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A. Marshak, Y. Knyazikhin, J. C. Chiu, and W. J. Wiscombe

Abstract

Certain algebraic combinations of single scattering albedo and solar radiation reflected from, or transmitted through, vegetation canopies do not vary with wavelength. These “spectrally invariant relationships” are the consequence of wavelength independence of the extinction coefficient and scattering phase function in vegetation. In general, this wavelength independence does not hold in the atmosphere, but in cloud-dominated atmospheres the total extinction and total scattering phase function vary only weakly with wavelength. This paper identifies the atmospheric conditions under which the spectrally invariant approximation can accurately describe the extinction and scattering properties of cloudy atmospheres. The validity of the assumptions and the accuracy of the approximation are tested with 1D radiative transfer calculations using publicly available radiative transfer models: Discrete Ordinate Radiative Transfer (DISORT) and Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART). It is shown for cloudy atmospheres with cloud optical depth above 3, and for spectral intervals that exclude strong water vapor absorption, that the spectrally invariant relationships found in vegetation canopy radiative transfer are valid to better than 5%. The physics behind this phenomenon, its mathematical basis, and possible applications to remote sensing and climate are discussed.

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H. W. Barker, A. Marshak, W. Szyrmer, J-P. Blanchet, A. Trishchenko, and Z. Li

Abstract

A method is introduced for inferring cloud optical depth τ from solar radiometric measurements made on an aircraft at altitude z. It is assessed using simulated radiometric measurements produced by a 3D Monte Carlo algorithm acting on fields of broken boundary layer clouds generated from Landsat imagery and a cloud-resolving model. The method uses upwelling flux and downwelling zenith radiance measured at two solar wavelengths where atmospheric optical properties above z are very similar but optical properties of the surface–atmosphere system below z differ. This enables estimation of cloud reflectance into nadir for upwelling diffuse flux and, finally, τ above z. An approximate one-dimensional radiative Green's function is used to roughly account for horizontal transport of photons in all, even broken, clouds. This method is compared to its surface-based counterpart and shown to be superior. Most notably, the aircraft-based approach deals easily with inhomogeneous land surfaces, is less susceptible to poor sampling, and need not account for aerosol below z.

The algorithm appears as though it will have little difficulty inferring high-resolution time series of τ ≲ 40 for most (single layer) clouds. For larger values of τ, biases emerge; particularly, underestimation for the statistically infrequent interiors of cumuliform clouds as photon leakage through cloud sides is not addressed. For the cumuliform and stratiform clouds used here, mean bias errors for retrieved τ are ∼1 (or ∼15%) and ∼0.3 (or ∼3%), respectively. For stratiform clouds with textured bases, performance is likely to improve slightly for flights just up from mean cloud base.

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H. Gerber, J. B. Jensen, A. B. Davis, A. Marshak, and W. J. Wiscombe

Abstract

Aircraft measurements of liquid water content (LWC) made at sampling frequencies of 1 and 2 kHz with a particle volume monitor (PVM) probe from horizontal traverses in stratocumulus clouds during the Southern Ocean Cloud Experiment and cumulus clouds during the Small Cumulus Microphysics Study are described. The spectral density of the LWC measurements is calculated and compared to the −5/3 scaling law. The effect of PVM sampling noise is found to be small in most cases. Most measurements follow approximately the −5/3 law until cloud scales decrease below about 5 m in length. Below this length LWC variance can exceed that predicted by the −5/3 law. It is suggested that the enhanced LWC variance at small scales is related to entrainment of environmental air into the clouds, which changes primarily the droplet concentration.

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Y. Knyazikhin, R. B. Myneni, A. Marshak, W. J. Wiscombe, M. L. Larsen, and J. V. Martonchik

Abstract

Most cloud radiation models and conventional data processing techniques assume that the mean number of drops of a given radius is proportional to volume. The analysis of microphysical data on liquid water drop sizes shows that, for sufficiently small volumes, this proportionality breaks down; the number of cloud drops of a given radius is instead proportional to the volume raised to a drop size–dependent nonunit power. The coefficient of proportionality, a generalized drop concentration, is a function of the drop size. For abundant small drops the power is unity as assumed in the conventional approach. However, for rarer large drops, it falls increasingly below unity. This empirical fact leads to drop clustering, with the larger drops exhibiting a greater degree of clustering. The generalized drop concentration shows the mean number of drops per cluster, while the power characterizes the occurrence frequency of clusters. With a fixed total number of drops in a cloud, a decrease in frequency of clusters is accompanied by a corresponding increase in the generalized concentration. This initiates a competing process missed in the conventional models: an increase in the number of drops per cluster enhances the impact of rarer large drops on cloud radiation while a decrease in the frequency suppresses it. Because of the nonlinear relationship between the number of clustered drops and the volume, these two opposite tendencies do not necessarily compensate each other. The data analysis suggests that clustered drops likely have a stronger radiative impact compared to their unclustered counterpart; ignoring it results in underestimation of the contribution from large drops to cloud horizontal optical path.

