Ocean Front Detection with Glider and Satellite-Derived SST Data in the Southern California Current System

This study proposes a method to detect ocean fronts from in situ temperature and density glider measurements. This method is applied to data collected along the CalCOFI Line 90, south of the California Current System (CCS), over the 2006–2013 period. It is based on image-processing techniques commonly applied to sea surface temperature (SST) satellite data. Front detection results using glider data are consistent with those obtained in other studies carried out in the CCS. SST images of the Multi-scale Ultra-high Resolution (MUR) dataset were also used to compare the probability of occurrence or front frequency (FF) obtained with the two datasets. Glider and MUR temperatures are highly correlated. Along Line 90, frontal frequency exhibited the same maxima near the transition zone (~130 km offshore) as derived from MUR and glider datasets. However, marked differences were found in the bimonthly FF probability with high (low) front frequency in spring-summer for glider (MUR) data. Methodological differences explaining these contrasting results are investigated. Thermohaline-compensated fronts are more abundant towards the oceanic zone, although most fronts are detected using both temperature and density criteria, indicating a significant contribution of temperature to density in this region.

Machine Learning-Based Front Detection in Central Europe

Extreme weather phenomena such as wind gusts, heavy precipitation, hail, thunderstorms, tornadoes, and many others usually occur when there is a change in air mass and the passing of a weather front over a certain region. The climatology of weather fronts is difficult, since they are usually drawn onto maps manually by forecasters; therefore, the data concerning them are limited and the process itself is very subjective in nature. In this article, we propose an objective method for determining the position of weather fronts based on the random forest machine learning technique, digitized fronts from the DWD database, and ERA5 meteorological reanalysis. Several aspects leading to the improvement of scores are presented, such as adding new fields or dates to the training database or using the gradients of fields.

The Intra-Tidal Characteristics of Tidal Front and Their Spring–Neap Tidal and Seasonal Variations in Bungo Channel, Japan

The intra-tidal variations of a tidal front in Bungo Channel, Japan and their dependence on the spring–neap tidal cycle and month were analyzed utilizing high-resolution (~2 km) hourly sea surface temperature (SST) data obtained from a Himawari-8 geostationary satellite from April 2016 to August 2020. A gradient-based front detection method was utilized to define the position and intensity of the front. Similar to previous ship-based studies, SST data were utilized to identify tidal fronts between a well-mixed strait and its surrounding stratified area. The hourly SST data confirmed the theoretical intra-tidal movement of the tidal front, which is mainly controlled by tidal current advection. Notably, the intensity of the front increases during the ebb current phase, which carries the front toward the stratified area, but decreases during the flood current phase that drives the front in the opposite direction. Due to a strong dependence on tidal currents, the intra-tidal variations appear in a fortnight cycle, and the fortnightly variations of the front are dependent on the month in which the background stratification and residual current changes occur. Additionally, tidal current convergence and divergence are posited to cause tidal front intensification and weakening.

A High Autonomous Sea Front Detection Algorithm Based on SAR Data

This paper has proposed a high autonomous sea front detection algorithm based on SAR data. Through the innovative introduction of empirical mode decomposition method, a good image de-trend and de-stripe effect is achieved. By introducing the calculation of the maximum interclass variance, the automatic conversion of binary images is realized; through the use of polynomial fitting method, the independent screening of front information is realized, and the continuity of front detection results is improved. After comparison, it is found that the new algorithm proposed in this paper has greatly improved detection accuracy and autonomy compared with the old algorithm. Finally, a SAR data of the GF-3 satellite on the west side of Taiwan Island is used to test the new algorithm proposed in this paper. The results show that the detection results are highly consistent with the original image in morphology, and the changes in frontal intensity are also very detailed, verifying the accuracy and autonomy of the new method.

Objective 3D atmospheric front detection in high-resolution numerical weather prediction data

