CN111832175B - Method and system for measuring sea surface wind speed of scatterometer under rainfall condition - Google Patents
Method and system for measuring sea surface wind speed of scatterometer under rainfall condition Download PDFInfo
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- CN111832175B CN111832175B CN202010676803.4A CN202010676803A CN111832175B CN 111832175 B CN111832175 B CN 111832175B CN 202010676803 A CN202010676803 A CN 202010676803A CN 111832175 B CN111832175 B CN 111832175B
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Abstract
The application relates to a method and a system for measuring the sea surface wind speed of a scatterometer under rainfall conditions, wherein the historical scatterometer wind speed and the historical scatterometer rainfall rate are used as input quantities, the buoy wind speed is used as output quantity, and after a sea surface wind speed measuring model is constructed, the current scatterometer wind speed and the current scatterometer rainfall rate are input into the sea surface wind speed measuring model to obtain the actual sea surface wind speed. According to the application, the model is built by directly utilizing the sea surface wind speed of the scatterometer and the rainfall rate of the scatterometer, the sea surface wind speed of the scatterometer is measured under the rainfall condition, and the measurement accuracy is improved.
Description
Technical Field
The application relates to the field of ocean remote sensing and application, in particular to a method and a system for measuring the sea surface wind speed of a scatterometer under rainfall conditions.
Background
The three aspects of attenuation of the signals by the raindrops, backward volume scattering of the raindrops and disturbance of the signals after the raindrops fall to the sea surface jointly act on the signals, so that the sea surface wind speed inversion precision of the scatterometer is greatly reduced.
The signal attenuation increases with increasing rainfall intensity and incidence angle, and the backscattering and rain surface turbulence effects increase with increasing rainfall intensity and decrease with increasing incidence angle. Wang, etc. indicate that the calculated value of the wind speed of the scatterometer is higher under rainfall condition, and the deviation of the calculated value of the wind direction is larger. Contreras et al studied the change in the backscatter coefficient of a scatterometer caused by raindrops changing sea surface roughness using observed data and theoretical models. Li and the like establish a sea surface scattering forward model considering rainfall attenuation, and analyze the influence of rainfall on sea surface backscattering coefficients under the condition of different incidence angles of Ku wave bands. Stills et al established a model of the wind field inversion experience of scatterometers under rainfall conditions using the QUIKSCAT scatterometer data and the matched radiometer rainfall data, and the results indicated that horizontal polarization was more severely affected by precipitation than vertical polarization. Tourn dre et al establish a theoretical model to calculate the influence of rainfall on the backscattering coefficient of the scatterometer based on the radiation transmission theory of rainfall signal attenuation and backscattering, and the result shows that the high-resolution rainfall data has better correction effect on the sea surface wind field of the scatterometer. Hilburn et al used radiometer rainfall data to correct the backscatter coefficient of SeaWinds scatterometer, and further developed wind field inversion. Draper et al establish an empirical model for Ku band scattering coefficient correction under rainfall conditions, quantify signal attenuation and volume scattering caused by rainfall, and analysis results show that the change of sea surface roughness caused by rainfall under low wind speed conditions plays a main role, and the atmospheric attenuation caused by rainfall under high wind speed conditions plays a main role. Nielsen et al established a model of SeaWinds sea surface wind farms and rainfall with which sea surface wind farms and rainfall could be inverted. Stiles et al also established improved geophysical model functions under rainfall conditions. Zhong Jian and the like discuss the sensitivity of the fuzzy removal effect of each parameter in the two-dimensional variation method from the theoretical analysis perspective, and then propose an optimal parameter selection method to improve the wind direction precision. Zhang Liang and the like construct a geophysical model function suitable for rainfall conditions, and wind field inversion is carried out on two typhoon processes of 'Capricorn' and 'Xiangshen' by adopting a two-dimensional variation and multi-solution scheme fuzzy removing method. Li Dawei A correction model of the backscatter coefficient of the scatterometer under rainfall condition is established, and the inversion precision of the wind field of the HY-2A scatterometer is improved.
The scheme is that sea surface wind speed precision improvement is developed based on the backscattering coefficient of the scatterometer and other radiometer rainfall rate products.
