Directional Validation of Wave Predictions*

2007 ◽  
Vol 24 (3) ◽  
pp. 504-520 ◽  
Author(s):  
W. Erick Rogers ◽  
David W. C. Wang
Keyword(s):  
Ground Truth ◽  
Wave Model ◽  
Peak Frequency ◽  
Adaptive Method ◽  
Parametric Model ◽  
Spectral Peak ◽  
Systematic Bias ◽  
Data Adaptive ◽  
Interpretation Of Results

Abstract A methodology for quantitative, directional validation of a long-term wave model hindcast is described and applied. Buoy observations are used as ground truth and the method does not require the application of a parametric model or data-adaptive method to the observations. Four frequency ranges, relative to the peak frequency, are considered. The validation of the hindcast does not suggest any systematic bias in predictions of directional spreading at or above the spectral peak. Idealized simulations are presented to aid in the interpretation of results.

scholarly journals Data-adaptive image-denoising for detecting and quantifying nanoparticle entry in mucosal tissues through intravital 2-photon microscopy

2014 ◽  
Vol 5 ◽  
pp. 2016-2025 ◽  
Author(s):  
Torsten Bölke ◽  
Lisa Krapf ◽  
Regina Orzekowsky-Schroeder ◽  
Tobias Vossmeyer ◽  
Jelena Dimitrijevic ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Ground Truth ◽  
Adaptive Method ◽  
Fluorescent Nanoparticles ◽  
Noise Characteristics ◽  
Mucosal Tissues ◽  
Noise Structure ◽  
Non Local ◽  
Fine Structures ◽  
Data Adaptive

Intravital 2-photon microscopy of mucosal membranes across which nanoparticles enter the organism typically generates noisy images. Because the noise results from the random statistics of only very few photons detected per pixel, it cannot be avoided by technical means. Fluorescent nanoparticles contained in the tissue may be represented by a few bright pixels which closely resemble the noise structure. We here present a data-adaptive method for digital denoising of datasets obtained by 2-photon microscopy. The algorithm exploits both local and non-local redundancy of the underlying ground-truth signal to reduce noise. Our approach automatically adapts the strength of noise suppression in a data-adaptive way by using a Bayesian network. The results show that the specific adaption to both signal and noise characteristics improves the preservation of fine structures such as nanoparticles while less artefacts were produced as compared to reference algorithms. Our method is applicable to other imaging modalities as well, provided the specific noise characteristics are known and taken into account.

Modeling of Sea Sea Surface Waves In Hurricane Basing On Self-Similarity Concept

2020 ◽  
Author(s):  
Mariya Yurovskaya ◽  
Vladimir Kudryavtsev ◽  
Bertrand Chapron
Keyword(s):  
Surface Waves ◽  
Wave Energy ◽  
Wind Field ◽  
Time Moment ◽  
Peak Frequency ◽  
Parametric Model ◽  
Spectral Peak ◽  
System Of Equations ◽  
Self Similarity ◽  
Wave Forecast

<p>The study is based on a simple parametric model, which is an extension of the self-similarity theory for surface waves generated by a wind field. According to the original similarity concept, the development of wind waves can be fully described using the scale of the fetch length (or time) and wind velocity. The aim of the work is to develop a parametric model to describe the wave generation in arbitrary spatio-temporal wind field. We assume that in this case similarity laws are also fulfilled, i.e., the rate of spectral the peak frequency and wave energy change is completely determined by the wave age. The source function is written in a form providing the stationary solution that corresponds to the well-known fetch law, confirmed in numerous experiments.</p><p>In order to extend the equations to the two-dimensional case, when the wind change occurs in both directions, it is assumed that the relations stay valid if the wind speed is replaced by its component in spectral peak direction. In this case, the system of equations should be supplemented by an expression for the evolution of spectral peak direction, describing its adaptating to the direction of non-uniform wind.</p><p>The algorithm for solving the complete system of equations describing the evolution of wave height, spectral peak frequency, its propagation direction and focusing/defocusing of wave energy, is based on the method of characteristics. To simulate the evolution of waves in a hurricane, we use the calculation in a non-stationary reference system associated with the hurricane. Coordinates, wave peak frequency, energy and direction are calculated along ray trajectory at every discrete time moment. To increase the stability of the numerical scheme, an implicit 4th-order Runge-Kutta method is used.</p><p>Test calculations were carried out for the case of the wave development from the coast with a uniform wind and then for an inhomogeneous cyclonic wind field for different hurricane speeds. The calculations reproduce the anisotropy of the energy distribution inside the hurricane and the effect of wave trapping by a moving cyclone. A comparison of the results with available field measurements of wave parameters in tropical cyclones showed their good agreement. The proposed algorithm can be used in wave forecast models and can serve for deeper understanding the wave field formation in extreme conditions.</p><p>The work was supported by Russian Science Foundation via grant 17-77-30019 and the Ministry of Education and Science of the Russian Federation under the State Assignment No. 0827-2018-0003.</p>

scholarly journals Evaluating the accuracy and uncertainty of atmospheric and wave model hindcasts during severe events using model ensembles

