scholarly journals SPATIOTEMPORAL VISUALIZATION OF TIME-SERIES SATELLITE-DERIVED CO2 FLUX DATA USING VOLUME RENDERING AND GPU-BASED INTERPOLATION ON A CLOUD-DRIVEN DIGITAL EARTH

2017 ◽  
Vol IV-4/W2 ◽  
pp. 77-85 ◽  
Author(s):  
S. Wu ◽  
Y. Yan ◽  
Z. Du ◽  
F. Zhang ◽  
R. Liu
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Volume Rendering ◽  
Satellite Data ◽  
Particle System ◽  
Realistic Simulation ◽  
Digital Earth ◽  
Flux Data ◽  
Location Aware ◽  
Ocean Carbon

The ocean carbon cycle has a significant influence on global climate, and is commonly evaluated using time-series satellite-derived CO<sub>2</sub> 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<sub>2</sub> 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.

scholarly journals Deglacial carbon cycle changes observed in a compilation of 127 benthic <i>δ</i><sup>13</sup>C time series (20–6 ka)

2018 ◽  
Vol 14 (8) ◽  
pp. 1229-1252 ◽  
Author(s):  
Carlye D. Peterson ◽  
Lorraine E. Lisiecki
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Carbon Storage ◽  
Large Scale ◽  
Deep Ocean ◽  
Atmospheric Co2 ◽  
Last Deglaciation ◽  
Terrestrial Biosphere ◽  
Spatial Coverage ◽  
Ocean Carbon

Abstract. We present a compilation of 127 time series δ13C records from Cibicides wuellerstorfi spanning the last deglaciation (20–6 ka) which is well-suited for reconstructing large-scale carbon cycle changes, especially for comparison with isotope-enabled carbon cycle models. The age models for the δ13C records are derived from regional planktic radiocarbon compilations (Stern and Lisiecki, 2014). The δ13C records were stacked in nine different regions and then combined using volume-weighted averages to create intermediate, deep, and global δ13C stacks. These benthic δ13C stacks are used to reconstruct changes in the size of the terrestrial biosphere and deep ocean carbon storage. The timing of change in global mean δ13C is interpreted to indicate terrestrial biosphere expansion from 19–6 ka. The δ13C gradient between the intermediate and deep ocean, which we interpret as a proxy for deep ocean carbon storage, matches the pattern of atmospheric CO2 change observed in ice core records. The presence of signals associated with the terrestrial biosphere and atmospheric CO2 indicates that the compiled δ13C records have sufficient spatial coverage and time resolution to accurately reconstruct large-scale carbon cycle changes during the glacial termination.

scholarly journals Deglacial carbon cycle changes observed in a compilation of 117 benthic δ<sup>13</sup>C time series (20–6 ka)

2018 ◽  
Author(s):  
Carlye Peterson ◽  
Lorraine Lisiecki
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Carbon Storage ◽  
Large Scale ◽  
Deep Ocean ◽  
Atmospheric Co2 ◽  
Last Deglaciation ◽  
Terrestrial Biosphere ◽  
Spatial Coverage ◽  
Ocean Carbon

Abstract. We present a compilation of 117 time series δ13C records from Cibicides wuellerstorfi spanning the last deglaciation (20–6 kyr) and well-suited for reconstructing large-scale carbon cycle changes, especially for comparison with isotope-enabled carbon cycle models. The age models for the δ13C records are derived from regional planktic radiocarbon compilations (Stern and Lisiecki, 2014). The δ13C records were stacked in nine different regions and then combined using volume-weighted averages to create intermediate, deep, and global δ13C stacks. These benthic δ13C stacks are used to reconstruct mean changes in the size of the terrestrial biosphere and deep ocean carbon storage. The timing of change in global mean δ13C is interpreted to indicate terrestrial biosphere expansion from 19–6 ka. The δ13C gradient between the intermediate and deep ocean, which we interpret as a proxy for deep ocean carbon storage, matches the pattern of atmospheric CO2 change observed in ice core records. The presence of signals associated with the terrestrial biosphere and atmospheric CO2 indicates that the compiled δ13C records have sufficient spatial coverage and time resolution to accurately reconstruct large-scale carbon cycle changes during the glacial termination.

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

The use of CO2 flux time series for parameter and carbon stock estimation in carbon cycle research

2011 ◽  
Vol 18 (1) ◽  
pp. 179-193 ◽  
Author(s):  
Timothy Charles Hill ◽  
Edmund Ryan ◽  
Mathew Williams
Keyword(s):  
Time Series ◽  
Carbon Cycle ◽  
Carbon Stock ◽  
Co2 Flux ◽  
Stock Estimation

scholarly journals Changes in the Arctic Ocean carbon cycle with diminishing ice cover

2020 ◽  
Author(s):  
Michael D. DeGrandpre ◽  
Wiley Evans ◽  
Mary-Louise Timmermans ◽  
Richard A. Krishfield ◽  
William J Williams ◽  
...  
Keyword(s):  
Carbon Cycle ◽  
Arctic Ocean ◽  
Ice Cover ◽  
The Arctic ◽  
Ocean Carbon Cycle ◽  
The Arctic Ocean ◽  
Ocean Carbon

The Use of Satellite TIR Time Series for Thermal Anomalies’ Detection on Natural and Urban Areas

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Malvina Silvestri ◽  
Federico Rabuffi ◽  
Massimo Musacchio ◽  
Sergio Teggi ◽  
Maria Fabrizia Buongiorno
Keyword(s):  
Time Series ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Time Series ◽  
Campi Flegrei ◽  
Thermal Anomalies ◽  
Pca Method ◽  
Temperature Time

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.

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