Disclosure of Invention
The application aims to provide a method and a system for directly measuring the sea surface wind speed of a scatterometer under rainfall conditions by utilizing the sea surface wind speed of the scatterometer and the rainfall rate of the scatterometer, so that the measurement accuracy is improved.
In order to achieve the above object, the present application provides the following solutions:
a method of scatterometer sea surface wind speed measurement under rainfall conditions, the method comprising:
s1: the method comprises the steps of taking a historical scatterometer wind speed and a historical scatterometer rainfall rate as input quantities, taking a buoy wind speed as output quantity, and constructing a sea surface wind speed measurement model;
s2: acquiring the current wind speed of a scatterometer and the current rainfall rate of the scatterometer;
s3: and inputting the current scatterometer wind speed and the current scatterometer rainfall rate into the sea surface wind speed measurement model to obtain the actual sea surface wind speed.
Optionally, the S1 specifically includes:
s101: acquiring the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2,3, …, n, n is the training sample size;
s102: from the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i Construction of binary linear model b i ＝β 0 +β 1 s i +β 2 r i +ε i ；ε i For random measurement errors, each ε i Independent of each other and obeys normal distribution with mathematical expectation of 0;
s103: establishing an objective function according to the binary linear model
S104: calculating the minimum value of the objective function to obtain a measurement vector beta= (beta) of the sea surface wind speed measurement model 0 ,β 1 ,β 2 ) T ；
Wherein, the step S104 specifically includes:
according to the objective functionRespectively to beta 0 、β 1 And beta 2 And (3) deriving to obtain:
and (3) shifting the equation set to obtain:
extracting Y and a historical observation matrix X, wherein
According to beta= (X' X) -1 X' Y calculates a measurement vector of the sea surface wind speed measurement model; the measurement vector of the sea surface wind speed measurement model comprises: ASCAT coefficient vector beta A And QUIKSCT coefficient vector beta Q 。
Optionally, the step S3 specifically includes:
s301: judging whether the current rainfall rate of the scatterometer is greater than 0; if yes, S302 is performed; if not, returning to the step S2;
s302: construction of the current observation matrix X input ；
S304: according to Y output ＝X input And beta is obtained to obtain the actual sea surface wind speed.
Optionally, the step S3 further includes:
s303: judging the current observation matrix X input The type of corresponding scatterometer wind speed; if the ASCAT marine measurement data is the ASCAT marine measurement data, selecting the ASCAT coefficient vector beta A If the QUIKSCT coefficient vector beta is selected for the QUIKSCT marine survey data Q ；
Wherein: the ASCAT marine survey data includes: sea surface wind speed and rainfall rate; the QUIKSAT marine survey data comprises: sea surface wind speed and columnar rainfall rate.
A scatterometer sea surface wind speed measurement system under rainfall conditions, the system comprising:
the sea surface wind speed measurement model construction module M1 is used for constructing a sea surface wind speed measurement model by taking a historical scatterometer wind speed and a historical scatterometer rainfall rate as input quantity and taking a buoy wind speed as output quantity;
the data acquisition module M2 is used for acquiring the current wind speed of the scatterometer and the current rainfall rate of the scatterometer;
the sea surface wind speed measuring module M3 is used for inputting the current scatterometer wind speed and the current scatterometer rainfall rate into the sea surface wind speed measuring model to obtain the actual sea surface wind speed.
