Characteristics of Global Ocean Abnormal Mesoscale Eddies Derived From the Fusion of Sea Surface Height and Temperature Data by Deep Learning

Liu, Yingjie; Zheng, Quanan; Li, Xiaofeng

doi:10.1029/2021gl094772

Cited by 53 publications

(39 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that previous studies usually focus on the cyclonic cold-core eddy (CCE) and anticyclonic warm-core eddy (AWE). In addition to these eddy statistical results (normal eddies, namely, CCE and AWE), recent studies show that there is also an abundance of eddies that do not meet the statistical characteristics (abnormal eddies, that are cyclonic warm-core eddy (CWE) and anticyclonic cold-core eddy (ACE); Itoh and Yasuda, 2010b;Itoh and Yasuda, 2010a;Ji et al, 2016;Sun et al, 2019;Liu et al, 2021;Ni et al, 2021;Sun et al, 2021a). The discovery of these abnormal eddies expands the research content and classification of mesoscale eddies from two types (CCE and AWE) to four (CCE, AWE, CWE, and ACE).…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of four types of mesoscale eddies in the Kuroshio-Oyashio extension region

Sun

Liu

et al. 2022

Front. Mar. Sci.

View full text Add to dashboard Cite

Oceanic mesoscale cyclonic (anticyclonic) eddies usually have cold (warm) cores and counterclockwise (clockwise) flow fields in the Northern Hemisphere. However, “abnormal” cyclonic (anticyclonic) eddies with warm (cold) cores and counterclockwise (clockwise) flow fields have recently been identified in the Kuroshio-Oyashio Extension (KOE) region. Here, traditional cyclonic cold-core eddies (CCEs) and anticyclonic warm-core eddies (AWEs) are termed normal eddies, and cyclonic warm-core eddies (CWEs) and anticyclonic cold-core eddies (ACEs) are called abnormal eddies. Applying a vector geometry-based automatic eddy detection method to the Ocean General Circulation Model for the Earth Simulator reanalysis data (OFES), a three-dimensional eddy dataset is obtained and used to quantify the statistical characteristics of these eddies. Results illustrate that the number of CCEs, AWEs, CWEs, and ACEs accounted for 38.46, 36.15, 13.40, and 11.99%, respectively. In the vertical direction, normal eddies are concentrated in the upper 2,000 m, while abnormal eddies are mainly found in the upper 600 m of the ocean. On seasonal scales, normal eddies are more abundant in winter and spring than in summer and autumn, with the opposite trend found for abnormal eddies. Potential density changes modulated by normal eddies are dominated by eddies-induced temperature anomalies, while salinity anomalies dominate the changes modulated by abnormal eddies. This study expands the types of eddies and enriches their understanding in the KOE region.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparative analysis of four types of mesoscale eddies in the Kuroshio-Oyashio extension region

Sun

Liu

et al. 2022

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…Sun et al (2019) proposed that the abnormal eddy needs to meet the following conditions: 1) the average temperature inside the CWE (ACE) is 0.1°C warmer (colder) than the average temperature of the annular area between the CWE (ACE) boundary and 1.5 times the eddy boundary; 2) the proportion of the warm (cold) temperature points inside the CWE (ACE) is at least 60% of the total points. Liu et al (2021) only used the positive and negative of the filtered temperature anomaly to define the abnormal eddy.…”

Section: Definition Of Four Types Of Eddiesmentioning

confidence: 99%

“…However, with the increasing number of studies focusing on the mesoscale eddy phenomenon, it has been found that there are not only normal eddies, i.e., cyclonic cold-core eddies (CCEs, with anticlockwise flow field and cold eddy core) and anticyclonic warm-core eddies (AWEs, with clockwise rotation flow field and warm eddy core). There are also mesoscale eddies with the opposite properties (Yasuda et al, 2000;Itoh and Yasuda, 2010;Ji et al, 2016;Sun et al, 2019;Sun et al, 2021a;Liu et al, 2021;Ni et al, 2021;Qi et al, 2022): cyclonic warm-core eddies (CWEs, with anticlockwise rotation flow field and warm eddy core), and anticyclonic cold-core eddies (ACEs, with clockwise rotation flow field and cold eddy core).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparative analysis of four types of mesoscale eddies in the north pacific subtropical countercurrent region – part I spatial characteristics

An¹,

Liu²,

Liu³

et al. 2022

Front. Mar. Sci.

