3 Merging and duty cycle

 

 

Table 1: Duty Cycles (%). ( a) Annual IRIS⁺⁺; ( b) 4-month summer IRIS⁺⁺ ; ( c) annual partial gap filled IRIS⁺⁺; ( d) 4-month summer partial gap filled IRIS⁺⁺. (NB: (1) Values for 1989 starts the 1st July, 1989. For the 4-months duty cycles values, only July, August and September are used / (2) Values for 1999 ends the 30 August, 1999. For the 4-months duty cycles values, only June, July and August are used.)

Years	a	b	c	d
1989	39.0	56.5	62.4	82.0
1990	36.6	54.5	63.6	86.0
1991	43.7	59.4	72.8	89.3
1992	44.3	60.3	73.1	89.0
1993	39.8	50.2	68.7	81.6
1994	59.8	80.6	82.4	97.0
1995	64.4	80.5	89.4	96.9
1996	59.3	72.2	85.0	93.6
1997	64.7	79.8	89.7	97.5
1998	61.5	75.1	88.0	95.9
1999	63.1	72.2	89.6	94.1

Figure 7: Monthly IRIS++ duty cycles and the contributions of each instrument (superimposed).

After these initial steps, the merging of the three data sets is now possible. It is made following the "weighted merging method'' (Fossat 1992b). As expected, the merging of IRIS, Mark-1 and LOWL results in an important improvement of the duty cycles values. IRIS only has an annual duty cycle of 20 to 40%. When merging IRIS with Mark-1 alone (before 1994), the annual duty cycle averages around 40%. Starting in 1994 when the IRIS⁺⁺ data base is complemented by LOWL, it achieves an annual duty cycle generally over 60%. The key importance of LOWL in this increase is well visible in Fig. 7, which shows the monthly duty cycles of IRIS⁺⁺, and the different contributions of Mark-1 and LOWL. The rate of duty cycle improvement is between 26% in 1994 and 43% in 1999. The seasonal summer-winter effect due to the prevailing northern hemisphere of our network deployment is clearly visible. But the important step upward due to LOWL after 1994 is also clear, and it decreases the relative amplitude of the seasonal variation of the duty cycle. The optimum longitude of Hawaii is the obvious reason of this efficiency. During the 4 months of June to September, the monthly duty cycle, from 1994 onward, is never less 63% and reaches 90% on some occurrences.

Figure 8: Power spectra of various sub-selections of IRIS++. Please note that the various y axes use different scales.

This performance can then be further improved by the so-called repetitive music partial gap filling method (Fossat et al. 1999) which is based on the fact that the oscillation signal has a very high level of correlation after slightly more than 4 hours. Its autocorrelation function shows a secondary maximum well above 70%, a number which is much higher than what it is just after one period of 5 min. It simply means that the time series is almost periodic in time, thus reflecting the quasi equidistance of p-mode peaks in the Fourier domain. The easy gap filling method consists of replacing a gap by the data collected 4 hours earlier or later. Table 1 shows the improvement obtained by this method on the annual (c versus a) and 4-month summer (d versus b) duty cycles. In summer, the 4-month duty cycles is now never less than 82%, reaching 97.5% in 1997.

Figure 9: Comparison of power spectra between Ground observations (IRIS++) and Space observations (GOLF, SoHO) for the same period (4-months summer 1996). Power spectra of GOLF in (b) and (d) are computed with the same temporal window than IRIS++.