Clusters, deformation, and dilation: Diagnostics for material accumulation regions

Journal of Geophysical Research: Oceans - Tập 120 Số 10 - Trang 6622-6636 - 2015
Helga S. Huntley1, B. L. Lipphardt2, Gregg Jacobs3, A. D. Kirwan1
1School of Marine Science and Policy, University of Delaware, Newark, Delaware, USA
2Formerly at School of Marine Science and Policy University of Delaware Newark Delaware USA
3Naval Research Laboratory, Stennis Space Center, Mississippi, USA

Tóm tắt

AbstractClusters of material at the ocean surface have been frequently observed. Such accumulations of material play an important role in a variety of applications, from biology to pollution mitigation. Identifying where clusters will form can aid in locating, for example, hotspots of biological activity or regions of high pollutant concentration. Here cluster strength is introduced as a new metric for defining clusters when all particle positions are known. To diagnose regions likely to contain clusters without the need to integrate millions of particle trajectories, we propose to use dilation, which quantifies area changes of Lagrangian patches. Material deformation is decomposed into dilation and area‐preserving stretch processes to refine previous approaches based on finite‐time Lyapunov exponents (FTLE) by splitting the FTLE into fundamental kinematic properties. The concepts are developed theoretically and illustrated in the context of a state‐of‐the‐art data‐assimilating predictive ocean model of the Gulf of Mexico. Regions of dilation less than one are shown to be much more likely (6 times more likely in the given example) to be visited by particles than those of dilation greater than one. While the relationship is nonlinear, dilation and cluster strength exhibit a fairly good correlation. In contrast, both stretch and Eulerian divergence are found to be uncorrelated with cluster strength. Thus, dilation maps can be used as guides for identifying cluster locations, while saving some of the computational cost of trajectory integrations.

Từ khóa


Tài liệu tham khảo

10.1016/j.ocemod.2005.01.004

Barron C. N. A. B.Kara R. C.Rhodes C.Rowley andL. F.Smedstad(2007) Validation test report for the 1/8° Global Navy Coastal Ocean Model Nowcast/Forecast System Tech. Rep. NRL/MR/7320–07‐9019 Nav. Res. Lab. Stennis Space Center Miss.

10.1029/2008GL033957

10.1016/S0167-2789(01)00330-X

10.1063/1.1667807

10.5194/npg-17-1-2010

10.1021/es0630691

10.1088/1367-2630/6/1/053

10.1256/qj.05.105

10.1016/S0025-326X(87)80017-6

Dempster A. P., 1977, Maximum likelihood from incomplete data via the EM algorithm, J. R. Stat. Soc., Ser. B, 39, 1, 10.1111/j.2517-6161.1977.tb01600.x

10.1029/2004GL020328

10.1103/PhysRevE.86.035301

10.1016/S0167-2789(00)00199-8

10.1063/1.1477449

10.1016/j.physd.2012.06.012

10.1017/jfm.2013.391

10.1016/S0167-2789(00)00142-1

10.1080/03091929.2010.532793

10.1016/j.ocemod.2010.12.006

10.1175/1520-0493(1997)125<1414:TNRLSC>2.0.CO;2

10.5670/oceanog.2002.39

10.1016/j.marpolbul.2011.04.034

10.1029/2011GM001097

Jacobs G. A., 2015, Ocean processes underlying surface clustering, J. Geophys. Res. Oceans

10.1007/BF02289588

10.1126/science.328.5985.1506

10.1134/1.558205

10.1007/s10236-010-0306-2

10.1016/j.dsr.2011.01.007

10.1016/j.marpolbul.2011.10.027

10.1016/j.physd.2005.06.023

10.1109/TIT.1982.1056489

10.1016/j.physrep.2006.09.005

10.1103/PhysRevLett.105.038501

10.1126/science.1194607

10.1063/1.3489987

10.1073/pnas.1118574109

10.1002/2013GL058624

10.1016/j.ocemod.2009.09.002

10.1073/pnas.1402452111

10.1175/2010JPO4336.1

10.1146/annurev-marine-120710-100819

10.1146/annurev.fluid.33.1.289

10.1103/PhysRevE.66.017303

10.1016/j.physd.2005.10.007

10.1029/2002JC001499

10.1038/371689a0

10.1215/21573689-1573372