CN110378074A - Infrared satellite radiance data cloud detection method of quality control based on particle filter - Google Patents
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Abstract
The present invention relates to the infrared satellite radiance data cloud detection method of quality control based on particle filter, the specific steps are as follows: (1) constructs a satellite visual field, the cloud coating ratio of defining mode layer and each mode layer；(2) all-sky emissivity values are fitted；(3) all particles are updated and make normalized；(4) the bright mild bright temperature of clear sky for comparing simulation differentiates whether channel is channel not influenced by cloud, to complete the control of the infrared satellite radiance data cloud detection quality based on particle filter.The characteristics of present invention is due to its imparametrization, effectively preventing the random quantity when handling Nonlinear Filtering Problem must satisfy the conditionality of Gaussian Profile, and then distribution more wider than Gauss model can be expressed, also there is stronger modeling ability to the nonlinear characteristic of variable parameter.Algorithm of the invention is fast and effective, and weather system can be identified for business by obtaining cloud detection mark, and numerical data assimilation provides effective reference information.
Description
Technical Field
The invention relates to the technical field of satellite meteorological data application in geoscience, in particular to an infrared satellite radiance data cloud detection quality control method based on particle filtering.
Background
Currently, there are three basic cloud detection methods internationally:
1) CO2 slicing method: smith and Frey (1990) calculated the cloud-top pressure and effective emissivity by the CO2 slicing method and cloud tested an ATOVS atmospheric infrared detector. The CO2 slicing method requires the assumption that only a thin opaque cloud exists in the atmosphere, and the calculation method is also complicated.
2) The method for utilizing the synchronous cloud product comprises the following steps: officli (2007) uses the MODIS-Resolution Imaging Spectro-radiometer (r) second-level cloud product synchronized with the aires (the Advanced InfraRed sounder) to determine the cloud-affected field of view. Chenjing et al (2011) bases on the AIRS cloud detection scheme of Goldberg et al (2003) and combines the features of a GRAPES-3DVAR system and an AIRS instrument to respectively perform cloud detection on the fields of view of the sea and the land. The implementation of this type of approach requires a higher spatiotemporal match for both data (Li et al 2005).
3) The minimization method comprises the following steps: huang et al (2004) proposed a minimum local variance of emissivity method to get an optimal estimate of single layer cloud emission by calculating the local variance of cloud spectral emissivity of a specific atmospheric layer in the ambient field. Also based on the variational approach, aurigne et al (2013a, 2013b) and Xu et al (2013, 2015) propose Multivariate Minimum residuals (MMR for short). The MMR cloud detection method adopts a minimization algorithm to carry out fitting observation by constructing a cost function, and cloud amount parameters of each mode layer are obtained through simulation. And determining whether the channel is polluted according to the difference between the radiance in clear sky and the radiance value in the simulated all-sky condition. The method needs convergence and an iteration process, and the calculation amount is relatively large.
In recent years, concepts based on probability density functions, such as Kalman Filter (KF) and Particle Filter (PF), have been widely applied to a plurality of meteorological fields, such as assimilation and inversion of atmospheric waveguides. The sample representative probability density distribution can effectively avoid the condition restriction that random quantity must meet Gaussian distribution when the nonlinear filtering problem is processed, so that the distribution which is wider than that of a Gaussian model can be expressed, and the nonlinear characteristic of a variable parameter has stronger modeling capability. The invention applies the idea of particle filtering algorithm in probability theory to the field of cloud detection of satellite data in weather for the first time, and provides necessary input information for quality control of infrared satellite data assimilation.
