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Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling'' in the atmospheric sciences: improving the spatial resolution of low-resolution images. The abil ...
2020
In this study, we develop statistical relationships between radar observables and drop size distribution properties in different latitude bands to inform radar rainfall retrieval techniques and understand underpinning microphysical reasons for differences ...
2019
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The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective ...
We demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks. We apply the CGAN to generating two-dimensional ...
The link between stratiform precipitation microphysics and multifrequency radar observables is thoroughly investigated by exploiting simultaneous airborne radar and in situ observations collected from two aircraft during the OLYMPEX/RADEX (Olympic Mountain ...
In this study, we analyze an in situ shipboard global ocean drop size distribution (DSD) 8-year database to understand the underpinning microphysical reasons for discrepancies between satellite oceanic rainfall products at high latitudes reported in the li ...
