Satellite-borne detection of high diurnal amplitude of sea surface temperature in the seas west of the Tsugaru Strait, Japan, during ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-02

AUTHORS

Anindya Wirasatriya, Hiroshi Kawamura, Magaly Koch, Muhammad Helmi

ABSTRACT

The purpose of this study is to investigate the variation of diurnal SST amplitude (∆SST) in the seas west of the Tsugaru Strait, Japan, which exhibit complex topography that affects the wind pattern during “Yamase” season, when cold easterly winds blow around northeastern Japan. We used infrared based satellite SSTs to obtain strong ∆SST signals. The objective analysis data on surface wind, air temperature and relative humidity patterns was used to explain possible mechanisms responsible for the observed phenomena. Although easterly winds are frequently accompanied by clouds that hinder frequent observation of SSTs from space, four cases of high ∆SST were obtained under clear sky conditions. The results show that high resolution satellite data can capture ∆SSTs of more than 5 °C. Further, the ∆SST variation was strongly influenced by topography. The existence of mountains near the study area have the effect of blocking the easterly winds, creating a low wind speed area on the lee side of the mountain range, which is responsible for the generation of high ∆SST, especially in the offshore area. In addition to the topographic effect, the ∆SST values measured in the in-shore area were influenced by the sea breeze. The warm-dry air mass is advected to the coastal seas by the anti-sea breeze which heats the in-shore area and keeps the ∆SST at the same level as that of the offshore area. This finding highlights the importance of understanding air, sea and land interactions in terms of the variation observed in ∆SST in the study area. More... »

PAGES

1-14

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10872-018-0481-1

DOI

http://dx.doi.org/10.1007/s10872-018-0481-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104369235


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