CN106646476A - Inversion method for microphysical parameters of liquid cloud - Google Patents

Abstract

The invention discloses an inversion method for microphysical parameters of liquid cloud. The inversion method mainly includes the following process: according to radar reflectivity factors provided by a millimeter waves cloud radar, based on the optimal estimation theory, adopting an empirical formula calculation value as a prior value, assuming that particle spectrum submits to logarithmic normal distribution, a function relationship of radar reflectivity factors and liquid cloud physical parameters is established, and the inversion optimal solution is obtained in a condition when the difference value weighing of to-be-inverted parameters, the prior value, the radar reflectivity factors and function calculation value achieves the minimum value. Besides, according to an error transmission theory, uncertainty of liquid cloud microphysical parameters is calculated. The invention can make up a shortcoming of poor adaptability of a traditional empirical formula. Also, besides, common cloud particle diameter and cloud water content, particle concentration and distribution width can also be obtained; the inversion result is more comprehensive. Since the radar reflectivity factor in real time monitoring is adopted for prior value calculation, the inversion result accuracy is improved.

Description

A kind of inversion method of liquid Cloud microphysical parameter

Technical field

The present invention relates to a kind of reflectivity factor inverting liquid Cloud microphysical parameter of utilization millimeter wave cloud radar acquisition Method, the inversion method of theoretical and empirical equation the liquid Cloud microphysical parameter of more particularly to a kind of combination optimal estimation.

Background technology

The distribution on global of cloud and its Microphysical Structure affect global climate and environmental change, and to the earth-Atmosphere System Radiation budget is balanced and Water, steam circulation has important regulative, by the global Cloud Layer Character of observation, is obtained from Bao Yun to dense The vertical section feature of cloud, inverting obtains measuring cloud water content, cloud particle size and other parameters, to climatic study, finely Change the aspects such as synoptic process analysis, weather forecasting, weather modification significant.

With the fast development for surveying cloud, Chinese scholars deepen continuously and study the inversion method of Cloud microphysical parameter, Achieve some application achievements.The empirical relation algorithm that traditional cloud physics parametric inversion is mainly obtained by statistical analysis has come Into that is, based on substantial amounts of experimental data, using the mode of Function Fitting the pass of radar reflectivity and cloud physics parameter being set up System.

Such as Atlas, Sauvageot, Kropfli and Kelly, Fox and Illingworth et al. combine 35GHz radars Mysorethorn is worn with aircraft and surveys spectrum supplemental characteristic, obtain the functional relation between radar reflectivity and particle effective radius, liquid water content, For exponential form；

Neil et al. has probed into the cloud water content and Effective radius size of stratus using 8mm ground cloud radars, checking Relation between Liquid water content three in radar reflectivity factor, particle effective radius, cloud, as a result with Atlas and Sauvageot Result coincide.

Austin and Stephens is theoretical based on optimal estimation, develops a kind of liquid Cloud microphysical based on optimal estimation The business inversion algorithm of parameter, using priori data information, state vector (treating inverting parameter) and priori data difference with And known measurement vectorial (radar reflectivity factor) and forward model value difference point weighting sum minimum when, obtain liquid cloud speck Reason parameter.

The advantage of experience inverting commonly used in the prior art is to calculate simple, needs substantial amounts of detection data to be counted Analysis, and it is different according to the difference key inverting coefficient of observation place, time and the varieties of clouds, but algorithm applicability and autgmentability It is poor, for example, when there is drizzle precipitation in cloud, due to contribution very little of the precipitation particles to liquid water content in cloud, utilize The result that Atlas empirical equation invertings are obtained is bigger than normal, and needs count the relational expression suitable for drizzle and light rain.Optimal estimation Inversion accuracy is affected by priori data, and observation statistical value adopt at present for priori data, have some limitations with not Foot.

Patent aspect, Anhui Normal University propose " one kind using A-Train series of satellites data collaborative Retrieval of Cloud phase and The new method of cloud parameter " (publication number：CN102707336A) a kind of " cloud radar is proposed with Meteorological Observation Centre of CMA With satellite sounding data fusion method and system " (publication number：CN105445816A), system adopts neural metwork training, sets up The relation of bright temperature and cloud-top height, the height of cloud base and reflectivity factor；Beijing Radio Measurement research department proposes a kind of " millimeter The data fusion method and system of ripple cloud radar " (publication number：CN104345312A), system globe area radar multi-mode working mode The detection data for obtaining, improves the quality of data and detection efficient.Additionally, in terms of detection means, Nanjing Information engineering Univ Shen A kind of please " cloud particle detection method and detector " (publication number：CN105115862A patent), using the scattering of cloud particle Signal detection obtains cloud particle phase and cloud particle size；The Chinese Academy of Space Technology has applied for a kind of " ground for surveying cloud Face Terahertz radar system " (publication number：) and a kind of " survey mysorethorn based on Terahertz active cloud detection radar CN104569980A Experiment device and method " (publication number：CN104597454A).In the Patents for being retrieved, correlative study master both domestic and external Cloud detecting devices or instrument are concentrated on, division unit proposes multisource data fusion processing method, the research of cloud inversion method It is less.

