Quantifying random measurement errors in Voluntary Observing Ships' meteorological observations

Kent, Elizabeth C.; Berry, David I.

doi:10.1002/joc.1167

Cited by 51 publications

(77 citation statements)

References 30 publications

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“…Gilhousen (1987) found wind speed variances of 2.0 and 3.2 m 2 s −2 from paired buoys located about 40 and 110 km apart. Our values also compare well with equivalent values for ships of between 4.0 and 4.8 m 2 s −2 in the region of interest for U 10N (Kent and Berry, 2005).…”

Section: 'Errors-in-variables' Least-squares Regressionsupporting

confidence: 82%

“…We use the semivariogram method (Lindau, 1995a;Kent and Taylor, 1997;Kent and Berry, 2005) to determine the ROEV for measured and estimated ship wind speeds. However, to prevent systematic differences from being included in the estimates of the ROEV, we worked with variances of differences rather than the mean of squared differences.…”

Section: 'Errors-in-variables' Least-squares Regressionmentioning

confidence: 99%

“…The effect of waves, including breaking waves, on near-surface airflow (Hare et al, 1997;Janssen, 1999) and on the buoy motion (Taylor et al, 2002) can also add a bias or increase uncertainty. Buoy winds have limited spatial and temporal coverage; however, moored buoy winds have smaller random observational error than ship winds (Gilhousen, 1987;Kent and Berry, 2005). Buoys report hourly, and are considered to be more reliable for validation of numerical model and remotely sensed data (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Methods to homogenize wind speeds from ships and buoys

Thomas¹,

Kent

Swail³

2005

Intl Journal of Climatology

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Marine winds reported by Voluntary Observing Ships (VOS) and moored buoys require adjustment to provide a homogeneous record of the marine climate. Known sources of inhomogeneity arise from differences in measurement height and method, averaging method and atmospheric stability; methods are available to correct for these. However, significant differences remain in a paired dataset of ship and buoy winds. Regression methods to remove this remaining inconsistency are discussed, and a ranked regression method chosen as most appropriate to adjust ship wind speeds to yield a similar distribution. We show the factors, such as vessel type, that affect the regression results. The corrections, derived from a high-quality paired dataset with rigorous quality control, are effective at reducing inhomogeneity in monthly mean wind speed distributions derived from the International Comprehensive Ocean-Atmosphere Data Set.

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Section: 'Errors-in-variables' Least-squares Regressionsupporting

confidence: 82%

Section: 'Errors-in-variables' Least-squares Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Methods to homogenize wind speeds from ships and buoys

Thomas¹,

Kent

Swail³

2005

Intl Journal of Climatology

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“…The random errors, or uncorrelated component of the uncertainty, have been estimated using a semi-variogram analysis following Kent and Berry (2005) but with several changes. Coincident observations made within 24 h of each other have been used to estimate the sample semi-variograms rather than the 1 h used in Kent and Berry (2005). This wider time separation limit has been used to include the representativeness errors in using a single value to represent a daily mean.…”

Section: Uncertainties Biases and Error Characteristicsmentioning

confidence: 99%

“…Observation errors have been estimated using a semi-variogram analysis (Lindau, 1995;Kent and Berry, 2005) and are summarised in Table I. Following Reynolds and Smith (1994) the previous day's analysis is used as the basis for the first guess field and the analysis error as the basis for the uncertainty in the first guess field.…”

Section: Application To Vos Observationsmentioning

confidence: 99%

Air–Sea fluxes from ICOADS: the construction of a new gridded dataset with uncertainty estimates

Berry

Kent

2011

Intl Journal of Climatology

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102

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ABSTRACT:The methods used to calculate a new in situ global dataset of air-sea exchanges, called the NOCS Flux Dataset v2.0, are described. The fluxes have been derived from in situ weather reports from Voluntary Observing Ships (VOS) covering the period 1973-2006. The reports have been adjusted for known biases and residual uncertainties estimated. The dataset is constructed using Optimal Interpolation (OI) using new estimates of random uncertainty in the observations. Daily fields have been calculated on a 1°latitude by 1°longitude grid, each grid box and time step have an associated uncertainty estimate. Monthly fields have been calculated from simple averages of the daily fields and monthly uncertainty estimates from the daily uncertainties, using estimates of the autocorrelation between the daily uncertainty estimates. The uncertainties due to the choice of flux parameterisation have not been accounted for. Bias adjustments applied to the data are shown to reduce trends in the data and to improve the consistency of estimates of air temperature, sea surface temperature (SST) and specific humidity. The bias adjustments also improve the agreement of NOCS v2.0 with independent data from research moorings. Cross-validation of the dataset suggests that the uncertainty estimates are realistic, but that the uncertainties are probably underestimated in high variability regions and overestimated in regions with lower variability. Copyright BackgroundReliable estimates of air-sea fluxes of heat, freshwater and momentum are needed to improve our understanding of the coupled ocean-atmosphere system. The main physical interactions at the sea surface are the turbulent heat, moisture and momentum fluxes between the ocean and the atmosphere, the radiative warming of the ocean surface by solar radiation and the net cooling of the ocean surface by thermal radiation. Direct measurements of the turbulent fluxes are typically only made on air-sea interaction research cruises and dedicated moored buoys and platforms. This limits the availability of direct flux measurements in space and time and their usefulness for global studies. As a result, global marine flux datasets are typically constructed using bulk estimates of the mean meteorological parameters and bulk parameterisations (or bulk formulae) developed using coincident measurements of the direct and radiative fluxes and mean meteorological variables from research cruises.The meteorological parameters required by the bulk formulae are: air temperature and humidity; sea surface temperature (SST); wind speed; sea level pressure; and cloud cover. These parameters are available from different sources including ship and buoy observations,

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A review of uncertainty in in situ measurements and data sets of sea surface temperature

2014

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Archives of in situ sea surface temperature (SST) measurements extend back more than 160 years.Quality of the measurements is variable, and the area of the oceans they sample is limited, especially early in the record and during the two world wars. Measurements of SST and the gridded data sets that are based on them are used in many applications so understanding and estimating the uncertainties are vital. The aim of this review is to give an overview of the various components that contribute to the overall uncertainty of SST measurements made in situ and of the data sets that are derived from them. In doing so, it also aims to identify current gaps in understanding. Uncertainties arise at the level of individual measurements with both systematic and random effects and, although these have been extensively studied, refinement of the error models continues. Recent improvements have been made in the understanding of the pervasive systematic errors that affect the assessment of long-term trends and variability. However, the adjustments applied to minimize these systematic errors are uncertain and these uncertainties are higher before the 1970s and particularly large in the period surrounding the Second World War owing to a lack of reliable metadata. The uncertainties associated with the choice of statistical methods used to create globally complete SST data sets have been explored using different analysis techniques, but they do not incorporate the latest understanding of measurement errors, and they want for a fair benchmark against which their skill can be objectively assessed. These problems can be addressed by the creation of new end-to-end SST analyses and by the recovery and digitization of data and metadata from ship log books and other contemporary literature.

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Quantifying random measurement errors in Voluntary Observing Ships' meteorological observations

Cited by 51 publications

References 30 publications

Methods to homogenize wind speeds from ships and buoys

Methods to homogenize wind speeds from ships and buoys

Air–Sea fluxes from ICOADS: the construction of a new gridded dataset with uncertainty estimates

A review of uncertainty in in situ measurements and data sets of sea surface temperature

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