LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Chen, Kai; Liu, Jun; Guo, Shanxin; Chen, Jinsong; Liu, Ping; Qian, Jing; Chen, Huijuan; Sun, Bo (2016)
Publisher: Copernicus Publications
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology
Short-term precipitation commonly occurs in south part of China, which brings intensive precipitation in local region for very short time. Massive water would cause the intensive flood inside of city when precipitation amount beyond the capacity of city drainage system. Thousands people’s life could be influenced by those short-term disasters and the higher city managements are required to facing these challenges. How to predict the occurrence of heavy precipitation accurately is one of the worthwhile scientific questions in meteorology. According to recent studies, the accuracy of short-term precipitation prediction based on numerical simulation model still remains low reliability, in some area where lack of local observations, the accuracy may be as low as 10%. The methodology for short term precipitation occurrence prediction still remains a challenge. In this paper, a machine learning method based on SVM was presented to predict short-term precipitation occurrence by using FY2-G satellite imagery and ground in situ observation data. The results were validated by traditional TS score which commonly used in evaluation of weather prediction. The results indicate that the proposed algorithm can present overall accuracy up to 90% for one-hour to six-hour forecast. The result implies the prediction accuracy could be improved by using machine learning method combining with satellite image. This prediction model can be further used to evaluated to predicted other characteristics of weather in Shenzhen in future.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Yu X.D., et al., 2012. The Advances in the Nowcasting Techniques on Thunderstorms And Severe Convection. Acta Meteorologica Sinica, (03), pp. 311-337.
    • Zou D.L., et al., 2014. Study of 0~3 Hour Short-Term Forecasting Algorithm for Rainfall. Journal of Tropical Meteorology, (02), pp. 249-260.
    • Feng Y.R., et al., 2013. A 0~6h Quantitative Snow(Rain) Forecast Technique and Its Application in Vancouver Winter Olympics. Guangdong Meteorology, (01), pp. 6-13.
    • Atencia A, et al., 2010. Improving QPF by blending techniques at the Meteorological Service of Catalonia. Natural Hazards and Earth System Sciences, 2010, Vol. 10, pp. 1443-1455.
    • Casati B., et al., 2004. A new intensity-scale approach for the verification of spatial precipitation forecasts. Meteorological Applications, 11(02), pp. 141-154.
    • Hu C.H., et al., 2015. Verification of Quantitative Precipitation Forecast Between Radar and Numerical Model Based on Intensity-Scale Method. Journal of Tropical Meteorology, (02), pp. 273-279.
    • James W. W., et al., 2010. Nowcasting Challenges during the Beijing Olympics: Successes, Failures, and Implications for Future Nowcasting Systems. Weather and Forecasting, 25(6), pp. 1691-1714.
    • Chen M.X., et al., 2010. Introduction of Auto-nowcasting System for Convective Storm and Its Performance in Beijing Olympics Meteorological Service. Journal of Applied Meteorological Science, (04), pp. 395-404.
    • Zhang L., et al., 2015. Discussion of Relative Accuracy of Short-Range Heavy Rain Nowcasting. Guangdong Meteorology, (02), pp. 1-6.
  • No related research data.
  • No similar publications.