Optionally, the sea surface wind speed measurement model building module M1 specifically includes:
a historical data acquisition unit for acquiring the historical scatterometer wind speed s i And the historic scatterometer rainfall rate r i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2,3, …, n, n is the training sample size;
a binary linear model construction unit for constructing a binary linear model based on the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i Construction of binary linear model b i ＝β 0 +β 1 s i +β 2 r i +ε i ；ε i For random measurement errors, each ε i Independent of each other and obeys normal distribution with mathematical expectation of 0;
an objective function establishing unit for establishing an objective function according to the binary linear model
A measurement vector determining unit for calculating the minimum value of the objective function and determining a measurement vector β= (β) of the sea surface wind speed measurement model 0 ,β 1 ,β 2 ) T ；
The measuring vector determining unit of the sea surface wind speed measuring model specifically comprises:
a derivation subunit for generating a target function according to the target functionRespectively to beta 0 、β 1 And beta 2 And (3) deriving to obtain:
the term shifting subunit is configured to shift the term for the equation set to obtain:
a data extraction subunit for extracting Y and a historical observation matrix X, wherein
Measurement vector calculation subunit of sea surface wind speed measurement model according to beta= (X' X) -1 X' Y calculates a measurement vector of the sea surface wind speed measurement model; the measurement vector of the sea surface wind speed measurement model comprises: ASCAT coefficient vector beta A And QUIKSCT coefficient vector beta Q 。
Optionally, the sea surface wind speed measurement module M3 specifically includes:
a judging unit, configured to judge whether the current precipitation rate of the scatterometer is greater than 0; if yes, S32 is carried out; if not, returning to the data acquisition module;
a matrix construction unit for constructing a current observation matrix X input ；
Sea surface wind speed calculating unit for calculating the wind speed according to Y output ＝X input And beta is obtained to obtain the actual sea surface wind speed.
Optionally, the sea surface wind speed measurement module M3 further includes:
a scatterometer wind speed type judging unit for judging the current observation matrix X input The type of corresponding scatterometer wind speed; if the ASCAT marine measurement data is the ASCAT marine measurement data, selecting the ASCAT coefficient vector beta A If the QUIKSCT coefficient vector beta is selected for the QUIKSCT marine survey data Q ；
Wherein: the ASCAT marine survey data includes: sea surface wind speed and rainfall rate; the QUIKSAT marine survey data comprises: sea surface wind speed and columnar rainfall rate.
According to the specific embodiment provided by the application, the application discloses the following technical effects: the application directly uses the wind speed of the scatterometer and the rainfall rate obtained by the scatterometer to carry out model construction, and then uses the actual wind speed of the scatterometer and the rainfall rate product of the scatterometer as input models to obtain the actual wind speed. By constructing the model, the measuring accuracy of the wind speed of the scatterometer under the rainfall condition is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for measuring the sea surface wind speed of a scatterometer under rainfall condition;
FIG. 2 is a plot of float wind speed versus ASCAT wind speed scatter under rainfall conditions;
FIG. 3 is an ASCAT sea surface wind speed error analysis chart under different rainfall rates;
FIG. 4 is a plot of buoy wind speed versus QUIKSCT wind speed scatter under rainfall conditions;
FIG. 5 is a graph of QUIKSAT sea surface wind speed error analysis under different columnar rainfall rates;
FIG. 6 is a graph comparing wind speed sample size, ASCAT wind speed error and improved wind speed error for different rainfall conditions;
FIG. 7 is a graph comparing wind speed sample size, QUIKSAT wind speed error and improved wind speed error for different columnar rainfall rates;
FIG. 8 is a graph of wind speed sample size, ASCAT wind speed error and improved wind speed error versus different wind speed conditions;
FIG. 9 is a graph of wind direction sample size, QUIKSAT wind direction error and improved wind direction error contrast for different wind speed conditions;
FIG. 10 is a flow chart of a method for measuring the wind speed of a scatterometer sea surface under rainfall condition of the application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application aims to provide a method for directly measuring the sea surface wind speed of a scatterometer under the rainfall condition by utilizing the sea surface wind speed of the scatterometer and the rainfall rate of the scatterometer, so that the measurement accuracy is improved.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description.
A flow diagram of the method is shown in figure 1, and specifically comprises the following steps:
s1: and constructing a sea surface wind speed measurement model by taking the historical scatterometer wind speed and the historical scatterometer rainfall rate as input quantities and taking the buoy wind speed as output quantity.
S2: the current scatterometer wind speed and the current scatterometer rainfall rate are obtained.
S3: inputting the current scatterometer wind speed and the current scatterometer rainfall rate into a sea surface wind speed measurement model to obtain the actual sea surface wind speed.
S1 specifically comprises:
s101: acquiring the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2,3, …, n, n is the training sample size.
S102: from the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i Construction of binary linear model b i ＝β 0 +β 1 s i +β 2 r i +ε i ；ε i For random measurement errors, each ε i Independent of each other and at the same time obeys a normal distribution for which a mathematical expectation is 0.
