A Neural Network to Retrieve Upper Level Winds from Ground Based Profilers
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چکیده
Accurate, real-time upper level wind measurements can provide essential input into operational mesoscale models for their initialization and verification. In artillery meteorology, measurements of upper level winds are important to the accuracy of calculated ballistic trajectories. Although there are a number of ground based wind profilers available (wind tracer lidar, Doppler radar, and acoustical sounders), measuring upper level winds can be problematic and is highly dependent on favorable atmospheric conditions. Other methods to obtain wind velocity profiles include satellite-based data, thermal wind approximations, cloud tracking (Nieman et al, 1997), and moisture field tracking (Velden et al, 1997). Each of these methods can provide useful information for some synoptic scale applications but each one has certain limitations. Pioneer neural network research was conducted at the former Atmospheric Sciences Laboratory in the early 1990's (Measure & Yee, 1992). The research involved experimentation with neural network methods to retrieve temperature profiles from ground based microwave radiometers (Yee & Measure, 1992) as well as from satellite radiance measurements (Bustamante, etal, 1994). Neural networks were trained using simulated microwave radiometric measurements and archived
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تاریخ انتشار 2004