著者
ITO Rui AOYAGI Toshinori HORI Naoto OH'IZUMI Mitsuo KAWASE Hiroaki DAIRAKU Koji SEINO Naoko SASAKI Hidetaka
出版者
Meteorological Society of Japan
雑誌
気象集誌. 第2輯 (ISSN:00261165)
巻号頁・発行日
pp.2018-053, (Released:2018-08-24)
被引用文献数
7

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.
著者
SEINO Naoko ODA Ryoko SUGAWARA Hirofumi AOYAGI Toshinori
出版者
Meteorological Society of Japan
雑誌
気象集誌. 第2輯 (ISSN:00261165)
巻号頁・発行日
pp.2018-029, (Released:2018-02-17)
被引用文献数
6

During the Tokyo Metropolitan Area Convection Study for Extreme Weather Resilient Cities (TOMACS) intensive observation period (IOP) in 2011-2013 summers, atmospheric environment of several heavy rainfalls was observed by means of radiosonde soundings in the Tokyo metropolitan area. We investigated formation and development processes of an extremely developed thunderstorm (Case 1 on 26 August 2011) and a moderately developed thunderstorm (Case 2 on 18 July 2013) observed in the TOMACS IOP, utilizing the radiosonde sounding data. Compared to Case 2, the mesoscale environment of the severe storm in Case 1 featured a lower level of free convection and a deeper layer of easterly flow. We carried out numerical simulations to investigate the formation processes of the convective systems in the two cases, using the Non-Hydrostatic Model (NHM) of the Japan Meteorological Agency (JMA) incorporating the Square Prism Urban Canopy (SPUC) scheme. Model results fairly represented the spatial distribution and amounts of the rainfall in both cases. In Case 1, the formation of a distinct convergence zone between easterly and southerly flows was the likely trigger of active convective systems around Tokyo. To further examine the urban impact on precipitation, we performed two comparative simulations, one using realistic current urban surface conditions (CRNT experiment) and the other using less urbanized surface conditions (LURB experiment). The CRNT experiment yielded more rainfall than the LURB experiment in the central urban area. It appears that higher temperatures caused by urbanization can lead to increased rainfall in Tokyo by intensifying convergence and ascending motion.