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Smoothing and Interpolating Noisy GPS Data with Smoothing Splinesof statistical significance. e. Optimal fits Table 2 summarizes the key results of this section by applying a smoothing spline ( S = 3) to 200 ensembles with three different slopes ( ω −2 , ω −3 , and ω −4 ) and five different strides. When the algorithm uses true values, uncontaminated by noise, we consider the process to be “unblinded,” in contrast to “blind” methods, in which the algorithm only uses noisy data. The second and third columns show the effective sample size and average mean
of statistical significance. e. Optimal fits Table 2 summarizes the key results of this section by applying a smoothing spline ( S = 3) to 200 ensembles with three different slopes ( ω −2 , ω −3 , and ω −4 ) and five different strides. When the algorithm uses true values, uncontaminated by noise, we consider the process to be “unblinded,” in contrast to “blind” methods, in which the algorithm only uses noisy data. The second and third columns show the effective sample size and average mean
meteorological station on the Knorr and converted to a stress using the algorithm of Large and Pond (1981) . (b) The streamwise component of the vertical shear ∂ u /∂ z , (c) the cross-stream component of the vertical shear ∂ υ /∂ z , and (d) the inverse Richardson number . (b)–(d) are derived from Knorr ADCP and TRIAXUS data. Density contours are superimposed with a spacing of 0.1 kg m −3 in each plot. The mixed layer depth, defined by a 0.03 kg m −3 density threshold, is indicated by a magenta line
meteorological station on the Knorr and converted to a stress using the algorithm of Large and Pond (1981) . (b) The streamwise component of the vertical shear ∂ u /∂ z , (c) the cross-stream component of the vertical shear ∂ υ /∂ z , and (d) the inverse Richardson number . (b)–(d) are derived from Knorr ADCP and TRIAXUS data. Density contours are superimposed with a spacing of 0.1 kg m −3 in each plot. The mixed layer depth, defined by a 0.03 kg m −3 density threshold, is indicated by a magenta line
measurements and the COARE 3.5 bulk formula ( Edson et al. 2013 ), using the wind speed relative to the mean water velocity between 10 and 30 m. The correction due to using the ocean currents averages −3.8%. The “3.5” modification of the COARE bulk stress calculation algorithm was developed from extensive direct wind stress observations during the Climate Variability and Predictability Program (CLIVAR) Mode Water Dynamics Experiment (CLIMODE; Marshall et al. 2009 ) in the Gulf Stream system during
measurements and the COARE 3.5 bulk formula ( Edson et al. 2013 ), using the wind speed relative to the mean water velocity between 10 and 30 m. The correction due to using the ocean currents averages −3.8%. The “3.5” modification of the COARE bulk stress calculation algorithm was developed from extensive direct wind stress observations during the Climate Variability and Predictability Program (CLIVAR) Mode Water Dynamics Experiment (CLIMODE; Marshall et al. 2009 ) in the Gulf Stream system during
near the surface to O (10) m near the bottom at 200 m. The model time step was 108 s. Hourly averaged wind stress computed from R/V Endeavor observations using the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) algorithm for 13–21 June was used in wind-forced runs. REFERENCES Badin , G. , A. Tandon , and A. Mahadevan , 2011 : Lateral mixing in the pycnocline by baroclinic mixed layer eddies . J. Phys. Oceanogr. , 41 , 2080 – 2101 , doi
near the surface to O (10) m near the bottom at 200 m. The model time step was 108 s. Hourly averaged wind stress computed from R/V Endeavor observations using the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) algorithm for 13–21 June was used in wind-forced runs. REFERENCES Badin , G. , A. Tandon , and A. Mahadevan , 2011 : Lateral mixing in the pycnocline by baroclinic mixed layer eddies . J. Phys. Oceanogr. , 41 , 2080 – 2101 , doi
