Submitted to: American Water Resources Association Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 5/10/1999
Publication Date: N/A
Citation: Interpretive Summary: Over the last decade, our understanding of global atmospheric circulation pattern, long-distance teleconnections, sea surface temperature-atmosphere interactions and their impact on continental scale weather patterns has significantly increased. This new atmospheric/climatic information has enabled NOAA to produce monthly and seasonal precipitation forecasts. The purpose of the research is to quantify the spatial variability of monthly precipitation within a region and relate this spatial variability to the temporal variations of monthly precipitation for the region. The spatial variability for a region in Central Oklahoma was found to be significant. The findings of this project are critical to understanding local implications of the regional climate forecast, because a high spatial varability reduces the accuracy and value of the forecast for local use.
Technical Abstract: The understanding of global atmospheric circulation patterns, long-distance teleconnections, seas surface temperature-atmosphere interactions and their impact on continental scale weather patterns has significantly increased over the last decade and has enabled the climate Prediction center (CPC) to produce monthly and seasonal precipitation forecasts. In this paper the spatial variability of monthly precipitation within a climate division is qualified and related to the temporal variations of the monthly precipitation for the climate division. The random spatial variability of the central Oklahoma climate division was found to be significant. The average of the absolute differences between the standardized value at a station and the divisional mean is about 36% of the mean temporal variation for the climate division. This implies that any forecasted temporal variation for the climate division will exhibit at a point an additional variability-envelope of 36% due to random spatial variability. This random spatial variability explains the observed large localized departures from divisional values. These findings are relevant to fully exploit the regional climate forecast provided by the CPC for local applications in agricultural management.