Arctic weather routing: a review of ship performance models and ice routing algorithms

With the accelerated melting of the Arctic sea ice, the opening of the Northeast Passage of the Arctic is becoming increasingly accessible. Nevertheless, the constantly changing natural environment of the Arctic and its multiple impacts on vessel navigation performance have resulted in a lack of confidence in the outcomes of polar automated route planning. This paper aims to evaluate the effectiveness of two distinct models by examining the advancements in two essential components of e-navigation, namely ship performance methods and ice routing algorithms. We also seek to provide an outlook on the future directions of model development. Furthermore, through comparative experiments, we have examined the existing research on ice path planning and pointed out promising research directions in future Arctic Weather Routing research

PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST

We describe PSR J1926-0652, a pulsar recently discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive single-pulse detections from FAST and long-term timing observations from the Parkes 64-m radio telescope, we probed phenomena on both long and short time scales. The FAST observations covered a wide frequency range from 270 to 800 MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at least four profile components, short-term nulling lasting from 4 to 450 pulses, complex subpulse drifting behaviours and intermittency on scales of tens of minutes. While the average band spacing P3 is relatively constant across different bursts and components, significant variations in the separation of adjacent bands are seen, especially near the beginning and end of a burst. Band shapes and slopes are quite variable, especially for the trailing components and for the shorter bursts. We show that for each burst the last detectable pulse prior to emission ceasing has different properties compared to other pulses. These complexities pose challenges for the classic carousel-type models.Comment: 13pages with 12 figure

Practical Dynamical-Statistical Reconstruction of Ocean’s Interior from Satellite Observations

The algorithms based on Surface Quasi-Geostrophic (SQG) dynamics have been developed and validated by many researchers through model products, however it is still doubtful whether these SQG-based algorithms are worth using in terms of observed data. This paper analyzes the factors impeding the practical application of SQG and makes amends by a simple “first-guess (FG) framework”. The proposed framework includes the correction of satellite salinity and the estimation of the FG background, making the SQG-based algorithms applicable in realistic circumstances. The dynamical-statistical method SQG-mEOF-R is thereafter applied to satellite data for the first time. The results are compared with two dynamical algorithms, SQG and isQG, and three empirical algorithms, multivariate linear regression (MLR), random forest (RF), and mEOF-R. The validation against Argo profiles showed that the SQG-mEOF-R presents a robust performance in mesoscale reconstruction and outperforms the other five algorithms in the upper layers. It is promising that the SQG-mEOF-R and the FG framework are applicable to operational reconstruction

Practical Dynamical-Statistical Reconstruction of Ocean’s Interior from Satellite Observations

The algorithms based on Surface Quasi-Geostrophic (SQG) dynamics have been developed and validated by many researchers through model products, however it is still doubtful whether these SQG-based algorithms are worth using in terms of observed data. This paper analyzes the factors impeding the practical application of SQG and makes amends by a simple “first-guess (FG) framework”. The proposed framework includes the correction of satellite salinity and the estimation of the FG background, making the SQG-based algorithms applicable in realistic circumstances. The dynamical-statistical method SQG-mEOF-R is thereafter applied to satellite data for the first time. The results are compared with two dynamical algorithms, SQG and isQG, and three empirical algorithms, multivariate linear regression (MLR), random forest (RF), and mEOF-R. The validation against Argo profiles showed that the SQG-mEOF-R presents a robust performance in mesoscale reconstruction and outperforms the other five algorithms in the upper layers. It is promising that the SQG-mEOF-R and the FG framework are applicable to operational reconstruction

Correction of Satellite Sea Surface Salinity Products Using Ensemble Learning Method

Although salinity satellites can provide high-resolution global sea surface salinity (SSS) data, the satellite data still display large errors close to the coast. In this paper, a nonlinear empirical method based on random forest is proposed to correct two Soil Moisture and Ocean Salinity (SMOS) L3 products in the tropical Indian Ocean, including SMOS BEC and SMOS CATDS data. The agreement between in-situ data and the corrected SMOS data is better than that between in-situ data and the original satellite data. The root-mean-square deviation (RMSD) of the satellite SSS data decreased from 0.366 to 0.275 and from 0.367 to 0.255 for SMOS BEC and SMOS CATDS, respectively. The effect of the correction model was better in the Arabian Sea than in the Bay of Bengal. The RMSD of corrected BEC (CATDS) SSS was reduced from 0.44 (0.48) to 0.276 (0.269), and the correlation coefficient was increased to 0.915 from 0.741(0.801) in the Arabian Sea while the correlation coefficient improved less than 0.02 in the Bay of Bengal. The cross-validation results highlight the robustness and effectiveness of the correction model. Additionally, the effects of different features on the correction model are discussed to demonstrate the vital role of geographical information in the correction of satellite SSS data. The proposed method outperformed other machine-learning methods with respect to the RMSD and correlation coefficient

A New Framework for Assessment of Offshore Wind Farm Location

Offshore wind energy has become a hot spot in new-energy development due to its abundant reserves, long power generation time, high unit capacity and low land occupation. In response to the current situation whereby wind energy, and natural and human factors have not been taken into account in the selection of sites for offshore wind-energy-resource development in the traditional “21st Century Maritime Silk Road” region, this paper intends to establish a new risk assessment framework that comprehensively considers the influence of wind resources, the natural environment, and the geopolitical and humanistic environment. The rationality of the new index system and weight determination methods are separately investigated. Some interesting results are obtained by comparing the new framework with traditional frameworks. The results show that the Persian Gulf, the Timor Sea in northern Australia, and the northern part of Sri Lanka in southern India are rich in wind-energy resources and have a low overall risk, making them recommended sites. In addition, unlike the results of previous studies, this paper does not recommend the Somali Sea as a priority area for wind-energy siting due to its high geographic humanity risks

A New Framework for Assessment of Offshore Wind Farm Location

Offshore wind energy has become a hot spot in new-energy development due to its abundant reserves, long power generation time, high unit capacity and low land occupation. In response to the current situation whereby wind energy, and natural and human factors have not been taken into account in the selection of sites for offshore wind-energy-resource development in the traditional “21st Century Maritime Silk Road” region, this paper intends to establish a new risk assessment framework that comprehensively considers the influence of wind resources, the natural environment, and the geopolitical and humanistic environment. The rationality of the new index system and weight determination methods are separately investigated. Some interesting results are obtained by comparing the new framework with traditional frameworks. The results show that the Persian Gulf, the Timor Sea in northern Australia, and the northern part of Sri Lanka in southern India are rich in wind-energy resources and have a low overall risk, making them recommended sites. In addition, unlike the results of previous studies, this paper does not recommend the Somali Sea as a priority area for wind-energy siting due to its high geographic humanity risks