Seasonality correction

Given that the RTS was carried out on weekdays (i.e. excluding weekends), outside school and pubhc holidays, in the winter period, we sought to correct for this in order to have year-round estimates. We introduced seasonality ratios these were estimated from the national household travel survey (NTS) performed in France in 2007-2008. We spht this survey into two periods (1) Ae RTS period , i.e. November-April on weekdays, outside school and pubhc holidays and (2) the outside RTS period , i.e. the rest of the year. We then divided the amount of each mobility measure associated with the outside RTS period by the total amount of the exposure measure associated with the RTS period to create seasonality ratios. These ratios were then applied to the mobility estimated from the RTS to obtain two full years of data between January 2005 and December 2006. 

The Rhone County being quite urbanized, a dense/non-dense areas variable was created. This distinction enables its to obtain results that do not depend on this characteristic. Moreover, this variable may be used as a proxy to identify different groups of cyclists according to their type of bicycle use most sport or leisure cycling take place in non-dense areas, and most cychng as a means of transport takes place in dense areas [AMO 11], 

We defined municipalities as being dense areas if their ZAUER category (as defined by INSEE - the French National Institute of Statistics and Economic Studies) was urban cluster , with a further population density of over 500 persons per km or a population of over 5,000, or if their ZAUER category was rural employment cluster , with (more than 5,000 jobs). All other locations were considered to be non-dense areas . 

For the crash data, the crash location was classified into dense or non-dense areas. For the mobihty data, trips were classified into dense or non-dense areas based on the origin and destination locations if these two differed, the characteristic of the destination location was considered. 

For each type of user (car occupants, pedestrians, cyclists and PTW riders), allinjury (whatever the severity), hospitalization and serious-injury (MAIS 3+) rates were estimated. These rates were estimated by dividing the number of injuries by the exposure measurement and scaled per one milhon trips, kilometers or hours. Rates by gender, age group and location ( dense areas and non-dense areas ) were estimated separately for each type of user (except for PTW riders because of insufficient mobility data). Only residents of the Rhone Coimty were included in the study. Crashes and trips were also restricted to the Rhone Coimty. 

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TREND-AND SEASONALITY-CORRECTED EXPONENTIAL SMOOTHING (WINTER S MODEL) This method is appropriate when the systematic component of d and has a level, a trend, and a seasonal factor. In this case we have... 

Consider the Tahoe Salt demand data in Table 7-1. Forecast demand for Period 1 using trend- and seasonality-corrected exponential smoothing with a = 0.1, /3 = 0.2, y = 0.1. 

FIGURE 7-10 Trend- and Seasonality-Corrected Exponential Smoothing... 

If the water circuit temperature rises above about 26°C, the cooling tower comes into operation to reject the surplus. If the circuit drops below 21°C, heat is taken from a boiler or other heat source to make up the deficiency. During mid-season operation within a large installation, many units may be cooling and many heating, so that energy rejected by the former can be used to the latter. With correct system adjustment, use of the boiler and tower can be minimized. 

Legates, D. R. and Willmott, C. J. (1990b). Mean seasonal and spatial variability in gauge-corrected, global precipitation, Int. ]. Climatol. 10, 111-127. 

The sometimes very large seasonal variation has also been corrected for. At least one measurement is supposed to be made in winter (November-March). The correction is based on the knowledge of typical winter-summer concentration ratios as shown in Figure 2. 

Some of the data presented above indicate that average radon levels are now only half as large in summer as in winter. Thus the summer air conditioning season contributes only 10-15% of our annual radon exposure, and the correction due to this factor is less than 10%. 

Table 4 shows the projected anomalies of annual and seasonal precipitation and air temperature for the Ebro River, whereas Figs. 7-12 show the spatial variation of these anomalies, computed using ordinary kriging. Anomalies are computed as the difference between projected bias-corrected values for the climate scenarios (January 2071 to December 2100) and the corresponding values observed during the control period (January 1961 to December 1990), and they can be viewed as expected values about which uncertainties of different origin exist. Table 4 shows that both RCMs predict a reduction in the mean annual precipitation, accompanied by an increase in the mean annual temperature with respect to the control period. In particular, the RCAO E model projects a reduction of 21.8% for the mean annual precipitation and an increase of +6.3°C for the mean annual temperature. 

The bias-correction is necessary to correct both the absolute magnitude and the seasonal cycle to that of the observations. This approach assumes that the same model biases persist in the future climate and thus GCMs more accurately simulate relative change than absolute values. It provides a correction of monthly mean climate only and does not correct biases in higher order statistics including the simulation of extreme events and persistence. 

Fig. 8.4 Discriminant analyses of the principal chemical components in L. catta scent secretions by (a) gland, (b) season, and (c) individual, (a) Accurate classification of 97.5% of labial, scrotal, and brachial samples in = 77) by gland of origin (Wilks lambda = 0.003 P < 0.001). (b) Reliable differentiation of 100% of labial samples (n = 26) into prebreeding, breeding, and nonbreeding seasons (Wilks lambda = 0.018, P < 0.01). (c) Individual scent signatures in the scrotal secretions from seven males. LDA performed on 17 principal components correctly classified 100% of these samples to the individuals from which they were collected (Wilks lambda = 0.000, P < 0.002)...

P002 drawdown based on seasonal differences in Pco2 concentrations in reference year 1995, corrected for temperature effects. This drawdown reflects the degree of biological utilization of CO2. Values exceeding 150 ji,atm (yellow-orange-red) are observed in the northwestern subarctic Pacific and Atlantic, the eastern equatorial Pacific, the northwestern Arabian Sea, and the Ross Sea. 

Withdrawal towers in dams allow corrections to the altered thermal regime without much additional effort. Especially in clear water reservoirs, withdrawal towers allow managers to choose water at its natural temperature and to reduce artificial seasonal temperature shifts. Again, retention basins have the same... 

A similar relationship was observed in Germany. Figure 12.36, for example, shows the deviation of the monthly mean ozone concentration after corrections for seasonal variations, long-term trends, the QBO and vortex effects, and the associated particle surface area concentration from 1991 to 1994 (Ansmann et al., 1996). The increase in the particle surface area due to Mount Pinatubo is clear associated with this increase in aerosol particles are negative monthly mean deviations in ozone that persist until fall 1993, when the surface area approaches the preeruption values. Similarly, the decrease in the total column ozone from 1980-1982 to 1993 observed at Edmonton, Alberta, Canada, and shown at the beginning of this chapter in Fig. 12.1 has been attributed to the effects of the Mount Pinatubo eruption (Kerr et al., 1993). 

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