Approximating snow surface temperature from standard temperature and humidity data: New possibilities for snow model and remote sensing evaluation. Issue 12 (9th December 2013)

• Record Type: Journal Article
• Authors: Raleigh, Mark S.; Landry, Christopher C.; Hayashi, Masaki; Quinton, William L.; Lundquist, Jessica D.
• Is Part Of: Water resources research. Volume 49:Issue 12(2013:Dec.)
• Page Start: 8053
• Page End: 8069
• Publication Date: 2013-12-09
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2013WR013958
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>[1] Snow surface temperature (<italic>T<sub>s</sub></italic>) is important to the snowmelt energy balance and land‐atmosphere interactions, but in situ measurements are rare, thus limiting evaluation of remote sensing data sets and distributed models. Here we test simple <italic>T<sub>s</sub></italic> approximations with standard height (2–4 m) air temperature (<italic>T<sub>a</sub></italic>), wet‐bulb temperature (<italic>T<sub>w</sub></italic>), and dew point temperature (<italic>T<sub>d</sub></italic>), which are more readily available than <italic>T<sub>s</sub></italic>. We used hourly measurements from seven sites to understand which <italic>T<sub>s</sub></italic> approximation is most robust and how <italic>T<sub>s</sub></italic> representation varies with climate, time of day, and atmospheric conditions (stability and radiation). <italic>T<sub>d</sub></italic> approximated <italic>T<sub>s</sub></italic> with the lowest overall bias, ranging from −2.3 to +2.6°C across sites and from −2.8 to 1.5°C across the diurnal cycle. Prior studies have approximated <italic>T<sub>s</sub></italic> with <italic>T<sub>a</sub></italic>, which was the least robust predictor of <italic>T<sub>s</sub></italic> at all sites. Approximation of <italic>T<sub>s</sub></italic> with <italic>T<sub>d</sub></italic> was most reliable at night, at sites with infrequent clear sky conditions, and at windier sites (i.e., more frequent turbulent instability). We illustrate how mean daily <italic>T<sub>d</sub></italic> can help detect surface energy balance bias in a physically based snowmelt model. The results imply that spatial <italic>T<sub>d</sub></italic> data sets may be useful for evaluating snow models and remote sensing products in data sparse regions, such as alpine, cold prairie, or Arctic regions. To realize this potential, more routine observations of humidity are needed. Improved understanding of <italic>T<sub>d</sub></italic> variations will advance understanding of <italic>T<sub>s</sub></italic> in space and time, providing a simple yet robust measure of snow surface feedback to the atmosphere.</p> </abstract>