CN110472648A - A kind of water-setting object Background error covariance construction method based on cloud amount classification - Google Patents

A kind of water-setting object Background error covariance construction method based on cloud amount classification Download PDF

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CN110472648A
CN110472648A CN201910365056.XA CN201910365056A CN110472648A CN 110472648 A CN110472648 A CN 110472648A CN 201910365056 A CN201910365056 A CN 201910365056A CN 110472648 A CN110472648 A CN 110472648A
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陈耀登
孟德明
王元兵
高玉芳
孙涛
陈海琴
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Nanjing University of Information Science and Technology
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Abstract

The constructing plan of water-setting object Background error covariance of the invention introduces water-setting object variable in Background error covariance, and after the water-setting object Background error covariance, assimilation system can realize the direct analysis to water-setting object variable.The utility model has the advantages that the cloud sector classification operator based on set sample can effectively classify water-setting object Background error covariance according to cloud amount, sorted water-setting object Background error covariance can more reasonably characterize the feature in cloud sector and clear sky area background error.

Description

A kind of water-setting object Background error covariance construction method based on cloud amount classification
Technical field
The present invention relates to atmospheric science technical field more particularly to a kind of water-setting object background field errors based on cloud amount classification Covariance construction method.
Background technique
Distribution, form and its variation of cloud or cloud system embody the situation and variation tendency of air motion, the related letter of cloud Ceasing analysis and forecast for carrying out weather system has important guide to be worth, however the main needle of assimilation of satellite data at present It is carried out under the conditions of clear sky, the satellite data largely influenced by cloud, which is often dropped, not to be had to.The satellite data influenced by cloud it is effective Using will be it is further improve numerical forecast initial fields, and then improve the important channel of numerical forecast accuracy rate.In variational Assimilation In system, background error covariance matrix (B matrix) is one of the key factor of performance for influencing assimilation system, therefore rationally Background error covariance be the key link for carrying out Data Assimilation, therefore construct and understanding sexual intercourse area background field error association side Difference is to improve assimilation system in one of the core work of sexual intercourse area assimilation performance.
At present in most of assimilation systems, Background error covariance only includes wind, temperature, surface pressure and humidity Equal conventional controls variable, in order to allow assimilation system to directly give the analysis field of water-setting object variable, need using water-setting object as The control variable of assimilation system introduces water-setting object variable in Background error covariance.
In Meteorological Data Assimilation, it is difficult to directly indicate and calculate there are ultra-large background error covariance matrix The problem of, it is again more true and reliable to can be convenient operation for construction for the Data Assimilation system at current major numerical forecast center Background error covariance matrix is general using control variable transformation approach (Control Variable Transforms, CVT). The conversion of control variable lies in background error covariance matrix in control variable operator, it is no longer necessary to directly indicate. By controlling change of variable, the storage and calculating of B matrix can be effectively relieved.But assimilate in research and application in region data, Approximate processing often is done to B matrix in such a way that horizontal lattice point is average in control variable conversion process, which simplify B's Construction, but have ignored in horizontal direction there is different background error features under synoptic background.The back of water-setting object variable Scape error is even more so, and since the distribution of water-setting object has the discontinuous feature in space, synoptic background is lauched condensate variable Background error difference is more obvious.
Summary of the invention
Present invention aims to overcome that the Background error covariance in most of assimilation systems not yet introduces water-setting at present Object controls variable, can not carry out the deficiency rationally directly analyzed to water-setting object, provide a kind of water-setting object based on cloud amount classification Background error covariance construction method realizes that water-setting object is introduced in the water-setting object Background error covariance newly constructed to be become Amount, while the water-setting object Background error covariance can more reasonably characterize cloud sector and clear sky area background error feature, specifically It is realized by the following technical scheme:
The water-setting object Background error covariance construction method based on cloud amount classification, includes the following steps: step 1) Using GEFS worldwide collection forecast model products as numerical model initial fields, mode initial fields are disturbed with different parameters scheme It is dynamic, one group of set sample is obtained, set sample contains the water-setting object change for corresponding respectively to Yun Shui, Yun Bing, rainwater, snow and graupel Measure Qcloud、Qice、Qrain、QsnowAnd Qgraupel；
