Heavy rain is a type of disastrous weather that affects many areas of the world, often triggering landslides, mudslides, floods, urban waterlogging, and many other secondary disasters.The accuracy of heavy rain forecasting on the 24-h time scale is currently about 20%, on average. Therefore, improving prediction accuracy is a challenge in heavy rain prediction studies, especially at the extended range (10--30 d) scale. Extended range (10-30 d) forecasting exceeds the limit of two weeks proposed by Lorenz in 1963. It’s regarded as seam prediction between medium range and long range and even to climatic prediction.
Recently, a paper describing the efforts to overcome this "blind point" led by XU Lisheng from Chengdu University of Information Technology (CUIT) and CHEN Hongbin from Institute of Atmospheric Physics (Chinese Academy of Sciences) has been accepted by Advances in Atmospheric Sciences. "A Nonlinear cross prediction error (NCPE) model was built in this study, Chaotic single-variable time series of precipitable water is tested in 100 global cases of heavy rain based on nonlinear science analysis methods including chaos, fractals and wavelets.” says Dr. XIA Zhiye, a research team member who just received his PhD degree from IAP and currently teaches at CUIT.
A heavy rain case occurred in Chongqing, China, 21 July 1996. Prediction eigen-peak is in segment 11 and heavy rain is in 21 calculated by NCPE model based on phase space reconstruction, so, forecasting validity period is 10 d. (Xia et al, 2015)
The research team found that, compared to global features analysis of traditional chaos methods, the local features analysis is more effective to depict dynamic change progress of attractor, and shows extended range forecasting characteristics. And it is also revealed that the prediction validity periods for 1-2 d, 3-9 d and 10-30 d are 4, 22 and 74 cases, respectively, without false alarm or omission.
The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10-30 d and it can provide new insights into extended range forecasting and atmospheric predictability, and is also instrumental to the development of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.
Reference: XIA Zhiye, CHEN Hongbin, XU Lisheng, and WANG Yongqiang, 2015: Extended Range (10－30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model. Adv. Atmos. Sci.. doi: 10.1007/s00376-015-4252-2. (in press)
Contact: Dr. XIA Zhiye, firstname.lastname@example.org