Ocean ensemble forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model

Milliff, Ralph F.; Bonazzi, A.; Wikle, Christopher K.; Pinardi, Nadia; Berliner, L. Mark

doi:10.1002/qj.767

Cited by 41 publications

(51 citation statements)

References 48 publications

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“…The BHM-SVW posterior distributions, developed in Part I of this article (Milliff et al, 2011) are used to design a new ensemble forecast method, the so-called BHM-SVW ocean ensemble forecast (OEF) method. As detailed in Part I, the BHM-SVW is a probabilistic model, the output of which is the posterior distribution of the SVW at each grid location for each output time (i.e.…”

Section: The Bhm-svw Ocean Ensemble Forecast Methodsmentioning

confidence: 99%

“…Figure 2 shows that the spread varies periodically during the period 1A-14A due to the insertion of scatteromenter data. The spread remains constant during the forecast period, since the ECMWF forecast wind variance has been modelled as time-independent (see Milliff et al, 2011). The EEPS wind spread (blue curve) instead mimics the growth of the forecast error (black curve), albeit offset to lower amplitude after day 6F.…”

Section: Comparison Between Bhm and Eeps Svw Distributionsmentioning

confidence: 99%

“…for notation see Part I (Milliff et al, 2011), where x, y, t denote space and time coordinates and σ W is the standard deviation represented in Figure 2, which provides a succinct depiction of the important differences in probabilistic BHM-SVW (red) and deterministic EEPS wind (blue) forcingensemble standard deviations. The variances in the wind forcing ensembles over the forecast time provide a context for the comparison of ocean ensemble forecast results.…”

Section: Comparison Between Bhm and Eeps Svw Distributionsmentioning

confidence: 99%

“…The aims of this article are (1) to analyze the impact of the Bayesian hierarchical model (BHM) surface vector winds (SVW), hereafter BHM-SVW, derived in Part I of this series (Milliff et al, 2011) on the ocean ensemble forecast (OEF) response and (2) to compare the response with other ensemble-forecast-generating methods. The assessment will be carried out for the short-term, open-ocean Mediterranean Forecasting System (MFS: Pinardi et al, 2003;Pinardi and Coppini, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…However, the perturbations at 100 m needed several months to grow, limiting the utility for real-time daily ensemble forecasting systems. These previous attempts did not use realistic estimates of the atmospheric wind errors such as we have developed with the BHM-SVW method and they did not use model-adjusted initial-condition perturbations, as we have shown in Part I of this article (Milliff et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

Pinardi

Bonazzi²,

Dobričić

et al. 2011

Quart J Royal Meteoro Soc

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Section: The Bhm-svw Ocean Ensemble Forecast Methodsmentioning

confidence: 99%

Section: Comparison Between Bhm and Eeps Svw Distributionsmentioning

confidence: 99%

Section: Comparison Between Bhm and Eeps Svw Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

Pinardi

Bonazzi²,

Dobričić

et al. 2011

Quart J Royal Meteoro Soc

Self Cite

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Comparison of full and empirical Bayes approaches for inferring sea‐level changes from tide‐gauge data

2017

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Tide‐gauge data are one of the longest instrumental records of the ocean, but these data can be noisy, gappy, and biased. Previous studies have used empirical Bayes methods to infer the sea‐level field from tide‐gauge records but have not accounted for uncertainty in the estimation of model parameters. Here we compare to a fully Bayesian method that accounts for uncertainty in model parameters, and demonstrate that empirical Bayes methods underestimate the uncertainty in sea level inferred from tide‐gauge records. We use a synthetic tide‐gauge data set to assess the skill of the empirical and full Bayes methods. The empirical‐Bayes credible intervals on the sea‐level field are narrower and less reliable than the full‐Bayes credible intervals: the empirical‐Bayes 95% credible intervals are 42.8% narrower on average than are the full‐Bayes 95% credible intervals; full‐Bayes 95% credible intervals capture 95.6% of the true field values, while the empirical‐Bayes 95% credible intervals capture only 77.1% of the true values, showing that parameter uncertainty has an important influence on the uncertainty of the inferred sea‐level field. Most influential are uncertainties in model parameters for data biases (i.e., tide‐gauge datums); letting data‐bias parameters vary along with the sea‐level process, but holding all other parameters fixed, the 95% credible intervals capture 92.8% of the true synthetic‐field values. Results indicate that full Bayes methods are preferable for reconstructing sea‐level estimates in cases where complete and accurate estimates of uncertainty are warranted.

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Modeling 3‐D spatio‐temporal biogeochemical processes with a forest of 1‐D statistical emulators

et al. 2012

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This paper focuses on the spatio‐temporal dynamical processes in lower trophic level marine ecosystems, where various sources of uncertainty make statistical modeling difficult. Such dynamical processes exhibit nonlinearity in time and potential nonstationarity in space. Planktonic organisms are microscopic, making it difficult to measure their abundance and resulting in limited data. Further, deterministic, component‐based ecosystem models contain a large number of parameters, some of which can be difficult to estimate. We consider a Bayesian hierarchical framework for parameter estimation that uses an approximation to the dynamical models for computational feasibility. Specifically, we develop a computationally inexpensive first‐order statistical emulator for a one‐dimensional NPZD model with iron limitation. Then, we introduce a novel approach to the modeling of three‐dimensional lower trophic level marine ecosystem processes, linking the one‐dimensional emulators via a two‐dimensional spatial field on the parameters. This methodology is used to estimate important biological parameters on the coastal Gulf of Alaska, leading to a reduction in Bayesian credible interval width compared with a nonspatial model. Copyright © 2012 John Wiley & Sons, Ltd.

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Ocean ensemble forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model

Cited by 41 publications

References 48 publications

Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

Ocean ensemble forecasting. Part II: Mediterranean Forecast System response

Comparison of full and empirical Bayes approaches for inferring sea‐level changes from tide‐gauge data

Modeling 3‐D spatio‐temporal biogeochemical processes with a forest of 1‐D statistical emulators

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