Step 2) reads the set sample, passes through the Q in ensemble average and set membercloudAnd QiceAccording to formula (1) Cloud sector discriminant classification standard is calculated, the cloud sector discriminant classification standard includes: ensemble average discrimination standard Pens_aveWith assemble Member's discrimination standard Pens_mem,
Wherein, top and bot respectively represents the air pressure on mode layer top and mode bottom, and "-" indicates that n set member's is flat ；
Step 3) classifies to aggregate error sample according to cloud sector discriminant classification standard P, formula (2),
Obtain sorted subregion operator P, respectively cloud sector operator (Pcloudy), clear sky area operator (Pclear) and mixing Area operator (Pmixed)；Step 4) carries out control variable conversion according to formula (3) to the error sample after subregion,
U=UpUvUh (3)
U indicates control variable conversion, U in formula (3)pIndicate physical conversion, UvIndicate vertical transitions, UhIndicate horizontal transformation；
Water-setting object Background error covariance B after obtaining subregion is accordingly indicated are as follows:
The further design of the water-setting object Background error covariance construction method based on cloud amount classification is, described The classification standard of aggregate error sample in step 3) are as follows: set member and ensemble average sample are all satisfied P >=0.01gkg-1 Lattice point be defined as cloud sector error sample；Set member and ensemble average sample are all satisfied P≤0.01gkg-1Lattice point it is fixed Justice is clear sky area error sample；When set member's lattice point identical as ensemble average occurs classifying inconsistent, it is defined as mixed zone Error sample.
3. the water-setting object Background error covariance construction method according to claim 1 based on cloud amount classification, It is characterized in that in the step 4) that the conversion of control variable includes the following steps:
Step 4-1) physical conversion is carried out, exist according between the state variable indicated by regression calculation or equilibrium equation Equilibrium relation state variable is divided into balance portion and non-equilibrium part；
Step 4-2) carry out vertical transitions, by empirical orthogonal function decomposition, obtain variable field characteristic value and feature to Amount, to characterize the magnitude and vertical structure feature of background error；
Step 4-3) according to formula (5) progress horizontal transformation, horizontal length scale is calculated；
In formula (5), L is horizontal length scale, and D indicates variance,Indicate nonequilibrium Physical Quantity Field.
The further design of the water-setting object Background error covariance construction method based on cloud amount classification is, described Step 4-2) in the error field for controlling variable is projected on the orthogonal modes of vertical direction, make inside each block diagonal matrix again Continue diagonalization, and then background error covariance matrix be decomposed into characteristic value and feature vector in vertical direction:
Bv=E ∧ ET (6)
In formula (6), the matrix that E is made of K feature vector, BvBecome for background error covariance matrix by vertical Part after changing and the symmetrical matrix for a positive definite, meet formula (6)
It is described based on cloud amount classification water-setting object Background error covariance construction method it is further design be, step 4) in, the acquisition of the water-setting object Background error covariance after subregion includes the following steps:
Step A) by background field error sample εbIt is decomposed into cloud sector cloudy, clear sky area clear and mixed zone mixed tri- The sum of a part:
εb=Pcloudyεb+Pclearεb+Pmixedεb (8)
In formula (8), PcloudyIndicate cloud sector classification operator；PclearIndicate that clear sky distinguishes class operator；PmixedIndicate mixed zone Classification operator；
Step B) Background error covariance B is decomposed are as follows:
In formula (9), εbIndicate that sample error, "-" indicate mathematic expectaion
B is further decomposed as:
B=PcloudyBcloudyPcloudy T+PclearBclearPclear T+PmixedBmixedPmixed T (10)
Water-setting object Background error covariance is just distinguished into cloud sector, fine hereby based on the cloud sector classification operator of set sample Dead zone and the part of mixed zone three.
The further design of the water-setting object Background error covariance construction method based on cloud amount classification is, described Step 4-1) the equilibrium equation such as formula (11),
In formula (11), cloudbIndicate the balanced field for the water-setting object variable being calculated by each variable, i and j indicate horizontal Direction lattice point number, k and l indicate the vertical direction sigma number of plies, k, l ∈ [0, NK], α indicates the regression coefficient between variable.
Advantages of the present invention is as follows:
The construction method of water-setting object Background error covariance of the invention, by being introduced in Background error covariance Water-setting object variable, after the water-setting object Background error covariance, assimilation system can realize directly dividing to water-setting object variable Analysis.
On the other hand, the cloud sector classification operator based on set sample can be effectively by water-setting object Background error covariance Classified according to cloud amount, sorted water-setting object Background error covariance can more reasonably characterize cloud sector and clear sky area back The feature of scape error.
Detailed description of the invention
Fig. 1 is the flow chart for obtaining subregion operator P.
Fig. 2 is the flow chart that Background error covariance calculating in cloud sector is carried out using subregion operator.
