S24230 Merge weight coefficient.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation method, are given using Bayesian model The precipitation data of surface precipitation data and each satellite is carried out normal distribution by the formula of probability density function of satellite fusion precipitation out After conversion, the fusion weight coefficient of each satellite is calculated using EM algorithm.When calculating data, in conjunction with being used Algorithm carry out data processing, fused precipitation can be made to the greatest extent close to true value, melted to improve more satellites Close the accuracy of Prediction of Precipitation.
Fig. 6 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in further embodiment Figure, more satellites as shown in FIG. 6 based on space interpolation merge Prediction of Precipitation method, comprising:
Step S24221 calculates each satellite according to the precipitation data of each satellite and the surface precipitation data Precipitation data error.
Specifically, calculating σ2, σ2For the variance of the opposite ground precipitation data of precipitation data of each satellite.
Step S24222, the posteriority of the error of the precipitation data of each satellite, the precipitation data of each satellite is general Rate, the initial parameter sets to be asked being determined as in the probability density function of more satellite fusion precipitation.
Specifically, with θ={ wk, σ2, k=1,2.., K } and it indicates in the probability density function of more satellite fusion precipitation Parameter sets to be asked, wherein σ2For the error of the precipitation data of each satellite, WkAfter precipitation data for each satellite Test probability.
Step S24223, according to the set of the parameter initially to be asked, the ground normal distribution precipitation data and described The normal distribution precipitation data of each satellite determines the log-likelihood function of the parameter sets initially to be asked.
Specifically, the log-likelihood function of the parameter sets to be asked is formula (8),
Wherein, fkFor the precipitation data of different satellites, wkFor the posterior probability of the precipitation data of each satellite,Expression mean value is fk, variance isNormal distribution.
Step S34224 iterates to calculate the log-likelihood function and determines the maximum likelihood of the log-likelihood function Value determines the parameter sets to be asked after optimization according to the maximum likelihood value, and the parameter sets to be asked after the optimization include excellent The posterior probability of the precipitation data of each satellite after change.
Specifically, be difficult to acquire the analytic solutions of θ hardly possible by formula (8), and expectation-maximization algorithm can by expectation and most Two step of bigization iterates until convergence, obtains maximum likelihood value, to obtain the numerical solution of θ.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation method, furthermore present satellite Merge the calculating process of the formula of probability density function of precipitation, used normal distribution conversion and EM algorithm, equal energy Enough guarantee that more satellite fusion Prediction of Precipitation results are more accurate.
Step S24224 includes: in one of the embodiments,
Step 1, according to each preset initial weight value of satellite, the drop of the surface precipitation data and each satellite Water number evidence calculates the initial variance of the precipitation data of each satellite.
Specifically, firstly, setting primary iteration number i=1, the initial weight value of each satellite are wk=1/k, each satellite The initial variance of precipitation data is formula (9):
Step 2, according to the initial weight value and the initial variance, the initial likelihood of the log-likelihood function is calculated Value.
Specifically, calculating initial likelihood value according to above-mentioned formula (8).
Step 3, according to the precipitation data of the satellite, the variance and the surface precipitation data, hidden variable is determined.
Specifically, introducing a hidden variable, formula (10) is determinedFor hidden variable.
Step 4, the hidden variable is iterated calculating, the hidden variable after obtaining iteration.
Specifically, by the number of iterations i+1, after calculating iteration according to formula (10)
Step 5, by the hidden variable after the iteration, the iteration weighted value and iteration variance of each satellite are calculated.
Specifically, indicating the iteration weighted value of each satellite with formula (11), changing for each satellite is indicated with formula (12) For variance.
Step 6, according to the iteration weighted value and iteration variance, the likelihood value of the log-likelihood function is calculated.
Specifically, iteration weighted value and iteration variance are substituted into formula (8), the likelihood value of log-likelihood function is calculated.
Step 7, according to the likelihood value and preset threshold value, the maximum likelihood value of the log-likelihood function is determined.
Specifically, convergence is examined, if l (θ) by continuous iterationi-l(θ)i-1Less than or equal to preset threshold value, then Determine that the likelihood value after current iteration calculates is the maximum likelihood value of the log-likelihood function.
More satellites fusion Prediction of Precipitation method based on space interpolation that the present embodiment provides, gives specific base area Face precipitation data and to satellite precipitation data carry out log-likelihood function maximum likelihood value calculating step, pass through iteration When calculating, so that the calculated result of more satellites fusion Prediction of Precipitation is more accurate.
Fig. 7 is the process signal of more satellites fusion Prediction of Precipitation method based on space interpolation in further embodiment Figure, more satellites based on space interpolation as shown in Figure 7 merge Prediction of Precipitation method, comprising:
Step S24221 calculates each satellite according to the precipitation data of each satellite and the surface precipitation data Precipitation data error.
Step S24222, the posteriority of the error of the precipitation data of each satellite, the precipitation data of each satellite is general Rate, the parameter sets to be asked being determined as in the probability density function of more satellite fusion precipitation.
Step S24223 the ground normal distribution precipitation data and described is respectively defended according to the set of the parameter to be asked The normal distribution precipitation data of star determines the log-likelihood function of the parameter sets to be asked.
Step S24224 iterates to calculate the log-likelihood function and determines the maximum likelihood of the log-likelihood function Value determines the parameter sets to be asked after optimization according to the maximum likelihood value, and the parameter sets to be asked after the optimization include excellent The posterior probability of the precipitation data of each satellite after change.
Step S24225 calculates the probability of more satellite fusion precipitation according to the parameter sets to be asked after the optimization The variance of density function；The variance of the probability density function of more satellite fusion precipitation is determined as more satellite fusion drops The control information of water.
Specifically, more satellites provided by the present embodiment merge precipitation parameter, it further include more satellite fusion precipitation Control information.
The mean value of the probability density function of more satellite fusion precipitation provided by the present embodiment is formula (13), and variance is public affairs Formula (14).
Bayes's multiple-model integration the result is that multiple single models continue to optimize after result of weighted average, the mean value For fused precipitation, the variance is the control information that more satellites merge precipitation.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation method, by providing fusion precipitation Variance, that is, merge precipitation control information, give judgement fusion precipitation accuracy probability statistics as a result, facilitate into One step improves the accuracy of more satellite fusion Prediction of Precipitation.
The structural schematic diagram for more satellites fusion Prediction of Precipitation system based on space interpolation that the following are provided by the present invention, Device provided by each embodiment is device corresponding to sending method provided by the present invention, heretofore described method institute The detailed description of corresponding each embodiment is suitable for corresponding device accordingly, repeats no more.
Fig. 8 is the structural schematic diagram of more satellites fusion Prediction of Precipitation system based on space interpolation in one embodiment, As Fig. 8 be shown in more satellites based on space interpolation merge Prediction of Precipitation system, comprising:
Grid dividing module 1000, it is described known for being known grid and grid to be predicted by region division to be predicted Grid is the grid comprising surface precipitation data, and the grid to be predicted is the grid not comprising surface precipitation data；
Known grid computing module 2000, for the surface precipitation data and at least two satellites according to the known grid Precipitation data, the more satellites for calculating the known grid merge precipitation parameter, and more satellites fusion precipitation parameters include each The fusion weight coefficient of satellite；
Spatial interpolation module 3000 is merged according to more satellites of the known grid and is dropped for utilizing spatial interpolation algorithm Water parameter, the more satellites for calculating the grid to be predicted merge precipitation parameter；
Prediction of Precipitation module 4000, for merging precipitation parameter, described to be predicted according to more satellites of the known grid More satellites of grid merge the satellite precipitation data of precipitation parameter, the grid to be predicted and known grid, calculate described to pre- Survey the fusion precipitation value in region.
The satellite fusion Prediction of Precipitation system of space interpolation is based on provided by the present embodiment, it will be by by known grid More satellites fusion precipitation parameter be interpolated into grid to be predicted, obtain region to be predicted more satellites fusion precipitation parameter weight Spatial distribution further calculates the fusion precipitation value in region to be predicted, avoids the fusion precipitation value directly by known grid It is interpolated into the problem of error brought by region to be predicted increases, improves the accuracy rate of more satellite fusion Prediction of Precipitation.
