CN110287986A - The typhoon target observation sensitizing range recognition methods of method is defined based on parallel gradient - Google Patents
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Abstract
The present invention relates to a kind of typhoon target observation sensitizing range recognition methods that method is defined based on parallel gradient, belong to computer and Atmosphere and Ocean subject crossing field, it can be used for the identification of Atmosphere and Ocean field typhoon target observation sensitizing range, the typhoon target observation sensitizing range identification that method is defined based on parallel gradient of the invention, method is defined using parallel gradient first to solve to obtain the condition nonlinear optimal perturbation (CNOP) of certain typhoon example, then " vertical integral energy " is used to obtain energy size distribution situation representated by CNOP, finally using the area energy great Zhi as the target observation sensitizing range of the typhoon.Compared with prior art, the present invention has many advantages, such as that accuracy height, solving speed are fast.
Description
Technical field
The present invention relates to computers and Atmosphere and Ocean subject crossing technical field, are based on parallel gradient more particularly, to one kind
The typhoon target observation sensitizing range recognition methods of definition method.
Background technique
In meteorological field, an important link of target observation first is that identification target observation sensitizing range, in identification
In target observation sensitizing range additionally increase observation, then by Data Assimilation system handle after, can provide for numerical model and more connect
Nearly true initial fields, to improve numerical model forecast skill.
It is a kind of method of effective identification typhoon target observation sensitizing range that condition is non-linear, which most to have disturbance (CNOP),.Tradition
The method for solving CNOP is adjoint method, but this method is highly dependent on adjoint mode.The exploitation of adjoint mode not only needs
Huge workload is consumed, and the calculation amount of adjoint mode is excessive for biggish business model, which greatly limits
Use of the CNOP in biggish business model.In order to avoid using adjoint method, the present invention uses parallel gradient definition side
Method solves CNOP to identify typhoon target observation sensitizing range.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on parallel gradient
The typhoon target observation sensitizing range recognition methods of definition method.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of typhoon target observation sensitizing range recognition methods defining method based on parallel gradient, comprising the following steps:
Step 1: the typhoon type of target observation sensitizing range identification will be carried out on demand by selecting；
Step 2: the selected corresponding final validation region of typhoon type starts to forecast moment and the moment of verifying and establishes correspondence
CNOP；
Step 3: defining method solution CNOP using parallel gradient and obtain solving result；
Step 4: showing that the target of selected typhoon type is seen using vertical integral energy method in conjunction with the solving result of CNOP
Survey the recognition result of sensitizing range.
Further, the parallel gradient in the step 3 define method the following steps are included:
Step S1: related optimized variable is initialized；
Step S2: method solution is defined using gradient and obtains condition nonlinear optimal perturbation optimal value result；
Step S3: loop iteration step S2 and judge whether meet stop iterated conditional；
Step S4: the condition nonlinear optimal perturbation optimal value of original solution room is exported when meeting and stopping iterated conditional
As a result.
Further, the step S2 specifically include it is following step by step:
Step S21: gradient of the objective function of solving condition nonlinear optimal perturbation about optimized variable；
Step S22: using SPG2 gradient project algorithms, along the direction that gradient declines, search can be converted into optimal value result
Objective function minimum value.
Further, gradient of the objective function in the step S21 about optimized variable, its calculation formula is:
In formula, f ' (X) indicates gradient of the objective function about optimized variable,Indicate optimization
First-order partial derivative of each variable of variable about objective function.
Further, calculation formula of each variable of the optimized variable about the first-order partial derivative of objective function are as follows:
In formula, Δ x indicates Delta, x1,…,xi,…,xnIndicate all optimized variables.
Further, the gradient in the step S21 optimization calculate using MPI technology carry out parallel computation, it is described simultaneously
Row calculates the MS master-slave process that uses, and the host process in the MS master-slave process is used to be responsible for the distribution of task and the collection of result and will
Task is divided equally, if task can not timesharing by be calculated as subprocess distribute task.
Further, when the dimension of numerical model needs dimensionality reduction, the parallel gradient in the step 3 defines method and also wraps
It includes: in spy after using singular value decomposition algorithm or Principal Component Analysis Algorithm to carry out dimensionality reduction the sample data of obtained optimized variable
Levy solution and optimizing that space carries out gradient.
Further, the stopping iterated conditional in the step S3 are as follows: whether be more than that greatest iteration step number or optimal value are
No is more than that setting number remains unchanged.