Disclosure of Invention
The invention aims to solve the technical problem of providing a particle filter-based infrared satellite radiance data cloud detection quality control method to solve the problems of large calculated amount and complex calculation of a cloud detection algorithm in the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows: the infrared satellite radiance data cloud detection quality control method based on particle filtering is provided, and has the innovation points that: the method specifically comprises the following steps:
(1) constructing a satellite field of view, constructing initial cloud amount profile particles in the field of view, defining each particle as a group of complete mode layer cloud coverage percentage combinations, and taking c as c1,c2,...,cnRepresents the effective cloud coverage ratio of each mode layer in the satellite field of view, wherein c0Is a clear sky proportion, n is the number of mode layers, and in a satellite view field, the cloud coverage proportion of the k-th layer of the mode layers is ckThe cloud coverage ratio of a clear sky area is 1-ck(ii) a And (3) carrying out inversion on the first time, and setting equal probability of cloud to be uniformly distributed in each mode layer, wherein the formula is as follows:
wherein, the background fields of other times of the experiment are obtained by mode prediction of the inversion result of the previous time.
(2) Fitting to obtain a total sky radiance value through the effective cloud coverage proportion of each mode layer defined in the step (1)Expressed as:
wherein the total number of mode layers is n +1, i.e. the total number of particles is n +1, and comprises the effective cloud coverage of n mode layersRatio c1,...,cnAnd percentage of clear sky c0；
(3) Particles c on the mode layer of the k-th layerkA posteriori probability ofThe particle ckThe update equation of (1) is:
wherein, sigma is the observation error,for the radiance observed at the wavenumber v,is a radiation value simulated by the radiation transmission mode CRTM at wave number v, assuming that the black body cloud is put in at the k layer of the mode layer;
particles c0A posteriori probability ofThen c is0The update equation of (1) is:
wherein,the radiance value is calculated by a radiance transmission mode under the clear sky condition; after updating each particle, performing normalization processing on all the particles to ensure that the sum of the cloud coverage proportion and the clear sky proportion of each mode layer is 1, and performing normalization processing on all the particles by adopting a formula as follows:
(4) for any channel, if the difference of the simulated bright temperature difference under the bright temperature and clear air conditions obtained by simulation under the cloud condition based on the cloud parameter inversion method is less than 1% of the bright temperature under the clear air condition, the channel can be judged to be a channel which is not influenced by cloud, and therefore the control of the infrared satellite radiance data cloud detection quality based on particle filtering is completed.
Further, the cloud coverage percentage of each particle defined in step (1) for a complete set of mode layers is expressed as: each particle represents a mode layer where all clouds are represented by the particle, and the last particle represents completely clear sky.
Further, the first time inversion set in step (1) is due to the fact that there is no previous time cloud inversion result in the assimilated background field, namely cold start.
Further, for any height surface of the k-th mode layer in the field of view in the step (2), the area covered by the cloud blocks the area from the lower part of the surface by the proportion ckIs radiated upwards, the remaining 1-ckIs effective.
Further, the specific judgment standard for judging that the channel is not affected by the cloud in the step (4) is as follows:
compared with the prior art, the invention has the following beneficial effects:
due to the non-parametric characteristic, the infrared satellite radiance data cloud detection quality control method based on particle filtering effectively avoids the condition restriction that random quantity must meet Gaussian distribution when the problem of nonlinear filtering is solved, can express the distribution wider than a Gaussian model, and has stronger modeling capability on the nonlinear characteristic of variable parameters. The algorithm is quick and effective, and the obtained cloud detection identification can provide effective reference information for business identification of a weather system and data assimilation.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments are briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a cloud detection quality control method for infrared satellite radiance data based on particle filtering according to the present invention.
Fig. 2 is a schematic diagram of cloud coverage percentages corresponding to n mode layers in a field of view according to the present invention.
Detailed Description
The technical solution of the present invention will be clearly and completely described by the following detailed description.
