A determination of the effect of topography upon precipitation

Spreen, William C.

doi:10.1029/tr028i002p00285

Cited by 132 publications

(74 citation statements)

References 2 publications

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“…Previous studies have shown that precipitation patterns are more highly correlated with broad-scale topographic features than with local features (e.g. Spreen, 1947;Burns, 1953;Schermerhorn, 1967;Daly et al, 1994;Kyriakidis et al, 2001). Many of these studies and results from other PRISM modelling activities suggest that 2.5 is near the optimum resolution for modelling precipitation.…”

Section: Elevation Datamentioning

confidence: 99%

Mapping the climate of Puerto Rico, Vieques and Culebra

Daly¹,

Helmer²,

Quiñones³

2003

Intl Journal of Climatology

240

216

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Spatially explicit climate data contribute to watershed resource management, mapping vegetation type with satellite imagery, mapping present and hypothetical future ecological zones, and predicting species distributions. The regressionbased parameter-elevation regressions on independent slopes model (PRISM) uses spatial data sets, a knowledge base and expert interaction to generate grids of climate variables that are compatible with geographical information systems. This study applied PRISM to generate maps of mean monthly and annual precipitation and minimum and maximum temperature for the Caribbean islands of Puerto Rico, Vieques and Culebra over the 1963-95 averaging period. PRISM was run under alternative parameterizations that simulated simpler interpolation methods as well as the full PRISM model. For temperature, the standard PRISM parameterization was compared with a hypsometric method (HYPS), in which the temperature-elevation slope was assumed to be −6.5°C/km. For precipitation, the standard PRISM parameterization was compared with an inverse-distance weighting interpolation (IDW). Spatial temperature patterns were linked closely to elevation, topographic position, and coastal proximity. Both PRISM and HYPS performed well for July maximum temperature, but HYPS performed relatively poorly for January minimum temperature, due primarily to lack of a spatially varying temperature-elevation slope, vertical atmospheric layer definition, and coastal proximity guidance. Mean monthly precipitation varied significantly throughout the year, reflecting seasonally differing moisture trajectories. Spatial precipitation patterns were associated most strongly with elevation, upslope exposure to predominant moisture-bearing winds, and proximity to the ocean. IDW performed poorly compared with PRISM, due largely to the lack of elevation and moisture-availability information. Overall, the full PRISM approach resulted in greatly improved performance over simpler methods for precipitation and January minimum temperature, but only a small improvement for July maximum temperature. Comparisons of PRISM mean annual temperature and precipitation maps with previously published handdrawn maps showed similar overall patterns and magnitudes, but the PRISM maps provided much more spatial detail.

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Section: Elevation Datamentioning

confidence: 99%

Mapping the climate of Puerto Rico, Vieques and Culebra

Daly¹,

Helmer²,

Quiñones³

2003

Intl Journal of Climatology

240

216

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“…Relatively high levels of correlation have been identified; the relationship is generally an increasing one (Spreen 1947;Arora et al, 2006): as elevation increases, precipitation increases. This is mainly due to the 'orographic effect' of the mountain terrain.…”

Section: Introductionmentioning

confidence: 99%

“…Many researchers have relied on establishing a correlation between precipitation and elevation to improve our understanding of precipitation patterns (Spreen, 1947;Daly et al, 1994;Pardo-Iguzquiza, 1998;Goovaerts, 1999aGoovaerts, , 1999bGoovaerts, , 2000KiefferWeisse and Bois, 2001;Marquinez et al, 2003;Kyriakidis et al, 2004;Arora et al, 2006). Relatively high levels of correlation have been identified; the relationship is generally an increasing one (Spreen 1947;Arora et al, 2006): as elevation increases, precipitation increases.…”