Further, the vertical integral energy method in the step 4 specifically includes: by the physics on each lattice point of CNOP
Amount vertically integrates vertical direction gross energy, so that the gross energy of each lattice point on horizontal plane is obtained, it then will be horizontal
Target observation sensitizing range of the region of direction gross energy maximum value as selected typhoon type.
Compared with prior art, the invention has the following advantages that
(1) accuracy is high, and the present invention is based on the typhoon target observation sensitizing range identifications that parallel gradient defines method, makes first
Method is defined with parallel gradient to solve to obtain the condition nonlinear optimal perturbation (CNOP) of certain typhoon example, is then used " vertical
Integral energy " obtains energy size distribution situation representated by CNOP, finally sees the area energy great Zhi as the target of the typhoon
Sensitizing range is surveyed, it is high for the identification accuracy of judgement degree of typhoon target observation sensitizing range.
(2) solving speed is fast, and it is that method is defined using gradient instead of companion that the present invention, which solves CNOP parallel gradient and defines method,
Gradient information of the objective function about optimized variable is solved with mode, and by the MPI concurrent technique of computer field by gradient
The calculating process parallel optimization of information, then uses the constrained optimization method based on gradient information --- SPG2 gradient project algorithms
Optimization Solution obtains CNOP, in addition to this, the calculating process defined present invention is alternatively directed to gradient be provided with parallel MPI principal and subordinate into
Journey, in addition, also introducing singular value decomposition algorithm SVD or PCA pairs of Principal Component Analysis Algorithm in the present invention if data dimension is excessively high
Luv space data carry out dimensionality reduction and are converted back to luv space from feature space when finally solving result to ensure entirety
The quick degree of solving speed.
Detailed description of the invention
Fig. 1 is the process signal for the typhoon target observation sensitizing range recognition methods that method is defined the present invention is based on parallel gradient
Figure.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiment is a part of the embodiments of the present invention, rather than whole embodiments.Based on this hair
Embodiment in bright, those of ordinary skill in the art's every other reality obtained without making creative work
Example is applied, all should belong to the scope of protection of the invention.
Embodiment
It is as shown in Figure 1 the stream that the typhoon target observation sensitizing range recognition methods of method is defined the present invention is based on parallel gradient
Journey schematic diagram, specific steps are described as follows:
A. the present invention be directed to the identifications of typhoon target observation sensitizing range, it is therefore desirable to first select typhoon.Different typhoon hairs
The path in raw region, process is not quite similar, and before carrying out sensitizing range identification, needs that numerical model is selected to have certain analog capability
Typhoon carry out the identification of target observation sensitizing range.
B. the determination of the sensitizing range of typhoon target observation and final validation region, start to forecast moment, verifying moment phase
It closes.The typhoon target observation sensitizing range that difference verifying moment and validation region finally solve is not quite similar: validation region needs
It to be determined according to specific typhoon example rationally, as far as possible be passed through typhoon from forecast start time to finish time in selection
The region crossed is included；Start to forecast the moment and verify the selection at moment to be determined according to specific needs.
C. typhoon target observation sensitizing range of the invention identification is based on CNOP method.CNOP, which is represented, to be represented
It forecasts that the moment has a kind of initial disturbance of maximum nonlinear development, by analyzing the space structure of CNOP, can obtain to pre-
It reports result to influence maximum sensitizing range, increases observation by being engraved in the sensitizing range at the beginning, improve initial field quality, Jin Erti
High forecast skill.
It is mathematically the optimization problem of a belt restraining when solution of D.CNOP.Objective function and optimized variable are not
It can or lack.CNOP represents a kind of initial disturbance for having maximum nonlinear development at the forecast moment, therefore objective function is
About the atmospheric physics sea state as caused by initial disturbance in the function for forecasting finish time increment, optimized variable is then and platform
Relevant Atmosphere and Ocean physical quantity occurs for wind.
E. the present invention defines method using parallel gradient and solves CNOP.Gradient define method be using gradient mathematically
Definition directly calculates gradient of the objective function of CNOP solution about optimized variable, rather than uses atmosphere and marine field tradition
Adjoint method.In order to improve gradient solution efficiency, using Computer Subject MPI concurrent technique to the calculating process of gradient into
Row is parallel.
F. parallel gradient define method solve CNOP when, the determination of concurrent process number be not it is The more the better, reach parallel bottle
After neck, even if increasing nucleus number, computational efficiency will not increase again.Therefore, it is necessary to select reasonable nucleus number, the bottleneck of computational efficiency
There is relationship with specific numerical model and server, needs to determine optimal process number according to specific experimental situation.
