Detailed knowledge of the radiative properties of atmospheric constituents is crucial to properly characterize climate forcing mechanisms and quantify the response of the climate system. An important challenge is detecting the three-dimensional (3-D) structure of clouds and aerosols, and properly modeling the effects of this structure on radiative transfer. This is essential to reduce ambiguity in the retrieval of atmospheric properties and to improve radiative parameterization in models. Current ability to resolve 3-D cloud structure is limited to scanning pulsed active sensors and imaging instruments. However, no single ground-based sensor has proven to be capable of doing the job for all of the wide variety of atmospheric cloud situations. In general, the laser devices are excellent for detecting essentially all clouds that are visible from the ground and are within the instruments' height range. The laser systems are unable to provide any information about higher cloud layers when lower liquid-water layers are present. The great strength of radar is its ability to penetrate clouds and reveal multiple layers aloft. Although its sensitivity is impressive, the millimeter-wave cloud radar fails to detect some of these clouds, especially if the clouds are composed of small hydrometeors, or the clouds may be thinner than the radar sample volume depth resulting in partial beam filling and reduced reflectivity [Clothiaux et al., 2000].
 Information of “missed” cloud layer is extremely important for the Broadband Heating Rate Profile (BBHRP), since “missed” upper layer clouds would substantially impact radiation heating profiles. Figure 1 shows the calculated shortwave (SW), longwave (LW), and total heating rates for a single-layer cloud, a double-layer water cloud, and an ice cloud over water cloud at solar zenith angle of 45°. For the LW calculation, we used the U.S. standard atmospheric profile. In the calculation of double-layer cloud cases, we added a “missed” water or ice cloud layer with water path of 10 g/m2 (cloud optical depth about 1) above the lower water cloud layer and reduced the lower layer water cloud path to 190 g/m2 to ensure the same total water path of 200 g/m2 for all cases. The SW reaching the surface for three cases are 124.1, 122.8, and 122.5 w/m2, respectively, whereas the upwelling SW at the top of the atmosphere (TOA) are 376.1, 377.5, and 379.5 w/m2, respectively. Clearly, the differences of SW at both boundaries with/without “missed” cloud layer are very small, within the measurement uncertainty. However, the heating rate profiles are substantially different. Although a “missed” cloud layer does not occur all the time, statistical information of “missed” cloud layer is extremely valuable for BBHRP. Furthermore, this simple calculation reinforces that the radiation closure at the boundaries cannot ensure the accuracy of the heating profile. There is an urgent need to exploit other means to detect the 3-D structure of clouds and aerosols.
 For a long time, the remote sensing community has recognized the advantages of using the oxygen A band and has sought ways to exploit these advantages to measure atmospheric properties and constituents. Because oxygen is a well-mixed gas in the atmosphere, the pressure dependence (as a surrogate of altitude) of oxygen A band absorption line parameters provides a vehicle for retrieving photon path length distributions from spectrometry of the oxygen A band. The concept underlying oxygen A band retrievals is the principle of equivalence, which allows assessment of atmospheric radiative properties at any nearby wavelength from a photon path length distribution measurement at one particular band [Irvine, 1964; 1966; van de Hulst, 1980]. This is possible because the scattering properties of cloud and aerosol vary slowly and predictably with wavelength and 760 nm is a useful central wavelength, reasonably representative of the entire solar shortwave. Photon path length distributions, a hidden property of standard radiation transfer models, are controlled by spatial distributions of scattering and absorption.
 Many efforts have been made to utilize photon path length distribution in oxygen A band as a tool in remote sensing [Grechko et al., 1973; Fischer and Grassl, 1991; Fischer et al., 1991; O'Brian and Mitchell, 1992; Harrison and Min, 1997; Pfeilsticker et al., 1998; Veitel et al., 1998; Min and Harrison, 1999; Portmann et al., 2001; Min et al., 2001; Min and Clothiaux, 2003; and Min et al., 2004; Min and Harrison, 2004; and many others]. In particular, Min and Clothiaux  demonstrated that two independent pieces of information (mean and variance) are retrievable from a modest resolution Rotating Shadowband Spectrometer (RSS). Analysis of the variance and mean of the photon path length distribution from RSS measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site illustrates how sensitive the photon path length distribution is to the cloud vertical profile. In this study, we further exploit the unique potential of photon path length distribution to detect the 3-D structure of clouds and investigate how many clouds may be “missed” by the combination of a millimeter-wave cloud radar (MMCR) and a micropulse lidar (MPL) in a 1 year routine observation. Simply flagging possible “missed” clouds in routine MMCR-MPL observation is extremely valuable, as most ARM cloud products primarily use cloud retrievals from the MMCR.