S103: establishing an objective function according to the binary linear model
S104: calculating the minimum value of the objective function to obtain a measurement vector beta= (beta) of the sea surface wind speed measurement model 0 ,β 1 ,β 2 ) T 。
Wherein S104 specifically includes:
according to the objective functionRespectively to beta 0 、β 1 And beta 2 And (3) deriving to obtain:
and (3) shifting the equation set to obtain:
extracting Y and a historical observation matrix X, wherein
According to beta= (X' X) -1 X' Y calculates a measurement vector of the sea surface wind speed measurement model; the measurement vector of the sea surface wind speed measurement model comprises: ASCAT coefficient vector beta A And QUIKSCT coefficient vector beta Q 。
S3 specifically comprises:
s301: judging whether the current rainfall rate of the scatterometer is greater than 0; if yes, S302 is performed; if not, returning to the step S2;
s302: construction of the current observation matrix X input ；
S304: according to Y output ＝X input And beta is obtained to obtain the actual sea surface wind speed.
Optionally, the step S3 further includes:
s303: judging the current observation matrix X input The type of corresponding scatterometer wind speed; if the ASCAT marine measurement data is the ASCAT marine measurement data, selecting the ASCAT coefficient vector beta A If the QUIKSCT coefficient vector beta is selected for the QUIKSCT marine survey data Q ；
Wherein: the ASCAT marine survey data includes: sea surface wind speed and rainfall rate; the QUIKSAT marine survey data comprises: sea surface wind speed and columnar rainfall rate.
Based on the method, ASCAT marine measurement data from 1 st 3 nd month to 17 th 6 th month 2019 are selected in the specific implementation process, wherein the ASCAT marine measurement data comprise sea surface wind speed and rainfall rate; selecting QUIKSAT marine survey data from 7.1999, 19, to 11.2009, 19, including sea surface wind speed and columnar rainfall rate; the site observation data adopts buoy data of the national data buoy center (National Data Buoy Center, NDBC); the ASCAT marine measurement data is matched with buoy data to obtain 1650 groups of rainfall samples, and the QUIKSAT marine measurement data is matched with buoy data to obtain 1453 groups of rainfall samples. The application takes the first 1000 groups of samples as an improved model sample library and the rest samples as a test sample library.
The ASCAT training set wind speed error was calculated to be 2.5m/s and the QUIKSAT wind speed error was calculated to be 4.3m/s, and after the improved model calculation, the wind speed error was reduced to 1.4m/s and 2.2m/s, respectively, as shown in Table 1.
TABLE 1 training set and test set error conditions
| Category(s) | ASCAT(m/s) | QUIKSCAT(m/s) |
| Training set | 2.5 | 4.3 |
| Test set | 1.4 | 2.2 |
And (3) analyzing ASCAT sea surface wind speed errors under different rainfall rate conditions: as shown in the scatter plot of float wind speed and ASCAT wind speed under rainfall conditions in fig. 2, it can be seen that the ASCAT wind speed under rainfall conditions is significantly overestimated, and the higher the float wind speed, the more significantly the ASCAT wind speed is overestimated. This may be due to rainfall causing the sea surface roughness to become greater and the signal strength received by the radar to become greater, resulting in overestimated wind speed inversion results.
The distribution range of the rainfall rate in ASCAT data under the rainfall condition is 0.4mm/h-12.4mm/h through statistics.
FIG. 3 is an ASCAT sea surface wind speed error analysis chart under different rainfall conditions, the value scheme of the different rainfall is that the rainfall sample size of the ith point is from the ith to the (i-0.5) th to the (i+0.5) th rainfall sample size S i And, it can be seen that the sample size decreases rapidly with increasing rainfall rate. The variation amplitude of the ASCAT wind speed error along with the increase of the rainfall rate is obviously increased, and the overall trend of gradual increase is not obvious.
Analyzing the wind speed error of the QUIKSAT sea surface under different columnar rainfall rate conditions: as shown in fig. 4, the float wind speed and the QUIKSCAT wind speed scatter plot under rainfall conditions, the QUIKSCAT wind speed under rainfall conditions is obviously overestimated, and the higher the float wind speed, the more obvious the ASCAT wind speed is overestimated. This may be due to rainfall causing the sea surface roughness to become greater and the signal strength received by the radar to become greater, resulting in overestimated wind speed inversion results. The QUIKSAT wind speed dispersion point is more divergent compared to FIG. 2, meaning that the wind speed error is larger, because the QUIKSAT scatterometer is in the Ku band and is more susceptible to rainfall.
