CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 10615 > Article
Paper
10 April 2018 Strong convective storm nowcasting using a hybrid approach of convolutional neural network and hidden Markov model
Wei Zhang, Ling Jiang, Lei Han
Author Affiliations +
Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106155E (2018) https://doi.org/10.1117/12.2302689
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Convective storm nowcasting refers to the prediction of the convective weather initiation, development, and decay in a very short term (typically 0 ~ 2 h) .Despite marked progress over the past years, severe convective storm nowcasting still remains a challenge. With the boom of machine learning, it has been well applied in various fields, especially convolutional neural network (CNN). In this paper, we build a servere convective weather nowcasting system based on CNN and hidden Markov model (HMM) using reanalysis meteorological data. The goal of convective storm nowcasting is to predict if there is a convective storm in 30min. In this paper, we compress the VDRAS reanalysis data to low-dimensional data by CNN as the observation vector of HMM, then obtain the development trend of strong convective weather in the form of time series. It shows that, our method can extract robust features without any artificial selection of features, and can capture the development trend of strong convective storm.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Wei Zhang, Ling Jiang, and Lei Han "Strong convective storm nowcasting using a hybrid approach of convolutional neural network and hidden Markov model", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106155E (10 April 2018); https://doi.org/10.1117/12.2302689
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 10 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Data modeling
Atmospheric modeling
Machine learning
Radar
Feature extraction
Pattern recognition
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top