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The atmosphere is one of the most important contamination sources in the
ground-based Cosmic Microwave Background (CMB) observations. In this paper, we
study three kinds of filters, which are polynomial filter, high-pass filter,
and Wiener filter, to investigate their ability for removing atmospheric noise,
as well as their impact on the data analysis process through the end-to-end
simulations of CMB experiment. We track their performance by analyzing the
response of different components of the data, including both signals and noise.
In the time domain, the calculation shows that the high-pass filter has the
smallest root mean square error and can achieve high filtering efficiency,
followed by the Wiener filter and polynomial filter. We then perform map-making
with the filtered time ordered data (TOD) to trace the effects from filters on
the map domain, and the results show that the polynomial filter gives high
noise residual at low frequency, which gives rise to serious leakage to small
scales in map domain during the map-making process, while the high-pass filter
and Wiener filter do not have such significant leakage. Then we estimate the
angular power spectra of residual noise, as well as those of the input signal
for comparing the filter effects in the power spectra domain. Finally, we
estimate the standard deviation of the filter corrected power spectra to
compare the effects from different filters, and the results show that, at low
noise level, the three filters give almost comparable standard deviations on
the medium and small scales, but at high noise level, the standard deviation of
the polynomial filter is significantly larger. These studies can be used for
the reduction of atmospheric noise in future ground-based CMB data processing.