The content of the invention

The radar reflectivity factor that the purpose of the present invention is provided according to millimeter wave cloud radar, it is theoretical based on optimal estimation, adopt It is priori value with empirical equation calculated value, it is assumed that particle spectra obeys logarithm normal distribution, sets up radar reflectivity factor and liquid The functional relation of cloud physics parameter, in the difference for treating inverting parameter and priori data and radar reflectivity factor and function value Value weighting tries to achieve inverting optimal solution in the case of obtaining minimum of a value.

In order to realize object above, the present invention is achieved by the following technical solutions：

A kind of inversion method of liquid Cloud microphysical parameter, inversion algorithm is asked in the case where cost function obtains minimum of a value Optimal solution is obtained, the cost function is state vector and priori data difference and known measurement vector sum forward model value difference point Weighting sum,

In formula, x_aIt is according to the calculated priori data of radar reflectivity factor, S_aFor the covariance square of priori data Battle array, F (x) for radar reflectivity factor forward physical pattern, S_yIt is the covariance square of radar reflectivity factor measure error Battle array, y is radar reflectivity factor for measurement vector, and x is that unknown state vector treats inverting parameter, described to treat inverting parameter bag Include cloud particle number density, geometrical mean radius and dispersion of distribution parameter.

By priori data x_aAs iterative initial value, cost function D is minimized by continuous, finally try to achieve and treat inverting parameter x Iterative solution.

Preferably, it is assumed that the size distribution of the cloud particle in air meets logarithm normal distribution, radar reflection is set up according to this The rate factor and liquid cloud physics parameter are cloud particle number density, efficient radius of cloud particle, the function of cloud particle dispersion of distribution parameter Relation；

In formula：N_TIt is cloud particle number density；R is efficient radius of cloud particle；r_gIt is geometrical mean radius；σ_logIt is the dispersion of distribution Parameter, is dimensionless variable；σ_gFor geometric standard deviation；Ln represents natural logrithm conversion；Horizontal line is represented seeks arithmetic mean of instantaneous value.

In non-rainfall or there is light rain, or in the case of having drizzle, it is believed that cloud particle yardstick is sufficiently small, meets Rayleigh scattering Condition, according to Liquid water content C in cloud_LW, efficient radius of cloud particle r_eWith the definition of radar reflectivity factor Z, it is derived by down Row formula.

In formula：ρ_ωRepresent water density.

Preferably, the priori data x_aWith the covariance matrix S of priori data_aGrain is obtained according to by historical statistical data Subnumber density N_TAnd distribution width sigma_logTo be calculated,

In formula, range bin z_i；z₁And z_nThe range bin on cloud base and cloud top in radar profile is represented respectively, and subscript a represents state The priori value of vector x, r_ga、N_TaAnd σ_logaIt is the priori for representing efficient radius of cloud particle, Particle number concentration and the dispersion of distribution respectively Value.

Preferably, it is efficient radius of cloud particle r by cloud particle size_e, Particle number concentration N_TAnd distribution width sigma_log, calculate Go out forward physical model F (x) of the radar reflectivity factor；

Measurement vector y and state vector x relation between the two, forward physical mould are set up by forward physical model F (x) Formula is represented by

Y=F (x)+ε_y

ε in formula_yRepresent measure error, Z_FM(z_i) it is each range bin z_iRadar reflectivity factor；z₁And z_nThunder is represented respectively The range bin on cloud base and cloud top up in profile.

Preferably, derived by the defined formula of radar reflectivity factor and calculate each range bin z_iRadar reflectivity factor Z_FM(z_i)；

K=(m²-1)/(m²+2)

In formula, m represents complex refractive index.Preferably, the measurement vector y is with unknown state vector x

In formula：Z_dB(z_i)、r_g(z_i)、N_T(z_i) and σ_log(z_i) represent respectively radar reflectivity factor, geometrical mean radius, Cloud particle number density and dispersion of distribution parameter are in range bin z_iThe value at place；N represents the cloud range bin number in profile.

