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Deep learning models for atmospheric pattern recognition require spatially consistent training labels that align precisely with input meteorological fields. This study introduces an automatic cold front detection method using the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) at 850 hPa, specifically designed to generate physically consistent labels for machine learning applications. The approach combines the Thermal Front Parameter (TFP) with temperature advection (AdvT), applying optimized thresholds (TFP < 5 × 10−11 K m−2; AdvT < −1 × 10−4 K s−1), morphological filtering, and polynomial smoothing. Comparison against 1426 manual charts from 2009 revealed systematic spatial displacement, with mean offsets of ~502 km. Although pixel-level overlap was low, with Intersection over Union (IoU) = 0.013 and Dice coefficient (Dice) = 0.034, spatial concordance exceeded 99%, confirming both methods identify the same synoptic systems. The automatic method detects 58% more fronts over the South Atlantic and 44% fewer over the Andes compared to manual charts. Seasonal variability shows maximum activity in austral winter (31.3%) and minimum in summer (20.1%). This is the first automatic front detection system calibrated for South America that maintains direct correspondence between training labels and reanalysis input fields, addressing the spatial misalignment problem that limits deep learning applications in atmospheric sciences.

Coastal and offshore waters are generally separated by a barrier or “ocean front” on the continental shelf. A basic question arises as to what the representative spatial scale across the front may be. To answer this question, we simply corrected skin sea surface temperatures (SSTs) estimated from Landsat 8 imagery with a resolution of 100 m using skin SSTs estimated from geostationary meteorological satellite Himawari 8 with a resolution of 2 km. We analyzed snapshot images of skin SSTs on 13 October 2016, when we performed a simultaneous ship survey. We focused in particular on submesoscale thermal fronts on the Pacific shelf off the southeastern coast of Hokkaido, Japan. The overall spatial distribution of skin SSTs was consistent between Landsat 8 and Himawari 8; however, the spatial distribution of horizontal gradients of skin SSTs differed greatly between the two datasets. Some parts of strong fronts on the order of 1 °C km−1 were underestimated with Himawari 8, mainly because of low resolution, whereas weak fronts on the order of 0.1 °C km−1 were obscured in the Landsat 8 imagery because the signal-to-noise ratios were low. The widths of the strong fronts were estimated to be 114–461 m via Landsat 8 imagery and 539–1050 m via in situ ship survey. The difference was probably attributable to the difference in measurement depth of the SST, i.e., about 10-μm skin layer by satellite and a few dozen centimeters below the sea surface by the in situ survey. Our results indicated that an ocean model with a grid size of no more than ≤100–200 m is essential for realistic simulation of the frontal structure on the shelf.

Using observational data from multiple satellites, we studied seasonal variations of the shape and location of the Luzon cold eddy (LCE) northwest of Luzon Island. The shape and location of the LCE have obvious seasonal variations. The LCE occurs, develops, and disappears from December to April of the next year. During this period, the shape of the LCE changed from a flat ellipse to a circular ellipse, and the change in shape can be reflected by the increase of the ellipticity of the LCE from 0.16 to 0.82. The latitude of center location of the LCE changes from 17.4°N to 19°N, and the change in latitude can reach 1.6°. Further study showed that seasonal variation of the northeast monsoon intensity leads to the change in the shape and location of the LCE. The seasonal variation of the LCE shape can significantly alter the spatial distribution of the thermal front and chlorophyll a northwest of the Luzon Island by geostrophic advection.

