Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: P. F. Linden x
  • All content x
Clear All Modify Search
J. R. Taylor, S. M. de Bruyn Kops, C. P. Caulfield, and P. F. Linden

Abstract

Direct numerical simulations of stratified turbulence are used to test several fundamental assumptions involved in the Osborn, Osborn–Cox, and Thorpe methods commonly used to estimate the turbulent diffusivity from field measurements. The forced simulations in an idealized triply periodic computational domain exhibit characteristic features of stratified turbulence including intermittency and layer formation. When calculated using the volume-averaged dissipation rates from the simulations, the vertical diffusivities inferred from the Osborn and Osborn–Cox methods are within 40% of the value diagnosed using the volume-averaged buoyancy flux for all cases, while the Thorpe-scale method performs similarly well in the simulation with a relatively large buoyancy Reynolds number (Reb ≃ 240) but significantly overestimates the vertical diffusivity in simulations with Reb < 60. The methods are also tested using a limited number of vertical profiles randomly selected from the computational volume. The Osborn, Osborn–Cox, and Thorpe-scale methods converge to their respective estimates based on volume-averaged statistics faster than the vertical diffusivity calculated directly from the buoyancy flux, which is contaminated with reversible contributions from internal waves. When applied to a small number of vertical profiles, several assumptions underlying the Osborn and Osborn–Cox methods are not well supported by the simulation data. However, the vertical diffusivity inferred from these methods compares reasonably well to the exact value from the simulations and outperforms the assumptions underlying these methods in terms of the relative error. Motivated by a recent theoretical development, it is speculated that the Osborn method might provide a reasonable approximation to the diffusivity associated with the irreversible buoyancy flux.

Full access
W. Kendall Melville, Luc Lenain, Daniel R. Cayan, Mati Kahru, Jan P. Kleissl, P. F. Linden, and Nicholas M. Statom

Abstract

Satellite remote sensing has enabled remarkable progress in the ocean, earth, atmospheric, and environmental sciences through its ability to provide global coverage with ever-increasing spatial resolution. While exceptions exist for geostationary ocean color satellites, the temporal coverage of low-Earth-orbiting satellites is not optimal for oceanographic processes that evolve over time scales of hours to days. In hydrology, time scales can range from hours for flash floods, to days for snowfall, to months for the snowmelt into river systems. On even smaller scales, remote sensing of the built environment requires a building-resolving resolution of a few meters or better. For this broad range of phenomena, satellite data need to be supplemented with higher-resolution airborne data that are not tied to the strict schedule of a satellite orbit. To address some of these needs, a novel, portable, high-resolution airborne topographic lidar with video, infrared, and hyperspectral imaging systems was integrated. The system is coupled to a highly accurate GPS-aided inertial measurement unit (GPS IMU), permitting airborne measurements of the sea surface displacement, temperature, and kinematics with swath widths of up to 800 m under the aircraft, and horizontal spatial resolution as low as 0.2 m. These data are used to measure ocean waves, currents, Stokes drift, sea surface height (SSH), ocean transport and dispersion, and biological activity. Hydrological and terrestrial applications include measurements of snow cover and the built environment. This paper describes the system, its performance, and present results from recent oceanographic, hydrological, and terrestrial measurements.

Full access
Nick A. Rayner, Renate Auchmann, Janette Bessembinder, Stefan Brönnimann, Yuri Brugnara, Francesco Capponi, Laura Carrea, Emma M. A. Dodd, Darren Ghent, Elizabeth Good, Jacob L. Høyer, John J. Kennedy, Elizabeth C. Kent, Rachel E. Killick, Paul van der Linden, Finn Lindgren, Kristine S. Madsen, Christopher J. Merchant, Joel R. Mitchelson, Colin P. Morice, Pia Nielsen-Englyst, Patricio F. Ortiz, John J. Remedios, Gerard van der Schrier, Antonello A. Squintu, Ag Stephens, Peter W. Thorne, Rasmus T. Tonboe, Tim Trent, Karen L. Veal, Alison M. Waterfall, Kate Winfield, Jonathan Winn, and R. Iestyn Woolway

Abstract

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

Full access