Journal of the Meteorological Society of Japan. Ser. II
Online ISSN : 2186-9057
Print ISSN : 0026-1165
Improvement of Snow Depth Reproduction in Japanese Urban Areas by the Inclusion of a Snowpack Scheme in the SPUC Model
Rui ITOToshinori AOYAGINaoto HORIMitsuo OH'IZUMIHiroaki KAWASEKoji DAIRAKUNaoko SEINOHidetaka SASAKI
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JOURNALS FREE ACCESS Advance online publication

Article ID: 2018-053

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Abstract

 Accurate simulation of urban snow accumulation/melting processes is important to provide reliable information about climate change in snowy urban areas. The Japan Meteorological Agency operates a square prism urban canopy (SPUC) model within their regional model to simulate urban atmosphere. However, presently, this model takes no account of snow processes. Therefore, in this study, we enhanced the SPUC by introducing a snowpack scheme, and the simulated snow over Japanese urban areas was assessed by comparing the snow depths from the enhanced SPUC and from a simple biosphere (iSiB) model with the observations. Snowpack schemes based on two approaches were implemented. The diagnostic approach (sSPUCdgn) uses empirical factors for snow temperature and melting/freezing amounts and the Penman equation for heat fluxes, whereas the prognostic approach (sSPUCprg) calculates snow temperatures using heat fluxes estimated from bulk equations. Both snowpack schemes enabled the model to accurately reproduce the seasonal variations and peaks in snow depth, but it is necessary to use sSPUCprg if we wish to consider the physical processes in the snow layer. Compared with iSiB, sSPUCprg resulted in a good performance for the seasonal variations in snow depth, and the error fell to 20 %. While iSiB overestimated the snow depth, a cold bias of over 1°C appeared in the daily mean temperature, which can be attributed to excessive decreases in the snow surface temperature. sSPUCprg reduces the bias by a different calculation method for the snow surface temperature and by the inclusion of heated building walls without snow; consequently, the simulated snow depth is improved. sSPUCprg generated a relationship between the seasonal variations in snowfall and snow depth close to the observed relationship, with the correlation coefficient getting large. Therefore, the simulation accuracy of snowfall becomes more crucial for simulating the surface snow processes precisely by the enhanced SPUC.

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© 2018 The Author(s) CC-BY 4.0 (Before 2018: Copyright © Meteorological Society of Japan)
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