Fig. 9 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in another embodiment Figure, more satellites based on space interpolation as shown in Figure 9 merge Prediction of Precipitation system, comprising:
Interpolation weights coefficient submodule 3100, for utilizing golden spatial interpolation algorithm in common gram, according to the Hownet More satellites of lattice merge precipitation parameter, calculate the interpolation weights coefficient of the grid to be predicted；
Grid submodule 3200 to be predicted, for according to more satellites of the known grid merge precipitation parameter and it is described to The interpolation weights coefficient of predicted grid, the more satellites for calculating the grid to be predicted merge precipitation parameter.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation system, empty by gold in common gram Value-based algorithm is interleave, merges precipitation parameter using more satellites of known grid, calculates more satellites fusion precipitation ginseng of grid to be predicted Number, the mutual spatial relationship between the distance between grid known to majority and grid to be predicted and the known grid is equal It is considered, the accuracy of more satellites fusion precipitation parameter of the grid to be predicted after ensure that interpolation calculation.
Figure 10 is the structural representation that Prediction of Precipitation system is merged by more satellites based on space interpolation in one embodiment Figure, more satellites based on space interpolation as shown in Figure 10 merge Prediction of Precipitation system, comprising:
Period samples submodule 10000, and for determining sampling periods, the sampling periods include each year pre- in N If the period, the duration of each preset period of time was less than 1 year；
Precipitation data acquisition submodule 20000, for obtaining surface precipitation data in the sampling periods and extremely respectively The precipitation data of few two satellites；
Prior probability determines submodule 30000, for determining the prior probability of each satellite；
Precipitation parameter computational submodule 40000, for the prior probability according to each satellite, the surface precipitation data With the precipitation data of each satellite, more satellites are calculated using probability statistics model and merge precipitation parameter.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation system, not according to sampling periods Together and the ground station selection difference, each satellite fusion weight coefficient calculated result it is also not identical, each satellite Fusion weight coefficient dynamic characteristic is presented on time and space, exclude to the greatest extent every satellite due to monitoring when Between or the difference in space caused by error so that more satellites fusion Prediction of Precipitation it is more accurate.
Figure 11 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in further embodiment Figure, more satellites based on space interpolation as shown in figure 11 merge Prediction of Precipitation system, comprising:
Probability density function determination unit 41000, for according to the posterior probability of the precipitation data of each satellite and more Satellite merges the Posterior distrbutionp of precipitation, determines the probability density function of more satellite fusion precipitation, wherein the precipitation of each satellite The posterior probability of data is posterior probability of the precipitation data of each satellite under the surface precipitation data qualification, more satellites The Posterior distrbutionp of precipitation is merged as more satellites under the precipitation data based on each satellite and the surface precipitation data qualification Merge the Posterior distrbutionp of precipitation；
Precipitation parameter computing unit 42000 is merged, for carrying out the probability density function of more satellite fusion precipitation Iterative calculation determines more satellite fusion precipitation parameters.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation system, are given using Bayesian model The formula of probability density function of satellite fusion precipitation out determines the precipitation data of each satellite in surface precipitation number by iterative calculation Posterior probability under the conditions of, so that it is determined that the fusion weight coefficient of each satellite.The present embodiment is true with surface precipitation data Value, comprehensively considers the internal association between data, at any time the difference of sampling periods and surface precipitation data, each satellite provided It is also different to merge weight coefficient, fused precipitation is made close to true value, to improve more satellite fusion Prediction of Precipitation to the greatest extent Accuracy.
Figure 12 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in further embodiment Figure, more satellites based on space interpolation as shown in figure 11 merge Prediction of Precipitation system, comprising:
Normal distribution conversion subunit 42100, for by the precipitation data of the surface precipitation data and each satellite Normal distribution conversion is carried out respectively, obtains the normal distribution precipitation data of ground normal distribution precipitation data and each satellite；
Greatest hope computation subunit 42200, for according to the ground normal distribution precipitation data, each satellite The probability density function of more satellite fusion precipitation is iterated meter using EM algorithm by normal distribution precipitation data It calculates, the posterior probability of the precipitation data of each satellite after determining optimization；And by the precipitation data of each satellite after the optimization Posterior probability is determined as the fusion weight coefficient of each satellite.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation system, are given using Bayesian model The precipitation data of surface precipitation data and each satellite is carried out normal distribution by the formula of probability density function of satellite fusion precipitation out After conversion, the fusion weight coefficient of each satellite is calculated using EM algorithm.When calculating data, in conjunction with being used Algorithm carry out data processing, fused precipitation can be made to the greatest extent close to true value, melted to improve more satellites Close the accuracy of Prediction of Precipitation.