The QUIKSAT sea surface wind speed error under different columnar rainfall rate conditions is shown in figure 5. As the columnar rainfall rate increases, the sample size decreases sharply, and the wind speed error oscillates up. Compared with ASCAT, the oscillation amplitude is smaller, and the rising trend is more obvious. When the columnar rainfall rate is larger than 20km mm/h, the QUIKSAT wind speed error is larger than 8m/s. So rainfall has serious influence on the accuracy of the QUIKSAT wind speed inversion.
The comparison of wind speed sample size, ASCAT wind speed error and improved wind speed error under different rainfall conditions is shown in FIG. 6, and the sample size gradually decreases with the increase of rainfall. Similar to fig. 3, the ASCAT wind speed error increases significantly with increasing rainfall rate, and the overall trend of increasing gradually is not obvious. The improved wind speed error is significantly lower than the ASCAT wind speed error. In addition, the trend of the improved wind speed error is similar to that of the ASCAT wind speed error, which shows that the ASCAT wind speed still has important influence on the improved wind speed in the algorithm implementation process.
The comparison of wind speed sample size, QUIKSAT wind speed error and modified wind speed error for different columnar rainfall conditions is shown in FIG. 7. The sample size drastically decreases with increasing columnar rainfall rate. The sample quantity is 0 when the columnar rainfall rate is 16km mm/h and 19km mm/h, so that a breakpoint occurs in wind speed error. With the increase of the columnar rainfall rate, the amplitude of the QUIKSAT wind speed error is increased, and the trend of the error increase is more obvious. The improved wind speed error is obviously reduced compared with the QUIKSAT wind speed error, the vibration amplitude is reduced, and the increasing trend is weakened.
As can also be seen by comparing fig. 6 and fig. 7, the ASCAT wind speed error before and after inversion is smaller than the QUIKSCAT wind speed error, which indicates that the C-band radar wind speed inversion accuracy is higher under rainfall conditions.
The wind speed sample size, ASCAT wind speed error and modified wind speed error under different wind speed conditions are compared to FIG. 8, with the sample size maximum occurring at 7m/s of buoy wind speed. The improved wind speed error is obviously smaller than the ASCAT wind speed error, and the effectiveness of the model is demonstrated.
The comparison of wind speed sample size, QUIKSAT wind speed error and improved wind direction error under different wind speed conditions is shown in figure 9, and the maximum value of the sample size is still 5m/s of the buoy wind speed. The minimum value of the improved wind speed error occurs between 8m/s and 10m/s of the buoy wind speed, and the improved wind speed error is obviously smaller than the QUIKSAT wind speed error.
The application also provides a system corresponding to the method for measuring the sea surface wind speed of the scatterometer under rainfall condition, as shown in fig. 10, the system comprises: the sea surface wind speed measurement model building module M1, the data acquisition module M2 and the sea surface wind speed measurement module M3.
The sea surface wind speed measurement model construction module M1 is used for constructing a sea surface wind speed measurement model by taking the historical scatterometer wind speed and the historical scatterometer rainfall rate as input quantities and taking the buoy wind speed as output quantity.
The data acquisition module M2 is configured to acquire a current scatterometer wind speed and a current scatterometer rainfall rate.
The sea surface wind speed measuring module M3 is used for inputting the current scatterometer wind speed and the current scatterometer rainfall rate into the sea surface wind speed measuring model to obtain the actual sea surface wind speed.
The sea surface wind speed measurement model construction module M1 specifically comprises: the device comprises a historical data acquisition unit, a binary linear model construction unit, an objective function construction unit and a measurement vector determination unit of a sea surface wind speed measurement model.
A historical data acquisition unit is used for acquiring the historical scatterometer wind speed s i And the historic scatterometer rainfall rate r i 。
A binary linear model building unit for building a model based on the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i Construction of binary linear model b i ＝β 0 +β 1 s i +β 2 r i +ε i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2,3, …, n, n is the training sample size; epsilon i For random measurement errors, each ε i Independent of each other and at the same time obeys a normal distribution for which a mathematical expectation is 0.
