This paper examines an approach to speaker adaptation called speaker cluster weighting (SCW) for rapid adaptation in the Jupiter weather information system. SCW extends the ideas of previous speaker cluster techniques by allowing the speaker cluster models (learned from training data) to be adaptively weighted to match the current speaker. We explore strategies for automatic speaker clustering as well as cluster model training procedures for use with this algorithm. As part of this exploration, we develop a novel algorithm called least squares linear regression (LSLR) clustering for the clustering of speakers for whom only a small amount of data is available.