G. the CNOP that method solves is defined based on parallel gradient, typhoon target observation sensitizing range primarily determines use
" vertical integral energy " scheme.Vertical energy angular quadrature scheme is by the physical quantity on each lattice point of CNOP in vertical direction gross energy
It is vertically integrated, so that the gross energy of each lattice point on horizontal plane is obtained, then by the area great Zhi of horizontal direction gross energy
As sensitizing range.
H. by improving the initial field quality of typhoon target observation sensitizing range, the journey that verifying sensitizing range improves forecast skill
Degree.For determine identification sensitizing range it is whether effective, can forecast start time improve sensitizing range in initial fields quality, and with
Control experiment compares, and obtains improvement degree of the improvement to typhoon forecast skill of initial fields in sensitizing range.
In above step, for step E: defining method using parallel gradient and solve CNOP, detailed process are as follows:
A. mathematically, gradient refers to single order local derviation vector of the function of many variables about multivariable.Assuming that objective function hair f
(X) it indicates, optimized variable is the vector of n dimension, is expressed as X (x1,x2,x3,…,xi,…,xn), i=1,2,3 ..., n, then excellent
Change variable per one-dimensional xiFirst-order partial derivative about objective function f (X) is represented byAnd so on, then obtain following formula
Gradient vector f ' (X), it may be assumed that
In formula, f ' (X) indicates gradient of the objective function about optimized variable,Indicate optimization
First-order partial derivative of each variable of variable about objective function.
B. per one-dimensional optimized variable xiSingle order local derviation about objective function f (X) is calculated using the definition of derivative, root
According to definition optimized variable xiSingle order local derviation be equal to when objective function f (X) independent variable xiOn certain point increase increment Delta x
When, the increment f (x of objective function output valve1,…,xi+Δx,…,xn)-f(x1,…,xi,…,xn) with independent variable increment Delta x's
The limit of the ratio when Δ x tends to 0, formula are expressed as follows, and wherein Δ x is a small increment:
In formula, Δ x indicates Delta, x1,…,xi,…,xnIndicate all optimized variables.
C. when carrying out CNOP solution, target function value needs logarithm mode to be integrated, if Δ x value is too small, that
It will lead to numerical model not develop, i.e. f (x1,…,xi+Δx,…,xn)-f(x1,…,xi,…,xn)=0, then finally
The gradient being calculated is 0 vector.And Δ x value is excessive, the definition with derivative is disagreed.In order to obtain most suitable Δ x value,
The value of Δ x is adaptively adjusted by the value of optimized variable, and specific scheme is true by following formula progress adaptability
It is fixed.
D. in Atmosphere and Ocean scientific domain, the optimized variable dimension that CNOP solves the objective function being related to is very high, gradient
The dimension of vector and the dimension of optimized variable are identical, serial to solve the excessive problem of gradient vector calculation amount.But gradient vector
It is mutually independent when the calculating of each dimension there is no dependence.The calculating of gradient vector can be carried out with MPI technology parallel
Change to improve computational efficiency.Information is carried out by mpi_send and mpi_recv function between each process during MPI is parallel
Interaction.
E. gradient vector parallel computation strategy uses the way to manage of MS master-slave process on the whole, and host process is responsible for task
Distribution and result collection.Gradient vector is calculated as a calculating task per one-dimensional, it is assumed that gradient vector is that n is tieed up, always into
Number of passes is PN, and n is indicated divided by the remainder of PN with rd, and n is indicated divided by the commercial qu of PN.If rd=0, then all processes are divided equally
It is fitted on qu task；If rd ≠ 0, preceding rd subprocess distributes qu+1 task, remaining subprocess and host process point
With qu task.
F. if the dimension of numerical model is very high, even calculation amount is also very big after parallel, at this moment can first make
Dimensionality reduction is carried out with singular value decomposition algorithm SVD/ Principal Component Analysis Algorithm PCA method.The sample data of optimized variable is obtained first,
Then dimensionality reduction is carried out using Principal Component Analysis Algorithm PCA/ singular value decomposition algorithm SVD, and selected characteristic value accounting is big and accumulative
Characteristic value accounting is more than 90% preceding PCs feature vector, as feature space.