An objective function establishing unit for establishing an objective function according to the binary linear model
A measurement vector determining unit for calculating the minimum value of the objective function and determining a measurement vector β= (β) of the sea surface wind speed measurement model 0 ,β 1 ,β 2 ) T 。
The measuring vector determining unit of the sea surface wind speed measuring model specifically comprises: the system comprises a derivation subunit, a term shifting subunit, a data extraction subunit and a measurement vector calculation subunit of a sea surface wind speed measurement model.
A derivation subunit for generating a target function according to the target functionRespectively to beta 0 、β 1 And beta 2 And (3) deriving to obtain:
the term shifting subunit is configured to shift the equation set to obtain:
the data extraction subunit is used for extracting Y and the historical observation matrix X, wherein
Measurement vector calculation subunit of sea surface wind speed measurement model according to beta= (X' X) -1 X' Y calculates a measurement vector of the sea surface wind speed measurement model; the measurement vector of the sea surface wind speed measurement model comprises: ASCAT coefficient vector beta A And QUIKSCT coefficient vector beta Q 。
The sea surface wind speed measuring module M3 specifically comprises a judging unit, a matrix constructing unit, a scatterometer wind speed type judging unit and a sea surface wind speed calculating unit which are connected in sequence.
A judging unit, configured to judge whether the current precipitation rate of the scatterometer is greater than 0; if yes, S32 is carried out; and if not, returning to the data acquisition module.
A matrix construction unit for constructing a current observation matrix X input 。
A scatterometer wind speed type judging unit for judging the current observation matrix X input The type of corresponding scatterometer wind speed; if the ASCAT marine measurement data is the ASCAT marine measurement data, selecting the ASCAT coefficient vector beta A If the QUIKSCT coefficient vector beta is selected for the QUIKSCT marine survey data Q 。
Sea surface wind speed calculating unit for calculating the wind speed according to Y output ＝X input And beta is obtained to obtain the actual sea surface wind speed.
Wherein: the ASCAT marine survey data includes: sea surface wind speed and rainfall rate; the QUIKSAT marine survey data comprises: sea surface wind speed and columnar rainfall rate.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present application have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present application and the core ideas thereof; also, it is within the scope of the present application to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the application.
Claims (6)
1. A method for measuring the wind speed of a scatterometer sea surface under rainfall conditions, the method comprising:
s1: the method comprises the steps of taking a historical scatterometer wind speed and a historical scatterometer rainfall rate as input quantities, taking a buoy wind speed as output quantity, and constructing a sea surface wind speed measurement model;
s2: acquiring the current wind speed of a scatterometer and the current rainfall rate of the scatterometer;
s3: inputting the current scatterometer wind speed and the current scatterometer rainfall rate into the sea surface wind speed measurement model to obtain an actual sea surface wind speed;
the S1 specifically comprises the following steps:
s101: acquiring the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2,3, …, n, n is the training sample size;
s102: from the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i Construction of binary linear model b i ＝β 0 +β 1 s i +β 2 r i +ε i ；ε i For random measurement errors, each ε i Independent of each other and obeys normal distribution with mathematical expectation of 0;
s103: establishing an objective function according to the binary linear model
S104: calculating the minimum value of the objective function to obtain a measurement vector beta= (beta) of the sea surface wind speed measurement model 0 ,β 1 ,β 2 ) T ；
Wherein, the step S104 specifically includes:
according to the objective functionRespectively to beta 0 、β 1 And beta 2 And (3) deriving to obtain:
and (3) shifting the equation set to obtain:
extracting Y and a historical observation matrix X, wherein
According to beta= (X' X) -1 X' Y calculates a measurement vector of the sea surface wind speed measurement model; the measurement vector of the sea surface wind speed measurement model comprises: ASCAT coefficient vector beta A And QUIKSCT coefficient vector beta Q The method comprises the steps of carrying out a first treatment on the surface of the Wherein, if the ASCAT marine measurement data is the ASCAT marine measurement data, the ASCAT coefficient vector beta is selected A If the QUIKSCT coefficient vector beta is selected for the QUIKSCT marine survey data Q 。
2. The method for measuring the sea surface wind speed of the scatterometer under the rainfall condition according to claim 1, wherein the step S3 specifically comprises:
s301: judging whether the current rainfall rate of the scatterometer is greater than 0; if yes, S302 is performed; if not, returning to the step S2;
s302: construction of the current observation matrix X input ；
S304: according to Y output ＝X input And beta is obtained to obtain the actual sea surface wind speed.
