SWIRLS-An Evolving Nowcasting System by

By P. W. Li, Wai-Kin Wong, K. Chan, E. Lai. 2000

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Hao Wang
2. Xingjian Shi
3. Dit-Yan Yeung
4. Zhourong Chen
5. Wai-Kin Wong
6. P. Cheung
7. Wang-chun Woo
8. H. Yeung

Citations in the corpus

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting (2015, methodology): “…at the pixel level, we project them back to radar echo intensities and calculate the rainfall at every cell of the grid using the Z-R relationship [15]: Z = 10 log a + 10 b log R , where Z is the radar echo intensity in dB, R is the rainfall rate in mm/h, and a, b are two constants with a = 118 .”
• Reprint 1025 Application of optical-flow technique to significant convection nowcast for terminal areas in Hong Kong (2012, methodology): “In SWIRLS, QPF is obtained basically by the extrapolation and accumulation of the observed reflectivity field converted suitably into rainfall rate (Li 2009, Li et al. 2000).”