Monthly temperature and precipitation fields on a storm‐facing mountain front: Statistical structure and empirical parameterization

Monthly temperature and precipitation fields on a storm‐facing mountain front: Statistical... This paper presents an analysis of monthly temperature (T) and precipitation (P) time series at 28 climatologic stations on the storm‐facing slope of the Wasatch Range, Utah. The goal is to examine the space‐time structure of T and P and to develop an empirical model incorporating both seasonal and elevation effects. Each time series (T or P) is decomposed into the sum of a long‐term mean, a seasonal cycle, and a residual random process. The seasonal cycle is well determined by the amplitude and phase of a few harmonics, and the residual noise is approximated by a power law form of the variance spectrum. Empirical correlations are found relating the temporal moments of altitude, allowing the construction of a parametric T‐P model as a function of altitude and season. The observed correlations are discussed within the context of the region's synoptic weather patterns. When combined with digital elevation data, the model can be used to estimate seasonal temperature and precipitation fields as input to mountain front hydrologic studies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Water Resources Research Wiley

Monthly temperature and precipitation fields on a storm‐facing mountain front: Statistical structure and empirical parameterization

Water Resources Research, Volume 29 (12) – Dec 1, 1993

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Publisher
Wiley
Copyright
Copyright © 1993 by the American Geophysical Union.
ISSN
0043-1397
eISSN
1944-7973
DOI
10.1029/93WR02141
Publisher site
See Article on Publisher Site

Abstract

This paper presents an analysis of monthly temperature (T) and precipitation (P) time series at 28 climatologic stations on the storm‐facing slope of the Wasatch Range, Utah. The goal is to examine the space‐time structure of T and P and to develop an empirical model incorporating both seasonal and elevation effects. Each time series (T or P) is decomposed into the sum of a long‐term mean, a seasonal cycle, and a residual random process. The seasonal cycle is well determined by the amplitude and phase of a few harmonics, and the residual noise is approximated by a power law form of the variance spectrum. Empirical correlations are found relating the temporal moments of altitude, allowing the construction of a parametric T‐P model as a function of altitude and season. The observed correlations are discussed within the context of the region's synoptic weather patterns. When combined with digital elevation data, the model can be used to estimate seasonal temperature and precipitation fields as input to mountain front hydrologic studies.

Journal

Water Resources ResearchWiley

Published: Dec 1, 1993

References

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