The invention provides a particle filter-based infrared satellite radiance data cloud detection quality control method, as shown in fig. 1 and 2, which comprises the following specific steps:
(1) constructing a satellite view field, constructing initial cloud amount profile particles in the view field, and defining each particle as a group of complete mode layer cloud coverage percentage combinations, namely each particle represents the mode layer represented by all clouds in the particle, the last particle represents the complete clear sky, and c is equal to c1,c2,...,cnRepresents the effective cloud coverage ratio of each mode layer in the satellite field of view, wherein c0Is a clear sky proportion, n is the number of mode layers, and in a satellite view field, the cloud coverage proportion of the k-th layer of the mode layers is ckThe cloud coverage ratio of a clear sky area is 1-ck(ii) a Because there is not the cloud inversion result of previous hour in the background field of assimilation, cold start promptly, then carry out the inversion to first hour, set up the cloud equiprobability evenly distributed at each mode layer, the formula is:
wherein, the background fields of other times of the experiment are obtained by mode prediction of the inversion result of the previous time.
(2) By passingFitting the effective cloud coverage proportion of each mode layer defined in the step (1) to obtain a total sky radiance valueExpressed as:
wherein the total number of the mode layers is n +1, i.e. the total number of the particles is n +1, and the effective cloud coverage ratio c of the n mode layers is included1,...,cnAnd percentage of clear sky c0(ii) a For any height surface of the field of view, the area covered by the cloud blocks the area from the lower part of the plane with the proportion ckIs radiated upwards, the remaining 1-ckIs effective;
(3) particles c on the mode layer of the k-th layerkA posteriori probability ofThe particle ckThe update equation of (1) is:
wherein, sigma is the observation error,for the radiance observed at the wavenumber v,is a radiation value simulated by the radiation transmission mode CRTM at wave number v, assuming that the black body cloud is put in at the k layer of the mode layer;
particles c0A posteriori probability ofThen c is0The update equation of (1) is:
wherein,the radiance value is calculated by a radiance transmission mode under the clear sky condition; after updating each particle, performing normalization processing on all the particles to ensure that the sum of the cloud coverage proportion and the clear sky proportion of each mode layer is 1, and performing normalization processing on all the particles by adopting a formula as follows:
(4) for any channel, if the difference between the simulated bright temperature difference under the cloud condition and the simulated bright temperature under the clear air condition, which is simulated based on the cloud parameter inversion method under the cloud condition, is less than 1% of the bright temperature under the clear air condition, the channel can be judged to be a channel which is not influenced by the cloud, wherein the specific judgment standard for judging the channel to be a channel which is not influenced by the cloud is as follows:therefore, the control of the infrared satellite radiance data cloud detection quality based on particle filtering is completed.
The above-mentioned embodiments are merely descriptions of the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention, and various modifications and improvements made to the technical solutions of the present invention by those skilled in the art should fall into the protection scope of the present invention without departing from the design concept of the present invention, and the technical contents of the present invention as claimed are all described in the technical claims.
Claims (5)
1. The infrared satellite radiance data cloud detection quality control method based on particle filtering is characterized by comprising the following steps: the method specifically comprises the following steps:
(1) constructing a satellite field of view, constructing initial cloud profile particles in the field of view, and defining each particle as a complete set of modesCombining the coverage percentage of the stratums, and taking c as c1,c2,...,cnRepresents the effective cloud coverage ratio of each mode layer in the satellite field of view, wherein c0Is a clear sky proportion, n is the number of mode layers, and in a satellite view field, the cloud coverage proportion of the k-th layer of the mode layers is ckThe cloud coverage ratio of a clear sky area is 1-ck(ii) a And (3) carrying out inversion on the first time, and setting equal probability of cloud to be uniformly distributed in each mode layer, wherein the formula is as follows:
wherein, the background fields of other times of the experiment are obtained by mode prediction of the inversion result of the previous time.
