Rainfall estimation method based on microwave rain attenuation and rainfall monitoring system

Abstract

The invention provides a rainfall estimation method based on microwave rainfall attenuation, which comprises the following steps: acquiring microwave rainfall attenuation data of an estimation area and corresponding rainfall data, and obtaining a rainfall estimation result to be corrected through a microwave rainfall inversion model; acquiring multi-source non-rainfall data corresponding to the rainfall data, training a deep belief network by using the multi-source non-rainfall data and the microwave rainfall data set, constructing a correction and optimization model, and performing correctionand optimization on estimated data output by the microwave rainfall inversion model; obtaining a preliminary rainfall estimation result of the estimation area through the microwave rainfall inversionmodel according to estimation request parameters proposed by a user, and carrying out the correction and optimization of the preliminary rainfall estimation result according to the correction and optimization model, so as to obtain a final rainfall estimation result of the estimation area. The invention also provides a rainfall monitoring system based on microwave rain attenuation and a data processing device for rainfall estimation by using the rainfall estimation method.

Description

technical field [0001] The invention relates to the fields of earth science and meteorological science computing, in particular to a microwave-based intelligent high-time-space resolution rainfall estimation method and a rainfall monitoring system. Background technique [0002] Precipitation is the most important link in describing the climate, and it is also the most important climate indicator. At present, under the background of global climate change, my country's precipitation presents a trend of "increased frequency of extreme precipitation" and "inhomogeneous distribution of precipitation in time and space", which has led to "frequent occurrence of heavy rain disasters", "increased economic losses from floods and droughts", " In order to minimize disaster losses and prevent problems before they happen, the local government and relevant departments need to pay close attention to precipitation at all times and make timely changes to precipitation indicators. , effective c...

AI Technical Summary

Problems solved by technology

At present, after many years of construction of the National Flood Control Command System Project, small and medium-sized rivers and mountain torrent early warning, and meteorological monitoring projects, my country's rainfall station observations have a certain number of scales, but rainfall monitoring is still limited by three aspects

First, in my country's high-altitude river source areas, middle-altitude mountain belts, and desert areas, the density of rainfall station networks is low, and rainfall stations often cannot work normally and continuously. At the same time, in remote mountainous cities, traditional rainfall telemetering stations require a lot of operation and personnel maintenance Cost, many places do not have the conditions to build a website

Second, in urban precipitation monitoring, precipitation radar echoes are easily affected by clutter and obstacles, resulting in inaccurate results. Urban micro-topography and local complex terrain have a great impact on measurement results. There are many blind spots in regional radar coverage, which cannot meet the needs of cities and Real-time Response Requirements for Emergency Handling in Suburbs

Third, there are still deficiencies in local precipitation monitoring methods. In terms of space, it is mainly manifested in "low density of monitoring station network in the west" and "limited monitoring in eastern cities". In terms of monitoring technology, there are also problems such as insufficient transmission distance, signal noise, and terrain interference.

[0004] Traditional rainfall monitoring is based on rain gauges, weather radars, and remote sensing satellites. However, due to time and space constraints, it is impossible to accurately estimate surface rainfall, and it is difficult to ensure good real-time monitoring requirements.

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