Auroral field-aligned currents (FACs) have an intrinsic complexity caused by the superposition of contributions from a broad spectrum of scales and diversity of locations. The complex FAC systems are investigated by using the multiscale minimum variance analysis. This technique provides a scale based decomposition of the FAC systems by identifying the constituting FAC elements as well as their structure. At the basis, the analysis exploits the scale dependence of the eigenvalues of the magnetic field variance matrix. The scale decomposition along the transversal (latitudinal) direction results from the scale derivative of the maximum eigenvalue. The complementary information from the scale derivative of the minimum eigenvalue helps to infer the full structure of each FAC element by providing estimates of the FAC length (longitudinal) scale. The scale derivative of minimum and maximum eigenvalues are used to identify FAC signatures associated to different types of aurora (e.g., highly extended, finite arcs, gradient regions) as well as to characterize the influence of the crossing location with respect to the FAC structures (e.g., near edge crossings). The multiscale analysis is applied to simulated FACs and to spacecraft observations made by the Swarm mission. The use with real world data illustrates the power of this analysis, whose full benefits for magnetosphere-ionosphere coupling investigations are yet to be explored.
极光场配向流(FACs)具有内在的复杂性,这是由来自各种尺度和各种地点的贡献叠加造成的。利用多尺度最小方差分析对复杂的极光场配向流系统进行了研究。该技术通过识别构成 FAC 的元素及其结构,对 FAC 系统进行基于尺度的分解。在此基础上,分析利用了磁场方差矩阵特征值的尺度依赖性。沿横向(纬向)的尺度分解来自最大特征值的尺度导数。最小特征值的尺度导数提供了补充信息,有助于通过对 FAC 长度(纵向)尺度的估计来推断每个 FAC 元素的完整结构。最小和最大特征值的尺度导数可用于识别与不同类型极光相关的 FAC 特征(如高度延伸、有限弧形、梯度区域),以及描述交叉位置对 FAC 结构的影响(如边缘交叉附近)。多尺度分析应用于模拟 FAC 和 Swarm 任务的航天器观测。实际数据的使用说明了这种分析的威力,其对磁层-电离层耦合研究的全部益处还有待探索。
{"title":"The Structure of Field-Aligned Current Systems as Inferred From the Multiscale Minimum Variance Analysis","authors":"Costel Bunescu","doi":"10.1029/2024EA003708","DOIUrl":"https://doi.org/10.1029/2024EA003708","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Auroral field-aligned currents (FACs) have an intrinsic complexity caused by the superposition of contributions from a broad spectrum of scales and diversity of locations. The complex FAC systems are investigated by using the multiscale minimum variance analysis. This technique provides a scale based decomposition of the FAC systems by identifying the constituting FAC elements as well as their structure. At the basis, the analysis exploits the scale dependence of the eigenvalues of the magnetic field variance matrix. The scale decomposition along the transversal (latitudinal) direction results from the scale derivative of the maximum eigenvalue. The complementary information from the scale derivative of the minimum eigenvalue helps to infer the full structure of each FAC element by providing estimates of the FAC length (longitudinal) scale. The scale derivative of minimum and maximum eigenvalues are used to identify FAC signatures associated to different types of aurora (e.g., highly extended, finite arcs, gradient regions) as well as to characterize the influence of the crossing location with respect to the FAC structures (e.g., near edge crossings). The multiscale analysis is applied to simulated FACs and to spacecraft observations made by the Swarm mission. The use with real world data illustrates the power of this analysis, whose full benefits for magnetosphere-ionosphere coupling investigations are yet to be explored.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the past century, scholars from both domestic and international communities have delved into the study of numerical weather prediction models to promptly understand meteorological factors and mitigate the impacts of extreme weather events on humanity. Effective and precise prediction models enable the forecasting of meteorological conditions in the upcoming days, empowering individuals to implement proactive measures to minimize the adverse effects of extreme weather (Liang et al., 2021). The WRF (Weather Research and Forecasting) modeling system is commonly used for forecasting meteorological elements. However, uncertainties terribly hamper the correctness of the forecasting results. To this end, the present study was conducted to build a secondary model on the basis of the WRF forecast model. The WRF-BPNN model was proposed for verification after constructing the network, the temperature vertical profile and the mixing ratio vertical profile were predicted, and the results on the validation set were tested. The results showed that the WRF-BPNN model could effectively predict the temperature profile and mixing ratio profile, presenting better performance than the traditional WRF model.
