This paper is concerned with temporal data requirements for the assessment of trends and for estimating spatial correlations of atmospheric species. We examine statistically three basic issues: (1) the effect of autocorrelations in monthly observations and the effect of the length of data record on the precision of trend estimates, (2) the effect of autocorrelations in the daily data on the sampling frequency requirements with respect to the representativeness of monthly averages for trend estimation, and (3) the effect of temporal sampling schemes on estimating spatial correlations of atmospheric species in neighboring stations. Our principal findings are (1) the precision of trend estimates depends critically on the magnitude of auto-correlations in the monthly observations, (2) this precision is insensitive to the temporal sampling rates of daily measurements under systematic sampling, and (3) the estimate of spatial correlation between two neighboring stations is insensitive to temporal sampling rate under systematic sampling, but is sensitive to the time lag between measurements taken at the two stations. These results are based on methodological considerations as well as on empirical analysis of total and profile ozone and rawinsonde temperature data from selected ground stations. ¿ American Geophysical Union 1990