|Home||<< 1 2 3 4 5 6 7 8 9 >>|
|Weissman, D. E., & Bourassa, M. A. (2008). Measurements of the Effect of Rain-induced Sea Surface Roughness on the Satellite Scatterometer Radar Cross Section. In XXIX General Assembly of the International Union of Radio Science, Union of Radio Science International (Vol. 4).|
|Weissman, D. E., & Bourassa, M. A. (2009). The combined effect of surface rain and wind on scatterometer observations of surface roughness. In 2009 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Cape Town, South Africa (I-pp. 108– I-111).|
|Weissman, D. E., & Bourassa, M. A. (2008). Measurements of the Effect of Rain-Induced Sea Surface Roughness on the QuikSCAT Scatterometer Radar Cross Section. IEEE Trans. Geosci. Remote Sensing, 46(10), 2882–2894.|
|Weissman, D. E., & Bourassa, M. A. (2011). The Influence of Rainfall on Scatterometer Backscatter Within Tropical Cyclone Environments-Implications on Parameterization of Sea-Surface Stress. In IEEE Transactions on Geoscience and Remote Sensing (Vol. 49, pp. 4805–4814).|
|Weissman, D. E., & Bourassa, M. A. (2011). The effect of rain on ASCAT observations of the sea surface radar cross section using simultaneous 3-d NEXRAD rain measurements. In IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 1171–1174).|
|Weissman, D. E., Bourassa, M. A., O'Brien, J. J., & Tongue, J. S. (2003). Calibrating the quikscat/seawinds radar for measuring rainrate over the oceans. IEEE Trans. Geosci. Remote Sensing, 41(12), 2814–2820.|
|Weissman, D. E., Morey, S., & Bourassa, M. (2017). Studies of the effects of rain on the performance of the SMAP radiometer surface salinity estimates and applications to remote sensing of river plumes. In IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 1491–1494).|
|Weissman, D. E., H. Winterbottom, and M. A. Bourassa. (2010). Studies of the influence of rainfall upon scatterometer estimates for sea surface stress: applications to boundary layer parameterization and drag coefficient models within tropical cyclone environments. In 2010 IEEE International Geoscience and Remote Sensing Symposium (pp. 4154–4157).|
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
|Yin, J., Schlesinger, M. E., & Stouffer, R. J. (2009). Model projections of rapid sea-level rise on the northeast coast of the United States. Nature Geosci, 2(4), 262–266.|