Work

A Data-driven Approach to Detecting Precipitation from Meteorological Sensor Data

Year: 2018

Type: preprint

Abstract: Precipitation is dependent on a myriad of atmospheric conditions. In this paper, we study how certain atmospheric parameters impact the occurrence of rainfall. We propose a data-driven, machine-learning based methodology to detect precipitation using various meteorological sensor data. Our approach achieves a true detection rate of 87.4% and a moderately low false alarm rate of 32.2%.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1805.01950

Authors: Manandhar, Shilpa, Dev, Soumyabrata, Lee, Yee Hui, Meng, Yu Song, Winkler, Stefan

Language: English

Topic: Precipitation Measurement and Analysis

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2800669362

Cites: 8

Cited by: 5

Related to: 10