The generation of monthly gridded datasets for a range of climatic variables over the UK

Abstract

Monthly or annual 5 km × 5 km gridded datasets covering the UK are generated for the 1961-2000 period, for 36 climatic parameters. As well as the usual elements of temperature, rainfall, sunshine, cloud, wind speed, and pressure, derived temperature variables (such as growing-season length, heating degree days, and heat and cold wave durations) and further precipitation variables (such as rainfall intensity, maximum consecutive dry days, and days of snow, hail and thunder) are analysed.The analysis process uses geographical information system capabilities to combine multiple regression with inversedistance-weighted interpolation. Geographic and topographic factors, such as easting and northing, terrain height and shape, and urban and coastal effects, are incorporated either through normalization with regard to the 1961-90 average climate, or as independent variables in the regression. Local variations are then incorporated through the spatial interpolation of regression residuals.For each of the climatic parameters, the choice of model is based on verification statistics produced by excluding a random set of stations from the analysis for a selection of months, and comparing the observed values with the estimated values at each point. This gives some insight into the significance, direction, and seasonality of factors affecting different climate elements. It also gives a measure of the accuracy of the method at predicting values between station locations.The datasets are being used for the verification of climate modelling scenarios and are available via the Internet.  Crown

Use of spatially distributed time-integrated sediment sampling networks and distributed fine sediment modelling to inform catchment management

“…through weighting upslope contributing areas by the amount of upstream contributed precipitation normalised across the catchment. Precipitation is based on the UK Met Office 5 km x 5 km rainfall grid, averaged over the period spanning 1961-90 (Perry and Hollis, 2005):…”

Developing drought resilience in irrigated agriculture in the face of increasing water scarcity

“…In addition, the maximum potential soil moisture deficit (PSMDmax) was used as an agroclimatic indicator to reflect the relationship between aridity and irrigation need (Knox et al 1997). PSMDmax values were calculated using a 5 km 9 5 km gridded monthly climatic data set from the UK Meteorological Office derived from observed historical weather data (Perry and Hollis 2004). ETo was calculated applying the FAO Penman-Monteith combination equation (Allen et al 1998).…”

Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale

“…Errors in the estimation could, however, increase with the use of local interpolation methods in areas where neighbouring data are not 'local enough' to show micro influences. If covariates are available, local variations can be incorporated through the spatial interpolation of regression residuals (Perry and Hollis, 2005); however, in this case, the drawbacks that will be illustrated for regression kriging (RK) will be applied.…”