Preferably for profile is observed per bar, state vector x is to treat inverting parameter in each range bin, it is known that measurement Vectorial y only has the radar reflectivity factor in respective distances storehouse, needs by priori data x_aRow constraint is entered to inverting, it is ensured that repeatedly Withhold hold back and inversion result reliability.

Preferably, it is described by priori data x_aAs iterative initial value, cost function D is minimized by continuous, try to achieve and treat anti- The iterative solution of parameter x is drilled, and the condition of convergence of iteration is met when calculating is iterated；

In formula：Subscript i and i+1 represent iterations, and L represents sensitivity of the forward physical pattern to state vector x.

Preferably, the condition of convergence of the iteration is：

In formula：S_xThe error co-variance matrix of iterative state vector, represent three variances for treating inverting physical quantity and Covariance between each parameter, S_yIt is the covariance matrix of radar reflectivity factor measure error.

Compared with prior art, present invention has the advantages that：

The present invention can make up traditional empirical equation shortcoming poor for applicability, and except common cloud particle radius and cloud Water content, is also obtained Particle number concentration and the dispersion of distribution, and the result for arriving of inverting is more fully；As a result of sight in real time The radar reflectivity factor of survey improves inversion result accuracy calculating priori value.

Description of the drawings

Fig. 1 is a kind of flow chart of the inversion method of liquid Cloud microphysical parameter of the invention；

Fig. 2 be a kind of inversion method of liquid Cloud microphysical parameter of the invention one embodiment in the cloud particle half that adopts Footpath priori data schematic diagram；

Fig. 3 be a kind of inversion method of liquid Cloud microphysical parameter of the invention one embodiment in the liquid that obtains of inverting Cloud microphysical parameter schematic diagram.

Specific embodiment

Below in conjunction with accompanying drawing, by describing a preferably specific embodiment in detail, the present invention is further elaborated.

Millimeter wave cloud radar utilizes scattering properties of the cloud particle to electromagnetic wave, and by the analysis on radar echoes to cloud cloud is understood Both macro and micro characteristic, you can detection diameter from several microns to weak precipitation particles, the vertical section of continuous detection cloud.Echo is strong Degree reflects the size and concentration of particle in cloud, and echo strength change over time and space reflects cloud micro-physical process and drills Become feature.The reflectivity factor that radar is surveyed is pre-processed, you can obtain continuous, effective profile in the domain of cloud sector and Section echo data, and its control information.

As shown in figure 1, a kind of inversion method of liquid Cloud microphysical parameter,

Inversion algorithm tries to achieve optimal solution in the case where cost function obtains minimum of a value, and the cost function is state vector The weighting sum divided with priori data difference and known measurement vector sum forward model value difference,

In formula, x_aIt is according to the calculated priori data of radar reflectivity factor, S_aFor the covariance square of priori data Battle array, F (x) for radar reflectivity factor forward physical pattern, S_yThe covariance matrix of radar measurement errors, y for measurement to Amount is radar reflectivity factor, and x is that unknown state vector treats inverting parameter, described to treat that inverting parameter includes that cloud particle subnumber is close Degree, geometrical mean radius and dispersion of distribution parameter.

By priori data x_aAs iterative initial value, cost function D is minimized by continuous, finally try to achieve and treat inverting parameter x Iterative solution.

Assume that the size distribution of cloud particle in air meets logarithm normal distribution, set up according to this radar reflectivity factor with Liquid cloud physics parameter is cloud particle number density, efficient radius of cloud particle, the functional relation of cloud particle dispersion of distribution parameter；

In formula：N_TIt is cloud particle number density；R is efficient radius of cloud particle；r_gIt is geometrical mean radius；σ_logIt is the dispersion of distribution Parameter, is dimensionless variable；σ_gFor geometric standard deviation；Ln represents natural logrithm conversion；Horizontal line is represented seeks arithmetic mean of instantaneous value.

For priori data is obtained and is calculated, rule of thumb formula, by the radar effective reflectivity factor cloud particle is calculated Sub- radius and liquid water content.Forefathers using cloud radar and other cloud particles spectrum detecting devices, measure cloud particle drop-size distribution distribution, Liquid water content in Particle number concentration, particle effective radius and cloud, the statistical fit for carrying out mass data draws cloud radar inverting The empirical relation of particle radii and aqueous water, draws the conventional empirical equation of classics,

Z=138 × 10^-12D⁶ (4)

Wherein C_LWLiquid water content in cloud is represented, D is particle diameter, and Z is the radar reflectivity that millimeter wave cloud radar is measured The factor.

