Climate data records typically exhibit considerable variation over short time scales both from natural variability and from instrumentation issues. The use of linear least squares regression can provide overall trend information from noisy data, however assessing intermediate time periods can also provide useful information unavailable from basic trend calculations. Extracting the short term information in these data for assessing changes to climate or for comparison of data series from different sources requires the application of filters to separate short period variations from longer period trends. A common method used to smooth data is the moving average, which is a simple digital filter that can distort the resulting series due to the aliasing of the sampling period into the output series. We utilized Hamming filters to compare MSU/AMSU satellite time series developed by three research groups (UAH, RSS and NOAA STAR), the results published in January 2017 (Swanson 2017).