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.
Water Resources Research – Wiley
Published: Dec 1, 1993
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera