Seasonal indicators

Sea salts appear to be less reliable seasonal indicators nearer the ocean on the Ross Ice Shelf. However, N03 and S042-have easily interpretable seasonal variations and serve as useful... 

Greenland, N03-, Cl , Na+, and A1 or microparticles have usable seasonal variations. Sea salt-derived Na and Cl are useful dating species in East Antarctic snow although the variations are lower than in Greenland and the dating errors are consequently higher. Sea salts are satisfactory seasonal indicators in the inland portion of the Ross Ice Shelf and presumably in West Antarctica. Nitrate and sulfate have good seasonal variations on the Ross Ice Shelf, even at the more seaward locations. 

Preliminary studies with field-grown alfalfa exposed to chronic ozone levels (200 yg/m throughout the growing season) indicated that the levels of amino acids and protein were increased 19 and 28% respectively (Neely and Tingey, unpublished). 

An analysis of bias by season indicates that the largest number of significant method bias values occur for most of the ions in the fall (September - November). 

NOTE Seasonality indices are not static. They can be updated as more data become available. 

Method Using Static Seasonality Indices (Hillier and Lieberman, 2001)... 

Step 1 Compute the seasonality indices as discussed in Section 2.5. 

Step 2 Using the seasonality indices, compute the deseasonalized demands. 

Step 5 Compute the actual forecasts using the seasonality indices again. 

First we compute the seasonality indices and the deseasonalized demands using the approach given in Section 2.5. 

Step 1 Compute seasonality indices (Si) as described in Example 2.3. 

A drawback of the previous approach is that the seasonality indices remain static and are not updated during the forecast horizon. Winters (1960) has extended Holt s method by updating seasonality indices also using exponential smoothing. 

The remaining calculations are shown in Table 2.8 using a spreadsheet. Comparing the forecast of Example 2.6 (Holt s method) and Example 2.7 (Winters method), we note that the forecasts are the same for the first four quarters (2008). However, they are different from 2009 onward due to the updating of the seasonality indices. The forecast for the first quarter of 2011 is given by... 

Since the deseasonalized level forecast does not change, the forecasts for future period are only affected by their respective seasonality indices. 

Note that the latest estimates of level, trend, and seasonality indices are used in Equation 2.19. 

Identify the components of a demand forecast. Demand consists of a systematic and a random component. The systematic component measures the expected value of demand. The random component measures fluctuations in demand from the expected value. The systematic component consists of level, trend, and seasonality. Level measures the current deseasonalized demand. Trend measures the current rate of growth or decline in demand. Seasonality indicates predictable seasonal fluctuations in demand. 

Leaves of tea (Camellia sinensis var. sinensis), camellia ( . japonlca) and sasanqua ( . sasanqua) were plucked in the Yamaguchi University Agricultural Experiment Station in the seasons indicated. Chloroplast thylakold was prepared as 4,000 g pellet as described previously (5). 

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