(2) Fitting to obtain a total sky radiance value through the effective cloud coverage proportion of each mode layer defined in the step (1)Expressed as:
wherein the total number of the mode layers is n +1, i.e. the total number of the particles is n +1, and the effective cloud coverage ratio c of the n mode layers is included1,...,cnAnd percentage of clear sky c0；
(3) Particles c on the mode layer of the k-th layerkA posteriori probability ofThe particle ckThe update equation of (1) is:
wherein, sigma is the observation error,for the radiance observed at the wavenumber v,is a radiation value simulated by the radiation transmission mode CRTM at wave number v, assuming that the black body cloud is put in at the k layer of the mode layer;
particles c0A posteriori probability ofThen c is0The update equation of (1) is:
wherein,the radiance value is calculated by a radiance transmission mode under the clear sky condition; after updating each particle, performing normalization processing on all the particles to ensure that the sum of the cloud coverage proportion and the clear sky proportion of each mode layer is 1, and performing normalization processing on all the particles by adopting a formula as follows:
(4) for any channel, if the difference of the simulated bright temperature difference under the bright temperature and clear air conditions obtained by simulation under the cloud condition based on the cloud parameter inversion method is less than 1% of the bright temperature under the clear air condition, the channel can be judged to be a channel which is not influenced by cloud, and therefore the control of the infrared satellite radiance data cloud detection quality based on particle filtering is completed.
2. The infrared satellite radiance data cloud detection quality control method based on particle filtering of claim 1, wherein: the cloud coverage percentage combination of each particle defined in the step (1) for a complete set of mode layers is expressed as: each particle represents a mode layer where all clouds are represented by the particle, and the last particle represents completely clear sky.
3. The infrared satellite radiance data cloud detection quality control method based on particle filtering of claim 1, wherein: the first time inversion set in the step (1) is because there is no cloud inversion result of the previous time in the assimilated background field, namely cold start.
4. The infrared satellite radiance data cloud detection quality control method based on particle filtering of claim 1, wherein: for any height surface of the k-th layer mode layer in the field of view in the step (2), the area covered by the cloud blocks the area from the lower part of the surface with the proportion ckIs radiated upwards, the remaining 1-ckIs effective.
5. The infrared satellite radiance data cloud detection quality control method based on particle filtering of claim 1, wherein: the specific judgment standard for judging whether the channel is not affected by the cloud in the step (4) is as follows:
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112990701A (en) * | 2021-03-12 | 2021-06-18 | 南京信息工程大学 | Automatic station temperature data quality control method based on EOF |
| CN113051529A (en) * | 2021-03-17 | 2021-06-29 | 哈尔滨工程大学 | Particle filter data assimilation method based on statistical observation and localized average weight |
| CN115165786A (en) * | 2022-07-06 | 2022-10-11 | 国家卫星气象中心(国家空间天气监测预警中心) | Equivalent clear sky radiation correction method for infrared hyperspectral atmosphere detector based on imager |
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| 许冬梅等: "基于卫星辐射率资料的两种三维云反演方法对比研究", 《大气科学》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990701A (en) * | 2021-03-12 | 2021-06-18 | 南京信息工程大学 | Automatic station temperature data quality control method based on EOF |
| CN112990701B (en) * | 2021-03-12 | 2023-06-23 | 南京信息工程大学 | EOF-based automatic station temperature data quality control method |
| CN113051529A (en) * | 2021-03-17 | 2021-06-29 | 哈尔滨工程大学 | Particle filter data assimilation method based on statistical observation and localized average weight |
| CN113051529B (en) * | 2021-03-17 | 2023-05-30 | 哈尔滨工程大学 | Localized uniform weight particle filtering data assimilation method based on statistical observation |
| CN115165786A (en) * | 2022-07-06 | 2022-10-11 | 国家卫星气象中心(国家空间天气监测预警中心) | Equivalent clear sky radiation correction method for infrared hyperspectral atmosphere detector based on imager |
| CN115165786B (en) * | 2022-07-06 | 2023-09-19 | 国家卫星气象中心(国家空间天气监测预警中心) | Equivalent clear sky radiation correction method for infrared hyperspectral atmospheric detector based on imager |
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