Showing papers on "World Ocean Atlas published in 2019"

A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU

Abstract: According to the complexity of ocean data, this paper adopts a parallel mining algorithm of association rules to explore the correlation and regularity of oxygen, temperature, phosphate, nitrate and silicate in the ocean. After the marine data is interpolated, this paper utilizes the parallel FP-growth algorithm to mine the data and then briefly analyzes the mining results of the frequent itemsets and association rules. The relationship between the parallel efficiency and the core number of CPU is analyzed through datasets with different scales. The experimental results indicate that the acceleration effect is ideal when each thread scored 200,000–300,000 data, which leads to more than 1.2 times of performance improvement.

A global monthly climatology of total alkalinity: a neural network approach

Abstract: Global climatologies of the seawater CO2 chemistry variables are necessary to assess the marine carbon cycle in depth The climatologies should adequately capture seasonal variability to properly address ocean acidification and similar issues related to the carbon cycle Total alkalinity ( AT ) is one variable of the seawater CO2 chemistry system involved in ocean acidification and frequently measured We used the Global Ocean Data Analysis Project version 22019 (GLODAPv2) to extract relationships among the drivers of the AT variability and AT concentration using a neural network (NNGv2) to generate a monthly climatology The GLODAPv2 quality-controlled dataset used was modeled by the NNGv2 with a root-mean-squared error (RMSE) of 53 µ mol kg −1 Validation tests with independent datasets revealed the good generalization of the network Data from five ocean time-series stations showed an acceptable RMSE range of 3–62 µ mol kg −1 Successful modeling of the monthly AT variability in the time series suggests that the NNGv2 is a good candidate to generate a monthly climatology The climatological fields of AT were obtained passing through the NNGv2 the World Ocean Atlas 2013 (WOA13) monthly climatologies of temperature, salinity, and oxygen and the computed climatologies of nutrients from the previous ones with a neural network The spatiotemporal resolution is set by WOA13: 1 ∘ × 1 ∘ in the horizontal, 102 depth levels (0–5500 m) in the vertical and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution The product is distributed through the data repository of the Spanish National Research Council (CSIC; https://doiorg/1020350/digitalCSIC/8644 , Broullon et al, 2019)

Evaluation of Arctic Ocean surface salinities from the Soil Moisture and Ocean Salinity (SMOS) mission against a regional reanalysis and in situ data

Abstract: . Recently two gridded sea surface salinity (SSS) products that cover the Arctic Ocean have been derived from the European Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) mission: one developed by the Barcelona Expert Centre (BEC) and the other developed by the Ocean Salinity Expertise Center of the Centre Aval de Traitement des Donnees SMOS at IFREMER (The French Research Institute for Exploitation of the Sea) (CEC). The uncertainties of these two SSS products are quantified during the period of 2011–2013 against other SSS products: one data assimilative regional reanalysis; one data-driven reprocessing in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS); two climatologies – the 2013 World Ocean Atlas (WOA) and the Polar science center Hydrographic Climatology (PHC); and in situ datasets, both assimilated and independent. The CMEMS reanalysis comes from the TOPAZ4 system, which assimilates a large set of ocean and sea-ice observations using an ensemble Kalman filter (EnKF). Another CMEMS product is the Multi-OBservations reprocessing (MOB), a multivariate objective analysis combining in situ data with satellite SSS. The monthly root mean squared deviations (RMSD) of both SMOS products, compared to the TOPAZ4 reanalysis, reach 1.5 psu in the Arctic summer, while in the winter months the BEC SSS is closer to TOPAZ4 with a deviation of 0.5 psu. The comparison of CEC satellite SSS against in situ data shows Atlantic Water that is too fresh in the Barents Sea, the Nordic Seas, and in the northern North Atlantic Ocean, consistent with the abnormally fresh deviations from TOPAZ4. When compared to independent in situ data in the Beaufort Sea, the BEC product shows the smallest bias (< 0.1 psu) in summer and the smallest RMSD (1.8 psu). The results also show that all six SSS products share a common challenge: representing freshwater masses (< 24 psu) in the central Arctic. Along the Norwegian coast and at the southwestern coast of Greenland, the BEC SSS shows smaller errors than TOPAZ4 and indicates the potential value of assimilating the satellite-derived salinity in this system.

Evaluation of Arctic Ocean surface salinities from SMOS and two CMEMS reanalyses against in-situ data sets

Abstract: . Although the stratification of the upper Arctic Ocean is mostly salinity-driven, the sea surface salinity (SSS) is still poorly known in the Arctic, due to its strong variability and the sparseness of in-situ observations. Recently, two gridded SSS products have been derived from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, independently developed by the Barcelona Expert Centre (BEC) in Spain and the Ocean Salinity Expertise Center (CECOS) of the Centre Aval de Traitemenent des Donnees SMOS (CATDS) in France, respectively. In parallel, there are two reanalysis products providing the Arctic SSS in the framework of the Copernicus Marine Environment Monitoring Services (CMEMS), one global, and another regional product. While the regional Arctic TOPAZ4 system assimilates a large set of sea-ice and ocean observations with an Ensemble Kalman Filter, the global reanalysis combines in-situ and satellite data using a multivariate ensemble optimal interpolation method. In this study, focused on the Arctic Ocean, these four salinity products, together with the climatology both World Ocean Atlas (WOA) of 2013 and Polar science center Hydrographic Climatology (PHC), are evaluated against in-situ datasets during 2011–2013. For the validation the in-situ observations are divided in two; those that have been assimilated and those that have not. The deviations of SSS between the different products and against the in-situ observations show largest disagreements below the sea-ice and in the marginal ice zone (MIZ), especially during the summer months. In the Beaufort Sea, the summer SSS from the BEC product has the smallest – saline – bias (~0.6 psu) with the smallest root mean squared difference (RSMD) of 2.6 psu. This suggests a potential value of assimilating of this product into the forthcoming Arctic reanalyses. Keywords: Arctic Ocean; sea surface salinity; SMOS; reanalysis; absolute deviation;

Potensi panas laut sebagai energi baru terbarukan di perairan papua barat dengan metode ocean thermal energy conversion (otec)

Abstract: Electrical energy is something that is needed in the continuation of human life. The need for electrical energy is increasing from time to time new innovations are purchased to produce environmentally friendly energy. Sea Heat Energy Conversion (OTEC) is a method to produce electrical energy using the temperature difference between the deep sea and the surface with a minimum temperature difference of 20 ° C to run a heat engine. West Papua is one of the provinces in Indonesia with electrification that is still low. The location of Indonesia in the tropics with differences in the temperature of sea water which has great potential to use the OTEC method in producing electricity. This study discusses to study the potential use of OTEC in Papua. The data used are data on the temperature of the deep sea surface and the period of 1955 - 2012 at twelve points in the waters around West Papua. The data was obtained from the World Ocean Atlas 2013 which was then processed using data software and Ocean Data View (ODV). The results of data processing obtained the greatest efficiency value of 7.67% and excess of 7.21%