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755 results on '"Krajewski, Witold F."'

5. Insights On Streamflow Predictability Across Scales Using Horizontal Visibility Graph Based Networks

Author

Ghimire, Ganesh R., Jadidoleslam, Navid, Krajewski, Witold F., and Tsonis, Anastasios A.

Subjects
Physics - Physics and Society, Statistics - Applications, physics.data-an
Abstract

Streamflow is a dynamical process that integrates water movement in space and time within basin boundaries. The authors characterize the dynamics associated with streamflow time series data from about seventy-one U.S. Geological Survey (USGS) stream-gauge stations in the state of Iowa. They employ a novel approach called visibility graph (VG). It uses the concept of mapping time series into complex networks to investigate the time evolutionary behavior of dynamical system. The authors focus on a simple variant of VG algorithm called horizontal visibility graph (HVG). The tracking of dynamics and hence, the predictability of streamflow processes, are carried out by extracting two key pieces of information called characteristic exponent, {\lambda} of degree distribution and global clustering coefficient, GC pertaining to HVG derived network. The authors use these two measures to identify whether streamflow process has its origin in random or chaotic processes. They show that the characterization of streamflow dynamics is sensitive to data attributes. Through a systematic and comprehensive analysis, the authors illustrate that streamflow dynamics characterization is sensitive to the normalization, and the time-scale of streamflow time-series. At daily scale, streamflow at all stations used in the analysis, reveals randomness with strong spatial scale (basin size) dependence. This has implications for predictability of streamflow and floods. The authors demonstrate that dynamics transition through potentially chaotic to randomly correlated process as the averaging time-scale increases. Finally, the temporal trends of {\lambda} and GC are statistically significant at about 40% of the total number of stations analyzed. Attributing this trend to factors such as changing climate or land use requires further research., Comment: 34 pages and 12 figures

Published
2019
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8. List of contributors

Author

Anagnostou, Emmanouil, primary, Anquetin, Sandrine, additional, Astitha, Marina, additional, Ayalew, Tibebu, additional, Calovi, Martina, additional, Cannon, Forest, additional, DeCastro, Amy, additional, Cerrai, Diego, additional, Cervone, Guido, additional, Chen, Dan, additional, Doan, Quang-Van, additional, Clemente, Laura, additional, Eghdami, Masih, additional, Di Giuseppe, F., additional, Juliano, Timothy W., additional, Muñoz-Esparza, Domingo, additional, Di Napoli, C., additional, Fonley, Morgan, additional, Frediani, Maria, additional, Hu, Weiming, additional, Jadidoleslam, Navid, additional, Jimenez, Pedro, additional, Knievel, Jason C., additional, Khan, Sana, additional, Kirschbaum, Dalia B., additional, Kosovi, Branko, additional, Krajewski, Witold F., additional, Kusaka, Hiroyuki, additional, Magnusson, Linus, additional, Mantilla, Ricardo, additional, Monache, Luca Delle, additional, Nikolopoulos, Efthymios, additional, Pappenberger, F., additional, Prudhomme, C., additional, Quintero, Felipe, additional, Ramamurthy, Prathap, additional, Ray, Pallav, additional, Ruin, Isabelle, additional, Small, Scott, additional, Siems-Anderson, Amanda, additional, Terti, Galateia, additional, Tewari, Mukul, additional, Velasquez, Nicolas, additional, and Wang, Zhihua, additional

Published
2023
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22. Hydrologic Validation and Flood Analysis

Author

Krajewski, Witold F., Quintero, Felipe, El Saadani, Mohamed, Goska, Radoslaw, Stoffel, Markus, Series Editor, Cramer, Wolfgang, Advisory Editor, Luterbacher, Urs, Advisory Editor, Toth, F., Advisory Editor, Levizzani, Vincenzo, editor, Kidd, Christopher, editor, Kirschbaum, Dalia B., editor, Kummerow, Christian D., editor, Nakamura, Kenji, editor, and Turk, F. Joseph, editor

Published
2020
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29. Compressed Sensing Applied to Weather Radar

Author

Mishra, Kumar Vijay, Kruger, Anton, and Krajewski, Witold F.

Subjects
Computer Science - Information Theory
Abstract

We propose an innovative meteorological radar, which uses reduced number of spatiotemporal samples without compromising the accuracy of target information. Our approach extends recent research on compressed sensing (CS) for radar remote sensing of hard point scatterers to volumetric targets. The previously published CS-based radar techniques are not applicable for sampling weather since the precipitation echoes lack sparsity in both range-time and Doppler domains. We propose an alternative approach by adopting the latest advances in matrix completion algorithms to demonstrate the sparse sensing of weather echoes. We use Iowa X-band Polarimetric (XPOL) radar data to test and illustrate our algorithms., Comment: 4 pages, 5 figrues

Published
2014

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