The Gerlache Strait is a narrow channel that separates the western coast of the Antarctic Peninsula (WAP) from the Palmer Archipelago. This area is characterized by the presence of interconnected fjords, bays, islands, and channels that serve as a refuge for megafauna during summer. Through the framework of FjordPhyto – a citizen science collaboration with the International Association of Antarctica Tour Operators (IAATO) vessels – we assessed phytoplankton biomass and composition in surface waters of six under-explored nearshore areas connected to the Gerlache Strait (between 64° and 65° S) during three consecutive seasons, from November to March (2016–2019). During the first two seasons, we found significant differences in the phytoplankton community distribution and successional patterns to the north and south of the sampling area; the greatest differences were evidenced mainly in the months of high biomass, December and January. During December, cryptophytes bloomed in the north, while microplanktonic diatoms dominated in the south, and during January, small centric diatoms dominated in the north, while prasinophytes bloomed in the south. This spatial distinction in phytoplankton communities were mainly associated with the occurrence of a surface thermal front in the Gerlache Strait around 64.5° S. The presence of the front separating warm waters to the north and colder waters to the south, during the months of December to February, was confirmed by the analysis of 10 years of remote sensing data. By contrast, during the third season, low biomass prevailed, and no differences in the phytoplankton composition between the north and south areas were observed. The third season was the coldest of the series, with smaller differences in water temperature north and south of the usual front location. This study shows for the first time a complete overview of the phytoplankton composition throughout the entire growth season (November through March) in the nearshore areas of the WAP between 64° and 65° S. The results of this work contribute to the understanding of the phytoplankton community in relation to small scale physical features during the Antarctic austral summer.

The thermal front can be used as an indicator to estimate the potential fishing ground in the ocean, especially for pelagic fishes. To determine the potential fishing areas, it is necessary to analyze the relationship between the distributions of the thermal front and mackerel catches. This research was conducted in the Java Sea, at the central part of the Indonesian seas by using oceanographic satellite datasets. The research method used was descriptive analysis with spatial and temporal approaches. Data on average sea surface temperatures and mackerel catches were analyzed from 2018-2020 (on a monthly basis). Based on the results, the thermal front distribution from 2018 to 2020 is mostly located near the coast, where the frequency of occurrence fluctuates every month and season. Data on mackerel catches are spread throughout the Java Sea, with an average sea surface temperature of 29°C. In general, it can be concluded that the mackerel catches distribution does not always coincide with the occurrence of a thermal front, except for January and August 2020. We highlighted that there is no direct relationship between the fishing location and the thermal front.

The thermal front in the oceanic system is believed to have a significant effect on biological activity. During an era of climate change, changes in heat regulation between the atmosphere and oceanic interior can alter the characteristics of this important feature. Using the simulation results of the 3D Regional Ocean Modelling System (ROMS), we identified the location of thermal fronts and determined their dynamic variability in the area between the southern Andaman Sea and northern Malacca Strait. The Single Image Edge Detection (SIED) algorithm was used to detect the thermal front from model-derived temperature. Results show that a thermal front occurred every year from 2002 to 2012 with the temperature gradient at the location of the front was 0.3 °C/km. Compared to the years affected by El Niño and negative Indian Ocean Dipole (IOD), the normal years (e.g., May 2003) show the presence of the thermal front at every selected depth (10, 25, 50, and 75 m), whereas El Niño and negative IOD during 2010 show the presence of the thermal front only at depth of 75 m due to greater warming, leading to the thermocline deepening and enhanced stratification. During May 2003, the thermal front was separated by cooler SST in the southern Andaman Sea and warmer SST in the northern Malacca Strait. The higher SST in the northern Malacca Strait was believed due to the besieged Malacca Strait, which trapped the heat and make it difficult to release while higher chlorophyll a in Malacca Strait is due to the freshwater conduit from nearby rivers (Klang, Langat, Perak, and Selangor). Furthermore, compared to the southern Andaman Sea, the chlorophyll a in the northern Malacca Strait is easier to reach the surface area due to the shallower thermocline, which allows nutrients in the area to reach the surface faster.

