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Oceanography in .NET Integrate datamatrix 2d barcode in .NET Oceanography

12.2 Oceanography use vs .net data matrix 2d barcode encoder tocreate data matrix ecc200 on .net Visal Basic .NET Overall, u .net framework data matrix barcodes se of NN (and its derivatives) as a low-cost substitute for the UES in the high resolution Multiscale Ocean Forecast system has accelerated density calculations by a factor of 10, with the errors in the density calculations not exceeding the natural uncertainty of 0.1 kg m 3 .

Hence the computational cost of the density calculations has dropped from 40% to 4 5% of the total cost (Krasnopolsky and Chevallier, 2003).. 12.2.3 Win d wave modelling Incoming water waves with periods of several seconds observed by sunbathers on a beach are called wind waves, as they are surface gravity waves (LeBlond and Mysak, 1978; Gill, 1982) generated by the wind.

As the ocean surface allows ef cient propagation of wind waves, they are usually generated by distant storms, often thousands of kilometres away from the calm sunny beach. The wave energy spectrum F on the ocean surface is a function of the 2dimensional horizontal wave vector k. We recall that the wavelength is 2 k 1 , while the wave s phase propagation is along the direction of k.

The evolution of F(k) is described by dF = Sin + Snl + Sds + Ssw , dt (12.10). where Sin .net vs 2010 Data Matrix barcode is the input source term, Snl the nonlinear wave wave interaction term, Sds the dissipation term, and Ssw is the term incorporating shallow-water effects, with Snl being the most complicated one. For surface gravity waves, resonant nonlinear wave wave interactions involve four waves satisfying the resonance conditions k1 + k2 = k3 + k4 , and 1 + 2 = 3 + 4 , (12.

11). where ki a nd i (i = 1, . . .

, 4) are, respectively, the wave vector and angular frequency of the ith wave. Letting k = k4 and = 4 , Snl in its full form can be written as (Hasselmann and Hasselmann, 1985) Snl (k) = (k1 , k2 , k3 , k) (k1 + k2 k3 k) ( 1 + 2 3 ) [n 1 n 2 (n 3 + n) + n 3 n(n 1 + n 2 )] dk1 dk2 dk3 , (12.12).

with n i = data matrix barcodes for .NET F(ki )/ i , a complicated net scattering coef cient and the Dirac delta function which ensures that the resonance conditions (12.11) are satis ed during the 6-dimensional integration in (12.

12). As this numerical integration requires 103 104 more computation than all other terms in the model, an approximation, e.g.

the Discrete Interaction Approximation (DIA) (Hasselmann et al., 1985), has. Applications in environmental sciences to be made .NET datamatrix 2d barcode to reduce the amount of computation for operational wind wave forecast models. Since (12.

12) is in effect a map from F(k) to Snl (k), Krasnopolsky et al. (2002) and Tolman et al. (2005) proposed the use of MLP NN to map from F(k) to Snl (k) using training data generated from (12.

12). Since k is 2-dimensional, both F and Snl are 2-dimensional elds, containing of the order of 103 grid points in the kspace. The computational burden is reduced by using PCA on F(k) and Snl (k) and retaining only the leading PCs (about 20-50 for F and 100-150 for Snl ) before training the NN model.

Once the NN model has been trained, computation of Snl from F can be obtained from the NN model instead of from the original (12.12). The NN model is nearly ten times more accurate than the DIA.

It is about 105 times faster than the original approach using (12.12) and only seven times slower than DIA (Krasnopolsky, 2007). 12.

2.4 Ocean temperature and heat content Due to the ocean s vast heat storage capacity, upper ocean temperature and heat content anomalies have a major in uence on global climate variability. The best known large-scale interannual variability in sea surface temperature (SST) is the El Ni o-Southern Oscillation (ENSO), a coupled ocean atmosphere interaction involving the oceanic phenomenon El Ni o in the tropical Paci c, and the associated atmospheric phenomenon, the Southern Oscillation (Philander, 1990; Diaz and Markgraf, 2000).

The coupled interaction results in anomalously warm SST in the eastern equatorial Paci c during El Ni o episodes, and cool SST in the central equatorial Paci c during La Ni a episodes. The ENSO is an irregular oscillation, but spectral analysis does reveal a broad spectral peak around the 4 5 year period. Because El Ni o is associated with the collapse in the Peruvian anchovy shery, and ENSO has a signi cant impact on extra-tropical climate variability, forecasting tropical Paci c SST anomalies at the seasonal to interannual time scale (Goddard et al.

, 2001) is of great scienti c and economic interest. Tangang et al. (1997) used MLP NN to forecast the SST anomalies at the Ni o3.

4 region (see Fig. 2.3 for location).

For predictors, the NN model used the seven leading principal components (PCs) of the tropical Paci c wind stress anomalies (up to four seasons prior) and the Ni o3.4 SST anomaly itself, hence a total of 7 4 + 1 predictors. Tangang et al.

(1998a) found that NN using the leading PCs of the tropical sea level pressure (SLP) anomalies forecast better than that using the PCs of the wind stress anomalies. Tangang et al. (1998b) found that using PCs from extended empirical orthogonal function analysis (also called space time PCA or singular spectrum analysis) led to far fewer predictor variables, hence a much smaller NN model.

Tang et al. (2000) compared the SST forecasts by MLP.
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