## 3 Ground-Based Instrumentation and Data Processing  
In the present study, we focus on the measurements from the ground-based radiometers, namely, the Multi-Filter Rotating Shadowband Radiometer (MFRSR, Model: MFR-7, Yankees Environmental Systems Inc.) and the profiling Microwave Radiometer (MWR, Model: MP-3000A, Radiometrics) for cloud retrievals. It should be pointed out that the MP-3000A at CCNY is an advanced radiometric system in comparison to the existing dual channel MWR at the ARM site. The MFRSR is a seven channel radiometer with six passbands of 10 nm FWHM (Full Width Half Maximum) centered at 415, 500, 615, 670, 870 and 940 nm and an unfiltered Si broadband (300-1,100 nm) channels. It uses an automated shadowbanding technique to measure the total-horizontal (global), diffuse-horizontal and direct-normal spectral irradiances through a single optical path [12, 37, 38]. The global and diffuse components are measured directly and the direct-normal component is computed from the difference of the two measured components. On a clear day, most of the solar radiation received by a horizontal surface will be direct-normal irradiance, while on a cloudy day most will be diffuse-horizontal irradiance. Observations of atmospheric irradiance (direct, diffuse and global) under overcast skies by MFRSR therefore provide the potential capability to infer cloud transmittance or the COD. At CCNY, we use an automated MFRSR data processing algorithm developed at NASA GISS by [PERSON] _et al._[39] with cloud screening procedure [40] being used for aerosol retrieval as well as calibration. In our application, the calibration is crucial since the cloud properties are determined radiatively from a diffuse transmission, which can only be obtained from the diffuse irradiance if calibrated. In the GISS method the calibration independent direct to diffuse ratio was used in the regression analysis to enhance the stability of regression and decrease the noise in the retrieved calibration coefficients when compared to traditional Langley technique. Since, this method does not require stable aerosol loading over long time periods; it takes care of data for which Langley regression fails. Thus, more observational data is included in the determination of the calibration coefficients. The most notable aspect about this MFRSR processing algorithm is that both calibration and retrieval procedures are not separated, while the traditional retrievals [41, 42, 43] require prior determination of calibration constants (through a calibration procedure) and applied to the data. In this approach, contributions due to different physical parameters (such as aerosol extinction, gaseous absorption) into total optical depths are separated first and then calibration is applied independently. Thus, calibration errors in one parameter do not affect the accuracy of the other retrievals. To improve the consistency over long time periods, the NASA GISS algorithm is applied simultaneously to a set of daily datasets covering at least a month (or a maximum of 4 months) of measurements through a sequential set of procedures: first, all days are cloud screened, then all 870 nm records are calibrated using compatibility between the direct and diffuse measurements. This approach allows for stabilization of the daily calibration constants at each step using a robust smoothing technique. Internal coefficients are instantaneous, derived for each day (using direct to diffuse ratio and size-regression methods) independently from other days, while the external coefficients result from averaging (smoothing) and/or interpolation of time series of internal coefficients over a long period (default \(\sim\)4 months). These external coefficients are the ones ultimately used to compute the top-of-atmosphere (TOA) irradiance. In our cloud retrieval algorithm, the measured irradiance is divided by a nominal TOA irradiance value to obtain the diffuse transmittance.