Thermal fronts are often used to explain the confinement of energetic electrons in the solar corona. In this paper, a 1-D particle-in-cell simulation model is used to explore the evolution of thermal fronts in the flaring magnetic loop. The numerical simulation starts with hot electrons in contact with background cold electrons along the magnetic field. The hot electrons transport along the magnetic field and induce a polarized electric field, drawing a return current from the background cold electrons. This return current can generate ion acoustic waves which could evolve into a double layer (DL). The DL is able to inhibit the transport of electron heat flux, which ultimately leads to the formation of a thermal front. However, the thermal front cannot persist for very long, since the DL will be dissipated by ambient cold protons. As a result, the cold protons trapped within the DL will be efficiently heated. After the vanishing of the first thermal front, hot electrons can again freely expand into the cold plasma, resulting in the growth of ion acoustic waves, which ultimately develop into a new DL. Then, a new thermal front will reform at a location farther into the regions of hot electrons. The new thermal front moves forward 200λD\\(200 \\lambda _{D}\\) (λD\\(\\lambda _{D}\\) is the cold electron Debye length) toward the regions containing hot electrons. The implications of simulation results to the observations of hard X-ray emission and confinement of energetic electrons in the corona are also discussed.

The seasonal structure and dynamic mechanism of oceanic surface thermal fronts (STFs) along the western Guangdong coast over the northern South China Sea shelf were analyzed using in situ observational data, remote sensing data, and numerical simulations. Both in situ and satellite observations show that the coastal thermal front exhibits substantial seasonal variability, being strongest in winter when it has the greatest extent and strongest sea surface temperature gradient. The winter coastal thermal front begins to appear in November and disappears after the following April. Although runoff water is more plentiful in summer, the front is weak in the western part of Guangdong. The frontal intensity has a significant positive correlation with the coastal wind speed, while the change of temperature gradient after September lags somewhat relative to the alongshore wind. The numerical simulation results accurately reflect the seasonal variation and annual cycle characteristics of the frontal structure in the simulated area. Based on vertical cross-section data, the different frontal lifecycles of the two sides of the Zhujiang (Pearl) River Estuary are analyzed.

Thermal front has been widely used as a parameter for determining fishing zones. Tis study aimed to  determine  the  thermal  front  distribution  and  to  analyze  its  association  with  the  Bali  Sardinella  fishing zones  in  the  Bali  Strait.  Termal  front  generated  using  sea  surface  temperature  (SST)  from  Aqua  MODIS imagery. Meanwhile, the fishing point data of Bali Sardinella were collected to validate our analysis results. Te data were analyzed into Spatio-temporal information. Te main facts that stand out are that the thermal front  was  predominantly  found  in  the  peak  of  first (April)  and  second  (September)  transitional  season, which was the peak season for the thermal front to occur in a year. Te least of the thermal front occurred in the  South-west  monsoon.  Te  linear  relationship  was found  when  the  peak  of  thermal  front  occurrence compared to the number of catch yields. Based on matching distance analysis, the maximum distance used (twenty  kilometres  buffer)  show  36  matching  points from  101  data  compared  or  at  range  35.6%.  In conclusion, there is a linear relationship between the thermal front parameter and catch yield. It is still used to predict the fishing zone, even though the correlation is not significantly found.

Understanding the heat transfer mechanism in the subsurface is essential for designing geothermal heat extraction system as well as oil extraction system in petroleum reservoirs. Due to the inherent variability in thermal diffusivity in the rock matrix–fracture system, this interface can either act as a source or sink for temperature. The main objective of this study is to investigate the influence of considering the heat transfer term either as a source or a sink at the fissure–matrix interface while describing the spatial and temporal distribution of temperature in a fractured reservoir at the scale of a single fissure. A numerical model is developed using implicit finite difference method to forecast the spatial and temporal propagation of the thermal front and to analyse the sensitivity of various coupling terms at the fissure–matrix interface. Following the numerical treatment, the velocity of thermal front as well as the degree of thermal dispersion encountered within the high-permeable fissure was computed using the method of spatial moments (first and second, respectively). Numerical results indicate that the mobile fluids within the fissure could reflect the exchange of stored heat energy from the rock-matrix quite efficiently for the cases with the heat gain term in the fissure equation. An enhanced mixing effect was observed for the cases having an explicit heat loss term for the matrix in the absence of any source/sink in the fissure equation. However, the thermal mixing regime remained insignificant for the cases having an explicit heat gain term in the fissure.