Interpolation of data on the surface of a sphere

Renka, Robert J.

doi:10.1145/2701.2703

Cited by 108 publications

(53 citation statements)

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“…Several spherically based interpolation procedures have been implemented (e.g. Wahba, 1981;Renka, 1984;Willmott et al, 1985a); however, inverse distance-weighting algorithms have been used most frequently in precipitation studies (Bradley et al, 1987;Diaz et al, 1989) and several are quite reliable (Legates, 1987;Bussieres and Hogg, 1989;Weber and Englund, 1992). One such procedure, a spherical version of Shepard's (1968) inverse-distance weighting method (Willmott et al, 1985a), is used here to interpolate precipitation data to continental and terrestrial grids.…”

Section: Spatial Sampling Interpolation and Averagingmentioning

confidence: 99%

Estimating continental and terrestrial precipitation averages from rain‐gauge networks

Willmott

Robeson

Feddema

1994

Intl Journal of Climatology

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Influences of varying rain‐gauge networks on continental and terrestrial precipitation averages (derived from data observed on those networks) are evaluated. Unsystematically and systematically designed station networks are considered, the latter being represented by the NCAR World Monthly Surface Station Climatology, which contains hand‐picked but time‐varying networks that date back to the 1800s. Biases arising from spatially uneven and temporally variable precipitation‐observing networks can be significant. For all the continents, except South America, sparse rain‐gauge networks produce overestimates of continental mean precipitation. Mean precipitation for South America, in contrast, is underestimated substantially by low densities of observing stations. Sampling errors tend to be large in areas of high precipitation and in regions with strong spatial precipitation gradients (e.g. in the Sahel). These patterns occur whether the station network has been selected systematically (as in the NCAR network) or unsystematically. Systematic sampling of mean precipitation (at the NCAR station locations), however, suggests that many yearly NCAR station networks are adequate for estimating continental average precipitation. As early as 1890, NCAR networks for Australia resolve continental average precipitation accurately. Not until 1960, however, do NCAR networks for South America begin to resolve continental mean precipitation adequately. Regional and continental NCAR network errors also tend to cancel one another, often giving accurate yearly estimates of terrestrial mean precipitation.

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Section: Spatial Sampling Interpolation and Averagingmentioning

confidence: 99%

Estimating continental and terrestrial precipitation averages from rain‐gauge networks

Willmott

Robeson

Feddema

1994

Intl Journal of Climatology

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“…Inverse-distance weighting is an example of a simple statistical interpolation method. Adaptations of this concept have been used for climate interpolation (Shepard 1968, Renka 1984, Willmott et al 1985, Dodson & Marks 1997, Thornton et al 1997. Kriging and its variants (Matheron 1971) have been applied extensively to the interpolation of climate data (Dingman et al 1988, Hevesi et al 1992, Phillips et al 1992, Garen et al 1994.…”

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confidence: 99%

A knowledge-based approach to the statistical mapping of climate

Daly

Gibson²,

Taylor³

et al. 2002

Clim. Res.

997

797

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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...

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“…Monthly values were interpolated at the centroid of each 0.5 ø grid cell using a triangle-based, piecewise linear interpolation designed for application on the surface of a sphere [Renka, 1982;Renka and Cline, 1984]. It is important for analyses of the impact of changing environmental conditions that the model be in equilibrium with climate conditions at the beginning of the simulation period.…”

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confidence: 99%