气候变化领域集成服务门户(CCPortal): Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning

CCPortal

DOI	10.1016/j.rse.2021.112355
	Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning
	Arulraj M.; Barros A.P.
发表日期	2021
ISSN	00344257
卷号	257
英文摘要	Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning. The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over three years (2016–2019), achieving large reductions in false alarms (77%) and missed detections (82%) relative to GPM Ku-PR precipitation products. The spatial distribution of predicted precipitation classes within the GPM overpass reflects the complex interactions between storms and topography that determine orographic precipitation regimes. For each GPM pixel, the local precipitation class informs on the vertical structure of rainfall microphysics aiming to capture low-level processes missed in GPM DPR reflectivity profiles contaminated by ground-clutter (i.e., the radar blind-zone). © 2021
英文关键词	Convolution neural network; Global precipitation measurement mission; Machine learning; Orographic precipitation; Precipitation detection; Precipitation radar
语种	英语
scopus关键词	Clutter (information theory); Complex networks; Convolutional neural networks; Decision trees; Machine learning; Precipitation (meteorology); Radar measurement; Reflection; Topography; Brightness temperatures; Dual-frequency precipitation radars; Global precipitation measurement missions; Orographic precipitation; Precipitation products; Random forest classifier; Southern Appalachian Mountains; Warm season precipitation; Space-based radar; artificial intelligence; automation; complex terrain; machine learning; precipitation assessment; radar; spatial distribution; Appalachians
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178910
作者单位	Cooperative Institute for Satellite Earth System Studies, Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, MD, United States; Department of Civil and Environmental Engineering, Duke University, Durham, NC, United States; Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Champaign, IL, United States
推荐引用方式 GB/T 7714	Arulraj M.,Barros A.P.. Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning[J],2021,257.
APA	Arulraj M.,&Barros A.P..(2021).Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning.Remote Sensing of Environment,257.
MLA	Arulraj M.,et al."Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning".Remote Sensing of Environment 257(2021).