With the advancement of Artificial Intelligence(Al)technologies and accumulation of big Earth data,Deep Learning(DL)has become an important method to discover patterns and understand Earth science processes in the pas...With the advancement of Artificial Intelligence(Al)technologies and accumulation of big Earth data,Deep Learning(DL)has become an important method to discover patterns and understand Earth science processes in the past several years.While successful in many Earth science areas,Al/DL applications are often challenging for computing devices.In recent years,Graphics Processing Unit(GPU)devices have been leveraged to speed up Al/DL applications,yet computational performance still poses a major barrier for DL-based Earth science applications.To address these computational challenges,we selected five existing sample Earth science Al applications,revised the DL-based models/algorithms,and tested the performance of multiple GPU computing platforms to support the applications.Application softwarepackages,performance comparisonsacross different platforms,along with other results,are summarized.This article can help understand how various Al/ML Earth science applications can be supported by GPU computing and help researchers in the Earth science domain better adopt GPU computing(such as supermicro,GPU clusters,and cloud computing-based)for their Al/ML applications,and to optimize their science applications to better leverage the computing device.展开更多
In recent years,our world has experienced significant disruptions due to the COviD-19 pandemic,and Russia's 2022 invasion of Ukraine,impacting human activities and the global environment.This paper explored air qu...In recent years,our world has experienced significant disruptions due to the COviD-19 pandemic,and Russia's 2022 invasion of Ukraine,impacting human activities and the global environment.This paper explored air quality changes in Ukraine due to COVID-19,and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approach.During the cOVID-19 pandemic,strict quarantine policies in Ukraine led to a 2%reduction in tropospheric NO_(2) concentration before the lockdown and 4%during the lockdown period.Cities like Kyiv,Donetsk,and Dnipro exhibited reductions of 5%,11%,and 16%,respectively.Total SO_(2) column concentration decreased by 6%before the lockdown and 2.5%during the lockdown period,except in high population density areas.Kyiv showed the highest reduction of 17%in SO_(2) concentration,while Donetsk and Dnipro exhibited an 11%reduction.However,during the Russian invasion,there was a significant increase in tropospheric NO_(2) concentration in heavily destroyed Kharkiv while most eastern regions experienced a reduction.The total SO_(2) column was 48%higher before the war but reduced throughout the country after the war,except for in Kyiv and a few central regions.These findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios.展开更多
基金supported by NSF F I/UCRC(1841520),NASA Goddard CISTO,and NASA AIST programs.
文摘With the advancement of Artificial Intelligence(Al)technologies and accumulation of big Earth data,Deep Learning(DL)has become an important method to discover patterns and understand Earth science processes in the past several years.While successful in many Earth science areas,Al/DL applications are often challenging for computing devices.In recent years,Graphics Processing Unit(GPU)devices have been leveraged to speed up Al/DL applications,yet computational performance still poses a major barrier for DL-based Earth science applications.To address these computational challenges,we selected five existing sample Earth science Al applications,revised the DL-based models/algorithms,and tested the performance of multiple GPU computing platforms to support the applications.Application softwarepackages,performance comparisonsacross different platforms,along with other results,are summarized.This article can help understand how various Al/ML Earth science applications can be supported by GPU computing and help researchers in the Earth science domain better adopt GPU computing(such as supermicro,GPU clusters,and cloud computing-based)for their Al/ML applications,and to optimize their science applications to better leverage the computing device.
基金supported by NSF I/UCRC and START programs(1841520)NASA Goddard CISTO,and NASA AIST programs.This research was,in part,carried out at the Jet Propulsion Laboratory,California Institute of Technology,under a contract with the National Aeronautics and Space Administration(80NM0018D0004).
文摘In recent years,our world has experienced significant disruptions due to the COviD-19 pandemic,and Russia's 2022 invasion of Ukraine,impacting human activities and the global environment.This paper explored air quality changes in Ukraine due to COVID-19,and Russia's invasion of Ukraine using on-demand with a what-you-see-is-what-you-get approach.During the cOVID-19 pandemic,strict quarantine policies in Ukraine led to a 2%reduction in tropospheric NO_(2) concentration before the lockdown and 4%during the lockdown period.Cities like Kyiv,Donetsk,and Dnipro exhibited reductions of 5%,11%,and 16%,respectively.Total SO_(2) column concentration decreased by 6%before the lockdown and 2.5%during the lockdown period,except in high population density areas.Kyiv showed the highest reduction of 17%in SO_(2) concentration,while Donetsk and Dnipro exhibited an 11%reduction.However,during the Russian invasion,there was a significant increase in tropospheric NO_(2) concentration in heavily destroyed Kharkiv while most eastern regions experienced a reduction.The total SO_(2) column was 48%higher before the war but reduced throughout the country after the war,except for in Kyiv and a few central regions.These findings can contribute to analyzing air pollution and building digital twin simulations for future reconstruction scenarios.
