Qian, C., Fu, C., Wu, Z., & Yan, Z. (2009). On the secular change of spring onset at Stockholm.
Geophys. Res. Lett., 36(12).
Qian, C., Wu, Z., Fu, C., & Wang, D. (2011). On Changing El Nino: A View from Time-Varying Annual Cycle, Interannual Variability, and Mean State.
J. Climate, 24(24), 6486–6500.
Qian, C., Wu, Z., Fu, C., & Zhou, T. (2010). On multi-timescale variability of temperature in China in modulated annual cycle reference frame.
Adv. Atmos. Sci., 27(5), 1169–1182.
Qian, C., Yan, Z., Wu, Z., Fu, C., & Tu, K. (2011). Trends in temperature extremes in association with weather-intraseasonal fluctuations in eastern China.
Adv. Atmos. Sci., 28(2), 297–309.
Sun, J., & Wu, Z. (2019). Isolating spatiotemporally local mixed Rossby-gravity waves using multi-dimensional ensemble empirical mode decomposition.
Clim Dyn, (3-4), 1383–1405.
Abstract: Tropical waves have relatively large amplitudes in and near convective systems, attenuating as they propagate away from the area where they are generated due to the dissipative nature of the atmosphere. Traditionally, nonlocal analysis methods, such as those based on the Fourier transform, are applied to identify tropical waves. However, these methods have the potential to lead to the misidentification of local wavenumbers and spatial locations of local wave activities. To address this problem, we propose a new method for analyzing tropical waves, with particular focus placed on equatorial mixed Rossby-gravity (MRG) waves. The new tropical wave analysis method is based on the multi-dimensional ensemble empirical mode decomposition and a novel spectral representation based on spatiotemporally local wavenumber, frequency, and amplitude of waves. We first apply this new method to synthetic data to demonstrate the advantages of the method in revealing characteristics of MRG waves. We further apply the method to reanalysis data (1) to identify and isolate the spatiotemporally heterogeneous MRG waves event by event, and (2) to quantify the spatial inhomogeneity of these waves in a wavenumber-frequency-energy diagram. In this way, we reveal the climatology of spatiotemporal inhomogeneity of MRG waves and summarize it in wavenumber-frequency domain: The Indian Ocean is dominated by MRG waves in the period range of 8–12 days; the western Pacific Ocean consists of almost equal energy distribution of MRG waves in the period ranges of 3–6 and 8–12 days, respectively; and the eastern tropical Pacific Ocean and the tropical Atlantic Ocean are dominated by MRG waves in the period range of 3–6 days. The zonal wavenumbers mostly fall within the band of 4–15, with Indian Ocean has larger portion of higher wavenumber (smaller wavelength components) MRG waves.
Wdowinski, S., Bray, R., Kirtman, B. P., & Wu, Z. (2016). Increasing flooding hazard in coastal communities due to rising sea level: Case study of Miami Beach, Florida.
Ocean & Coastal Management, 126, 1–8.
Wu, Z., Chassignet, E. P., Ji, F., & Huang, J. (2014). Reply to 'Spatiotemporal patterns of warming'.
Nature Climate change, 4(10), 846–848.
Wu, Z., Feng, J., Qiao, F., & Tan, Z. - M. (2016). Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatio-temporal datasets.
Philos Trans A Math Phys Eng Sci, 374(2065), 20150197.
Abstract: In this big data era, it is more urgent than ever to solve two major issues: (i) fast data transmission methods that can facilitate access to data from non-local sources and (ii) fast and efficient data analysis methods that can reveal the key information from the available data for particular purposes. Although approaches in different fields to address these two questions may differ significantly, the common part must involve data compression techniques and a fast algorithm. This paper introduces the recently developed adaptive and spatio-temporally local analysis method, namely the fast multidimensional ensemble empirical mode decomposition (MEEMD), for the analysis of a large spatio-temporal dataset. The original MEEMD uses ensemble empirical mode decomposition to decompose time series at each spatial grid and then pieces together the temporal-spatial evolution of climate variability and change on naturally separated timescales, which is computationally expensive. By taking advantage of the high efficiency of the expression using principal component analysis/empirical orthogonal function analysis for spatio-temporally coherent data, we design a lossy compression method for climate data to facilitate its non-local transmission. We also explain the basic principles behind the fast MEEMD through decomposing principal components instead of original grid-wise time series to speed up computation of MEEMD. Using a typical climate dataset as an example, we demonstrate that our newly designed methods can (i) compress data with a compression rate of one to two orders; and (ii) speed-up the MEEMD algorithm by one to two orders.
Wu, Z., & Huang, N. E. (2009). Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method.
Adv. Adapt. Data Anal., 01(01), 1–41.
Wu, Z., Huang, N. E., & Chen, X. (2009). The Multi-Dimensional Ensemble Empirical Mode Decomposition Method.
Adv. Adapt. Data Anal., 01(03), 339–372.