Specific embodiment
The present invention is described in detail with reference to the accompanying drawing.
Water-setting object Background error covariance construction method provided in this embodiment based on cloud amount classification, including walk as follows It is rapid:
As Fig. 1 needs to carry out following steps to obtain subregion operator P:
Step 1) is using GEFS worldwide collection forecast model products as numerical model initial fields, with different parameters scheme to mode Initial fields are disturbed, and the set sample of one group of 80 member is obtained, and set sample contains Qcloud(Yun Shui), Qice(Yun Bing), Qrain(rainwater), Qsnow(snow) and QgraupelFive kinds of water-setting object variables such as (graupel)；
Step 2) reads the set sample, passes through the Q in ensemble average and set membercloudAnd QiceAccording to formula (1) Calculate cloud sector discriminant classification standard, ensemble average discrimination standard: Pens_ave, set member's discrimination standard: Pens_mem,
Step 3) classifies to aggregate error sample according to cloud sector discriminant classification standard P, reference formula (2),
The classification standard of aggregate error sample in step 3) are as follows: by set member and ensemble average sample be all satisfied P >= 0.01g·kg-1Lattice point be defined as cloud sector error sample；Set member and ensemble average sample are all satisfied P < 0.01g kg-1Lattice point be defined as clear sky area error sample；It is fixed when set member's lattice point identical as ensemble average occurs classifying inconsistent Justice is mixed zone error sample.Obtain sorted subregion operator P, respectively cloud sector operator (Pcloudy), clear sky area operator (Pclear) and mixed zone operator (Pmixed)；
Such as Fig. 2, the calculating of cloud sector Background error covariance is carried out using subregion operator and is comprised the steps of:
Step 4) carries out control variable conversion according to formula (3) to the error sample after subregion,
U=UpUvUh (3)
U indicates control variable conversion, U in formula (3)pIndicate physical conversion, UvIndicate vertical transitions, UhIndicate horizontal transformation；
Water-setting object Background error covariance B after obtaining subregion is accordingly indicated are as follows:
The conversion of control variable includes the following steps: in step 4)
Step 4-1) physical conversion is carried out, exist according between the state variable indicated by regression calculation or equilibrium equation Equilibrium relation state variable is divided into balance portion and non-equilibrium part；Variable is controlled for water-setting object, equilibrium equation is such as Formula (5),
In formula (5), cloudbIndicate the balanced field for the water-setting object variable being calculated by each variable, i and j indicate level side To lattice point number, k and l indicate the vertical direction sigma number of plies, k, l ∈ [0, NK], α indicates the regression coefficient between variable.
Step 4-2) carry out vertical transitions, by empirical orthogonal function decomposition, obtain variable field characteristic value and feature to Amount, to characterize the magnitude and vertical structure feature of background error；The error field for controlling variable is being projected into vertical direction just It hands in mode, makes to be further continued for carrying out diagonalization inside each block diagonal matrix, and then background error covariance matrix is being hung down Histogram is decomposed into characteristic value and feature vector upwards:
Bv=E ∧ ET (6)
Wherein, the matrix that E is made of K feature vector, BvPass through vertical transitions for background error covariance matrix Part afterwards, and be the symmetrical matrix of a positive definite, meet formula (7)
Step 4-3) according to formula (8) progress horizontal transformation, horizontal length scale is calculated；
Wherein, L is horizontal length scale, and D indicates variance,Indicate nonequilibrium Physical Quantity Field.
In step 4), the acquisition of the water-setting object Background error covariance after subregion includes the following steps:
Step A) by background field error sample εbIt is decomposed into cloud sector cloudy, clear sky area clear and mixed zone mixed tri- The sum of a part:
εb=Pcloudyεb+Pclearεb+Pmixedεb (9)
In formula (9), PcloudyIndicate cloud sector classification operator；PclearIndicate that clear sky distinguishes class operator；PmixedIndicate mixed zone Classification operator；
Step B) Background error covariance B is decomposed are as follows:
In formula (10), εbIndicate that sample error, "-" indicate mathematic expectaion
B is further decomposed as:
B=PcloudyBcloudyPcloudy T+PclearBclearPclear T+PmixedBmixedPmixed T (11)
Water-setting object Background error covariance is just distinguished into cloud sector, fine hereby based on the cloud sector classification operator of set sample Dead zone and the part of mixed zone three.
The construction method of the water-setting object Background error covariance of the present embodiment, by drawing in Background error covariance Enter water-setting object variable, after the water-setting object Background error covariance, assimilation system can be realized to the direct of water-setting object variable Analysis.In addition, the cloud sector classification operator based on set sample can be effectively by water-setting object Background error covariance according to cloud Amount is classified, and sorted water-setting object Background error covariance can more reasonably characterize cloud sector and clear sky area background error Feature.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto, In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art, It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of protection of the claims Subject to.