Figure 13 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in further embodiment Figure, more satellites based on space interpolation merge Prediction of Precipitation system as shown in fig. 13 that, comprising:
Error computation component 42210, for according to each satellite precipitation data and the surface precipitation data, calculate The error of the precipitation data of each satellite；
Initially parameter obtaining component 42220 to be asked, for by the error of the precipitation data of each satellite, each satellite Precipitation data posterior probability, the initial parameter set to be asked being determined as in the probability density function of the more satellites fusion precipitation It closes；
Likelihood function securing component 42230, for the set according to the parameter initially to be asked, the ground normal distribution The normal distribution precipitation data of precipitation data and each satellite determines the log-likelihood letter of the parameter sets initially to be asked Number；
Maximum likelihood value securing component 42240, for iterating to calculate the log-likelihood function and determining the logarithm seemingly The maximum likelihood value of right function determines the parameter sets to be asked after optimization according to the maximum likelihood value, after the optimization to Seeking parameter sets includes the posterior probability of the precipitation data of each satellite after optimization.Specifically for preset according to each satellite Initial weight value, the precipitation data of the surface precipitation data and each satellite calculate the precipitation data of each satellite Initial variance；According to the initial weight value and the initial variance, the initial likelihood value of the log-likelihood function is calculated；Root According to the precipitation data of the satellite, the variance and the surface precipitation data, hidden variable is determined；By the hidden variable into Row iteration calculates, the hidden variable after obtaining iteration；By the hidden variable after the iteration, the iteration weighted value of each satellite is calculated With iteration variance；According to the iteration weighted value and iteration variance, the likelihood value of the log-likelihood function is calculated；According to described Likelihood value and preset threshold value determine the maximum likelihood value of the log-likelihood function.
More satellites provided by the present embodiment merge Prediction of Precipitation, furthermore present the probability density of satellite fusion precipitation The calculating process of function formula, used normal distribution conversion and EM algorithm can guarantee more satellite fusion drops The calculated result of water prediction is more accurate.
Figure 14 is the structural representation of more satellites fusion Prediction of Precipitation system based on space interpolation in further embodiment Figure, more satellites based on space interpolation merge Prediction of Precipitation system as shown in fig. 13 that, further includes:
Error computation component 42210, for according to each satellite precipitation data and the surface precipitation data, calculate The error of the precipitation data of each satellite；
Initially parameter obtaining component 42220 to be asked, for by the error of the precipitation data of each satellite, each satellite Precipitation data posterior probability, the initial parameter set to be asked being determined as in the probability density function of the more satellites fusion precipitation It closes；
Likelihood function securing component 42230, for the set according to the parameter initially to be asked, the ground normal distribution The normal distribution precipitation data of precipitation data and each satellite determines the log-likelihood letter of the parameter sets initially to be asked Number；
Maximum likelihood value securing component 42240, for iterating to calculate the log-likelihood function and determining the logarithm seemingly The maximum likelihood value of right function determines the parameter sets to be asked after optimization according to the maximum likelihood value, after the optimization to Seeking parameter sets includes the posterior probability of the precipitation data of each satellite after optimization.Specifically for preset according to each satellite Initial weight value, the precipitation data of the surface precipitation data and each satellite calculate the precipitation data of each satellite Initial variance；According to the initial weight value and the initial variance, the initial likelihood value of the log-likelihood function is calculated；Root According to the precipitation data of the satellite, the variance and the surface precipitation data, hidden variable is determined；By the hidden variable into Row iteration calculates, the hidden variable after obtaining iteration；By the hidden variable after the iteration, the iteration weighted value of each satellite is calculated With iteration variance；According to the iteration weighted value and iteration variance, the likelihood value of the log-likelihood function is calculated；According to described Likelihood value and preset threshold value determine the maximum likelihood value of the log-likelihood function.It is also used to calculate more satellite fusion precipitation Parameter, wherein more satellite fusion precipitation parameters include the control information of more satellite fusion precipitation.
Variance computation module 42250, for calculating more satellite fusions according to the parameter sets to be asked after the optimization The variance of the probability density function of precipitation；
Control information determines component 42260, for the variance of the probability density function of more satellite fusion precipitation is true It is set to the control information of more satellite fusion precipitation.
