NRIET quantitive precipation estimation method based on cloud classification and machine learning

A machine learning and cloud classification technology, applied in ensemble learning, ICT adaptation, rainfall/precipitation scale, etc., can solve problems such as limiting radar QPE accuracy

Inactive Publication Date: 2019-10-18
NANJING NRIET IND CORP
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AI Technical Summary

Problems solved by technology

[0003] The quantitative precipitation estimation algorithm based on the radar three-dimensional network mosaic data has been initially applied in the business, but factors such as the Z-R relationship and the radar-rain gauge fusion method limit the accuracy of the radar QPE. In the precipitation estimation, these algorithm links Both are subject to further optimization and improvement

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  • NRIET quantitive precipation estimation method based on cloud classification and machine learning
  • NRIET quantitive precipation estimation method based on cloud classification and machine learning
  • NRIET quantitive precipation estimation method based on cloud classification and machine learning

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Embodiment Construction

[0067] Such as figure 1 As shown, taking a heavy precipitation process from southern Jiangsu to Shanghai on May 25, 2018 as an example, real-time quantitative precipitation estimation is performed.

[0068] 1. Data preprocessing

[0069] Using the reflectivity data of 16 Doppler weather radars in Jiangsu Province and Shanghai, as well as surrounding Anhui, Zhejiang and other provinces and cities, the data is networked after quality control, and the combined reflectivity is calculated to form 120°E-123°E, Combined reflectivity grid field data in the Cartesian coordinates at 0.02° intervals in the 30°N-33°N range area, and the data time interval is 6 minutes.

[0070] 2. Rain gauge data preprocessing: use more than 2,000 rain gauges in Jiangsu Province and Shanghai to calculate the 6-minute cumulative precipitation after quality control.

[0071] 3. Cloud Classification

[0072] The parameters related to the cloud classification algorithm are set as follows:

[0073] Convec...

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Abstract

The invention discloses an NRIET quantitive precipation estimation method based on cloud classification and machine learning. The quantitive precipation estimation method based on cloud classificationand machine learning comprises the steps of first preprocessing radar data and rain gauge data, and matching a radar reflectivity with rain gauge precipation data based on a site; identifying different cloud systems such as strati and convective clouds according to the radar reflectivity intensity; then performing fitting training in real time using a machine learning regression algorithm to obtain a relationship model between cumulative precipation and radar combined reflectivity; and finally, applying the relationship model between cumulative precipation and radar combined reflectivity to radar combined reflectivity lattice field data in real time to obtain a quasi-real-time quantitive precipation estimation field.

Description

technical field [0001] The invention relates to an NRIET quantitative precipitation estimation method based on cloud classification and machine learning, and belongs to the technical field of precipitation inversion. Background technique [0002] With the frequent occurrence of disastrous weather in recent years, high-temporal-spatial resolution radar quantitative precipitation estimation (QPE: Quantitive Precipitation Estimation) information is used in short-term approaching and refined early warning and forecasting of disastrous weather such as heavy rain, typhoon, and flood. play an increasingly important role at work. [0003] The quantitative precipitation estimation algorithm based on the radar three-dimensional network mosaic data has been initially applied in the business, but factors such as the Z-R relationship and the radar-rain gauge fusion method limit the accuracy of the radar QPE. In the precipitation estimation, these algorithm links Both are subject to furt...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01W1/14G01S13/95G06K9/62G06N20/20
CPCG01W1/14G01S13/95G06N20/20G06F18/24Y02A90/10
Inventor 吴雪
Owner NANJING NRIET IND CORP
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