Claims (6)

1. a kind of water-setting object Background error covariance construction method based on cloud amount classification, it is characterised in that including walking as follows It is rapid:
Step 1) is initial to mode with different parameters scheme using GEFS worldwide collection forecast model products as numerical model initial fields Field is disturbed, and one group of set sample is obtained, and set sample, which contains, corresponds respectively to Yun Shui, Yun Bing, rainwater, snow and graupel Water-setting object variable Qcloud、Qice、Qrain、QsnowAnd Qgraupel；
Step 2) reads the set sample, passes through the Q in ensemble average and set membercloudAnd QiceIt is calculated according to formula (1) Cloud sector discriminant classification standard, the cloud sector discriminant classification standard include: ensemble average discrimination standard Pens_aveSentence with set member Other standard Pens_mem,
Wherein, top and bot respectively represents the air pressure on mode layer top and mode bottom, and "-" indicates being averaged for n set member；
Step 3) classifies to aggregate error sample according to cloud sector discriminant classification standard P, formula (2),
Obtain sorted subregion operator P, respectively cloud sector operator Pcloudy, clear sky area operator PclearAnd mixed zone operator Pmixed；
Step 4) carries out control variable conversion according to formula (3) to the error sample after subregion,
U=UpUvUh (3)
U indicates control variable conversion, U in formula (3)pIndicate physical conversion, UvIndicate vertical transitions, UhIndicate horizontal transformation；
Water-setting object Background error covariance B after obtaining subregion is accordingly indicated are as follows:
2. the water-setting object Background error covariance construction method according to claim 1 based on cloud amount classification, feature Be the classification standard of aggregate error sample in the step 3) are as follows: by set member and ensemble average sample be all satisfied P >= 0.01g·kg-1Lattice point be defined as cloud sector error sample；Set member and ensemble average sample are all satisfied P≤0.01g kg-1Lattice point be defined as clear sky area error sample；It is fixed when set member's lattice point identical as ensemble average occurs classifying inconsistent Justice is mixed zone error sample.
3. the water-setting object Background error covariance construction method according to claim 1 based on cloud amount classification, feature It is in the step 4) that the conversion of control variable includes the following steps:
Step 4-1) physical conversion is carried out, according to existing flat between the state variable indicated by regression calculation or equilibrium equation State variable is divided into balance portion and non-equilibrium part by weighing apparatus relationship；
Step 4-2) vertical transitions are carried out, by empirical orthogonal function decomposition, the characteristic value and feature vector of variable field are obtained, is used To characterize the magnitude and vertical structure feature of background error；
Step 4-3) according to formula (5) progress horizontal transformation, horizontal length scale is calculated；
In formula (5), L is horizontal length scale, and D indicates variance,Indicate nonequilibrium Physical Quantity Field.
4. the water-setting object Background error covariance construction method according to claim 1 based on cloud amount classification, feature Be the step 4-2) in the error field for controlling variable is projected on the orthogonal modes of vertical direction, make each piece to angular moment Battle array is internal to be further continued for carrying out diagonalization, and then background error covariance matrix is decomposed into characteristic value and spy in vertical direction Levy vector:
Bv=E ∧ ET (6)
In formula (6), the matrix that E is made of K feature vector, BvFor and be a positive definite symmetrical matrix, meet formula (7)
5. the water-setting object Background error covariance construction method according to claim 4 based on cloud amount classification, feature It is in step 4), the acquisition of the water-setting object Background error covariance after subregion includes the following steps: step A) by ambient field Error sample εbIt is decomposed into the sum of cloud sector cloudy, clear sky area clear and tri- parts mixed zone mixed:
εb=Pcloudyεb+Pclearεb+Pmixedεb (8)
In formula (8), PcloudyIndicate cloud sector classification operator；PclearIndicate that clear sky distinguishes class operator；PmixedIndicate that mixed zone classification is calculated Son；
Step B) Background error covariance B is decomposed are as follows:
In formula (9), εbIndicate that sample error, "-" indicate mathematic expectaion
B is further decomposed as:
B=PcloudyBcloudyPcloudy T+PclearBclearPclear T+PmixedBmixedPmixed T (10)
Water-setting object Background error covariance is just distinguished into cloud sector, clear sky area hereby based on the cloud sector classification operator of set sample With the part of mixed zone three.
6. the water-setting object Background error covariance construction method according to claim 1 based on cloud amount classification, feature It is the step 4-1) the equilibrium equation such as formula (11),
In formula (11), cloudbIndicate the balanced field for the water-setting object variable being calculated by each variable, i and j indicate horizontal direction lattice Points, k and l indicate the vertical direction sigma number of plies, k, l ∈ [0, NK], α indicates the regression coefficient between variable.
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CN117706512A (en) * 2023-12-13 2024-03-15 安徽省气象台 Hydrogel inversion method and system integrating temperature judgment and background dependence
CN117706512B (en) * 2023-12-13 2024-05-10 安徽省气象台 Hydrogel inversion method and system integrating temperature judgment and background dependence

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US20160291203A1 (en) * 2015-04-06 2016-10-06 The Government Of The United States Of America, As Represented By The Secretary Of The Navy Adaptive ecosystem climatology
CN108875254A (en) * 2018-07-03 2018-11-23 南京信息工程大学 A kind of One-Dimensional Variational inversion method of Atmosphere and humidity profiles

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