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Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, and they are as hazardous and fatal as many tornadoes and hurricanes. Yet, automated detection of derechos remains challenging due to the absence of spatiotemporally continuous observations and the complex criteria employed to define the phenomenon. This study proposes a physically based definition of derechos that contains the key features of derechos described in the literature and allows their automated objective identification using either observations or model simulations. The automated detection is composed of three algorithms: the Flexible Object Tracker algorithm to track mesoscale convective systems (MCSs), a semantic segmentation convolutional neural network to identify bow echoes, and a comprehensive algorithm to classify MCSs as derechos or non-derecho events. Using the new approach, we develop a novel high-resolution (4 km and hourly) observational dataset of derechos over the United States east of the Rocky Mountains from 2004 to 2021. The dataset is analyzed to document the derecho climatology in the United States. Many more derechos (increased by ~50–400 %) are identified in the dataset (~31 events per year) than in previous estimations (~6–21 events per year), but the spatial distribution and seasonal variation patterns resemble earlier studies with a peak occurrence in the Great Plains and Midwest during the warm season. In addition, around 20 % of damaging gust (≥ 25.93 m s-1) reports are produced by derechos during the dataset period over the United States east of the Rocky Mountains. The dataset is available at https://doi.org/10.5281/zenodo.10884046 (Li et al., 2024). [ABSTRACT FROM AUTHOR]

The direct interactions of atmospheric aerosols with radiation significantly impact the Earth's climate and weather and are important to represent accurately in simulations of the atmosphere. This work introduces two new contributions to enable more accurate representation of aerosol optics in atmosphere models: 1) 'TAMie,' a new Python-based Mie scattering code that can represent both homogeneous and coated particles and achieves comparable speed and accuracy to established Fortran Mie codes. 2) 'NeuralMie,' a neural network Mie code emulator trained on data from TAMie, that can directly compute the bulk optical properties of a diverse range of aerosol populations and is appropriate for use in atmosphere simulations where aerosol optical properties are parameterized. NeuralMie is highly flexible and can be used for a large range of particle types and wavelengths. It can represent core-shell scattering, and by directly estimating bulk optical properties, is more efficient than existing Mie code and Mie code emulators while incurring negligible error (0.08 % mean absolute percentage error). [ABSTRACT FROM AUTHOR]

Recently, deep convolutional neural networks (CNNs) have shown remarkable results for single-image super resolution. Super resolution involves enhancing the resolution of data beyond its native resolution, and is an ill-posed [...]

Atmospheric aerosols have a substantial impact on climate and remain one of the largest sources of uncertainty in climate forecasts. Accurate representation of their direct radiative effects is a crucial component of modern climate models. Direct computation of the radiative properties of aerosols is far too computationally expensive to perform in a climate model however, so optical properties are typically approximated using a parameterization. This work develops artificial neural net-works (ANNs) capable of replacing the current aerosol optics parameterization used in the Energy Exascale Earth System Model (E3SM). A large training dataset is generated by using Mie code to directly compute the optical properties of a range of atmospheric aerosol populations given a large variety of particle sizes, wavelengths, and refractive indices. Optimal neural architectures for shortwave and longwave bands are identified by evaluating ANNs with randomly generated wirings. Randomly generated deep ANNs are able to outperform conventional multi-layer perceptron style architectures with comparable parameter counts. Finally, the ANN-based parameterization is found to dramatically outperform the current parameterization. The success of this approach makes possible the future inclusion of much more sophisticated representations of aerosol optics in climate models that cannot be captured through simple expansion of the existing parameterization scheme. [ABSTRACT FROM AUTHOR]

Linear temporal trends in cloud fraction over the extratropical oceans, observed by NASA's Multiangle Imaging Spectro-Radiometer (MISR) during the period 2000-2013, are examined in the context of coincident ECMWF reanalysis data using a maximum covariance analysis. Changes in specific cloud types defined with respect to cloud top height and cloud optical depth are related to trends in reanalysis variables. A pattern of reduced high altitude optically thick cloud and increased low altitude cloud of moderate optical depth is found to be associated with increased temperatures, geopotential heights, and anticyclonicity over the extratropical oceans. These and other trends in cloud occurrence are shown to be correlated with changes in the El Niño Southern Oscillation, the Pacific Decadal Oscillation, the North Pacific Index, and the Southern Annular Mode. [ABSTRACT FROM AUTHOR]