Section: Introductionmentioning

confidence: 99%

“…Other researchers have expanded the list of potential predictive variables to include derivatives of elevation and other variables summarizing geographic location (Spreen, 1947;Hutchinson, 1998b;Kieffer Weisse and Bois, 2001;Marquinez et al, 2003;Diodato, 2005;Oettli and Camberlin, 2005). For example, Spreen (1947) found that distribution of rainfall also depends on variables such as slope, exposure, and orientation. Similarly, Keiffer Weisse and Bois (2001) found that topographic variables were correlated with heavy rainfall events (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A spatial regression analysis of the influence of topography on monthly rainfall in East Africa

Hession¹,

Moore²

2011

Intl Journal of Climatology

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Precipitation in Kenya is highly variable and dominated by a variety of physical processes. Statistical studies of climate patterns have historically focused on application of ordinary least squares (OLS) regression to test hypotheses related to multiple predictive variables, perhaps in an attempt to better understand the physical mechanisms that drive precipitation, or on use of spatially explicit models, typically kriging-or spline-based analyses, for the purpose of improving predictions. Each of these approaches may be individually useful; however, they all possess limitations. OLS approaches have yielded biased results in the presence of spatially autocorrelated data. Kriging-and spline-based studies often focus on providing improved predictions rather than understanding. Here we use spatial regression, a method not commonly used in analysis of climate data, to assess the role of predictive variables while explicitly incorporating spatial autocorrelation in parameter estimation and hypothesis testing. This approach can yield a better understanding of relationships between precipitation and multiple predictive variables with improved statistical rigour. Using spatial regression, we show that topographic variables such as elevation and slope strongly influence rainfall during the 'long rains' and 'short rains', which are vital for Kenyan agriculture. Outside these seasons, we find that smaller (mesoscale) variations in elevation are statistically significant. Further, we demonstrate the shortcomings of automated selection procedures such as stepwise regression through the identification of spurious results due to confounding.

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“…The orographic effect may be the reason to increase the parameters A in the high altitude zone. The rainfall intensity can be increased due to the ascending air current following the mountain slopes, which can also be strengthened when the humidity and wind velocity are all high [26][27][28][29][30][31].…”

Section: Parameter Estimation Of Z-r Relationmentioning

confidence: 99%

Calibrating Radar Data in an Orographic Setting: A Case Study for the Typhoon Nakri in the Hallasan Mountain, Korea

Yoo

2017

Atmosphere

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Abstract:The Typhoon Nakri passed the Jeju Island in Korea (1-3 August 2014) and recorded one-day rainfall of 1182 mm at Witse-Oreum (a place name where a small volcanic cone is) of the Hallasan Mountain, Korea. This one-day rainfall amount was the highest rainfall that was ever recorded in Korea. As the altitude of Witse-Oreum is 1673 m, it was believed that the orographic effect enhanced the rainfall depth significantly. It was also argued that the maximum rainfall could be recorded in some other locations in the Hallasan Mountain. In this study, the rainfall event due to the Typhoon Nakri in the Jeju Island was analyzed using the radar and rain gauge data that were collected during this rainfall event. Fortunately, two radars are available on the eastern and western side of the Jeju Island. The entire Hallasan Mountain is covered by these two radars. A total of 23 rain gauges are also available in the Jeju Island. As a first step, independent ground-level Z-R relations were derived for every 250 m interval from the sea-level. Each Z-R relation was then applied to the corresponding-altitude radar reflectivity data to generate the rain rate field over the Jeju Island. Finally, the generated radar rain rate data were examined fully to evaluate the orographic effect in the Hallasan Mountain, also to locate the point where the maximum rain rate was recorded. This result shows that the maximum one-day rainfall amount could be up to 1500 mm, which was about 30% higher than the rain gauge measurement.

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A determination of the effect of topography upon precipitation

Cited by 132 publications

References 2 publications

Mapping the climate of Puerto Rico, Vieques and Culebra

Mapping the climate of Puerto Rico, Vieques and Culebra

A spatial regression analysis of the influence of topography on monthly rainfall in East Africa

Calibrating Radar Data in an Orographic Setting: A Case Study for the Typhoon Nakri in the Hallasan Mountain, Korea

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