When plane waves diffract through fractal-patterned apertures, the resulting far-field profiles or diffractals also exhibit iterated, self-similar features. Here we show that this specific architecture enables robust signal processing and spatial multiplexing: arbitrary parts of a diffractal contain sufficient information to recreate the entire original sparse signal.

We present a new dust source area map for the Sahara and Sahel region, derived from the spatiotemporal variability of composite images of Meteosat Second Generation (MSG) using the 8.7, 10.8 and 12.0 μm wavelength channels for March 2006–February 2007. Detected dust events have been compared to measured aerosol optical thickness (AOT) and horizontal visibility observations. Furthermore the monthly source area map has been compared with the Ozone Monitoring Instrument aerosol index (AI). A spatial shift of the derived frequency patterns and the local maxima of AI‐values can be explained by wind‐transport of airborne dust implicitly included in the AI signal. To illustrate the sensitivity of a regional model using the new dust source mask, we present a case study analysis that shows an improvement in reproducing aerosol optical thickness in comparison to the original dust source parameterization.

Fifteen‐minute Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared dust index images are used to identify dust source areas. The observations of dust source activation (DSA) are compiled in a 1° × 1° map for the Sahara and Sahel, including temporal information at 3‐hourly resolution. Here we use this data set to identify the most active dust source areas and the time of day when dust source activation occurs most frequently. In the Sahara desert 65% of DSA (March 2006 to February 2008) occurs during 0600–0900 UTC, pointing toward an important role of the breakdown of the nocturnal low‐level jet (LLJ) for dust mobilization. Other meteorological mechanisms may lead to dust mobilization including density currents initiated by deep convective systems which mobilize dust fronts (haboobs) occurring preferentially in the afternoon hours and cyclonic activities. The role of the nocturnal LLJ for dust mobilization in the Sahara is corroborated by regional model studies and analysis of meteorological station data.

Large-eddy simulations (LES) with the newThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. R. Heinze et al.at building confidence in the model's ability to simulate small-to mesoscale variability in turbulence, clouds and precipitation. The results are encouraging: the high-resolution model matches the observed variability much better at small-to mesoscales than the coarser resolved reference model. In its highest grid resolution, the simulated turbulence profiles are realistic and column water vapour matches the observed temporal variability at short time-scales. Despite being somewhat too large and too frequent, small cumulus clouds are well represented in comparison with satellite data, as is the shape of the cloud size spectrum. Variability of cloud water matches the satellite observations much better in ICON than in the reference model. In this sense, it is concluded that the model is fit for the purpose of using its output for parametrization development, despite the potential to improve further some important aspects of processes that are also parametrized in the high-resolution model.

[1] The paper presents the current status of the Maritime Aerosol Network (MAN), which has been developed as a component of the Aerosol Robotic Network (AERONET). MAN deploys Microtops handheld Sun photometers and utilizes the calibration procedure and data processing (Version 2) traceable to AERONET. A web site dedicated to the MAN activity is described. A brief historical perspective is given to aerosol optical depth (AOD) measurements over the oceans. A short summary of the existing data, collected on board ships of opportunity during the NASA Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project is presented. Globally averaged oceanic aerosol optical depth (derived from island-based AERONET measurements) at 500 nm is $0.11 and Angstrom parameter (computed within spectral range 440-870 nm) is calculated to be $0.6. First results from the cruises contributing to the Maritime Aerosol Network are shown. MAN ship-based aerosol optical depth compares well to simultaneous island and near-coastal AERONET site AOD.

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Abstract.The recently increasing development of whole sky imagers enables temporal and spatial high-resolution sky observations. One application already performed in most cases is the estimation of fractional sky cover. A distinction between different cloud types, however, is still in progress. Here, an automatic cloud classification algorithm is presented, based on a set of mainly statistical features describing the color as well as the texture of an image. The k-nearestneighbour classifier is used due to its high performance in solving complex issues, simplicity of implementation and low computational complexity. Seven different sky conditions are distinguished: high thin clouds (cirrus and cirrostratus), high patched cumuliform clouds (cirrocumulus and altocumulus), stratocumulus clouds, low cumuliform clouds, thick clouds (cumulonimbus and nimbostratus), stratiform clouds and clear sky. Based on the Leave-One-Out CrossValidation the algorithm achieves an accuracy of about 97%. In addition, a test run of random images is presented, still outperforming previous algorithms by yielding a success rate of about 75%, or up to 88% if only "serious" errors with respect to radiation impact are considered. Reasons for the decrement in accuracy are discussed, and ideas to further improve the classification results, especially in problematic cases, are investigated.

An international Intercomparison of 3D Radiation Codes (I3RC) underscores the vast progress of recent years, but also highlights the challenges ahead for routine implementation in remote sensing and global climate modeling applications. Modeling atmospheric and oceanic processes is one of the most important methods of the earth sciences for understanding the interactions of the various components of the surface-atmosphere system and predicting future weather and climate states. Great leaps in the availability of computing power at continuously decreasing costs have led to widespread popularity of computer models for research and operational applications. As part of routine scientific work, output from models built for AFFILIATIONS: CAHALAN-NASA