LIN Huiling,XIAO Hui,YAO Zhendong,et al.Auto-classification of Solid Precipitation Particles based on A 2DVD into Snowflake and Graupel[J].Journal of Chengdu University of Information Technology,2020,35(04):382-391.[doi:10.16836/j.cnki.jcuit.2020.04.004]
基于二维粒子谱仪的固态降水粒子自动分类研究—雪花和霰
- Title:
- Auto-classification of Solid Precipitation Particles based on A 2DVD into Snowflake and Graupel
- 文章编号:
- 2096-1618(2020)04-0382-10
- 分类号:
- TP731
- 文献标志码:
- A
- 摘要:
- 固态降水粒子进行准确而细致的分类对许多大气过程及天气雷达的应用是十分重要的。使用二维光学粒子谱仪(2DVD)对单个降水粒子进行测量,并基于测得的粒子微物理参数及特性提供降水过程中一分钟单位时间间隔内主要降水粒子类型的估测,对固态降水粒子进行自动分类。为实现自动分类任务,考虑将该工作与常用的机器学习分类算法相结合,应用朴素贝叶斯,支撑向量机(SVM),决策树三种监督学习算法对单位时间间隔内的粒子分类。文中将降水粒子归类为雪花和霰两种主要类型,并结合人工检测进行结果验证,最终利用独立的数据集进一步验证,证明分类算法的准确性
- Abstract:
- Giving an accurate and detailed classification of solid precipitation particles is of a paramount importance to most of the atmospheric processes and the application of weather radar. This paper aims to use two-dimensional optical disdrometer( hereinafter referred to as 2DVD)to measure the precipitation of a single particle, and based on its microphysical parameters and characteristics of precipitation in the course of a minute of the main types of precipitation particles in the unit Interval estimation, this paper classifies the solid precipitation particles. In order to actualize the automatic classification, this paper also attempts to make this task and common classification of machine learning algorithms combined, and the three supervised learning algorithm, naive Bayesian algorithm, support vector machine(SVM),and Decision Tree, applied to classify particles in the unit interval. In this paper, precipitation particles is classified mainly as snowflake and graupel, and its result is tested with the help of Manual detection. Besides,the independent data has been searched to do further examination, proving the accuracy of classification algorithms
参考文献/References:
[1] 杨军,陈宝君,银燕.云降水物理学[M].北京:气象出版社,2011.
[2] Christophe Praz1,Yves Alain Roulet,Alexis Berne.Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera[J].Atmos.Meas.Tech.,2017,10:1335-1357.
[3] K.Nurzyńska,Mamoru Kubo,Ken ichiro Muramoto.Texture operator for snow particle classification into snowflake and graupel[J].Atmospheric Research,2012,118:121-132.
[4] Grazioli J,Tuia D,Monhart S,et al.Hydrometeor classification from two-dimensional video disdrometer data[J].Atmos.Meas.Tech,2014,7:2869-2882.
[5] Dusan S Z,Alexander Ryzhkov,Jerry Straka.Testing a Procedure for Automatic Classification of Hydrometeor Types[J].Journal of Atmospheric and Oceanic Technology,2011,18:892-913.
[6] 黄敏松.云降水粒子图像识别方法研究及其在云微物理分析中的应用[D].北京:中国科学院大学,2015.
[7] Operating instructions Present Weather Sensor OTT Parsival2[Z].OTTHydromet GmbH.( OTT Parsival2 用户手册(English)).2006.
[8] MJ Bartholomew.Two-dimensional Video Disdrometer(VDIS)Instrument Handbook[Z].Brookhaven National Laboratory,2017.
[9] Huang G,Bringi V N,Cifelli R,et al.A methodology to derive radar reflectivity liquid equivalent snow rate relations using C-band radar and a 2D video disdrometer[J].J.Atmos.Oceanic Technol,2010,27:637-651.
[10] Bernauer F,Hürkamp K,Rühm W,et al.On the consistency of 2-D video disdrometers in measuring micro physical parameters of solid precipitation[J].Atmos.Meas.Tech,2015,8:3251-3261.
[11] 李舰,肖凯.数据科学中的R语言[M].西安:西安交通大学出版社,2015.
[12] Lee J E,S H Jung,H M Park,S,et al.Classification of precipitation types using fall velocity diameter relationships from 2D-videodistrometer measurements[J].Adv.Atmos.Sci.,2015,32(9):1277-1290.
[13] Locatelli J D,P V.Hobbs Fall speeds and masses of solid precipitation particles[J].J.Geophys.Res.,1974,79:2185-2197.
[14] Bernauer F,Hürkamp K,Rühm W,et al.Snow event classification with a 2D video disdrometer-A decision tree approach[J].Atmos.Res.,2016,172:186-195.
[15] 马刚.朴素贝叶斯算法的改进与应用[D].合肥:安徽大学,2018.
[16] 周志华.机器学习[M].北京:清华大学出版社,2016.
[17] Nicoletta Roberto,Luca Baldini.A Support Vector Machine Hydrometeor Classification Algorithm for Dual-Polarization Radar[J].Atmosphere,2017,8:134.
[18] BenHur A.Weston J A user's guide to support vector machines In Data mining techniques for the life sciences[M].New Jersey:Humana Press,2010,223-239.
[19] Wen G,H Xiao,H Yang,et al.Characteristics of summer and winter precipitation over northern China[J].Atmos.Res.,2017,197:390-406.
备注/Memo
收稿日期:2020-03-17
基金项目:国家重点研发计划战略性国际科技创新合作重点专项资助项目(2016YFE0201900-02); 国家自然科学基金面上资助项目(41575037); 国家重点基础研究发展计划资助项目(2014CB441403)