A knowledge-based approach to the statistical mapping of climate

Abstract

The demand for spatial climate data in digital form has risen dramatically in recent years. In response to this need, a variety of statistical techniques have been used to facilitate the production of GIS-compatible climate maps. However, observational data are often too sparse and unrepresentative to directly support the creation of high-quality climate maps and data sets that truly represent the current state of knowledge. An effective approach is to use the wealth of expert knowledge on the spatial patterns of climate and their relationships with geographic features, termed 'geospatial climatology', to help enhance, control, and parameterize a statistical technique. Described here is a dynamic knowledge-based framework that allows for the effective accumulation, application, and refinement of climatic knowledge, as expressed in a statistical regression model known as PRISM (parameter-elevation regressions on independent slopes model). The ultimate goal is to develop an expert system capable of reproducing the process a knowledgeable climatologist would use to create high-quality climate maps, with the added benefits of consistency and repeatability. However, knowledge must first be accumulated and evaluated through an ongoing process of model application; development of knowledge prototypes, parameters and parameter settings; testing; evaluation; and modification. This paper describes the current state of a knowledge-based framework for climate mapping and presents specific algorithms from PRISM to demonstrate how this framework is applied and refined to accommodate difficult climate mapping situations. A weighted climate-elevation regression function acknowledges the dominant influence of elevation on climate. Climate stations are assigned weights that account for other climatically important factors besides elevation. Aspect and topographic exposure, which affect climate at a variety of scales, from hill slope to windward and leeward sides of mountain ranges, are simulated by dividing the terrain into topographic facets. A coastal proximity measure is used to account for sharp climatic gradients near coastlines. A 2-layer model structure divides the atmosphere into a lower boundary layer and an upper free atmosphere layer, allowing the simulation of temperature inversions, as well as mid-slope precipitation maxima. The effectiveness of various terrain configurations at producing orographic precipitation enhancement is also estimated. Climate mapping examples are presented.KEY WORDS: Climate map · Knowledge-based system · Climate interpolation · Spatial climate · Climate data sets · GIS · PRISM · Geospatial climatology Resale or republication not permitted without written consent of the publisherClim Res 22: [99][100][101][102][103][104][105][106][107][108][109][110][111][112][113] 2002 expert and statistical. Human-expert methods use human experience, expertise, and knowledge acquisition capabilities to infer climate patterns from meteorological regimes, physiographic features, biotic character...

Mapping the climate of Puerto Rico, Vieques and Culebra

“…In the general PRISM formulation, the combined weight of a station is a function of distance, elevation, cluster, vertical layer, topographic facet, coastal proximity, and effective terrain weights. A full discussion of the station weighting functions is available in Daly (2002) and Daly et al (2002). A subset of these functions was used for Puerto Rico.…”

“…The parameter-elevation regressions on independent slopes model (PRISM; Daly and Neilson, 1992;Daly et al, 1994Daly et al, , 1997Daly et al, , 2002) is especially suited to mapping climate in a complex landscape like that of Puerto Rico. This study describes the application of PRISM to the particular landscapes of Puerto Rico, Vieques and Culebra; alternative parameterizations of PRISM that simulate simpler interpolation methods are also used.…”

Comparison of regression techniques for mapping fog frequency: application to the Aragón region (northeast Spain)

“…This has been observed in the mapping of minimum temperature, for which the common occurrence of thermal inversions makes it difficult to obtain reliable estimates (Daly et al, 2002), and the mapping of snow depth, which is strongly affected by topographic features in mountainous areas (López-Moreno and Nogués-Bravo, 2006). Fogs are strongly influenced by thermal inversions, which are a common occurrence in the centre of the study area; this explains the high frequency of fogs in the winter months in association with topographic features.…”

“…Previous studies sought to take thermal inversions into account. For example, Daly et al (2002) divided a set of observatories into two sub-samples (those above and below a certain elevation) and modelled them independently to take into account the effect of thermal inversions in creating minimum-temperature models. Nevertheless, the inclusion of terrain variables such as TWI and the elevation range within a given radius enabled us to consider those topographic characteristics that favour the occurrence of fogs and facilitated the creation of the models via a unique step.…”

“…The potential predictors used in the present analysis are listed in Table I. For elevation available from a DEM at a resolution of 1000 m, which is the variable that most strongly influences the spatial distribution of climate parameters (Daly et al, 1994(Daly et al, , 2002, we performed transformations using low-pass filters with radii of 2.5, 5, 10, and 25 km TWI within x, where x has a radius of 2.5, 5 and 10 km TWI x ELEV TWI, within x, where x has a radius of 2.5, 5 and 10 km × elevation to measure the wider influence of elevation on fog frequency. We also calculated the maximum elevation, minimum elevation, and range in elevation using the same filters and radii to take into account the possible effect of topographic gradients that reflect the terrain structure (valleys, depressions, etc.).…”