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  • Author or Editor: Robert F. Cahalan x
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Joachim H. Joseph
and
Robert F. Cahalan

Abstract

Histograms of nearest neighbor spacings of fair weather cumulus at 15 locations Over the world's oceans are presented based on the analysis of high resolution LANDSAT 3 Multispectral Scanner images for amounts of cloud cover ranging from 0.6% to 37.6%. These histograms are found to be essentially the same at all locations analysed, similarly to our previous findings on the size distributions and the fractal dimensions of the perimeters for this cloud type.

The nearest neighbor spacings are linearly dependent on the effective cloud radii, with a proportionality factor ranging from five to twenty. The histograms peak at about 0.5 km. Nearest-neighbor spacings smaller than about a kilometer, associated with cumulus clouds with an effective radius less than a few hundred meters, have a distribution of cloud centers that is almost independently distributed in the horizontal plane and show a tendency for the formation of clumps. Larger spacings of up to thirty kilometers occur and are associated with the larger clouds. These latter spacings are not independent.

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Lazaros Oreopoulos
,
Robert F. Cahalan
,
Alexander Marshak
, and
Guoyong Wen

Abstract

The authors propose a new cloud property retrieval technique that accounts for cloud side illumination and shadowing effects present at high solar zenith angles. The technique uses the normalized difference of nadir reflectivities (NDNR) at a conservative and an absorbing (with respect to liquid water) wavelength. It can be further combined with the inverse nonlocal independent pixel approximation (NIPA) of that corrects for radiative smoothing, thus providing a retrieval framework where all 3D cloud effects can potentially be accounted for. The effectiveness of the new technique is demonstrated using Monte Carlo simulations. Real-world application is shown to be feasible using Thematic Mapper (TM) radiance observations from Landsat-5 over the Southern Great Plains (SGP) site of the Atmospheric Radiation Measurement (ARM) Program. For the moderately oblique (45°) solar zenith angle of the available Landsat scene, NDNR gives similar regional statistics and histograms when compared with standard independent pixel approximation (IPA), but significant differences at the pixel level. Inverse NIPA is also applied for the first time on observed high-resolution radiances of overcast Landsat subscenes. The dependence of the NIPA-retrieved cloud fields on the parameters of the method is illustrated and practical issues related to the optimal choice of these parameters are discussed.

It is natural to compare novel cloud retrieval techniques with standard IPA retrievals. IPA is useful in revealing the inadequacy of plane parallel theory in certain situations and in demonstrating sensitivities to parameter choices, parameterizations, and assumptions. For example, it is found that IPA has problems in matching modeled and observed band-7 (2.2 μm) reflectance values for ∼6% of the pixels, most of which are at cloud edges. For simultaneous cloud optical depth–droplet effective radius retrievals (where a conservative and an absorptive TM band are needed), it is found that the band-4 (0.83 μm)–band-7 pair was the most well behaved, having less saturation, smaller changes in nominal calibration, and better overall consistency with modeled values than other bands. Mean values of optical depth, effective radius, and liquid water path (LWP) for typical IPA retrievals using this pair are τ = 22, r e = 11 μm, and LWP = 157 g m−2, respectively. Inclusion of aerosol scattering above clouds results in ∼8% decrease in mean cloud optical depth for an aerosol optical depth of 0.2. Degradation of instrument resolution up to ∼2 km has small effects on the optical property means and histograms, suggesting small actual cloud variability at these scales and/or radiative smoothing. Comparisons with surface instruments (microwave radiometer, pyranometer, and pyrgeometer) verify the statisitical adequacy of the IPA retrievals. Last, cloud fractions derived with a simple threshold method are compared with those from an automated procedure called Automatic Cloud Cover Assessment now in operational use for Landsat-7. For the northernmost 2000 scanlines of the scene, the cloud fraction A c is 0.585 from thresholding, as compared with A c = 0.563 for the automated procedure, and the full scene values are A c = 0.870 and A c = 0.865, respectively. This suggests that the Landsat-7 automated procedure will likely give reliable scene-averaged cloud fractions for moderately thick clouds over continental U.S. scenes similar to SGP.

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Nicola L. Pounder
,
Robin J. Hogan
,
Tamás Várnai
,
Alessandro Battaglia
, and
Robert F. Cahalan

Abstract

Liquid clouds play a profound role in the global radiation budget, but it is difficult to retrieve their vertical profile remotely. Ordinary narrow-field-of-view (FOV) lidars receive a strong return from such clouds, but the information is limited to the first few optical depths. Wide-angle multiple-FOV lidars can isolate radiation that is scattered multiple times before returning to the instrument, often penetrating much deeper into the cloud than does the single-scattered signal. These returns potentially contain information on the vertical profile of the extinction coefficient but are challenging to interpret because of the lack of a fast radiative transfer model for simulating them. This paper describes a variational algorithm that incorporates a fast forward model that is based on the time-dependent two-stream approximation, and its adjoint. Application of the algorithm to simulated data from a hypothetical airborne three-FOV lidar with a maximum footprint width of 600 m suggests that this approach should be able to retrieve the extinction structure down to an optical depth of around 6 and a total optical depth up to at least 35, depending on the maximum lidar FOV. The convergence behavior of Gauss–Newton and quasi-Newton optimization schemes are compared. Results are then presented from an application of the algorithm to observations of stratocumulus by the eight-FOV airborne Cloud Thickness from Off-Beam Lidar Returns (THOR) lidar. It is demonstrated how the averaging kernel can be used to diagnose the effective vertical resolution of the retrieved profile and, therefore, the depth to which information on the vertical structure can be recovered. This work enables more rigorous exploitation of returns from spaceborne lidar and radar that are subject to multiple scattering than was previously possible.

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