More satellites provided by the present embodiment based on space interpolation merge Prediction of Precipitation system, by providing fusion precipitation Variance, that is, merge precipitation control information, give judgement fusion precipitation accuracy probability statistics as a result, facilitate into One step improves the accuracy of more satellite fusion Prediction of Precipitation.
Further, it the present invention also provides a kind of verification method of more satellite fusion precipitation methods, is mentioned based on the present invention The more satellites supplied merge precipitation method, and specific evaluation index of verifying includes absolute relative error (RBE), root-mean-square error (RMSE), Pearson correlation coefficient (CC), standard deviation (SD), detectivity (POD), Euclid approach degree (e).
Wherein, absolute relative error (RBE), root-mean-square error (RMSE), standard deviation (SD) describe the ground of ground station Error and deviation between data and satellite fusion precipitation.Pearson correlation coefficient (CC) for describe ground station observation with Satellite merges the degree of fitting between precipitation.Euclid approach degree (e) is a kind of measurement of degree of closeness between two fuzzy subsets, Above-mentioned five other indexs can be combined, be the overall target for the description satellite estimation effect that can be quantified.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with Relevant hardware is instructed to complete by computer program, the program can be stored in a computer-readable storage medium In, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access Memory, RAM) etc..
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.

Claims (16)

1. a kind of more satellites based on space interpolation merge Prediction of Precipitation method, which is characterized in that the described method includes:
It is known grid and grid to be predicted by region division to be predicted, the known grid is the net comprising surface precipitation data Lattice, the grid to be predicted are the grid not comprising surface precipitation data；
According to the precipitation data of the surface precipitation data of the known grid and at least two satellites, the known grid is calculated More satellites merge precipitation parameter, and more satellites fusion precipitation parameter of the known grid includes the fusion weight coefficient of each satellite；
Using spatial interpolation algorithm, precipitation parameter is merged according to more satellites of the known grid, calculates the grid to be predicted More satellites merge precipitation parameter；
According to more satellites of the known grid merge precipitation parameter, the grid to be predicted more satellites fusion precipitation parameter, The satellite precipitation data of the grid to be predicted and known grid calculates the fusion precipitation value in the region to be predicted.
2. more satellites according to claim 1 based on space interpolation merge Prediction of Precipitation method, which is characterized in that described Using spatial interpolation algorithm, precipitation parameter is merged according to more satellites of the known grid, calculates the more of the grid to be predicted Satellite merges precipitation parameter, comprising:
Using spatial interpolation algorithm golden in common gram, precipitation parameter is merged according to more satellites of the known grid, described in calculating The interpolation weights coefficient of grid to be predicted；
The interpolation weights coefficient of precipitation parameter and the grid to be predicted is merged according to more satellites of the known grid, calculates institute State more satellites fusion precipitation parameter of grid to be predicted.
3. more satellites according to claim 2 based on space interpolation merge Prediction of Precipitation method, which is characterized in that described The interpolation weights coefficient of grid to be predicted, comprising:
Keep the error between the actual value and estimated value of more satellites fusion precipitation parameter of each known grid minimum, and it is expected The difference of value is equal to 0 interpolation weights coefficient.
4. more satellites according to claim 1 based on space interpolation merge Prediction of Precipitation method, which is characterized in that described According to the precipitation data of the surface precipitation data of the known grid and at least two satellites, calculates known grid more and defend Star merges precipitation parameter, and more satellites fusion precipitation parameter of the known grid includes the fusion weight coefficient of each satellite, comprising:
Sampling periods are obtained, the sampling periods include each year in N preset period of time, the duration of each preset period of time Less than 1 year, wherein N was positive integer；
The precipitation data of the surface precipitation data and at least two satellites in the sampling periods is obtained respectively；
Determine the prior probability of each satellite；
According to the prior probability of each satellite, the precipitation data of the surface precipitation data and each satellite, probability is utilized Statistical model calculates more satellites and merges precipitation parameter.
