Unraveling vertical inhomogeneity in vapour phase polymerized PEDOT:Tos films

Vertical inhomogeneity found in vapour phase polymerized thin films of the conducting polymer PEDOT:Tos.

Use of spectral cloud emissivity to infer ice cloud boundaries: Methodology and assessment using CALIPSO cloud products

Satellite-based operational cloud height retrievals generally assume a plane-parallel homogeneous cloud exists in each field of regard, or pixel, but this assumption ignores vertical inhomogeneity, which is of particular importance for optically thin, but geometrically thick, ice clouds. This study demonstrates that ice cloud emissivity uncertainties can be used to provide a reasonable range of ice cloud layer boundaries, i.e., the minimum to maximum heights. Here ice cloud emissivity uncertainties are obtained for three IR channels centered at 11, 12, and 13.3 µm. The range of cloud emissivities is used to infer a range of ice cloud temperature/heights, rather than a single value per pixel as provided by operational cloud retrievals. Our methodology is tested using MODIS observations over the western North Pacific Ocean during August 2015. We estimate minimum/maximum heights for three cloud regimes, i.e., single-layer thin and thick ice clouds, and multi-layered clouds. Our results are assessed through comparison with CALIOP Version 4 cloud products for a total of 11873 pixels. The cloud boundary heights for single-layer optically thin clouds show good agreement with those from CALIOP; bias for maximum (minimum) heights versus the cloud top (base) heights of CALIOP are 0.13 km (−1.01 km). For optically thick and multi-layered clouds, the biases of the estimated cloud heights from the cloud top/base become larger. Our method is applicable to measurements provided by most geostationary weather satellites including the GK-2A advanced multi-channel infrared imager. The vertically resolved heights for ice clouds can contribute new information for studies involving weather prediction and cloud radiative effects.

Smart composite hydrogel with pH-, ionic strength- and temperature-induced actuation

A facile and versatile photo-patterning method to fabricate “smart” hydrogels with defined lateral and vertical inhomogeneity of hydrogel composition and dimensions has been developed via generating programmable composite hydrogels and bilayer hydrogels based on thermal and ionic strength-responsive poly(N-isopropylacrylamide) and pH-sensitive poly(acrylic acid) hydrogels.

Interpretation of Multiwavelength-Retrieved Droplet Effective Radii for Warm Water Clouds in Terms of In-Cloud Vertical Inhomogeneity by Using a Spectral Bin Microphysics Cloud Model

This study examines the impact of in-cloud vertical inhomogeneity on cloud droplet effective radii (CDERs) of water-phase cloud retrieved from 1.6-, 2.1-, and 3.7-μm-band measurements (denoted by r1.6, r2.1, and r3.7, respectively). Discrepancies between r1.6, r2.1, and r3.7 due to in-cloud vertical inhomogeneity are simulated by using a spectral bin microphysics cloud model and one-dimensional (1D) remote sensing simulator under assumptions that cloud properties at the subpixel scale have horizontal homogeneity and 3D radiative transfer effects can be ignored. Two-dimensional weighting functions for the retrieved CDERs with respect to cloud optical depth and droplet size are introduced and estimated by least squares fitting to the relation between the model-simulated droplet size distribution functions and the retrieved CDERs. The results show that the 2D weighting functions can explain CDER discrepancies due to in-cloud vertical inhomogeneity and size spectrum characteristics. The difference between r1.6 and r2.1 is found to primarily depend on the vertical difference in droplet size distribution because the peak widths of their weighting functions differ in terms of cloud optical depth. The difference between r3.7 and r2.1, in contrast, is highly dependent on r2.1 because the magnitude of its weighting function is always greater than that of r3.7 over the entire range of optical depths and droplet sizes, except for the cloud top. The overestimation of retrieved CDER compared with in situ CDER in a typical adiabatic cloud case is also interpreted in terms of in-cloud vertical inhomogeneity based on the 2D weighting functions and simulation results.