G. it is defined after objective function is calculated about the gradient information of optimized variable in method using gradient, reuses SPG2 ladder
It spends projection algorithm and obtains CNOP in solution room Optimization Solution.SPG2 gradient project algorithms are a kind of algorithms for solving minimum value,
If optimization problem is not minimum problems.It can carry out the Solve problems that certain processing is converted to minimum value.SPG2 gradient projection
Algorithm needs to provide gradient information of the objective function about optimized variable, solves to obtain target using the definition of gradient in the present invention
Gradient information of the function about optimized variable.
H. parallel gradient defines the process that method is an iteration optimizing, and iteration can all record optimal solution each time, if repeatedly
Generation number is more than greatest iteration step number (being denoted as maxIter) or the optimal value that finds is more than to remain unchanged for limits times, optimizing
Terminate, algorithm exports to obtain optimal value, if having carried out dimensionality reduction, needs to revert to optimal value original solution space and obtains CNOP, also
Former process is carried out using eigenvectors matrix.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (9)
1. a kind of typhoon target observation sensitizing range recognition methods for defining method based on parallel gradient, which is characterized in that including with
Lower step:
Step 1: the typhoon type of target observation sensitizing range identification will be carried out on demand by selecting；
Step 2: the selected corresponding final validation region of typhoon type starts to forecast moment and the moment of verifying and establishes correspondence
CNOP；
Step 3: defining method solution CNOP using parallel gradient and obtain solving result；
Step 4: showing that the target observation of selected typhoon type is quick using vertical integral energy method in conjunction with the solving result of CNOP
The recognition result of sensillary area.
2. a kind of typhoon target observation sensitizing range identification side for defining method based on parallel gradient according to claim 1
Method, which is characterized in that the parallel gradient in the step 3 define method the following steps are included:
Step S1: related optimized variable is initialized；
Step S2: method solution is defined using gradient and obtains condition nonlinear optimal perturbation optimal value result；
Step S3: loop iteration step S2 and judge whether meet stop iterated conditional；
Step S4: the condition nonlinear optimal perturbation optimal value knot of original solution room is exported when meeting and stopping iterated conditional
Fruit.
3. a kind of typhoon target observation sensitizing range identification side for defining method based on parallel gradient according to claim 2
Method, which is characterized in that the step S2 specifically include it is following step by step:
Step S21: gradient of the objective function of solving condition nonlinear optimal perturbation about optimized variable；
Step S22: the target of optimal value result can be converted into along the direction that gradient declines search using SPG2 gradient project algorithms
Function minimum.
4. a kind of typhoon target observation sensitizing range identification side for defining method based on parallel gradient according to claim 3
Method, which is characterized in that gradient of the objective function about optimized variable in the step S21, its calculation formula is:
In formula, f ' (X) indicates gradient of the objective function about optimized variable,Indicate optimized variable
First-order partial derivative of each variable about objective function.
5. a kind of typhoon target observation sensitizing range identification side for defining method based on parallel gradient according to claim 4
Method, which is characterized in that calculation formula of each variable of the optimized variable about the first-order partial derivative of objective function are as follows:
In formula, Δ x indicates Delta, x1,…,xi,…,xnIndicate all optimized variables.
6. a kind of typhoon target observation sensitizing range identification side for defining method based on parallel gradient according to claim 3
Method, which is characterized in that the optimization of the gradient in the step S21, which is calculated, carries out parallel computation, the parallel meter using MPI technology
It calculates and uses MS master-slave process, the host process in the MS master-slave process is for being responsible for the distribution of task and the collection of result and by task
Respectively, if task can not timesharing by be calculated as subprocess distribute task.
7. a kind of typhoon target observation sensitizing range identification side for defining method based on parallel gradient according to claim 1
Method, which is characterized in that when the dimension of numerical model needs dimensionality reduction, the parallel gradient in the step 3 defines method further include:
In feature after using singular value decomposition algorithm or Principal Component Analysis Algorithm to carry out dimensionality reduction the sample data of obtained optimized variable
The solution and optimizing of space progress gradient.
8. a kind of typhoon target observation sensitizing range identification side for defining method based on parallel gradient according to claim 2
Method, which is characterized in that the stopping iterated conditional in the step S3 are as follows: whether be more than whether greatest iteration step number or optimal value surpass
Setting number is crossed to remain unchanged.
9. a kind of typhoon target observation sensitizing range identification side for defining method based on parallel gradient according to claim 1
Method, which is characterized in that the vertical integral energy method in the step 4 specifically includes: by the physical quantity on each lattice point of CNOP
Vertical direction gross energy is vertically integrated, so that the gross energy of each lattice point on horizontal plane is obtained, then by level side
Target observation sensitizing range to the region of gross energy maximum value as selected typhoon type.
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