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Y. Knyazikhin, A. Marshak, W. J. Wiscombe, J. V. Martonchik, and R. B. Myneni

Abstract

Most of the existing cloud radiation models treat liquid water drops of a variety of sizes as an ensemble of particles. The ensemble approach assumes that all drop sizes are well represented in an elementary volume, and its scattering and absorbing properties can be accurately specified through the use of the drop size probability density distribution function. The concentration of large drops, however, can be so low that a chance to capture them in the elementary volume is rare. Thus the drop ensemble assumption is not always true, though classical radiative transfer theory uses this assumption to simplify the radiative transfer process, as if scattering takes place from an “average drop” rather than from a particular drop. The theoretical analysis presented in this paper demonstrates that if a cumulative distribution function is used to describe drop size variability with jumps accounting for the probability of finding large drops in the elementary volume, one obtains an extra term, the Green's function, in the solution of the radiative transfer equation. The analysis of data on cloud drop size distribution acquired during the First International Satellite Cloud Climatology Project (ISCCP) Research Experiment (FIRE) field campaign clearly shows jumps in the cumulative drop size distribution; the magnitudes of the jumps are related to the frequencies of large drop occurrence. This discontinuity is primarily responsible for the additional terms that must be added to the solution to properly account for the photon interaction with the large drops. The enhancement of cloud absorption due to accounting for the “missing solution” exhibits a jump-like response to continuous variation in the concentration of large drops and may exceed the enhancement predicted by the ensemble-based models. The results presented here indicate that the missing term might be plausible to explain the enhanced value of the ratio of the shortwave cloud forcing at the surface to the forcing at top of the atmosphere.

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Mikhail D. Alexandrov, Alexander Marshak, Brian Cairns, Andrew A. Lacis, and Barbara E. Carlson

Abstract

Statistical scale-by-scale analysis, for the first time, has been applied to the aerosol optical thickness (AOT) retrieved from the Multi-Filter Rotating Shadowband Radiometer (MFRSR) network. The MFRSR data were collected in September 2000 from the dense local network operated by the U.S. Department of Energy Atmospheric Radiation Measurement program, located in Oklahoma and Kansas. These data have 20-s temporal resolution. The instrument sites form an irregular grid with the mean distance between neighboring sites about 80 km. It is found that temporal variability of AOT can be separated into two well-established scale-invariant regimes: 1) microscale (0.5–15 km), where fluctuations are governed by 3D turbulence, and 2) intermediate scale (15–100 km), characterized by a transition toward large-scale 2D turbulence. The spatial scaling of AOT was determined by the comparison of retrievals between different instrument sites (distance range 30–400 km). The authors investigate how simultaneous determination of AOT scaling in space and time can provide means to examine the validity of Taylor's frozen turbulence hypothesis. The temporal evolution of AOT scaling exponents during the month appeared to be well correlated with changes in aerosol vertical distribution, while their spatial variability reflects the concavity/convexity of the site topography. Explanations based on dynamical processes in atmospheric convective boundary layer are suggested.

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A. Marshak, A. Ackerman, A. Da Silva, T. Eck, B. Holben, R. Kahn, R. Kleidman, K. Knobelspiesse, R. Levy, A. Lyapustin, L. Oreopoulos, L. Remer, O. Torres, T. Varnai, G. Wen, and J. Yorks

Abstract

Aerosol properties are fundamentally different near clouds than distant from clouds. This paper reviews the current state of knowledge of aerosol properties in the near low cloud environment and quantitatively compares them with aerosols far from clouds, limited in scope to remote sensing observations. It interprets observations of aerosol properties from different sensors using satellite, aircraft and ground-based observations. The correlation (and anticorrelation) between proximity to cloud and aerosol properties is discussed. Retrieval artifacts in the near-cloud environment are demonstrated and quantified for different sensor attributes and environmental conditions. Finally, the paper describes the possible corrections for near-cloud enhancement in remote-sensing retrievals. This study is timely in view of science definition studies for NASA’s Aerosols-Clouds, Convection and Precipitation (ACCP) mission, which will also seek to directly links aerosol properties to nearby clouds.

