Southeastern Tropical Atlantic Changing From Subtropical to Tropical Conditions

A warming and freshening trend of the mixed layer in the upper southeastern tropical Atlantic Ocean (SETA) is observed by the Argo float array during the time period of 2006–2020. The associated ocean surface density reduction impacts upper-ocean stratification that intensified by more than 30% in the SETA region since 2006. The initial typical subtropical stratification with a surface salinity maximum is shifting to more tropical conditions characterized by warmer and fresher surface waters and a subsurface salinity maximum. During the same period isopycnal surfaces in the upper 200 m are shoaling continuously. Observed wind stress changes reveal that open ocean wind curl-driven upwelling increased, however, partly counteracted by reduced coastal upwelling due to weakened alongshore southerly winds. Weakening southerly winds might be a reason why tropical surface waters spread more southward reaching further into the SETA region. The mixed layer warming and freshening and associated stratification changes might impact the marine ecosystem and pelagic fisheries in the Angolan and northern Namibian upwelling region.

Long-Endurance Dynamic Path Planning Method of NSV Considering Wind Energy Capture

To meet the mission requirements of long-endurance and unmanned marine environment observation, the natural energy-driven unmanned surface vehicle (NSV) usually takes special sailing paths to increase energy capture to achieve the purpose of improving endurance. Aiming at the route planning problems of the “Wave Rider” NSV in the time-varying ocean wind field, this paper is organized as follows. Firstly, a visual modeling method of the real-time-varying ocean wind field for NSV is proposed. Then, through the wind energy capture experiment, the NSV system energy net output model is calculated, and a Dynamic Dijkstra algorithm considering wind energy capture (DW–Dijkstra) has been proposed in this paper based on a Dijkstra algorithm, of which weight function has been improved. Accordingly, the NSV long-endurance dynamic path planning method is designed. Finally, the DW–Dijkstra algorithm has been verified through a set of comparison simulations and a set of semi-physical comparison simulations. The results show that the DW–Dijkstra algorithm can plan a collision-free and high-efficiency energy capture path in the real-time-varying ocean wind field environment in the southern waters of China. Compared with the traditional A* algorithm and the Wind_A* algorithm, the proposed method can save energy by between 15.07% and 6.50%, respectively, which effectively increases the endurance of the NSV.

IoT BASED SMART AND EFFICIENT WIND TURBINE MONITORING SYSTEM

Wind Turbine industry has the improved latest generation of Wind turbines with bigger flexible blades, high tower, Good efficiency & low cost repairing in all platforms of wind turbines from Small wind mills to Ocean wind turbines. The Control centre is responsible for Monitoring and Controlling wind turbines in wind power farms. Various parameters like Oil level, Gas leakage, air pressure, vibrations & linear velocity, environmental condition like rain & humidity are to be monitored and controlled for proper working of the wind turbines. In the proposed work, smart and efficient turbine network architecture is designed to automate this process. The aim of the proposed work is to monitor the different parameters of the turbine using respective sensors. The acquired sensor data are uploaded to the cloud via WiFi module for online monitoring and further data analysis. IFTTT Server of Adafruit io cloud is used to send the warning notification of the critical sensor value to the concerned person. Also the sensor node life time is taken care by implementing a proposed compression algorithm in each node that reduces the amount of data transmitted and thereby the energy consumed during transmission.

Towards a Topographically-Accurate Reflection Point Prediction Algorithm for Operational Spaceborne GNSS Reflectometry—Development and Verification

GNSS Reflectometry (GNSS-R), a method of remote sensing using the reflections from satellite navigation systems, was initially envisaged for ocean wind speed sensing. In recent times there has been significant interest in the use of GNSS-R for sensing land parameters such as soil moisture, which has been identified as an Essential Climate Variable (ECV). Monitoring objectives for ECVs set by the Global Climate Observing System (GCOS) organisation include a reduction in data gaps from spaceborne sources. GNSS-R can be implemented on small, relatively cheap platforms and can enable the launch of constellations, thus reducing such data gaps in these important datasets. However in order to realise operational land sensing with GNSS-R, adaptations are required to existing instrumentation. Spaceborne GNSS-R requires the reflection points to be predicted in advance, and for land sensing this means the effect of topography must be considered. This paper presents an algorithm for on-board prediction of reflection points over the land, allowing generation of DDMs on-board as well as compression and calibration. The algorithm is tested using real satellite data from TechDemoSat-1 in a software receiver with on-board constraints being considered. Three different resolutions of Digital Elevation Model are compared. The algorithm is shown to perform better against the operational requirements of sensing land parameters than existing methods and is ready to proceed to flight testing.

An Assessment of CYGNSS Ocean Wind Speed Products

<p>Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from Global Navigation Satellite Systems, which have been reflected off the Earth’s surface. CYGNSS is a constellation of 8 satellites launched in 2016 which use GNSS-R technology for the remote sensing of ocean wind speed. The ESA ECOLOGY project aims to evaluate CYGNSS data which has recently undergone a series of improvements in the calibration approach. Using CYGNSS collections above the ocean surface, an assessment of Level-1 calibration is presented, alongside a performance evaluation of Level-2 wind speed products. L1 data collected by the individual satellites are shown to be generally well inter-calibrated and remarkably stable over time, a significant improvement over previous versions. However, some geographical biases are found, which appear to be linked to a number of factors including the transmitter-receiver pair considered, viewing geometry, and surface elevation. These findings provide a basis for further improvement of CYGNSS products and have wider applicability to improving calibration of GNSS-R sensors for remote sensing of the Earth.</p>