In non-rainfall or there is light rain, or in the case of having drizzle, it is believed that cloud particle yardstick is sufficiently small, meets Rayleigh scattering Condition, according to Liquid water content C in cloud_LW, efficient radius of cloud particle r_eWith the definition of radar reflectivity factor Z, it is derived by down Row formula.

In formula：ρ_ωRepresent water density.

The priori data x_aWith the covariance matrix S of priori data_aPopulation density is obtained according to by historical statistical data N_TAnd distribution width sigma_logObtained by historical statistical data to calculate Particle number concentration and the dispersion of distribution, and calculate priori according to this The covariance matrix of data.

In formula, range bin z_i；z₁And z_nThe range bin on cloud base and cloud top in radar profile is represented respectively, and subscript a represents priori Value, r_gaRepresent the priori data of efficient radius of cloud particle, in the same manner, N_TaAnd σ_lo_gaIt is to represent Particle number concentration and distribution width respectively The priori value of degree.

Forward physical model F (x) of described radar reflectivity factor is by known cloud particle size i.e. cloud particle Effective radius r_e, Particle number concentration N_TAnd distribution width sigma_log, extrapolate forward physical model F (x) of radar reflectivity factor；I.e. When considering that millimeter cloud radar receives the cloud particle scattering energy of given distance, the two-way decay on institute's pathway footpath obtains Jing decay Revised radar reflectivity factor Z_FM；Derived by the defined formula of radar reflectivity factor Z and calculate each range bin z_iThunder Up to reflectivity factor Z_FM(z_i)；

K=(m²-1)/(m²+2) (12)

In formula, z₁And z_nThe range bin on cloud base and cloud top in radar profile is represented respectively, and subscript FM represents forward physical pattern Calculated value, m represents complex refractive index.

Measurement vector y is set up by forward physical model F (x) and inverting parameter x relations between the two, forward physical is treated Pattern is represented by

Y=F (x)+ε_y (13)

ε in formula_yRepresent measure error.

The measurement vector y is with unknown state vector x

In formula：Z_dB(z_i)、r_g(z_i)、N_T(z_i) and σ_log(z_i) represent respectively radar reflectivity factor, geometrical mean radius, Cloud particle number density and dispersion of distribution parameter are in range bin z_iThe value at place；N represents the cloud range bin number in profile.

For profile is observed per bar, state vector x is to treat inverting parameter in each range bin, it is known that measurement vector y is only There is the radar reflectivity factor in respective distances storehouse, need by priori data x_aRow constraint is entered to inverting, it is ensured that iteration convergence And the reliability of inversion result.

It is described by priori data x_aAs iterative initial value, cost function D is minimized by continuous, try to achieve and treat inverting parameter x Iterative solution, and the condition of convergence of iteration is met when calculating is iterated；

In formula：Subscript i and i+1 represent iterations, and L represents sensitivity of the forward physical pattern to state vector x.

The condition of convergence of the iteration is：

In formula：S_xThe error co-variance matrix of iterative state vector, represent three variances for treating inverting physical quantity and Covariance between each parameter, S_yIt is the covariance matrix of cloud radar measurement errors.

Inversion error is mainly derived from radar reflectivity factor and priori data acquisition algorithm, theoretical according to error propagation, Calculate the standard deviation and error for the treatment of inverted parameters of inverting, also referred to as uncertainty.

According to the inversion method of the liquid Cloud microphysical parameter, refutation process is realized using CloudSat measured datas, And analyze inversion result.Selected inverting example be stratocumulus, inverting adopt cloud particle radius priori data as shown in Fig. 2 with The fixed priori data adopted in existing refutation process compare (i.e. effective radius priori data for fixed value, and not with height and Longitude and latitude changes), can more reflect the true of cloud physics parameter by the calculated Effective radius of actual measurement radar reflectivity factor True property.

The liquid Cloud microphysical parametric results that inverting is obtained are as shown in Figure 3.Cloud microphysical parameter include Effective radius, The maximum of Liquid water content, Particle number concentration and dispersion of distribution parameter, Effective radius and cloud liquid water content occurs The stronger region of portion's radar return in the clouds, cloud particle Particle density is presented with the trend that gradually increases highly is increased, is distributed width Degree parameter is then gradually reduced with height increase, and spatial distribution and variation tendency and the CloudSat of each parameter issue result basic Cause, and meet the water dust characteristic parameter scope of the cumulus of Chinese scholars statistics, inversion result is credible.

