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A recent study in MWR explores the use of underused data from both the GOES-16 satellite and the national network of WSR-88D radars to better define the atmospheric state used to start weather prediction models, with the hope that this will lead to better forecasts of new thunderstorm development.
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A recent article in WAF describes the tornado probability algorithm (TORP), which uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and its performance. TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making.
Using a high-resolution (∼2 km) ocean model, a new study in JPO characterizes the spatial and temporal distribution of turbulent cascades in the Gulf Stream over a period of 5 years.
There are a wide variety of meteorological processes that can generate high winds and dust, and thus the physical processes underlying dust emission and subsequent transport are not always understood. A recent article in JAS uses in situ measurements and numerical modeling of three dust outbreaks that occurred in the northwestern Sonoran Desert to demonstrate that vertically trapped atmospheric waves generated by air flowing over a mountain are one mechanism that can produce dust storms.