View full text Add to dashboard Cite

The North Pacific Subtropical Countercurrent (STCC) region has high mesoscale eddy activities due to its complex circulation structure. This study divides these mesoscale eddies into four types: cyclonic cold-core eddy (CCE), anticyclonic warm-core eddy (AWE), cyclonic warm-core eddy (CWE), and anticyclonic cold-core eddy (ACE) according to the rotation direction of the eddy flow field and the sign of average temperature anomaly within the eddy after spatial high-pass filtering. CCE and ACE are called normal eddies, while CWE and ACE are named abnormal eddies. Using eddy-resolving model data (OFES), this work finds that the abnormal eddy phenomenon mainly occurs in the ocean’s upper layer. The eddy number proportion for CCEs, AWEs, CWEs, and ACEs at the sea surface is 35.60, 32.08, 12.95, and 19.37%. The corresponding average radius is 79.14 ± 3.7, 83.34 ± 3.75, 73.74 ± 4.14, and 79.46 ± 3.89 km, respectively. Each type of eddy’s average amplitude is about 3 cm. Regarding the eddy average eccentricity, the four types of eddies have very close eccentricities, with a range of 0.73 ~ 0.76. If the types of eddies are not distinguished, the eddies generated north of 21°N tend to move southward, while eddies generated south of that latitude tend to move northward. The depth of CCEs, AWEs, CWEs, and ACEs with average eddy nonlinearity larger than one is concentrated in the ocean’s upper layer at 109.0, 116.0, 159.0, and 52.0 m, respectively. This study deepens the understanding of the spatial distribution characteristics of mesoscale eddies in the STCC region.

show abstract

“…With the increase of computing power and the development of novel algorithms, deep learning models have become powerful tools that have been widely utilized in hydrology studies (Fang et al, 2017;Orland et al, 2020;Liu et al, 2021;Feng et al, 2021a) in the past few years. The majority of studies focus on daily rainfall-runoff modeling and streamflow forecasting (Kratzert et al, 2018;Kratzert et al, 2019;Feng et al, 2020;Qian et al, 2020;Sarkar et al, 2020;Van et al, 2020;Lees et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Xiang¹,

Demir²

2022

Preprint

View full text Add to dashboard Cite

Rainfall-runoff modeling and streamflow prediction using deep learning algorithms have been studied significantly in the last few years. The majority of these studies focus on the simulation and testing of historical datasets. Deployment and operation of a real-time streamflow forecast model using deep learning will face additional data and computational challenges such as inaccurate rainfall forecast data and real-time data assimilation with limited studies guiding on these difficulties. We proposed a real-time streamflow forecast framework that includes pre-event model training using deep learning, real-time data acquisition, and post-event analysis. We implemented the framework for 124 USGS gauged watersheds across Iowa to forecast 120-hour streamflow rates since April 2021. This is the first time deep learning models have been used to predict streamflow in real-time operational settings at a large scale, and we anticipate seeing more real-time implementations of deep learning models in the future.

show abstract

Characteristics of Global Ocean Abnormal Mesoscale Eddies Derived From the Fusion of Sea Surface Height and Temperature Data by Deep Learning

Abstract: Mesoscale eddies are broadly distributed in the global ocean and play a significant role in transporting energy, heat, and mass alongside nutrients and chemical elements across ocean basins (Chelton, Gaube,

Cited by 53 publications

References 58 publications

Comparative analysis of four types of mesoscale eddies in the Kuroshio-Oyashio extension region

Comparative analysis of four types of mesoscale eddies in the Kuroshio-Oyashio extension region

Comparative analysis of four types of mesoscale eddies in the north pacific subtropical countercurrent region – part I spatial characteristics

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Contact Info

Product

Resources

About