2021 ◽  
Author(s):  
Ali Abdolali ◽  
Andre van der Westhuysen ◽  
Zaizhong Ma ◽  
Avichal Mehra ◽  
Aron Roland ◽  
...  
Keyword(s):  
Extreme Event ◽  
Numerical Models ◽  
Model Performance ◽  
Ground Truth ◽  
Wave Model ◽  
Atmospheric Model ◽  
Stationary Source ◽  
Source Point ◽  
Spectral Wave Model ◽  
Model Ensembles

AbstractVarious uncertainties exist in a hindcast due to the inabilities of numerical models to resolve all the complicated atmosphere-sea interactions, and the lack of certain ground truth observations. Here, a comprehensive analysis of an atmospheric model performance in hindcast mode (Hurricane Weather and Research Forecasting model—HWRF) and its 40 ensembles during severe events is conducted, evaluating the model accuracy and uncertainty for hurricane track parameters, and wind speed collected along satellite altimeter tracks and at stationary source point observations. Subsequently, the downstream spectral wave model WAVEWATCH III is forced by two sets of wind field data, each includes 40 members. The first ones are randomly extracted from original HWRF simulations and the second ones are based on spread of best track parameters. The atmospheric model spread and wave model error along satellite altimeters tracks and at stationary source point observations are estimated. The study on Hurricane Irma reveals that wind and wave observations during this extreme event are within ensemble spreads. While both Models have wide spreads over areas with landmass, maximum uncertainty in the atmospheric model is at hurricane eye in contrast to the wave model.

scholarly journals Wave Directions in a Narrow Bay

2010 ◽  
Vol 40 (1) ◽  
pp. 155-169 ◽  
Author(s):  
Heidi Pettersson ◽  
Kimmo K. Kahma ◽  
Laura Tuomi
Keyword(s):  
Wave Model ◽  
Wave Climate ◽  
Spectral Peak ◽  
Step Size ◽  
Weakly Nonlinear ◽  
Spectral Wave Model ◽  
Directional Coupling ◽  
Directional Wave ◽  
The Baltic ◽  
The Waves

Abstract In slanting fetch conditions the direction of actively growing waves is strongly controlled by the fetch geometry. The effect was found to be pronounced in the long and narrow Gulf of Finland in the Baltic Sea, where it significantly modifies the directional wave climate. Three models with different assumptions on the directional coupling between the wave components were used to analyze the physics responsible for the directional behavior of the waves in the gulf. The directionally decoupled model produced the direction at the spectral peak correctly when the slanting fetch geometry was narrow but gave a weaker steering than observed when the fetch geometry was broader. The method of Donelan estimated well the direction at the spectral peak in well-defined slanting fetch conditions, but overestimated the longer fetch components during wave growth from a more complex shoreline. Neither the decoupled nor the Donelan model reproduced the observed shifting of direction with the frequency. The performance of the third-generation spectral wave model (WAM) in estimating the wave directions was strongly dependent on the grid resolution of the model. The dominant wave directions were estimated satisfactorily when the grid-step size was dropped to 5 km in the gulf, which is 70 km in its narrowest part. A mechanism based on the weakly nonlinear interactions is proposed to explain the strong steering effect in slanting fetch conditions.

Prediction of long-term cumulative incidences based on short-term parametric model for competing risks: application in early breast cancer

2016 ◽  
Vol 156 (3) ◽  
pp. 577-585 ◽  
Author(s):  
B. Cabarrou ◽  
L. Belin ◽  
S. M. Somda ◽  
M. C. Falcou ◽  
J. Y. Pierga ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Competing Risks ◽  
Parametric Model ◽  
Short Term

A Method to Implement the Random Signals for Time Varying Offshore Wave Height During Storm Condition in an Intra-Wave Period Wave Model

2006 ◽  
Author(s):  
Yuliang Zhu ◽  
Shunqi Pan ◽  
Premanandan T. Fernando ◽  
Xiaoyan Zhou
Keyword(s):  
Wave Height ◽  
Wave Model ◽  
Peak Frequency ◽  
Wave Period ◽  
Storm Event ◽  
Time Varying ◽  
Wave Heights ◽  
Offshore Wave ◽  
Storm Condition ◽  
Period Wave