5. more satellites according to claim 4 based on space interpolation merge Prediction of Precipitation method, which is characterized in that described According to the prior probability of each satellite, the precipitation data of the surface precipitation data and each satellite, probability statistics are utilized Model calculates more satellites and merges precipitation parameter, comprising:
According to the Posterior distrbutionp of the posterior probability of the precipitation data of each satellite and more satellites fusion precipitation, determine that more satellites melt Close the probability density function of precipitation, wherein the posterior probability of the precipitation data of each satellite is that the precipitation data of each satellite exists Posterior probability under the surface precipitation data qualification；The Posterior distrbutionp of more satellite fusion precipitation is based on each satellite Precipitation data and the surface precipitation data qualification under more satellites fusion precipitation Posterior distrbutionp；
The probability density function of more satellite fusion precipitation is iterated calculating, determines more satellite fusion precipitation ginsengs Number.
6. more satellites according to claim 5 based on space interpolation merge Prediction of Precipitation method, which is characterized in that described The probability density function of more satellite fusion precipitation is iterated calculating, determines more satellite fusion precipitation parameters, packet It includes:
The precipitation data of the surface precipitation data and each satellite is subjected to normal distribution conversion respectively, obtains ground normal state It is distributed the normal distribution precipitation data of precipitation data and each satellite；
According to the normal distribution precipitation data of the ground normal distribution precipitation data and each satellite, more satellites are melted The probability density function for closing precipitation is iterated calculating using EM algorithm, the precipitation data of each satellite after determining optimization Posterior probability；
The posterior probability of the precipitation data of each satellite after the optimization is determined as to the fusion weight coefficient of each satellite.
7. more satellites according to claim 6 based on space interpolation merge Prediction of Precipitation method, which is characterized in that described According to the normal distribution precipitation data of the ground normal distribution precipitation data and each satellite, by more satellite fusion drops The probability density function of water is iterated calculating using EM algorithm, after the precipitation data of each satellite after determining optimization Test probability, comprising:
According to the precipitation data of each satellite and the surface precipitation data, the mistake of the precipitation data of each satellite is calculated Difference；
By the posterior probability of the error of the precipitation data of each satellite and the precipitation data of each satellite, it is determined as described more Satellite merges the initial parameter sets to be asked in the probability density function of precipitation；
According to the set of the parameter initially to be asked, the normal distribution of the ground normal distribution precipitation data and each satellite Precipitation data determines the log-likelihood function of the parameter sets initially to be asked；
It iterates to calculate the log-likelihood function and determines the maximum likelihood value of the log-likelihood function, seemingly according to the maximum Right value determines the parameter sets to be asked after optimization, and after the optimization includes the precipitation of each satellite after optimizing wait seek parameter sets The posterior probability of data.
8. more satellites according to claim 7 based on space interpolation merge Prediction of Precipitation method, which is characterized in that described More satellites fusion precipitation parameter of known grid further includes the control information of more satellite fusion precipitation, the calculating of the control information Method includes:
According to the parameter sets to be asked after the optimization, the variance of the probability density function of more satellite fusion precipitation is calculated；
The variance of the probability density function of more satellite fusion precipitation is determined as to the error letter of more satellite fusion precipitation Breath.
9. a kind of more satellites based on space interpolation merge Prediction of Precipitation system characterized by comprising
Grid dividing module, for being known grid and grid to be predicted by region division to be predicted, the known grid is packet The grid of the data containing surface precipitation, the grid to be predicted are the grid not comprising surface precipitation data；
Known grid computing module, for according to the surface precipitation data of the known grid and the precipitation number of at least two satellites According to the more satellites for calculating the known grid merge precipitation parameter, and more satellites fusion precipitation parameter of the known grid includes The fusion weight coefficient of each satellite；
Spatial interpolation module merges precipitation parameter, meter according to more satellites of the known grid for utilizing spatial interpolation algorithm Calculate more satellites fusion precipitation parameter of the grid to be predicted；
Prediction of Precipitation module, for according to more satellites of the known grid merge precipitation parameter, the grid to be predicted it is more Satellite merges the satellite precipitation data of precipitation parameter, the grid to be predicted and known grid, calculates the region to be predicted Merge precipitation value.
10. more satellites according to claim 9 based on space interpolation merge Prediction of Precipitation system, which is characterized in that institute State spatial interpolation module, comprising:
Interpolation weights coefficient submodule, for being defended according to known grid using spatial interpolation algorithm golden in common gram more Star merges precipitation parameter, calculates the interpolation weights coefficient of the grid to be predicted；
Grid submodule to be predicted, for merging precipitation parameter and the grid to be predicted according to more satellites of the known grid Interpolation weights coefficient, the more satellites for calculating the grid to be predicted merge precipitation parameter.