3. The method for measuring the wind speed of a scatterometer sea surface under rainfall conditions according to claim 2, wherein the step S3 further comprises:
s303: judging the current observation matrix X input The type of corresponding scatterometer wind speed;
wherein: the ASCAT marine survey data includes: sea surface wind speed and rainfall rate; the QUIKSAT marine survey data comprises: sea surface wind speed and columnar rainfall rate.
4. A scatterometer sea surface wind speed measurement system under rainfall conditions, the system comprising:
the sea surface wind speed measurement model construction module M1 is used for constructing a sea surface wind speed measurement model by taking a historical scatterometer wind speed and a historical scatterometer rainfall rate as input quantity and taking a buoy wind speed as output quantity;
the data acquisition module M2 is used for acquiring the current wind speed of the scatterometer and the current rainfall rate of the scatterometer;
the sea surface wind speed measuring module M3 is used for inputting the current scatterometer wind speed and the current scatterometer rainfall rate into the sea surface wind speed measuring model to obtain an actual sea surface wind speed;
the sea surface wind speed measurement model construction module M1 specifically comprises:
a historical data acquisition unit for acquiring the historical scatterometer wind speed s i And the historic scatterometer rainfall rate r i The method comprises the steps of carrying out a first treatment on the surface of the Where i=1, 2,3, …, n, n is the training sample size;
a binary linear model construction unit for constructing a binary linear model based on the historic scatterometer wind speed s i And the historic scatterometer rainfall rate r i Construction of binary linear model b i ＝β 0 +β 1 s i +β 2 r i +ε i ；ε i For random measurement errors, each ε i Independent of each other and obeys normal distribution with mathematical expectation of 0;
an objective function establishing unit for establishing an objective function according to the binary linear model
A measurement vector determining unit for calculating the minimum value of the objective function and determining a measurement vector β= (β) of the sea surface wind speed measurement model 0 ,β 1 ,β 2 ) T ；
The measuring vector determining unit of the sea surface wind speed measuring model specifically comprises:
a derivation subunit for generating a target function according to the target functionRespectively to beta 0 、β 1 And beta 2 And (3) deriving to obtain:
the term shifting subunit is configured to shift the term for the equation set to obtain:
a data extraction subunit for extracting Y and a historical observation matrix X, wherein
Measurement vector calculation subunit of sea surface wind speed measurement model according to beta= (X' X) -1 X' Y calculates a measurement vector of the sea surface wind speed measurement model; the measurement vector of the sea surface wind speed measurement model comprises: ASCAT coefficient vector beta A And QUIKSCT coefficient vector beta Q The method comprises the steps of carrying out a first treatment on the surface of the Wherein, if the ASCAT marine measurement data is the ASCAT marine measurement data, the ASCAT coefficient vector beta is selected A If the QUIKSCT coefficient vector beta is selected for the QUIKSCT marine survey data Q 。
5. The system for measuring the sea surface wind speed of a scatterometer under rainfall conditions according to claim 4, wherein the sea surface wind speed measuring module M3 specifically comprises:
a judging unit, configured to judge whether the current precipitation rate of the scatterometer is greater than 0; if yes, S32 is carried out; if not, returning to the data acquisition module;
a matrix construction unit for constructing a current observation matrix X input ；
Sea surface wind speed calculating unit for calculating the wind speed according to Y output ＝X input And beta is obtained to obtain the actual sea surface wind speed.
6. The system for measuring the sea surface wind speed of a scatterometer under rainfall conditions according to claim 5, wherein the sea surface wind speed measuring module M3 further comprises:
a scatterometer wind speed type judging unit for judging the current observation matrix X input The type of corresponding scatterometer wind speed;
wherein: the ASCAT marine survey data includes: sea surface wind speed and rainfall rate; the QUIKSAT marine survey data comprises: sea surface wind speed and columnar rainfall rate.
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