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Precipitation inversion method and system based on sunflower stationary satellite

A geostationary satellite and inversion technology, applied in neural learning methods, instruments, meteorology, etc., can solve problems such as limited estimation accuracy and large computing power consumption, and achieve the effect of improving accuracy

Pending Publication Date: 2022-05-13
北京玖天气象科技有限公司
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  • Application Information

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Problems solved by technology

Among the above methods, method ① has regional limitations, and there are great limitations in areas with a latitude higher than 40°, method ② has limited estimation accuracy, and method ③ consumes a lot of computing power

Method used

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  • Precipitation inversion method and system based on sunflower stationary satellite
  • Precipitation inversion method and system based on sunflower stationary satellite
  • Precipitation inversion method and system based on sunflower stationary satellite

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Experimental program
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Effect test

Embodiment 1

[0033] Embodiment 1 of the present application provides a precipitation inversion method based on sunflower geostationary satellites. As the most advanced means of remote sensing observation, geostationary satellites can effectively perform real-time Precipitation monitoring, using the characteristics of high temporal resolution of satellites, can retrieve precipitation on a large scale, and it will be a good supplement for areas with few observations or lack of observations.

[0034] see figure 1 , based on the sunflower geostationary satellite precipitation retrieval method, which specifically includes the following steps:

[0035] Step 110, pre-selecting a large number of satellite channel data and elevation data corresponding to the station as the first training sample set, and selecting a large number of station real-time precipitation statistics under the corresponding area of ​​the station as the second training sample set;

[0036] figure 2 Design a schematic diagra...

Embodiment 2

[0054] Embodiment 2 of the present application provides a precipitation inversion system based on sunflower geostationary satellites, including: a training sample set selection module, a training module, an acquisition module, and a model output module;

[0055] The training sample set selection module pre-selects a large number of satellite channel data and elevation data as the first training sample set, and selects a large number of station real-time precipitation statistics as the second training sample set; the training module takes the first sample set and the second sample set Input into the convolutional neural network for training, through the three-layer convolutional classifier and the residual neural network regressor, the rain model set and the precipitation intensity model set are obtained; the acquisition module collects the satellite channel data and elevation data of the current site, The channel data and elevation data are input into the rain model set; the mo...

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Abstract

The invention discloses a rainfall inversion method and system based on a sunflower stationary satellite. The method comprises the following steps: pre-selecting a large amount of satellite channel data and elevation data as a first training sample set, and selecting a large amount of site live rainfall statistics as a second training sample set; inputting the first sample set and the second sample set into a convolutional neural network for training, and obtaining a weather model set and a rainfall intensity model set through a three-layer convolution classifier and a residual neural network regression device; and satellite channel data and elevation data of a current station are collected, the current satellite channel data and elevation data are input into the sun and rain model set, whether the current station is a rainfall area or a non-rainfall area is determined, and for the rainfall area, the rainfall statistic of the rainfall area is input into the rainfall intensity model, namely, a satellite inversion live rainfall index is output. According to the method of combining the rain and sun classification and the rainfall intensity model based on the neural network, the accuracy of rainfall inversion is improved.

Description

technical field [0001] The present application relates to the field of early warning of meteorological disasters, in particular to a precipitation inversion method and system based on Sunflower geostationary satellites. Background technique [0002] Precipitation is a fundamental component of the Earth's water cycle and has important meteorological, climatological and hydrological implications. Accurately measuring precipitation and its regional and global distribution has long been a challenging scientific research goal. Accurate measurement of rainfall is of great significance to industrial and agricultural production, water resource utilization, and forecasting of natural disasters such as floods and droughts. Monitoring and tracking torrential rain and other severe weather systems is difficult due to insufficient density of surface rain gauge and ground-based rain radar observation networks. The use of satellite remote sensing data to retrieve rainfall has a wide cover...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06N3/04G06N3/08G06F17/18G01W1/10
CPCG06F30/27G06N3/08G06F17/18G01W1/10G06F2119/02G06N3/045G06F18/2414G06F18/214
Inventor 郭禹琛何晓凤武正天王仁磊郭鹏
Owner 北京玖天气象科技有限公司
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