11. more satellites according to claim 10 based on space interpolation merge Prediction of Precipitation system, which is characterized in that institute Interpolation weights coefficient submodule is stated, is specifically used for:
The interpolation weights coefficient is calculated, the interpolation weights coefficient meets the more satellites fusion precipitation for making each known grid Error between the actual value and estimated value of parameter is minimum, and the difference of desired value is equal to 0.
12. more satellites according to claim 9 based on space interpolation merge Prediction of Precipitation system, which is characterized in that institute State known grid computing module, comprising:
Period samples submodule, and for obtaining sampling periods, the sampling periods include each year in N preset period of time, often The duration of a preset period of time was less than 1 year, and wherein N is positive integer；
Precipitation data acquisition submodule, for obtaining surface precipitation data and at least two satellites in the sampling periods respectively Precipitation data；
Prior probability determines submodule, for determining the prior probability of each satellite；
Precipitation parameter computational submodule, for according to the prior probability of each satellite, surface precipitation data and described each The precipitation data of satellite calculates more satellites using probability statistics model and merges precipitation parameter.
13. more satellites according to claim 12 based on space interpolation merge Prediction of Precipitation system, which is characterized in that institute State precipitation parameter computational submodule, comprising:
Probability density function determination unit, for according to the posterior probability of the precipitation data of each satellite and more satellites fusion drop The Posterior distrbutionp of water determines the probability density function of more satellite fusion precipitation, wherein the posteriority of the precipitation data of each satellite Probability is posterior probability of the precipitation data of each satellite under the surface precipitation data qualification, more satellite fusion precipitation Posterior distrbutionp is that more satellites under the precipitation data based on each satellite and the surface precipitation data qualification merge precipitation Posterior distrbutionp；
Precipitation parameter computing unit is merged, for the probability density function of more satellite fusion precipitation to be iterated calculating, Determine more satellite fusion precipitation parameters.
14. more satellites according to claim 13 based on space interpolation merge Prediction of Precipitation system, which is characterized in that institute State fusion precipitation parameter computing unit, comprising:
Normal distribution conversion subunit, for carrying out just the precipitation data of the surface precipitation data and each satellite respectively State distribution conversion, obtains the normal distribution precipitation data of ground normal distribution precipitation data and each satellite；
Greatest hope computation subunit, for the normal distribution according to the ground normal distribution precipitation data and each satellite The probability density function of more satellite fusion precipitation is iterated calculating using EM algorithm, determined by precipitation data The posterior probability of the precipitation data of each satellite after optimization；And by the posterior probability of the precipitation data of each satellite after the optimization It is determined as the fusion weight coefficient of each satellite.
15. more satellites according to claim 14 based on space interpolation merge Prediction of Precipitation system, which is characterized in that institute State greatest hope computation subunit, comprising:
Error computation component, for according to each satellite precipitation data and the surface precipitation data, calculate and described respectively defend The error of the precipitation data of star；
Initially parameter obtaining component to be asked, for by the precipitation number of the error of the precipitation data of each satellite and each satellite According to posterior probability, the initial parameter sets to be asked being determined as in the probability density function of the more satellites fusion precipitation；
Likelihood function securing component, for the set according to the parameter initially to be asked, the ground normal distribution precipitation data With the normal distribution precipitation data of each satellite, the log-likelihood function of the parameter sets initially to be asked is determined；
Maximum likelihood value securing component, for iterating to calculate the log-likelihood function and determining the log-likelihood function most Maximum-likelihood value determines the parameter sets to be asked after optimization according to the maximum likelihood value, the parameter sets to be asked after the optimization The posterior probability of precipitation data including each satellite after optimization.
16. more satellites according to claim 15 based on space interpolation merge Prediction of Precipitation system, which is characterized in that
The known grid computing module is also used to calculate more satellites fusion precipitation parameter of the known grid, described known More satellites fusion precipitation parameter of grid further includes the control information of more satellite fusion precipitation；
The greatest hope computation subunit, further includes:
Variance computation module, for calculating the general of more satellite fusion precipitation according to the parameter sets to be asked after the optimization The variance of rate density function；
Control information determines component, described more for the variance of the probability density function of more satellite fusion precipitation to be determined as The control information of satellite fusion precipitation.
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