2.7
Stochastic generation of multi-station daily temperatures using a neural network
Douglas A. Stewart, Environmental Dynamics Research, Inc., Lantana, FL
Stochastic algorithms for generating daily weather elements have been developed over the past two decades to provide synthetic time series in support of a variety of agricultural and hydrological issues. Although often multivariate with respect to the number of weather elements, the models are usually designed to provide time sequences of weather at a single location.
In this study, a neural network is developed to generate a stochastic sequence of daily average temperatures at over 150 stations distributed across the lower 48 states. Close replication of the auto- and cross-covariance structure of the observed temperature fluctuations is the desired objective. The neural network operates on the space-time principal components of the temperature variability. The skill of the methodology is assessed through cross validation.
Session 2, Artificial Neural Networks
Monday, 10 January 2000, 1:30 PM-4:30 PM
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