Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-1019-2020
https://doi.org/10.5194/amt-13-1019-2020
Research article
 | 
03 Mar 2020
Research article |  | 03 Mar 2020

Atmospheric condition identification in multivariate data through a metric for total variation

Nicholas Hamilton

Related authors

One-to-one aeroservoelastic validation of operational loads and performance of a 2.8 MW wind turbine model in OpenFAST
Kenneth Brown, Pietro Bortolotti, Emmanuel Branlard, Mayank Chetan, Scott Dana, Nathaniel deVelder, Paula Doubrawa, Nicholas Hamilton, Hristo Ivanov, Jason Jonkman, Christopher Kelley, and Daniel Zalkind
Wind Energ. Sci., 9, 1791–1810, https://doi.org/10.5194/wes-9-1791-2024,https://doi.org/10.5194/wes-9-1791-2024, 2024
Short summary
Observations of wind farm wake recovery at an operating wind farm
Raghavendra Krishnamurthy, Rob Newsom, Colleen Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna M. Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-29,https://doi.org/10.5194/wes-2024-29, 2024
Revised manuscript under review for WES
Short summary
Implementation of a Near-Wake Region within the Curled-Wake Model
Paul Hulsman, Luis A. Martínez-Tossas, Nicholas Hamilton, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-112,https://doi.org/10.5194/wes-2023-112, 2023
Manuscript not accepted for further review
Short summary
Evolution of eddy viscosity in the wake of a wind turbine
Ryan Scott, Luis Martínez-Tossas, Juliaan Bossuyt, Nicholas Hamilton, and Raúl B. Cal
Wind Energ. Sci., 8, 449–463, https://doi.org/10.5194/wes-8-449-2023,https://doi.org/10.5194/wes-8-449-2023, 2023
Short summary
Design and analysis of a wake model for spatially heterogeneous flow
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021,https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
The role of time averaging of eddy covariance fluxes on water use efficiency dynamics of maize
Arun Rao Karimindla, Shweta Kumari, Saipriya S R, Syam Chintala, and BVN P. Kambhammettu​​​​​​​
Atmos. Meas. Tech., 17, 5477–5490, https://doi.org/10.5194/amt-17-5477-2024,https://doi.org/10.5194/amt-17-5477-2024, 2024
Short summary
Number- and size-controlled rainfall regimes in the Netherlands: physical reality or statistical mirage?
Marc Schleiss
Atmos. Meas. Tech., 17, 4789–4802, https://doi.org/10.5194/amt-17-4789-2024,https://doi.org/10.5194/amt-17-4789-2024, 2024
Short summary
The Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) – Part 2: First measurements of the emissivity of water in the far-infrared
Laura Warwick, Jonathan E. Murray, and Helen Brindley
Atmos. Meas. Tech., 17, 4777–4787, https://doi.org/10.5194/amt-17-4777-2024,https://doi.org/10.5194/amt-17-4777-2024, 2024
Short summary
Hailstorm events in the Central Andes of Peru: insights from historical data and radar microphysics
Jairo M. Valdivia, José Luis Flores-Rojas, Josep J. Prado, David Guizado, Elver Villalobos-Puma, Stephany Callañaupa, and Yamina Silva-Vidal
Atmos. Meas. Tech., 17, 2295–2316, https://doi.org/10.5194/amt-17-2295-2024,https://doi.org/10.5194/amt-17-2295-2024, 2024
Short summary
Hybrid instrument network optimization for air quality monitoring
Nishant Ajnoti, Hemant Gehlot, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 17, 1651–1664, https://doi.org/10.5194/amt-17-1651-2024,https://doi.org/10.5194/amt-17-1651-2024, 2024
Short summary

Cited articles

Ali, N., Hamilton, N., Calaf, M., and Cal, R. B.: Turbulence kinetic energy budget and conditional sampling of momentum, scalar, and intermittency fluxes in thermally stratified wind farms, J. Turbul., 1, 32–63, https://doi.org/10.1080/14685248.2018.1564831, 2019. a
Anderson, T. W.: An introduction to multivariate statistical analysis, Tech. rep., Wiley New York, 1962. a
Barthelmie, R., Crippa, P., Wang, H., Smith, C., Krishnamurthy, R., Choukulkar, A., Calhoun, R., Valyou, D., Marzocca, P., Matthiesen, D., et al.: 3D wind and turbulence characteristics of the atmospheric boundary layer, B. Am. Meteorol. Soc., 95, 743–756, 2014. a
Barthelmie, R., Churchfield, M. J., Moriarty, P. J., Lundquist, J. K., Oxley, G., Hahn, S., and Pryor, S.: The role of atmospheric stability/turbulence on wakes at the Egmond aan Zee offshore wind farm, in: Journal of Physics: Conference Series, 625, p. 012002, IOP Publishing, 2015. a
Belušić, D. and Mahrt, L.: Is geometry more universal than physics in atmospheric boundary layer flow?, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2011JD016987, 2012. a
Download
Short summary
The identification of atmospheric conditions within a multivariable atmospheric data set is an important step in validating emerging and existing models used to simulate wind plant flows and operational strategies. The total variation approach developed here offers a method founded in tested mathematical metrics and can be used to identify and characterize periods corresponding to quiescent conditions or specific events of interest for study or wind energy development.