摘要
The continuous, over two-decade data record from the Multi-Filter Rotating Shadowband Radiometer （MFRSR） is ideal for climate research which requires timely and accurate information of important atmospheric components such as gases, aerosols, and clouds. Except for parameters derived from MFRSR measurement ratios, which are not impacted by calibration error, most applications require accurate calibration factor（s）, angular correction, and spectral response function（s） from calibration. Although a laboratory lamp （or reference） calibration can provide all the information needed to convert the instrument readings to actual radiation, in situ calibration methods are implemented routinely （daily） to fill the gaps between lamp calibrations. In this paper, the basic structure and the data collection and pretreatment of the MFRSR are described. The laboratory lamp calibration and its limita- tions are summarized. The cloud screening algorithms for MFRSR data are presented. The in situ calibration methods, the standard Langley method and its variants, the ratio-Langley method, the general method, Alexandrov＇s comprehensive method, and Chen＇s multi-channel method, are outlined. The reason that all these methods do not fit for all situations is that they assume some properties, such as aerosol optical depth （AOD）, total optical depth （TOD）, precipitable water vapor （PWV）, effective size of aerosol particles, or angstrom coefficient, are invariant over time. These properties are not universal and some of them rarely happen. In practice, daily calibration factors derived from these methods should be smoothed to restrain elTor.
The continuous, over two-decade data record from the Multi-Filter Rotating Shadowband Radiometer （MFRSR） is ideal for climate research which requires timely and accurate information of important atmospheric components such as gases, aerosols, and clouds. Except for parameters derived from MFRSR measurement ratios, which are not impacted by calibration error, most applications require accurate calibration factor（s）, angular correction, and spectral response function（s） from calibration. Although a laboratory lamp （or reference） calibration can provide all the information needed to convert the instrument readings to actual radiation, in situ calibration methods are implemented routinely （daily） to fill the gaps between lamp calibrations. In this paper, the basic structure and the data collection and pretreatment of the MFRSR are described. The laboratory lamp calibration and its limita- tions are summarized. The cloud screening algorithms for MFRSR data are presented. The in situ calibration methods, the standard Langley method and its variants, the ratio-Langley method, the general method, Alexandrov＇s comprehensive method, and Chen＇s multi-channel method, are outlined. The reason that all these methods do not fit for all situations is that they assume some properties, such as aerosol optical depth （AOD）, total optical depth （TOD）, precipitable water vapor （PWV）, effective size of aerosol particles, or angstrom coefficient, are invariant over time. These properties are not universal and some of them rarely happen. In practice, daily calibration factors derived from these methods should be smoothed to restrain elTor.
