|Home||<< 1 2 3 4 5 6 >>|
|Weihs, R. (2016). Surface and Atmospheric Boundary Layer Responses to Diurnal Variations of Sea Surface Temperature in an NWP Model. Ph.D. thesis, Florida State University, Tallahassee, FL.|
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.
Keywords: adaptive and local data analysis; data compression; empirical orthogonal function; fast algorithm; multidimensional ensemble empirical mode decomposition; principal component analysis
|Xu, X., Rhines, P. B., & Chassignet, E. P. (2016). Temperature-Salinity Structure of the North Atlantic Circulation and Associated Heat and Freshwater Transports. J. Climate, 29(21), 7723–7742.|
|Xue, W., Xin, X., Zhang, J., Zhang, W., Wu, H., Huang, Z., et al. (2016). Development and Testing of a Multi-model Ensemble Coupling Framework. In Development and Evaluation of High Resolution Climate System Models (pp. 163–208). Springer.|
|Yan, Y., Liu, Y., & Lu, J. (2016). Cloud vertical structure, precipitation, and cloud radiative effects over Tibetan Plateau and its neighboring regions. J. Geophys. Res. Atmos., 121(10), 5864–5877.|
|Zhao, X., Zhou, C., Zhao, W., Tian, J., & Xu, X. (2016). Deepwater overflow observed by three bottom-anchored moorings in the Bashi Channel. Deep Sea Research Part I: Oceanographic Research Papers, 110, 65–74.|
|Zheng, Y., Ali, M. M., & Bourassa, M. A. (2016). Contribution of Monthly and Regional Rainfall to the Strength of Indian Summer Monsoon. Mon. Wea. Rev., 144(9), 3037–3055.|
|Zheng, Y., Bourassa, M. A., Ali, M. M., & Krishnamurti, T. N. (2016). Distinctive features of rainfall over the Indian homogeneous rainfall regions between strong and weak Indian summer monsoons. J. Geophys. Res. Atmos., 121(10), 5631–5647.|
|Özgökmen, T., Chassignet, E., Dawson, C., Dukhovskoy, D., Jacobs, G., Ledwell, J., et al. (2016). Over What Area Did the Oil and Gas Spread During the 2010 Deepwater Horizon Oil Spill? Oceanog, 29(3), 96–107.|