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We introduce the concepts, methodologies, and applications of meteorological electromagnetics with a focus on snow, which currently is the least understood component of the global water cycle. As "no two snowflakes are alike," the intricacies of snowflakes and snowfall are both truly fascinating and extremely challenging to measure, analyze, and predict. We describe a unique approach to the characterization of winter precipitation through the synergistic use of advanced optical instrumentation for in situ microphysical and geometrical measurements of ice and snow particles; image processing techniques to obtain the fall speed, size distribution, 3D shape (mesh), density, and effective dielectric constant of snowflakes; method of moments (MoM) scattering computations of precipitation particles; and state-of-the-art dualpolarization radars for the measurement of polarimetric scattering observables. We discuss the operations, observations, and analyses using this approach during a snow field campaign that took place in Colorado, United States, from 2014 to 2017, and we also introduce an international collaborative field program in association with the 2018 Winter Olympics in South Korea. One goal of this article is to promote meteorological electromagnetics as an interdisciplinary field where nature, science, and technology meet in some of the most fascinating and rewarding ways and where many key areas of interest and endeavors of the antennas and propagation community play an indispensable role. [ABSTRACT FROM AUTHOR]

The European Space Agency’s soil moisture ocean salinity (SMOS) satellite was launched in 2009 to measure land soil moisture and sea surface salinity (SSS). It carries an L-band microwave imaging radiometer that measures brightness temperatures that are used to produce global ocean salinity (OS) maps every three days. Similar maps are obtained with NASA’s L-band push-broom radiometer Aquarius (AQ) on board of the AQ/SAC-D satellite that was launched in 2011. In previous studies, the Central Florida Remote Sensing Laboratory (CFRSL) has analyzed AQ SSS retrievals during rain and has developed a model to predict the effect of precipitation on the SSS measurements. This rain impact model (RIM) estimates the transient near-surface salinity stratification based upon the corresponding rain accumulation over the previous 24 h to the satellite observation. In this paper, the RIM methodology has been adapted to the SMOS geometry, presenting comparisons with its SSS measurements; also, spatial correlations are performed between SMOS salinity images with those predicted by RIM for different wind speed ranges. Therefore, the main objective of this research is to better understand the processes of near-surface salinity stratification, which impact the interpretation of satellite-based SSS measurements to measure the ocean bulk salinity (5–10-m depth). The results presented in this paper show an excellent performance of RIM when applied to SMOS SSS data. Also, the SSS comparisons show that significant rain events are rapidly diluted for wind speeds of \sim 12 \text m/\texts and above. [ABSTRACT FROM PUBLISHER]

This paper proposes an image-based prediction method that can physically predict near-future spatio-temporal image changes using fluid-like image sequences, i.e., dynamic texture, from different image sources such as ground-based radar imagers, satellite sensors, and lightning detectors. Previous alternatives, i.e., tracking radar echo by correlation or thunderstorm identification, tracking, analysis, and nowcasting, employ pattern matching or linear extrapolation of the centroid of an image object to predict the next time image with many tuning model parameters. However, such methods fail to handle the high degree of motion and deformation of fluid-like images, i.e., vortex. To remedy this issue, this paper presents a spatio-temporal prediction method based on a computer vision framework; it employs a physics-based model with time-variant optical flow. Initial local motions from image sequences are estimated by the extended optical flow method, where a locally optimal weighting parameter and a statistically robust function are applied to Horn and Schunck's model. The next time image sequence from the past image sequence is physically predicted by the extended advection equation for image intensities and the Navier–Stokes equation with a continuity equation for varying optical flow over time. For different source images, our method offers no prior knowledge of size, shape, texture, and motion of moving objects. Experiments demonstrate that the proposed prediction method outperforms a previous prediction method with respect to prediction accuracy. [ABSTRACT FROM PUBLISHER]