Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorith...Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.展开更多
In the past two decades, artificial intelligence （AI） algorithms have proved to be promising tools for solving several tough scientific problems, As a broad subfield of AI, machine learning is concerned with algorit...In the past two decades, artificial intelligence （AI） algorithms have proved to be promising tools for solving several tough scientific problems, As a broad subfield of AI, machine learning is concerned with algorithms and techniques that allow computers to ＂learn＂. The machine learning approach covers main domains such as data mining, difficult-to-program applications, and soft- ware applications. It is a collection of a variety of algorithms that can provide multivariate, nonlinear, nonparametric regression or classification. The remarkable simulation capabilities of the ma- chine learning-based methods have resulted in their extensive ap- plications in science and engineering. Recently, the machine learning techniques have found many applications in the geoscien- ces and remote sensing. More specifically, these techniques are proved to be practical for cases where the system＇s deterministic model is computationally expensive or there is no deterministic model to solve the problem （Lary, 2010）.展开更多
This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were ide...This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were identified by Defense Meteorological Satellite Program(DMSP)F16 during the years 2011 to 2022.A temperature ratio of ion/electron temperature(T_(i)/T_(e))<0.68 is recommended to define a hot patch in the Southern Hemisphere,otherwise it is defined as a cold patch.The cold and hot patches have different dependencies on IMF clock angle,while their dependencies on IMF cone angle are similar.Both cold and hot patches appear most often on the duskside,and the distribution of cold patches gradually decreases from the dayside to the nightside,while hot patches have a higher occurrence rate near 14 and 21 magnetic local time(MLT).Moreover,we compared the key plasma characteristics of polar cap cold and hot patches in the Southern and Northern Hemispheres.The intensity of the duskside upward field-aligned current of patches in the Southern Hemisphere(SH)is stronger than that in the Northern Hemisphere(SH),which may be due to the discrepancy in conductivities between the two hemispheres,caused by the tilted dipole.In both hemispheres,the downward soft-electron energy flux of the dawnside patches is significantly greater than that of the duskside patches.展开更多
Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically...Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.展开更多
The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles(EPBs),that in turn lead to ionospheric scintillation which can severely degrade prec...The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles(EPBs),that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System(GNSS).Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services.The objective of this study is to propose a neural network,trained with radio occultation data from the COSMIC-1 mission,that generates average ionospheric electron density profiles during dusk,focusing on the pre-reversal enhancement of the zonal electric field.Results show that the estimated profiles exhibit a clear seasonal pattern,and reproduce adequately the climatological behavior of the ionosphere,thus presenting strong appeal on ionospheric error attenuation.展开更多
文摘Learning incorporates a broad range of complex procedures. Machine learning（ML） is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficultto-program applications, and software applications. It is a collection of a variety of algorithms（e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc.） that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore,nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.
文摘In the past two decades, artificial intelligence （AI） algorithms have proved to be promising tools for solving several tough scientific problems, As a broad subfield of AI, machine learning is concerned with algorithms and techniques that allow computers to ＂learn＂. The machine learning approach covers main domains such as data mining, difficult-to-program applications, and soft- ware applications. It is a collection of a variety of algorithms that can provide multivariate, nonlinear, nonparametric regression or classification. The remarkable simulation capabilities of the ma- chine learning-based methods have resulted in their extensive ap- plications in science and engineering. Recently, the machine learning techniques have found many applications in the geoscien- ces and remote sensing. More specifically, these techniques are proved to be practical for cases where the system＇s deterministic model is computationally expensive or there is no deterministic model to solve the problem （Lary, 2010）.
基金supported by the National Natural Science Foundation of China(Grants 42325404,42120104003,42204164,42474219 and U22A2006)the Chinese Meridian Project,the International Partnership Program of Chinese Academy of Sciences(Grant 183311KYSB20200003)+7 种基金Shandong Provincial Natural Science Foundation(Grants ZR2022QD077,ZR2022MD034)the Stable-Support Scientific Project of China Research Institute of Radiowave Propagation(Grant A132312191)the foundation of the National Key Laboratory of Electromagnetic Environment(Grant 6142403180204)the Chongqing Natural Science Foundation(Grants cstc2021ycjh-bgzxm0072,CSTB2023NSCQ-LZX0082)National Program on Key Basic Research Project(Grant 2022173-SD-1)The work in Norway is supported by the Research Council of Norway Grant 326039Work at UCLA has been supported by NSF grant AGS-2055192This research was supported by the International Space Science Institute(ISSI)in Bern and Beijing,through ISSI International Team project#511(Multi-Scale Magnetosphere-Ionosphere-Thermosphere Interaction).
文摘This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were identified by Defense Meteorological Satellite Program(DMSP)F16 during the years 2011 to 2022.A temperature ratio of ion/electron temperature(T_(i)/T_(e))<0.68 is recommended to define a hot patch in the Southern Hemisphere,otherwise it is defined as a cold patch.The cold and hot patches have different dependencies on IMF clock angle,while their dependencies on IMF cone angle are similar.Both cold and hot patches appear most often on the duskside,and the distribution of cold patches gradually decreases from the dayside to the nightside,while hot patches have a higher occurrence rate near 14 and 21 magnetic local time(MLT).Moreover,we compared the key plasma characteristics of polar cap cold and hot patches in the Southern and Northern Hemispheres.The intensity of the duskside upward field-aligned current of patches in the Southern Hemisphere(SH)is stronger than that in the Northern Hemisphere(SH),which may be due to the discrepancy in conductivities between the two hemispheres,caused by the tilted dipole.In both hemispheres,the downward soft-electron energy flux of the dawnside patches is significantly greater than that of the duskside patches.
文摘Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.
基金CAPES scholarships 88887.570088/2020-00 and 88887.634447/2021-00 and worked on this research in collaboration to the framework CNPq 465648/2014-2 and FAPESP 2017/01150-0.GSFAOM are supported by CNPq awards 165561/2023-8 and 309389/2021-6 respectively+1 种基金PRPS and JS were supported by CAPES awards 850937/2023-00 and 88887.901203/2023-00 respectivelyJS also acknowledges FAPESP 2018/06158-9.
文摘The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles(EPBs),that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System(GNSS).Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services.The objective of this study is to propose a neural network,trained with radio occultation data from the COSMIC-1 mission,that generates average ionospheric electron density profiles during dusk,focusing on the pre-reversal enhancement of the zonal electric field.Results show that the estimated profiles exhibit a clear seasonal pattern,and reproduce adequately the climatological behavior of the ionosphere,thus presenting strong appeal on ionospheric error attenuation.
