Spatio-temporal visualization of air–sea CO2 flux and carbon budget using volume rendering

Du, Zhenhong; Fang, Lei; Bai, Yan; Zhang, Feng; Liu, Renyi

doi:10.1016/j.cageo.2015.01.004

Cited by 12 publications

(5 citation statements)

References 37 publications

(33 reference statements)

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“…Calculation of CO2 flux or CO2 gas flow exchange is calculated using a formula (Takahashi et al, 2002;Du et al, 2015;Fachri et al, 2015;Takahashi and Sutherland, 2017):…”

Section: Estimation Of the Sea-air Co2 Fluxmentioning

confidence: 99%

“…Several variables, including pCO2sea, pCO2atm, kwa, kH, sea surface temperature, chlorophyll-a, salinity, wind, pH, DIC, TA influence the role of these waters as a CO2 sink or CO2 source. The determination of these variables is based on the CO2 calculation formula, which requires the values of the variables pCO2sea, pCO2atm, seawater solubility (kH) calculated as a function of temperature and salinity, gas transfer rate (kwa) calculated as a function of the wind speed, and the Schmidt number which is affected by sea surface temperature (Weiss, 1974;Wanninkhof, 1992;Takahashi et al, 2002;Zhu et al, 2009;Robbins et al, 2010;Kartadikaria et al, 2015;Du et al, 2015;Takahashi and Sutherland, 2017;Yan et al, 2018;. Based on the results of Spearman's correlation (Figure 9.)…”

Section: Fluxes Of Co2mentioning

confidence: 99%

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The Partial Pressure of CO2 and Fluxes of CO2 in Semarang Bay, Indonesia

et al. 2023

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The CO2 characteristics of the Semarang Bay system on the Northern coast of Java were investigated to understand the roles in carbon dioxide flux. The flux of carbon dioxide at the sea surface between seawater and atmosphere is essential for mitigating atmospheric CO2. The aims of this study were to assess the variation of CO2 partial pressure seawater (pCO2sea) and CO2 flux and to analyse the water quality variable which has the larger influence on CO2 flux in the waters of Semarang Bay. The variables in this study were temperature, pH, salinity, and DO. Different methods were adopted in the analysis of water samples: spectrophotometric to measure chlorophyll-a and phosphate and titration method to measure Total Alkalinity (TA) and Dissolved Inorganic Carbon (DIC). A CO2 meter was used to measure the CO2atm. The lowest CO2 flux value was (1.86 mmol CO2 m-2 day-1) during the morning period while the pCO2sea reached 461.04 μatm and CO2 flux 83.79 mmol CO2 m-2 day-1 at night. At noon time, with increased temperature, pCO2sea was 461.04 μatm, and CO2 flux was 83.79 mmol CO2 m-2 day-1. The high concentration of chlorophyll-a ranged between 3.55–4.11 mg.L-1. This chlorophyll-a concentration has a negative correlation with CO2 flux, and it was found that TA and DIC concentrations have no relationship with CO2 flux. Based on PCA analysis, it was found that the variability of CO2 flux in Semarang Bay is influenced by the variability of pCO2sea, sea surface temperature and kwa (affected by wind speed).

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“…Calculation of CO2 flux or CO2 gas flow exchange is calculated using a formula (Takahashi et al, 2002;Du et al, 2015;Fachri et al, 2015;Takahashi and Sutherland, 2017):…”

Section: Estimation Of the Sea-air Co2 Fluxmentioning

confidence: 99%

Section: Fluxes Of Co2mentioning

confidence: 99%

The Partial Pressure of CO2 and Fluxes of CO2 in Semarang Bay, Indonesia

et al. 2023

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“…where (x, y) is the position in S, and t is the time in T. To render CO 2 flux, a data model proposed by Du (2015) is adopted to construct the air-sea SateCO 2 flux data to a particle system. The initial position of each particle is generated from a random function and is located around its raster grid cell center.…”

