Graduate Student / Postdoc Seminar
Predicting the Cloud Patterns of the Madden-Julian Oscillation Through a Low-Order Nonlinear Stochastic Model
Speaker: Nan Chen
Location: Warren Weaver Hall 1302
Date: Friday, November 18, 2016, 1 p.m.
We assess the limits of predictability of the large-scale cloud patterns in the boreal winter Madden-Julian Oscillation (MJO) as measured through outgoing longwave radiation (OLR) alone, a proxy for convective activity. A recent advanced nonlinear time series technique, nonlinear Laplacian spectral analysis, is applied to the OLR data to define two spatial modes with high intermittency associated with the boreal winter MJO. A recent data-driven physics-constrained low-order stochastic modeling procedure is applied to these time series. The result is a four-dimensional nonlinear stochastic model for the two observed OLR variables and two hidden variables involving correlated multiplicative noise defined through energy-conserving nonlinear interaction. Systematic calibration via information theory and prediction experiments show the skillful prediction by these models for 40, 25, and 18 days in strong, moderate, and weak MJO winters, respectively. Furthermore, the ensemble spread is an accurate indicator of forecast uncertainty at long lead times.
If time permits, calibration and prediction of another type of the MJO indices with a novel information-theoretic framework will be briefly mentioned at the end of the talk. The information-theoretic framework overcomes the fundamental insufficiency of the path-wise RMS error and pattern correlation in capturing the extremely events. The applications to the monsoon intraseasonal variability with spatial-temporal reconstruction will be roughly discussed as well. The mathematical tools developed here are also useful in material science, biological science and neuroscience.