Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects.

There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100-500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01 + 5%AOD, and the mean bias is approximately -2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.

Testing the Two-Layer Model for Correcting Near Cloud Reflectance Enhancement Using LES/SHDOM Simulated Radiances.

A transition zone exists between cloudy skies and clear sky, such that clouds scatter solar radiation into clear sky regions. From a satellite perspective, it appears that clouds enhance the radiation nearby. We seek a simple method to estimate this enhancement, since it is so computationally expensive to account for all 3-dimensional (3D) scattering processes. In previous studies, we developed a simple two-layer model (2LM) that estimated the radiation scattered via cloud-molecular interactions. Here we have developed a new model to accounts for cloud-surface interaction (CSI). We test the models by comparing to calculations provided by full 3D radiative transfer simulations of realistic cloud scenes. For these scenes, the MODIS-like radiance fields were computed from the Spherical Harmonic Discrete Ordinate Method (SHDOM), based on a large number of cumulus fields simulated by the UCLA Large Eddy Simulation (LES) model. We find that the original 2LM model that estimates cloud-air molecule interactions accounts for 64% of the total reflectance enhancement, and the new model (2LM+CSI) that also includes cloud-surface interactions accounts for nearly 80%. We discuss the possibility of accounting for cloud-aerosol radiative interactions in 3D cloud induced reflectance enhancement, which may explain the remaining 20% of enhancements. Because these are simple models, these corrections can be applied to global satellite observations (e.g. MODIS) and help to reduce biases in aerosol and other clear-sky retrievals.

New heterocyclic tetrathiafulvalene compounds with an azobenzene moiety: photomodulation of the electron-donating ability of the tetrathiafulvalene moiety.

New heterocyclic TTF compounds 1a-c and 2 with an azobenzene moiety were described. The oxidation potential of 1a could be reversibly modulated by alternating UV and visible light irradiation. As a result, a molecular switch with UV/visible light as the inputs and the electrochemical signal as the output was achieved. Moreover, it was found that the influence of the azobenzene photoisomerization on the electronic property of the TTF unit became stronger with shorter spacers in compounds 1a-c.

Photomodulation of the electrode potential of a photochromic spiropyran-modified Au electrode in the presence of Zn2+: a new molecular switch based on the electronic transduction of the optical signals.

The electrode potential of a photochromic spiropyran-modified Au electrode could be reversibly modulated by UV/visible light irradiation in the presence of Zn2+, and a new molecular switch and an "AND" logic gate based on this electronic transduction of the optical signals were established.