THE PROBLEM 



Develop statistical, physical, and computer techniques 

 for interpreting, summarizing, and extrapolating oceanic 

 and meteorologic data for reliable estimation of the sound 

 velocity distribution in the ocean. Specifically, determine 

 the effect of random missing data and the effect of several 

 long periods of missing data on the regression and auto- 

 correlation analyses used in the estimation of sea-surface 

 temperatures. 



RESULTS 



Analysis of records of sea-surface temperature, 

 taken in the N. Atlantic and N. Pacific and up to 40 years 

 in length, has shown that: 



1. For many stations, the time series of sea-surface 

 temperatures have missing temperatures scattered at ran- 

 dom throughout the series. For each day there is a certain 

 probability that the temperature will be missing. For such 

 series, proper adjustments can be made in the computations 

 of the regression and autocorrelation coefficients. The 

 random deletion of data yields coefficients whose variances 

 exceed those of a complete time series by an amount as 

 predicted by the reduction in sample size. 



2. For certain stations, there are an excessive 

 number of longer sequences of missing data. For the time 

 series considered, the increase in the variances of the re- 

 gression coefficients attributable to this nonrandom missing 

 data is twice the increase attributable to random missing 

 data. Alternatively, for fractions of missing data greater 

 than 0.2, time series with nonrandom missing data will 

 have regression coefficient variances equal to those the 

 same series with 0. 15 more missing data would have, if 



all the missing data were random. 



MBL/WHOI 



D 03Q1 0DMQ517 1 