In this paper, a method to implement the surface elevation at the offshore boundary during storm conditions is presented in the intra-wave period wave model. At storm condition, the offshore incident significant wave height is time varying. In the case of time varying incident wave height, the JONSWAP energy spectrum can be manipulated as follows: H1/32s(f). s(f) is the energy density function for a unit wave height. During a storm event not only the offshore boundary significant wave heights but also the peak frequency varies. If we choose a mean peak frequency during a storm event, s(f) can be calculated for the mean peak frequency for the storm event. The amplitudes of the component waves for the random signals are calculated from the unit energy density function s(f), and the phase angle of the component wave, So we can numerically generate surface elevation time series for the time varying offshore wave heights. The method was verified in the intra-wave period wave model using field measurements at Sea Palling site Norfolk UK.

scholarly journals Global and long-term comparison of SCIAMACHY limb ozone profiles with correlative satellite data (2002–2008)

2011 ◽  
Vol 4 (4) ◽  
pp. 4867-4910
Author(s):  
S. Mieruch ◽  
M. Weber ◽  
C. von Savigny ◽  
A. Rozanov ◽  
H. Bovensmann ◽  
...  
Keyword(s):  
Satellite Data ◽  
Convective Cloud ◽  
Residual Effects ◽  
Statistical Hypothesis ◽  
Systematic Bias ◽  
Statistical Hypothesis Testing ◽  
Trend Model ◽  
The Tropics ◽  
Linear Trends

Abstract. SCIAMACHY limb scatter ozone profiles from 2002 to 2008 have been compared with MLS (2005–2008), SABER (2002–2008), SAGE II (2002–2005), HALOE (2002–2005) and ACE-FTS (2004–2008) measurements. The comparison is performed for global zonal averages and heights from 10 to 50 km in one km steps. The validation was performed by comparing monthly mean zonal means and by comparing averages over collocated profiles within a zonal band and month. Both approaches yield similar results. For most of the stratosphere SCIAMACHY agrees to within 10 % or better with other correlative data. A systematic bias of SCIAMACHY ozone of up to 100 % between 10 and 20 km in the tropics points to some remaining issues with regard to convective cloud interference. Statistical hypothesis testing reveals at which altitudes and in which region differences between SCIAMACHY and other satellite data are statistically significant. We also estimated linear trends from monthly mean data for different periods where SCIAMACHY has common observations with other satellite data using a classical trend model with QBO and seasonal terms in order to draw conclusions on potential instrumental drifts as a function of latitude and altitude. SCIAMACHY exhibits a statistically significant negative trend in the range of of about 1–3 % per year depending on latitude during the period 2002–2005 (overlapping with HALOE and SAGE II) and somewhat less during 2002–2008 (overlapping with SABER) in the altitude range of 30–40 km, while in the period 2004–2008 (overlapping with MLS and ACE-FTS) no significant trends are observed. The statistically significant negative trends only observed with SCIAMACHY data point at some residual effects from errors in the tangent height registration.

A stable data adaptive method for matched‐field array processing in acoustic waveguides

1989 ◽  
Vol 85 (S1) ◽  
pp. S17-S17
Author(s):  
C. L. Byrne ◽  
R. I. Brent ◽  
C. Feuillade ◽  
D. R. DelBalzo
Keyword(s):  
Array Processing ◽  
Adaptive Method ◽  
Acoustic Waveguides ◽  
Data Adaptive

scholarly journals Shifts in broadband power and alpha peak frequency observed during long-term isolation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jan Weber ◽  
Timo Klein ◽  
Vera Abeln
Keyword(s):  
Sensory Deprivation ◽  
Peak Frequency ◽  
Neuronal Population ◽  
Confined Space ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
Induced Changes ◽  
Alpha Peak

Abstract Prolonged periods of social isolation and spatial confinement do not only represent an issue that needs to be faced by a few astronauts during space missions, but can affect all of us as recently shown during pandemic situations. The fundamental question, how the brain adapts to periods of sensory deprivation and re-adapts to normality, has only received little attention. Here, we use eyes closed and eyes open resting-state electroencephalographic (EEG) recordings to investigate how neural activity is altered during 120 days of isolation in a spatially confined, space-analogue environment. After disentangling oscillatory patterns from 1/f activity, we show that isolation leads to a reduction in broadband power and a flattening of the 1/f spectral slope. Beyond that, we observed a reduction in alpha peak frequency during isolation, but did not find strong evidence for isolation-induced changes that are of oscillatory nature. Critically, all effects reversed upon release from isolation. These findings suggest that isolation and concomitant sensory deprivation lead to an enhanced cortical deactivation which might be explained by a reduction in the mean neuronal population firing rate.

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