Although present disclosure has been made to be discussed in detail by above preferred embodiment, but it should be appreciated that above-mentioned Description is not considered as limitation of the present invention.After those skilled in the art have read the above, for the present invention's Various modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.

Claims (8)

1. a kind of inversion method of liquid Cloud microphysical parameter, it is characterised in that

Inversion algorithm tries to achieve optimal solution in the case where cost function obtains minimum of a value, and the cost function is state vector and elder generation The weighting sum of data difference and known measurement vector sum forward model value difference point is tested,

$D={(x-x_a)}^T{S}_a^{-1}(x-x_a)+{\[y-F\left(x\right)\]}^T{S}_y^{-1}\[y-F\left(x\right)\]$

In formula, x_aIt is according to the calculated priori data of radar reflectivity factor, S_aFor the covariance matrix of priori data, F (x) for radar reflectivity factor forward physical pattern, S_yIt is the covariance matrix of radar reflectivity factor measure error, y is Measurement vector is radar reflectivity factor, and x is that unknown state vector treats inverting parameter, described to treat that inverting parameter includes cloud particle Subnumber density, geometrical mean radius and dispersion of distribution parameter；

By priori data x_aAs iterative initial value, cost function D is minimized by continuous, finally try to achieve the iteration for treating inverting parameter x Solution.

2. a kind of inversion method of liquid Cloud microphysical parameter as claimed in claim 1, it is characterised in that in assuming air The size distribution of cloud particle meets logarithm normal distribution, sets up radar reflectivity factor and liquid cloud physics parameter i.e. cloud particle subnumber Density, efficient radius of cloud particle, the functional relation of cloud particle dispersion of distribution parameter；

$N\left(r\right)=\frac{N_T}{\sqrt{2\mathrm{\π}}{\mathrm{\σ}}_{log}r}\exp \[\frac{-{\ln }^2(r/r_g)}{2{{\mathrm{\σ}}_{log}}^2}\]$

$\ln r_g=\stackrel{\‾}{\ln r},{\mathrm{\σ}}_{log}={\mathrm{ln\σ}}_g,{{\mathrm{\σ}}_g}^2=\stackrel{\‾}{{(\ln r-\ln r_g)}^2}$

In formula：N_TIt is cloud particle number density；R is efficient radius of cloud particle；r_gIt is geometrical mean radius；σ_logIt is dispersion of distribution ginseng Number, is dimensionless variable；σ_gFor geometric standard deviation；Ln represents natural logrithm conversion；Horizontal line is represented seeks arithmetic mean of instantaneous value；

In the case where rainfall is not considered, it is believed that cloud particle yardstick is sufficiently small, Rayleigh scattering condition is met, according to aqueous water in cloud Content C_LW, efficient radius of cloud particle r_eWith the definition of radar reflectivity factor Z, following equation is derived by,

$C_{LW}={\∫}_0^{\mathrm{\∞}}\frac{4}{3}{\mathrm{\πr}}^3{\mathrm{\ρ}}_{\mathrm{\ω}}N\left(r\right)dr=\frac{4}{3}{\mathrm{\πN}}_T{\mathrm{\ρ}}_{\mathrm{\ω}}{r_g}^3\exp \left(\frac{9}{2}{{\mathrm{\σ}}_{log}}^2\right)$

$r_e=\frac{{\∫}_0^{\mathrm{\∞}}N\left(r\right)r^{3}dr}{{\∫}_0^{\mathrm{\∞}}N\left(r\right)r^{2}dr}=r_g\exp \left(\frac{5}{2}{{\mathrm{\σ}}_{\log }}^2\right)$

$Z=64{\∫}_0^{\mathrm{\∞}}N\left(r\right)r^{6}dr=64N_T{r_g}^6\exp \left(18{{\mathrm{\σ}}_{\log }}^2\right)$

In formula：ρ_ωRepresent water density.

3. a kind of inversion method of liquid Cloud microphysical parameter as claimed in claim 2, it is characterised in that the priori data x_aWith the covariance matrix S of priori data_aPopulation density N is obtained according to by historical statistical data_TAnd distribution width sigma_logTo count Obtain,

In formula, range bin z_i；z₁And z_nThe range bin on cloud base and cloud top in radar profile is represented respectively；Subscript a represents state vector The priori value of x, r_ga、N_TaAnd σ_logaIt is the priori value for representing efficient radius of cloud particle, Particle number concentration and the dispersion of distribution respectively.

