Neural Network Implementation of a Mesoscale Meteorological Model
Numerical weather prediction is a computationally expensive task that requires not only the numerical solution to a complex set of non-linear partial differential equations, but also the creation of a parameterization scheme to estimate sub-grid scale phenomenon. This paper outlines an alternative approach to developing a mesoscale meteorological model – a modified recurrent neural network that learns to simulate the solution to these equations. Along with an appropriate time integration scheme and learning algorithm, this method can be used to create multi-day forecasts for a large region. The learning method presented in this paper is an extended form of Backpropagation Through Time for a recurrent network with outputs that feed back through as inputs only after undergoing a fixed transformation.
Firth, R., Chen, J.: Neural Network Implementation of a Mesoscale Meteorological Model. In: T. Andreasen et al. (Eds.): ISMIS 2014, LNAI 8502, pp. 164-173. Springer International Publishing Switzerland (2014)
The final publication is
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Satellite Surveillance of the Gulf and Beyond: An Overview of the LSU Earth Scan Laboratory's Ocean Observing Capabilities
The Earth Scan Laboratory (ESL), a facility in the Coastal Studies Institute (CSI) of the Department of Oceanography and Coastal Sciences (DOCS) at LSU was founded in 1988, and was one of the first universities in the United States to have a real-time satellite capture capability. The ESL's unique location, north of the central Gulf of Mexico, provides a radio horizon for polar orbiting satellites that extends north, into the Hudson Bay, south beyond Panama, and east/west to cover both seaboards of the United States. Over the past two decades, the ESL has built a reputation for providing access to a variety of sensor systems on board a large array of satellites. With it ability to capture several of the National Oceanographic and Atmospheric Administration (NOAA) environmental satellites, including the eastern GOES (Geostationary Operational Environmental Satellite) satellite, the ESL is able to observe local ocean phenomena, such as the Gulf of Mexico Loop Current (LC) as well as the El Nino/La Nina oscillations of the eastern Pacific ocean. The ESL provides a large archive of imagery on its web site (http://www.esl.lsu.edu), and focuses on access to real-time data. An overview of the ESL capabilities and activities is given, with attention to ocean observation, and its role in research, education, commerce, and emergency response.
Haag, Alaric, Nan Walker, Chet Pilley, Jessica Comeaux, John Calvasina, and Robert Firth. "Satellite Surveillance of the Gulf and Beyond: An Overview of the LSU Earth Scan Laboratory's Ocean Observing Capabilities ." Conference Papers - Oceans (Annual Meeting 2009): MTS/IEEE Conference (October 2009)