The ESMD method can be seen as a new alternate of the well-known Hilbert-Huang transform (HHT) for non-steady data processing. It is good at finding the optimal adaptive global mean fitting curve, which is superior to...The ESMD method can be seen as a new alternate of the well-known Hilbert-Huang transform (HHT) for non-steady data processing. It is good at finding the optimal adaptive global mean fitting curve, which is superior to the common least-square method and running-mean approach. Take the air-sea momentum flux investigation as an example, only when the non-turbulent wind components is well extracted, can the remainder signal be seen as actual oscillations caused by turbulence. With the aid of —5/3 power law for the turbulence, a mode-filtering approach based on ESMD decomposition is developed here. The test on observational data indicates that this approach is very feasible and it may greatly reduce the error caused by the non-turbulent components.展开更多
文摘The ESMD method can be seen as a new alternate of the well-known Hilbert-Huang transform (HHT) for non-steady data processing. It is good at finding the optimal adaptive global mean fitting curve, which is superior to the common least-square method and running-mean approach. Take the air-sea momentum flux investigation as an example, only when the non-turbulent wind components is well extracted, can the remainder signal be seen as actual oscillations caused by turbulence. With the aid of —5/3 power law for the turbulence, a mode-filtering approach based on ESMD decomposition is developed here. The test on observational data indicates that this approach is very feasible and it may greatly reduce the error caused by the non-turbulent components.