4. a kind of inversion method of liquid Cloud microphysical parameter as claimed in claim 1, it is characterised in that by cloud particle chi Very little i.e. efficient radius of cloud particle r_e, Particle number concentration N_TAnd distribution width sigma_log, extrapolate the forward direction of the radar reflectivity factor Multiplicative model F (x)；

Measurement vector y and state vector x relation between the two are set up by forward physical model F (x), forward physical pattern can It is expressed as

Y=F (x)+ε_y

$F\left(x\right)=\left[\begin{array}{c}{Z}_{FM}\left(z_1\right)\\ .\\ .\\ .\\ Z_{FM}\left(z_n\right)\end{array}\right]$

ε in formula_yRepresent measure error；Z_FM(z_i) it is each range bin z_iRadar reflectivity factor；z₁And z_nIt is wide that radar is represented respectively The range bin on cloud base and cloud top in line.

5. a kind of inversion method of liquid Cloud microphysical parameter as claimed in claim 4, it is characterised in that by radar reflection The defined formula of the rate factor is derived and calculates each range bin z_iRadar reflectivity factor Z_FM(z_i)；

$Z_{FM}\left(z_i\right)=10log\left\{\begin{array}{c}64N_{Ti}{r}_{gi}^6\exp \left(18{\mathrm{\σ}}_{\log i}^2\right)\\ \×\exp \[\frac{-16{\mathrm{\π}}^2{N}_{Ti}}{\mathrm{\λ}}K\exp \left(\frac{9}{2}{\mathrm{\σ}}_{\log i}^2\right)\mathrm{\Δ}z\underset{j=i+1}{\overset{n}{\Σ}}{r}_{gi}^3\]\end{array}\right\},i=1,...,n-1$

$Z_{FM}\left(z_n\right)=10log\[64N_{Tn}{r}_{gn}^6\exp \left(18{\mathrm{\σ}}_{\log n}^2\right)\]$

K=(m²-1)/(m²+2)

In formula, m represents complex refractive index.

6. a kind of inversion method of the liquid Cloud microphysical parameter as described in claim 1 or 4, it is characterised in that the measurement Vectorial y is with state vector x

$\begin{array}{cc}x=\left[\begin{array}{c}{r}_g\left(z_1\right)\\ .\\ .\\ .\\ r_g\left(z_n\right)\\ N_T\left(z_1\right)\\ .\\ .\\ .\\ N_T\left(z_n\right)\\ {\mathrm{\σ}}_{\log }\left(z_1\right)\\ .\\ .\\ .\\ {\mathrm{\σ}}_{\log }\left(z_n\right)\end{array}\right]& y=\left[\begin{array}{c}{Z}_{dB}\left(z_1\right)\\ .\\ .\\ .\\ Z_{dB}\left(z_n\right)\end{array}\right]\end{array}$

In formula：Z_dB(z_i)、r_g(z_i)、N_T(z_i) and σ_log(z_i) radar reflectivity factor, geometrical mean radius, cloud particle are represented respectively Subnumber density and dispersion of distribution parameter are in range bin z_iThe value at place；N represents the cloud range bin number in profile.

7. a kind of inversion method of liquid Cloud microphysical parameter as claimed in claim 6, it is characterised in that by the priori number According to x_aAs iterative initial value, by continuously minimizing the cost function D, the iterative solution for treating inverting parameter x is tried to achieve, and The condition of convergence of iteration is met when being iterated calculating,

${\hat{x}}^{i+1}={(S_a^{-1}+L^{iT}{S}_y^{-1}{L}^i)}^{-1}\×\{S_a^{-1}{x}_a+L^{iT}{S}_y^{-1}\[y-F\left({\hat{x}}^i\right)+L^i{\hat{x}}^i\]\}$

In formula：Subscript i and i+1 represent iterations, and L represents sensitivity of the forward physical pattern to state vector x.

8. a kind of inversion method of liquid Cloud microphysical parameter as claimed in claim 7, it is characterised in that the receipts of the iteration The condition of holding back is：

$\mathrm{\&Delta;}{\hat{x}}^T{S}_x^{-1}\mathrm{\&Delta;}\hat{x}<0.01n$

$S_x={(S_a^{-1}+L^T{S}_y^{-1}L)}^{-1}$

In formula：S_xIt is the error co-variance matrix of iterative state vector, represents three variances and each parameter for treating inverting physical quantity Between covariance；S_yIt is the covariance matrix of radar measurement errors.