User-based skill assessment techniques for operational hydrodynamic forecast systems

Zhang, A; Heß, Kurt; Aikman, Frank

doi:10.1080/1755876x.2010.11020114

Cited by 29 publications

(20 citation statements)

References 15 publications

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“…Another measure, the relative central frequency (RCF), which reports the proportion of percentage error that lies within ± 50% of observed values, is also calculated (Zhang et al, 2010). All analyses were conducted using the R software package (R Core Team, 2014).…”

Section: Field Datamentioning

confidence: 99%

“…The accuracy of different models was assessed using several measures used in other remote sensing (IOCCG, 2006;Moore et al, 2009) and modeling studies (Zhang et al, 2010). Calculation of the regression parameters for the observed vs. derived data (i.e., slope and intercept), the deviance explained (r square), the root mean square error (RMSE) and the average absolute percentage error (ε) were reported for the smoothed data, with a subset, deviance explained and RMSE only calculated for closest match data.…”

Section: Field Datamentioning

confidence: 99%

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Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

et al. 2017

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Monitoring of the phytoplankton pigment chlorophyll-a is often used as an indicator of eutrophication in coastal waters. Improved water quality monitoring using data sourced from MODIS (Moderate Resolution Imaging Spectroradiometer)-sourced data allows for infrequently sampled sites to be interrogated for long-term trends. Despite the wide availability and good spatial and temporal coverage of MODIS data, these data have had little use in operational coastal monitoring of chlorophyll-a in New Zealand. This is in part due to the poor performance of global oceanic algorithms applied in the coastal waters. Accessible algorithm tuning methods that can be validated by in situ measurements may assist the uptake of satellite data for coastal monitoring. This study presents results from regional tuning and validation of two empirical algorithm approaches, including a new simple exponential model, to estimate chlorophyll-a for two coastal locations in New Zealand. A novel method of training chlorophyll-a models using smoothed in situ data to match spatial scales of satellite observations was applied, and shows promise for improving tuned model performance. This approach shows potential for lowering barriers for researchers and coastal managers wishing to make use of the growing satellite data resource in their coastal environments.

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Section: Field Datamentioning

confidence: 99%

Section: Field Datamentioning

confidence: 99%

Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

et al. 2017

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“…At short time-scales, initialization uncertainty dominates, while at longer time-scales, scenario uncertainty is generally the most important (Figure 1). Marine science has not quantified its understanding of uncertainty in such a formal and quantitative manner, although the evaluation of model skill is clearly a rapidly developing area in the various disciplines of marine and climatological science (Lynch et al, 2009;Stow et al, 2009;Zhang et al, 2010;Link et al, 2015). However, it is also unclear how predictions from ecological models map onto this partitioning of uncertainty over time, and what role parametric uncertainty, for example, plays in this partitioning.…”

Section: Introductionmentioning

confidence: 99%

Uncertainties in projecting climate-change impacts in marine ecosystems

Payne

Barangé

Cheung

et al. 2015

ICES Journal of Marine Science

140

112

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Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale. We conclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections.

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“…Tidal data assimilation is essential to improve accuracy in a number of important modeling applications in the National Ocean Service (NOS), National Oceanic and Atmospheric Administration (NOAA). For example, Operational Forecast Systems (OFS) are the backbone of the operational coastal ocean forecast system [1] in NOAA's NOS. Every day, OFSs provide up to 5-day forecasts of water levels, currents, temperature and salinity in US coastal, Great Lakes, and estuarine waters.…”

Section: Introductionmentioning

confidence: 99%

Variational Data Assimilation of Tides

Shi

Tang

Myers

2020

JMSE

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This paper presents an incremental variational method to assimilate the observed tidal harmonic constants using a frequency domain linearized shallow water equation. A cost function was constructed with tidal boundary conditions and tidal forcing as its control (independent) variables. To minimize the cost function, optimal boundary conditions and tidal forcing were derived using a conventional dual 4-Dimensional Variational (4D-Var) Physical-space Statistical Analysis System. The tangent linear and adjoint model were solved by using a finite element method. By adapting the incremental form, the variational method streamlines the workflow to provide the incremental correction to the boundary conditions and tidal forcing of a hydrodynamic forward model. The method was tested for semi-diurnal M2 tides in a regional sea with a complex tidal system. The results demonstrate a 65–72% reduction of tidal harmonic constant vector error by assimilating the observed M2 tidal harmonic constants. In addition to improving the tides of a hydrodynamic model by optimizing boundary conditions and tidal forcing, the method computes a spatially varying uncertainty of individual tidal constituents in the model. The method provides a versatile tool for mapping the spatially continuous tides and currents in coastal and estuarine waters by assimilating the harmonic constants of individual tidal constituents of observed tides and currents.

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User-based skill assessment techniques for operational hydrodynamic forecast systems

Cited by 29 publications

References 15 publications

Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters

Uncertainties in projecting climate-change impacts in marine ecosystems

Variational Data Assimilation of Tides

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