气候变化领域集成服务门户(CCPortal): Application of machine learning to large hail prediction - The importance of radar reflectivity; lightning occurrence and convective parameters derived from ERA5

CCPortal

DOI	10.1016/j.atmosres.2019.05.010
	Application of machine learning to large hail prediction - The importance of radar reflectivity; lightning occurrence and convective parameters derived from ERA5
	Czernecki B.; Taszarek M.; Marosz M.; Półrolniczak M.; Kolendowicz L.; Wyszogrodzki A.; Szturc J.
发表日期	2019
ISSN	0169-8095
起始页码	249
结束页码	262
卷号	227
英文摘要	This study presents a concept for coupling remote sensing data and environmental variables with machine learning techniques for the prediction of large hail events. In particular, we want to address the following question: How would one improve the performance of large hail warnings / forecasts if thermodynamic and kinematic parameters derived from a numerical weather prediction model are combined with real-time remote sensing data? For this purpose, POLRAD radar reflectivity, EUCLID lightning detection data, and convective indices calculated from the ERA5 reanalysis are combined and then compared with large hail reports from Poland (2008–2017). The data fusion of multiple sources, coupled with the machine learning approach, makes it possible to greatly improve the robustness of large hail prediction compared to any single product commonly used in operational forecasting. This is especially noticeable with the reduced number of false alarms. Although the created machine learning models are mainly driven by radar reflectivity, composite thermodynamic and kinematic indices such as Hail Size Index (HSI), Significant Hail Parameter (SHIP), Large Hail Parameter (LGHAIL), and WMAXSHEAR provide an added value to a model's performance. The accuracy achieved by a random forest model brings with it encouraging prospects for future research with respect to operational forecasters (who may fill in the gaps within NWP-derived data with remotely sensed measurement) and climatological studies that aim to investigate past and future changes in severe weather occurrences. © 2019
英文关键词	ERA5; ESWD; EUCLID; Forecasting; Large hail; Machine learning; Thunderstorm
语种	英语
scopus关键词	Couplings; Data fusion; Decision trees; Forecasting; Kinematics; Learning systems; Lightning; Machine learning; Radar; Reflection; Remote sensing; Thunderstorms; Weather forecasting; Environmental variables; ERA5; ESWD; EUCLID; Machine learning approaches; Machine learning techniques; Numerical weather prediction models; Operational forecasters; Precipitation (meteorology); climatology; hail; lightning; machine learning; parameterization; prediction; radar; remote sensing; Poland [Central Europe]
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/162043
作者单位	Department of Climatology, Institute of Physical Geography and Environmental Planning, Adam Mickiewicz University, Poznań, Poland; Department of Meteorology and Climatology, University of Gdańsk, Poland; Institute of Meteorology and Water Management – National Research Institute, Warsaw, Poland
推荐引用方式 GB/T 7714	Czernecki B.,Taszarek M.,Marosz M.,et al. Application of machine learning to large hail prediction - The importance of radar reflectivity; lightning occurrence and convective parameters derived from ERA5[J],2019,227.
APA	Czernecki B..,Taszarek M..,Marosz M..,Półrolniczak M..,Kolendowicz L..,...&Szturc J..(2019).Application of machine learning to large hail prediction - The importance of radar reflectivity; lightning occurrence and convective parameters derived from ERA5.Atmospheric Research,227.
MLA	Czernecki B.,et al."Application of machine learning to large hail prediction - The importance of radar reflectivity; lightning occurrence and convective parameters derived from ERA5".Atmospheric Research 227(2019).