2016 Articles

Prediction of Northern Summer Low-Frequency Circulation Using a High-Order Vector Auto-Regressive Model.

Wang, Lei; Ting, Mingfang; Chapman, David; Lee, Dong Eun; Henderson, Naomi L.; Yuan, Xiaojun

A data-driven, high-order vector auto-regressive (VAR) model is evaluated for predicting the Northern Hemisphere summer time (May through September) low frequency (>10 days or so) variability. The VAR model is suitable for linear stationary time series, similar to the commonly used linear inverse model (LIM), with additional temporal information incorporated to improve forecast skill. The intraseasonal forecast skill of the 250/750 hPa streamfunction is investigated using observational data since 1979, which shows significant improvements in high-order VAR models than the first-order model LIM. Furthermore, the tropical diabatic heating is found to significantly improve the forecast skill of the atmospheric low frequency circulation when included in the VAR model. The forecast skill of 250 hPa streamfunction at Arabian Peninsula is particularly enhanced for up to 5 weeks lead-time through circumglobal wave propagation associated with the persistent tropical eastern Pacific and equatorial Atlantic heating anomalies and the intraseasonal evolution of the tropical Indian Ocean and western Pacific heating anomalies.

Keywords: Low frequency variability; Intraseasonal predictability; Madden–Julian oscillation; Circumglobal wave train