In this study, multi‐year near‐surface observations were conducted at two sites in Jakarta, Indonesia to analyse seasonal sea breeze variation. Seasonal length was defined using observed zonal wind components, the rainy season was defined as December–March, and the dry season was defined as May–September. We found that the sea breeze in Jakarta started earlier, propagated more rapidly, and lasted for a shorter period of time during the rainy than dry season. Variation in the air temperature difference between urban and coastal areas of Jakarta was the major factor driving sea breeze seasonal variation. During the rainy season, night‐time cloud downwelling decreased this air temperature difference, causing earlier sea breeze onset and more rapid sea breeze propagation due to a weaker land breeze. By contrast, during the dry season, intense night‐time radiative cooling inland caused a strong negative temperature difference that produced a stronger land breeze, thus, slowing sea breeze propagation. Seasonal differences in urban surface heating and urban heat island circulation may also affect sea breeze onset and propagation speed. Discrepancies in thermal properties between urban core and coastal areas of Jakarta also prolonged positive temperature differences after sunset, thus extending the sea breeze duration in the dry season.
Identifying the pre-convective condition of the atmospheric boundary layer plays an important role in the prediction of extreme weather events. To achieve this, acquisition of low-level flow fields is necessary. Latest available wind vector dataset, called Atmospheric Motion Vector (AMV), is still incapable for estimating near-ground level fields. We investigated the possibility applying the Thermal Image Velocimetry (TIV) method to Himawari-8 satellite retrievals to estimate near-ground level fields in cloud-free areas, identified during days of calm weather with sea-breeze penetration.A sea-breeze day event on August 4, 2015 was selected. Average sea-breeze inland penetration speed was estimated to be ~3.6 m/s and ~1.8 m/s from Sagami Bay and Tokyo Bay, respectively. The derived motion vector of advection flow from TIV revealed the general pattern of near-surface atmospheric flow due to sea-breeze phenomena.
<p><em>The sea breeze is a meteorological phenomenon that occurs due to the contrast temperature between land and oceans. The propagation velocity of sea breeze are influenced strongly by e.g., synoptic wind and geographical conditions. Therefore, it is important to understand the relationship between the spatial distribution of sea breeze velocity and the surface characteristic, for instance over urbanized and less-urbanized coastal areas. When the sea breeze propagates inland, a cumulus cloudline will form in the vicinity of the sea breeze front (SBF). Previous studies have successfully detected the cloudline automatically using the morphological-snake algorithm. In this paper, we estimate the SBF velocity using Himawari-8 satellite images. The proposed method segmented the cloudline data points using a clustering approach, named machine learning-based k-means++, on the level-set obtained from snake algorithm. We then estimate the SBF velocity by calculating the haversine distance of the segmented cloudline points that propagate over time. The comparison of estimated cloudline speed with SBF speed measured at two observation sites, namely KKP and BPL, reveals the root mean square errors 1.39 m/s and 1.41 m/s, respectively. And the propagation direction was mainly southward.</em></p>
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