Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System

Chu, Philip; Kelley, John G. W.; Mott, Gregory V.; Zhang, Aijun; Lang, Gregory A.

doi:10.1007/s10236-011-0424-5

Cited by 25 publications

(15 citation statements)

References 12 publications

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“…The datasets available for 5 validation of ocean dynamical models, for example, include satellite-based surface water temperatures (Reynolds et al, 2007), sea surface height (Lambin et al, 2010), and, when available, in situ measurements of sensible and latent heat fluxes (Edson et al, 1998). Dynamical and thermodynamic models for large lakes are often verified using similar measurements (Chu et al, 2011;Croley, 1989aCroley, , 1989bMoukomla and Blanken, 2017;Xiao et al, 2016;Xue et al, 2016). 10 However the spatiotemporal resolution of in situ measurements for these variables in lakes is comparatively sparse (Gronewold and Stow, 2014), particularly for latent and sensible heat fluxes.…”

Section: Introductionmentioning

confidence: 99%

Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes

Charusombat¹,

Fujisaki‐Manome²,

Gronewold³

et al. 2018

Preprint

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Section: Introductionmentioning

confidence: 99%

Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes

Charusombat¹,

Fujisaki‐Manome²,

Gronewold³

et al. 2018

Preprint

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“…While some lakes (e.g., surface temperature on Lake Michigan, Figure ) did exhibit pronounced seasonal differences between the physical models and ship measurements, such patterns or their magnitude were not consistent year‐to‐year. Additionally, it is worth noting that Lake Erie most likely does not experience drastic MAE seasonality due to the greater density of buoys available for calibration of the physical models as well as the considerably greater amount of resources put into the operational model for this lake [ Chu et al ., ].…”

Section: Discussionmentioning

confidence: 99%

“…It is a regionally focused model that deterministically outputs meteorological estimates based on measurements from regional weather stations [ Glahn and Ruth , ]. One model that uses the NDFD for forcing is the Great Lakes Coastal Forecasting System, which models a suite of variables not covered by the NDFD such as the temperature profile of the lakes, significant wave height, and other variables of interest to researchers and stakeholders [ Chu et al ., ]. As with the NDFD, the GLCFS is a deterministic model underpinned by assumptions about overlake conditions, for which measurements are unavailable.…”

Section: Introductionmentioning

confidence: 99%

Big Ship Data: Using vessel measurements to improve estimates of temperature and wind speed on the Great Lakes

Fries

Kerkez

2017

Water Resources Research

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The sheer size of many water systems challenges the ability of in situ sensor networks to resolve spatiotemporal variability of hydrologic processes. New sources of vastly distributed and mobile measurements are, however, emerging to potentially fill these observational gaps. This paper poses the question: How can nontraditional measurements, such as those made by volunteer ship captains, be used to improve hydrometeorological estimates across large surface water systems? We answer this question through the analysis of one of the largest such data sets: an unprecedented collection of one million unique measurements made by ships on the North American Great Lakes from 2006 to 2014. We introduce a flexible probabilistic framework, which can be used to integrate ship measurements, or other sets of irregular point measurements, into contiguous data sets. The performance of this framework is validated through the development of a new ship‐based spatial data product of water temperature, air temperature, and wind speed across the Great Lakes. An analysis of the final data product suggests that the availability of measurements across the Great Lakes will continue to play a large role in the confidence with which these large surface water systems can be studied and modeled. We discuss how this general and flexible approach can be applied to similar data sets, and how it will be of use to those seeking to merge large collections of measurements with other sources of data, such as physical models or remotely sensed products.

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“…Horizontal diffusion in the GLCFS is prescribed by the Smagorinsky parameterization and vertical diffusion is governed by the Mellor-Yamada level 2.5 turbulence closure scheme. Forcing conditions for the hydrodynamic model are prescribed using a naturalneighbor interpolation from land-and buoy-based observations, which have yielded successful prediction of water levels, temperatures, and currents in the lake (Schwab and Bedford 1994; D r a f t Chu et al 2011). Although recent work has shown that the interpolated meteorology can cause errors in the summer circulation in the central basin of Lake Erie (Beletsky et al 2013), our study focuses on spring transport (April -May) in the western basin, in which wind-fieldinduced errors are presumed to be reduced due to the influence of hydraulically-driven flow and the density of meteorological stations surrounding the western basin.…”

Section: Hydrodynamic Backtracking Of Larval Dispersalmentioning

confidence: 99%

Larval dispersal underlies demographically important intersystem connectivity in a Great Lakes yellow perch (Perca flavescens) population

Brodnik

Fraker

Anderson

et al. 2016

Can. J. Fish. Aquat. Sci.

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Ability to quantify connectivity among spawning subpopulations and their relative contribution of recruits to the broader population is a critical fisheries management need. By combining microsatellite and age information from larval yellow perch (Perca flavescens) collected in the Lake St. Clair – Detroit River system (SC-DRS) and western Lake Erie with a hydrodynamic backtracking approach, we quantified subpopulation structure, connectivity, and contributions of recruits to the juvenile stage in western Lake Erie during 2006–2007. After finding weak (yet stable) genetic structure between the SC-DRS and two western Lake Erie subpopulations, microsatellites also revealed measurable recruitment of SC-DRS larvae to the juvenile stage in western Lake Erie (17%–21% during 2006–2007). Consideration of precollection larval dispersal trajectories, using hydrodynamic backtracking, increased estimated contributions to 65% in 2006 and 57% in 2007. Our findings highlight the value of complementing subpopulation discrimination methods with hydrodynamic predictions of larval dispersal by revealing the SC-DRS as a source of recruits to western Lake Erie and also showing that connectivity through larval dispersal can affect the structure and dynamics of large lake fish populations.

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Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System

Cited by 25 publications

References 12 publications

Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes

Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes

Big Ship Data: Using vessel measurements to improve estimates of temperature and wind speed on the Great Lakes

Larval dispersal underlies demographically important intersystem connectivity in a Great Lakes yellow perch (Perca flavescens) population

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