MWHTS simulation brightness temperature calculation method based on deep neural network

A technology of deep neural network and calculation method, which is applied in the calculation of MWHTS simulated brightness temperature based on deep neural network, and the calculation field of MWHTS simulated brightness temperature. Increased linearity and other issues to achieve the effect of simple operation, improved calculation accuracy, and high calculation accuracy

Pending Publication Date: 2020-10-02
LUOYANG NORMAL UNIV
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Problems solved by technology

Insufficient current understanding of the interaction between microwaves and atmospheric molecules is the main cause of errors in radiative transfer models
Especially under cloudy and rainy atmospheric conditions, the nonlinearity

Method used

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  • MWHTS simulation brightness temperature calculation method based on deep neural network
  • MWHTS simulation brightness temperature calculation method based on deep neural network
  • MWHTS simulation brightness temperature calculation method based on deep neural network

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Embodiment 1

[0031] The climatology data set is selected as the reanalysis data set ERA-Interim of the European Center for Medium-Range Weather Forecasting (ECMWF). 25°N—45°N, 160°E—220°E), the data resolution is 0.5°×0.5°, using the temperature profile, humidity profile, cloud water profile, surface temperature, and surface humidity in this data set , surface pressure, 10m wind speed and cloud water content to calculate the simulated brightness temperature. The matching data set (1060162 groups) was established according to the matching rule that the time error is less than 10 minutes and the latitude and longitude error is less than 0.1° with the brightness temperature observed by FY-3D / MWHTS. In the matching data set, the clear sky data set is selected according to the cloud water content of 0, and the clear sky analysis data set (13810 groups) and the clear sky verification data set (3453 groups) are respectively established; the cloud water content is greater than 0 and less than 0.5m...

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Abstract

The invention discloses an MWHTS simulation brightness temperature calculation method based on a deep neural network. The MWHTS simulation brightness temperature calculation method comprises the steps: establishing a matching data set of an MWHTS observation brightness temperature and a climate data set in space and time; dividing the matching data set into a clear sky data set, a cloud data set and a rain data set according to the cloud water content, and respectively forming a corresponding analysis data set and a corresponding verification data set; training a deep neural network model by utilizing the three analysis data sets, inputting atmospheric parameters in the corresponding verification data sets into the trained deep neural network model, and calculating MWHTS simulation brightness temperature; inputting the atmospheric parameters in the three verification data sets into a radiation transmission model to calculate the MWHTS simulated brightness temperature, comparing the calculation precision with the MWHTS simulated brightness temperature calculation precision based on the deep neural network, and selecting MWHTS channels with higher precision to form an MWHTS simulatedbrightness temperature calculation result. According to the method, the interaction between microwaves and atmospheric molecules is modeled by using the deep neural network, the calculation precisionhigher than that of a business radiation transmission model RTTOV is obtained, and the operation is simple and easy to implement.

Description

technical field [0001] The invention relates to a calculation method for MWHTS simulated brightness temperature, which belongs to the technical field of microwave remote sensing, and in particular to a calculation method for MWHTS simulated brightness temperature based on a deep neural network. Background technique [0002] In the field of microwave remote sensing, the problems dealt with can be divided into two categories: forward modeling and inversion. For spaceborne microwave remote sensing, the so-called forward modeling is to calculate the brightness temperature observed by the spaceborne microwave radiometer by modeling the radiation transmission process of microwaves in the atmosphere, and the inversion is usually a mathematical calculation of the inversion of the radiation transmission model , that is, the process of obtaining atmospheric parameters by observing the brightness temperature with a spaceborne microwave radiometer. The radiation transfer model of micro...

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/08G06N3/045
Inventor 贺秋瑞金彦龄李德光张永新任桢琴周莉高新科朱艺萍
Owner LUOYANG NORMAL UNIV
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