Atmosphere. Aerosols, clouds, and climate.
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Comment↗
Biomedical subjects
Daniel Rosenfeld
Publications and source records attributed to Daniel Rosenfeld.
The effect of smoke, dust, and pollution aerosol on shallow cloud development over the Atlantic Ocean.
Clouds developing in a polluted environment tend to have more numerous but smaller droplets. This property may lead to suppression of precipitation and longer cloud lifetime. Absorption of incoming solar radiation by aerosols, however, can reduce the cloud cover. The net aerosol effect on clouds is currently the largest uncertainty in evaluating climate forcing. Using large statistics of 1-km resolution MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data, we study the aerosol effect on shallow water clouds, separately in four regions of the Atlantic Ocean, for June through August 2002: marine aerosol (30 degrees S-20 degrees S), smoke (20 degrees S-5 degrees N), mineral dust (5 degrees N-25 degrees N), and pollution aerosols (30 degrees N- 60 degrees N). All four aerosol types affect the cloud droplet size. We also find that the coverage of shallow clouds increases in all of the cases by 0.2-0.4 from clean to polluted, smoky, or dusty conditions. Covariability analysis with meteorological parameters associates most of this change to aerosol, for each of the four regions and 3 months studied. In our opinion, there is low probability that the net aerosol effect can be explained by coincidental, unresolved, changes in meteorological conditions that also accumulate aerosol, or errors in the data, although further in situ measurements and model developments are needed to fully understand the processes. The radiative effect at the top of the atmosphere incurred by the aerosol effect on the shallow clouds and solar radiation is -11 +/- 3 W/m2 for the 3 months studied; 2/3 of it is due to the aerosol-induced cloud changes, and 1/3 is due to aerosol direct radiative effect.
Air Pollutants↗
The role of sea spray in cleansing air pollution over ocean via cloud processes.
Particulate air pollution has been shown to strongly suppress precipitation from convective clouds over land. New observations show that precipitation from similar polluted clouds over oceans is much less affected, because large sea salt nuclei override the precipitation suppression effect of the large number of small pollution nuclei. Raindrops initiated by the sea salt grow by collecting small cloud droplets that form on the pollution particles, thereby cleansing the air. Therefore, sea salt helps cleanse the atmosphere of the air pollution via cloud processes. This implies that over oceans, the climatic aerosol indirect effects are significantly smaller than current estimates.
Air Pollution↗
New approach to gridding using regularization and estimation theory.
When sampling under time-varying gradients, data is acquired over a non-equally spaced grid in k-space. The most computationally efficient method of reconstruction is first to interpolate the data onto a Cartesian grid, enabling the subsequent use of the inverse fast Fourier transform (IFFT). The most commonly used interpolation technique is called gridding, and is comprised of four steps: precompensation, convolution with a Kaiser-Bessel window, IFFT, and postcompensation. Recently, the author introduced a new gridding method called Block Uniform ReSampling (BURS), which is both optimal and efficient. The interpolation coefficients are computed by solving a set of linear equations using singular value decomposition (SVD). BURS requires neither the pre- nor the postcompensation steps, and resamples onto an n x n grid rather than the 2n x 2n matrix required by conventional gridding. This significantly decreases the computational complexity. Several authors have reported that although the BURS algorithm is very accurate, it is also sensitive to noise. As a consequence, even in the presence of a low level of measurement noise, the resulting image is often highly contaminated with noise. In this work, the origin of the noise sensitivity is traced back to the potentially ill-posed matrix inversion performed by BURS. Two approaches to the solution are presented. The first uses regularization theory to stabilize the inversion process. The second formulates the interpolation as an estimation problem, and employs estimation theory for the solution. The new algorithm, called rBURS, contains a regularization parameter, which is used to trade off the accuracy of the result against the signal-to-noise ratio (SNR). The results of the new method are compared with those obtained using conventional gridding via simulations. For the SNR performance of conventional gridding, it is shown that the rBURS algorithm exhibits equal or better accuracy. This is achieved at a decreased computational cost compared to conventional gridding.
Algorithms↗