<p><span>Atmospheric fronts, a widely used conceptual model in meteorology, describe sharp boundaries between two air masses of different thermal properties. In the mid-latitudes, these sharp boundaries are commonly associated with extratropical cyclones. The passage of a frontal system is accompanied by significant weather changes, and therefore fronts are of particular interest in weather forecasting. Over the past decades, several two-dimensional, horizontal feature detection methods to objectively identify atmospheric fronts in numerical weather prediction (NWP) data were proposed in the literature (e.g. Hewson, Met.Apps. 1998). In addition, recent research (Kern et al., IEEE Trans. Visual. Comput. Graphics, 2019) has shown the feasibility of detecting atmospheric fronts as three-dimensional surfaces representing the full 3D frontal structure. In our work, we build on the studies by Hewson (1998) and Kern et al. (2019) to make front detection usable for forecasting purposes in an interactive 3D visualization environment. We consider the following aspects: (a) As NWP models evolved in recent years to resolve atmospheric processes on scales far smaller than the scale of midlatitude-cyclone- fronts, we evaluate whether previously developed detection methods are still capable to detect fronts in current high-resolution NWP data. (b) We present integration of our implementation into the open-source “Met.3D” software (http://met3d.wavestoweather.de) and analyze two- and three-dimensional frontal structures in selected cases of European winter storms, comparing different models and model resolution. (c) The considered front detection methods rely on threshold parameters, which mostly refer to the magnitude of the thermal gradient within the adjacent frontal zone - the frontal strength. If the frontal strength exceeds the threshold, a so-called feature candidate is classified as a front, while others are discarded. If a single, fixed, threshold is used, unwanted “holes” can be observed in the detected fronts. Hence, we use transparency mapping with fuzzy thresholds to generate continuous frontal features. We pay particular attention to the adjustment of filter thresholds and evaluate the dependence of thresholds and resolution of the underlying data.</span></p>

Tapping the Potential of Earth Observation - Calving Front Detection in SAR Images using Deep Learning Techniques

<p>The calving fronts of lake or marine terminating glaciers provide information about the state of glaciers. A change in its position can affect the flow of the entire glacier system, and the loss of ice mass as icebergs calve-off and discharge into the ocean has a multi-scale impact on the global hydrological cycle. The calving fronts can be manually delineated in Synthetic Aperture Radar (SAR) images. However, this is a time-consuming, tedious and expensive task. As deep learning approaches have achieved tremendous success in various disciplines, such as medical image processing and computer vision, the project Tapping the Potential of Earth Observation (TAPE) is amongst other things dedicated to applying deep learning techniques to calving front detection. So far, all our experiments have employed U-Net based architectures, as the U-Net is state-of-the-art in semantic image segmentation. A major challenge of front detection is the class imbalance: The front has significantly fewer pixels than the remaining parts of the SAR image. Hence, we developed variants of the U-Net specifically addressing this challenge including an Attention U-Net, a probabilistic Bayesian U-Net, as well as a U-Net with a distance map-based binary cross-entropy (BCE) loss function and a Mathews correlation coefficient (MCC) as early stopping criterion. In future work, we plan to investigate multi-task learning and a segmentation of the SAR image into different classes (i.e. ocean, glacier and rocks) to enhance the quality and efficiency of the front detection.</p>

Remote Sensing of Ocean Fronts in Marine Ecology and Fisheries

This paper provides a concise review of the remote sensing of ocean fronts in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms and techniques, including those proposed by Canny, Cayula and Cornillon, Miller, Shimada et al., Belkin and O’Reilly, and Nieto et al.. A case is made for a feature-based approach that emphasizes fronts as major structural and circulation features of the ocean realm that play key roles in various aspects of marine ecology.

Observed and Projected Frontal Activities in East Asia

An objective front detection method is applied to ERA5, CMIP5 historical, and RCP8.5 simulations to evaluate climate model performance in simulating front frequency and understand future projections of seasonal front activities. The study area is East Asia for two natural seasons, defined as winter (December 2nd –February 14th) and spring (February 15th –May 15th), in accordance with regional circulation and precipitation patterns. Seasonal means of atmospheric circulation and thermal structures are analyzed to understand possible factors responsible for future front changes.The front location and frequency in CMIP5 historical simulations are captured reasonably. Frontal precipitation accounts for more than 30% of total precipitation over subtropical regions. Projections suggest that winter fronts will decrease over East Asia, especially over southern China. Frontal precipitation is projected to decrease for 10-30%. Front frequency increases in the South China Sea and tropical western Pacific because of more tropical moisture supply, which enhances local moisture contrasts. During spring, southern China and Taiwan will experience fewer fronts and less frontal precipitation while central China, Korea, and Japan may experience more fronts and more frontal precipitation due to moisture flux from the south that enhances 𝜽𝒘 gradients.Consensus among CMIP5 models in front frequency tendency is evaluated. The models exhibit relatively high consensus in the decreasing trend over polar and subtropical frontal zone in winter and over southern China and Taiwan in spring that may prolong the dry season. Spring front activities are crucial for water resource and risk management in the southern China and Taiwan.