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Yuekui Yang, Alexander Marshak, J. Christine Chiu, Warren J. Wiscombe, Stephen P. Palm, Anthony B. Davis, Douglas A. Spangenberg, Louis Nguyen, James D. Spinhirne, and Patrick Minnis

Abstract

Laser beams emitted from the Geoscience Laser Altimeter System (GLAS), as well as other spaceborne laser instruments, can only penetrate clouds to a limit of a few optical depths. As a result, only optical depths of thinner clouds (< about 3 for GLAS) are retrieved from the reflected lidar signal. This paper presents a comprehensive study of possible retrievals of optical depth of thick clouds using solar background light and treating GLAS as a solar radiometer. To do so one must first calibrate the reflected solar radiation received by the photon-counting detectors of the GLAS 532-nm channel, the primary channel for atmospheric products. Solar background radiation is regarded as a noise to be subtracted in the retrieval process of the lidar products. However, once calibrated, it becomes a signal that can be used in studying the properties of optically thick clouds. In this paper, three calibration methods are presented: (i) calibration with coincident airborne and GLAS observations, (ii) calibration with coincident Geostationary Operational Environmental Satellite (GOES) and GLAS observations of deep convective clouds, and (iii) calibration from first principles using optical depth of thin water clouds over ocean retrieved by GLAS active remote sensing. Results from the three methods agree well with each other. Cloud optical depth (COD) is retrieved from the calibrated solar background signal using a one-channel retrieval. Comparison with COD retrieved from GOES during GLAS overpasses shows that the average difference between the two retrievals is 24%. As an example, the COD values retrieved from GLAS solar background are illustrated for a marine stratocumulus cloud field that is too thick to be penetrated by the GLAS laser. Based on this study, optical depths for thick clouds will be provided as a supplementary product to the existing operational GLAS cloud products in future GLAS data releases.

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D. D. Turner, A. M. Vogelmann, R. T. Austin, J. C. Barnard, K. Cady-Pereira, J. C. Chiu, S. A. Clough, C. Flynn, M. M. Khaiyer, J. Liljegren, K. Johnson, B. Lin, C. Long, A. Marshak, S. Y. Matrosov, S. A. McFarlane, M. Miller, Q. Min, P. Minimis, W. O'Hirok, Z. Wang, and W. Wiscombe

Many of the clouds important to the Earth's energy balance, from the Tropics to the Arctic, contain small amounts of liquid water. Longwave and shortwave radiative fluxes are very sensitive to small perturbations of the cloud liquid water path (LWP), when the LWP is small (i.e., < 100 g m−2; clouds with LWP less than this threshold will be referred to as “thin”). Thus, the radiative properties of these thin liquid water clouds must be well understood to capture them correctly in climate models. We review the importance of these thin clouds to the Earth's energy balance, and explain the difficulties in observing them. In particular, because these clouds are thin, potentially mixed phase, and often broken (i.e., have large 3D variability), it is challenging to retrieve their microphysical properties accurately. We describe a retrieval algorithm intercomparison that was conducted to evaluate the issues involved. The intercomparison used data collected at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and included 18 different algorithms to evaluate their retrieved LWP, optical depth, and effective radii. Surprisingly, evaluation of the simplest case, a single-layer overcast stratocumulus, revealed that huge discrepancies exist among the various techniques, even among different algorithms that are in the same general classification. This suggests that, despite considerable advances that have occurred in the field, much more work must be done, and we discuss potential avenues for future research.)

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Andrew M. Vogelmann, Greg M. McFarquhar, John A. Ogren, David D. Turner, Jennifer M. Comstock, Graham Feingold, Charles N. Long, Haflidi H. Jonsson, Anthony Bucholtz, Don R. Collins, Glenn S. Diskin, Hermann Gerber, R. Paul Lawson, Roy K. Woods, Elisabeth Andrews, Hee-Jung Yang, J. Christine Chiu, Daniel Hartsock, John M. Hubbe, Chaomei Lo, Alexander Marshak, Justin W. Monroe, Sally A. McFarlane, Beat Schmid, Jason M. Tomlinson, and Tami Toto

A first-of-a-kind, extended-term cloud aircraft campaign was conducted to obtain an in situ statistical characterization of continental boundary layer clouds needed to investigate cloud processes and refine retrieval algorithms. Coordinated by the Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign operated over the ARM Southern Great Plains (SGP) site from 22 January to 30 June 2009, collecting 260 h of data during 59 research flights. A comprehensive payload aboard the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft measured cloud microphysics, solar and thermal radiation, physical aerosol properties, and atmospheric state parameters. Proximity to the SGP's extensive complement of surface measurements provides ancillary data that support modeling studies and facilitates evaluation of a variety of surface retrieval algorithms. The five-month duration enabled sampling a range of conditions associated with the seasonal transition from winter to summer. Although about twothirds of the flights during which clouds were sampled occurred in May and June, boundary layer cloud fields were sampled under a variety of environmental and aerosol conditions, with about 77% of the cloud flights occurring in cumulus and stratocumulus. Preliminary analyses illustrate use of these data to analyze aerosol– cloud relationships, characterize the horizontal variability of cloud radiative impacts, and evaluate surface-based retrievals. We discuss how an extended-term campaign requires a simplified operating paradigm that is different from that used for typical, short-term, intensive aircraft field programs.

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