JWST data on NGTS-10A b shows nightside CH4 depletion caused by day-to-night horizontal transport rather than vertical mixing or non-solar abundances.
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2 Pith papers cite this work. Polarity classification is still indexing.
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LSTM networks predict HRRR forecast errors with average improvements of 48% for precipitation, 25% for temperature, and 15% for wind using mesonet ground truth.
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Horizontal transport as a source of disequilibrium chemistry on the nightside of a hot exoplanet
JWST data on NGTS-10A b shows nightside CH4 depletion caused by day-to-night horizontal transport rather than vertical mixing or non-solar abundances.
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Predicting Forecast Error for the HRRR Using LSTM Neural Networks: A Comparative Study Using New York and Oklahoma State Mesonets
LSTM networks predict HRRR forecast errors with average improvements of 48% for precipitation, 25% for temperature, and 15% for wind using mesonet ground truth.