Section: Modeling Of Time-series Sateco 2 Flux Datamentioning

confidence: 99%

“…However, the behaviors of the ocean carbon cycle are controlled by various processes in global ocean environment (Canadell, 2003). Although the method suggested by Du (2015) has achieved visually realistic simulation of the ocean carbon fluxes, their study mainly focused on regions. The role of these regions in the global carbon cycle system has yet to be discussed.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Visualization of Time-Series Satellite-Derived Co2 Flux Data Using Volume Rendering and Gpu-Based Interpolation On A Cloud-Driven Digital Earth

Yan

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The ocean carbon cycle has a significant influence on global climate, and is commonly evaluated using time-series satellite-derived CO 2 flux data. Location-aware and globe-based visualization is an important technique for analyzing and presenting the evolution of climate change. To achieve realistic simulation of the spatiotemporal dynamics of ocean carbon, a cloud-driven digital earth platform is developed to support the interactive analysis and display of multi-geospatial data, and an original visualization method based on our digital earth is proposed to demonstrate the spatiotemporal variations of carbon sinks and sources using time-series satellite data. Specifically, a volume rendering technique using half-angle slicing and particle system is implemented to dynamically display the released or absorbed CO 2 gas. To enable location-aware visualization within the virtual globe, we present a 3D particlemapping algorithm to render particle-slicing textures onto geospace. In addition, a GPU-based interpolation framework using CUDA during real-time rendering is designed to obtain smooth effects in both spatial and temporal dimensions. To demonstrate the capabilities of the proposed method, a series of satellite data is applied to simulate the air-sea carbon cycle in the China Sea. The results show that the suggested strategies provide realistic simulation effects and acceptable interactive performance on the digital earth.

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“…Advances in the field of information visualization offer a number of innovative and promising approaches (Kehrer et al 2010). The applications of the visualization techniques has grown rapidly for different geologic purposes (Best and Lewis 2010;Gorricha and Lobo 2012;Romaňach et al 2012;Du et al 2015). Effective computer tools, incorporate the Microsoft office package can provide intelligence from raw paleontological data by creating visual graphs, thus new concepts will be constructed to understand the paleo-ecosystems easily and efficiently.…”

Section: Introductionmentioning

confidence: 99%

Short Communication: Categorization models as a powerful tool in paleontological data analyses – the Phanerozoic bivalves

Abdelhady

Abdalla

2018

Biodiversitas

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Abdelhady AA, Abdalla MM. 2018. Short Communication: Categorization models as a powerful tool in paleontological data analyses – the Phanerozoic bivalves. Biodiversitas 19: 1769-1776. Predicting biotic responses to current and future global change can be acquired through understanding how biological and environmental traits shaped the past origination, dispersion and extinction patterns. A global dataset encompasses 161,357 taxon occurrences belonging to 2,378 bivalve genera from past and recent environments were analyzed based on the categorization model, a widely-used machine-learning analysis, using MS-SQL and Excel PowerView. The occurrence data was standardized using square-root transformation to downplay the effect of sampling effort. Thus, the examined traits are resulting from reliable ecological interactions. The results indicate that the biotic traits of the bivalve can be determined by the abiotic ones. Moreover, ecological traits such as life habit (i.e., infaunal vs. epifaunal), diet (suspension vs. deposit feeders, herbivores vs. carnivores), composition (aragonite vs. calcite), and locomotion (stationary vs. mobile) all exhibit significant relation to a specific environment. The results demonstrated that decision tree and association rules are primary powerful tools in analyzing huge biological data and in testing many useful bio-ecological hypotheses.

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Spatio-temporal visualization of air–sea CO2 flux and carbon budget using volume rendering

Cited by 12 publications

References 37 publications

The Partial Pressure of CO2 and Fluxes of CO2 in Semarang Bay, Indonesia

The Partial Pressure of CO2 and Fluxes of CO2 in Semarang Bay, Indonesia

Spatiotemporal Visualization of Time-Series Satellite-Derived Co2 Flux Data Using Volume Rendering and Gpu-Based Interpolation On A Cloud-Driven Digital Earth

Short Communication: Categorization models as a powerful tool in paleontological data analyses – the Phanerozoic bivalves

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