Missing data days are randomly introduced into a 

 complete time series using computer-generated, uniformly 

 distributed random numbers. Any desired fraction of missing 

 data can be introduced by associating with each daily tem- 

 perature one of the random numbers with range to 1. If 

 the random number has a value greater than the desired 

 fraction, the temperature is retained; if not, the tempera- 

 ture is deleted. Although two probabilities are used to 

 generate each of the computed histograms of figure 4, it is 

 more convenient in the analyses below to use single prob- 

 abilities yielding the same fractions of missing data. The 

 resulting computed histograms decrease more rapidly as a 

 function of length of sequence than do those of figure 4, but 

 the analysis used is fairly insensitive to the shape of the 

 histograms. Since the gross characteristics of the time 

 series are similar for all stations, any deletion of tempera- 

 tures from complete Scripps Pier and Triple Island time 

 series yields sample time series which are like those with 

 naturally missing larger fractions of data, and which have 

 whatever weaknesses are implied by the missing data. In 

 the remainder of this paper, the name samp 1 e time series 

 refers to a comp 1 et e time s e r i es with data deleted by the 

 above described process. 



MONTE CARLO APPROACH 



For a sample time series, the harmonic and autocor- 

 relation analyses can be performed just as for a complete 

 series, the proper adjustments being made in the computa- 

 tions. The regression and autocorrelation coefficients ob- 

 tained from a sample time series are different from those 

 obtained from the corresponding complete series. If many 

 sample time series with the same fraction of missing data 

 are independently generated from the same complete time 

 series, and if regression and autocorrelation analyses are 

 performed for each sample time series, then the variabilities 



19 



