Winter daily precipitation variability over the South West Peninsula of England

Summary Daily precipitation totals for five consecutive winters (1995–99) were obtained for 127 stations in Devon and Cornwall to explore spatial variations in rainfall. This dataset was assembled with the explicit aim of assessing the appropriateness of current arrangements for daily rainfall forecasts in the SW Peninsula of England. Firstly, the extent to which fundamental geographic variables determine precipitation was investigated by correlating each station’s mean wet day amount (WDA) and percentage of wet days (PERWET) with altitude, latitude, longitude and distance from the coast. Altitude emerged as the most important control on precipitation, with a two-variable multiple linear regression model containing altitude and latitude being able to explain 39.3% (29.8%) of the variance in WDA (PERWET) values. The main spatial modes of variability in the region’s precipitation field were identified by using S mode principal components analysis (PCA). Six PCs were statistically significant and explained 83.4% of the geographic variance in precipitation over Devon and Cornwall. The components were interpreted physically by examining the synoptic flow environment (pressure and wind anomalies) on days with high positive and negative PC scores. Explaining 25.1% of the variance, the most important pattern (PC1) depicts a location’s degree of exposure or shelter in a moist, unstable W–NW airflow. The higher PCs describe modes of variability that accentuate rainfall in East Devon (PC2), Cornwall (PC3), Dartmoor and Bodmin Moor (PC4), South Devon (PC5), and North Cornwall and NW Devon (PC6) relative to other areas of the Peninsula. Finally, a winter precipitation regionalisation was derived by applying agglomerative hierarchical cluster analysis to the PC loadings of the significant components. In most cases, the six coherent precipitation regions do not reflect the familiar administrative or topographic areas used for forecasting, suggesting that forecasts issued on such a basis are likely to be insufficiently detailed and misleading.