{"title":"Enhancing Weather Forecast Accuracy Through the Integration of WRF and BP Neural Networks: A Novel Approach","authors":"Zeyang Liu, Jing Zhang, Yadong Yang, Yaping Wang, Wangjun Luo, Xiancun Zhou","doi":"10.1029/2024EA003613","DOIUrl":"https://doi.org/10.1029/2024EA003613","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>In the past century, scholars from both domestic and international communities have delved into the study of numerical weather prediction models to promptly understand meteorological factors and mitigate the impacts of extreme weather events on humanity. Effective and precise prediction models enable the forecasting of meteorological conditions in the upcoming days, empowering individuals to implement proactive measures to minimize the adverse effects of extreme weather (Liang et al., 2021). The WRF (Weather Research and Forecasting) modeling system is commonly used for forecasting meteorological elements. However, uncertainties terribly hamper the correctness of the forecasting results. To this end, the present study was conducted to build a secondary model on the basis of the WRF forecast model. The WRF-BPNN model was proposed for verification after constructing the network, the temperature vertical profile and the mixing ratio vertical profile were predicted, and the results on the validation set were tested. The results showed that the WRF-BPNN model could effectively predict the temperature profile and mixing ratio profile, presenting better performance than the traditional WRF model.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tyler F. M. Brown, Michele T. Bannister, Laura E. Revell, Timofei Sukhodolov, Eugene Rozanov
The rate of rocket launches is accelerating, driven by the rapid global development of the space industry. Rocket launches emit gases and particulates into the stratosphere, where they impact the ozone layer via radiative and chemical processes. We create a three-dimensional per-vehicle inventory of stratospheric emissions, accounting for flight profiles and all major fuel types in active use (solid, kerosene, cryogenic and hypergolic). In 2019, stratospheric (15–50 km) rocket launch emissions were 5.82 Gg , 6.38 Gg O, 0.28 Gg black carbon, 0.22 Gg nitrogen oxides, 0.50 Gg reactive chlorine and 0.91 Gg particulate alumina. The geographic locations of launch sites are preserved in the inventory, which covers all active launch sites in 2019. We also report the emissions data from contemporary vehicles that were not launched in 2019, so that users have freedom to construct their own launch activity scenarios. A subset of the inventory—stratospheric emissions for successful launches in 2019—is freely available and formatted for direct use in global chemistry-climate or Earth system models.
{"title":"Worldwide Rocket Launch Emissions 2019: An Inventory for Use in Global Models","authors":"Tyler F. M. Brown, Michele T. Bannister, Laura E. Revell, Timofei Sukhodolov, Eugene Rozanov","doi":"10.1029/2024EA003668","DOIUrl":"https://doi.org/10.1029/2024EA003668","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The rate of rocket launches is accelerating, driven by the rapid global development of the space industry. Rocket launches emit gases and particulates into the stratosphere, where they impact the ozone layer via radiative and chemical processes. We create a three-dimensional per-vehicle inventory of stratospheric emissions, accounting for flight profiles and all major fuel types in active use (solid, kerosene, cryogenic and hypergolic). In 2019, stratospheric (15–50 km) rocket launch emissions were 5.82 Gg <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>C</mi>\u0000 <mi>O</mi>\u0000 </mrow>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${mathrm{C}mathrm{O}}_{2}$</annotation>\u0000 </semantics></math>, 6.38 Gg <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>H</mi>\u0000 <mn>2</mn>\u0000 </msub>\u0000 </mrow>\u0000 <annotation> ${mathrm{H}}_{2}$</annotation>\u0000 </semantics></math>O, 0.28 Gg black carbon, 0.22 Gg nitrogen oxides, 0.50 Gg reactive chlorine and 0.91 Gg particulate alumina. The geographic locations of launch sites are preserved in the inventory, which covers all active launch sites in 2019. We also report the emissions data from contemporary vehicles that were not launched in 2019, so that users have freedom to construct their own launch activity scenarios. A subset of the inventory—stratospheric emissions for successful launches in 2019—is freely available and formatted for direct use in global chemistry-climate or Earth system models.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yangkang Chen, Alexandros Savvaidis, Daniel Siervo, Dino Huang, Omar M. Saad
Artificial intelligence (AI) seismology has witnessed enormous success in a variety of fields, especially in earthquake detection and P and S-wave arrival picking. It has become widely accepted that DL techniques greatly help routine seismic monitoring by enabling more accurate picking than traditional pickers like STA/LTA. However, a completely automatic AI-driven earthquake monitoring framework has not been reported due to the concerns of potential false positives using DL pickers. Here, we propose a novel AI-facilitated near real-time monitoring framework using a recently developed deep learning (DL) picker (EQCCT) that has been deployed in the Texas seismological network (TexNet). For the West Texas area, TexNet's seismic monitoring relies on the EQCCT picker to report earthquake events. For earthquakes with a magnitude above two, the picks are further validated by analysts to output the final TexNet catalog. Due to the fast-increasing seismicity caused by continuing oil&gas production in West Texas, this AI-facilitated framework significantly relieves the workload of TexNet analysts. We show the mean absolute error (MAE) of automatic magnitude estimation for the magnitude-above-two earthquakes is smaller than 0.15 in West Texas and MAEs of hypocenter locations within 2.6 km in both distance and depth estimates. This research provides more evidence that DL pickers can play a fundamental role in daily earthquake monitoring.
{"title":"Near Real-Time Earthquake Monitoring in Texas Using the Highly Precise Deep Learning Phase Picker","authors":"Yangkang Chen, Alexandros Savvaidis, Daniel Siervo, Dino Huang, Omar M. Saad","doi":"10.1029/2024EA003890","DOIUrl":"https://doi.org/10.1029/2024EA003890","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Artificial intelligence (AI) seismology has witnessed enormous success in a variety of fields, especially in earthquake detection and <i>P</i> and <i>S</i>-wave arrival picking. It has become widely accepted that DL techniques greatly help routine seismic monitoring by enabling more accurate picking than traditional pickers like STA/LTA. However, a completely automatic AI-driven earthquake monitoring framework has not been reported due to the concerns of potential false positives using DL pickers. Here, we propose a novel AI-facilitated near real-time monitoring framework using a recently developed deep learning (DL) picker (EQCCT) that has been deployed in the Texas seismological network (TexNet). For the West Texas area, TexNet's seismic monitoring relies on the EQCCT picker to report earthquake events. For earthquakes with a magnitude above two, the picks are further validated by analysts to output the final TexNet catalog. Due to the fast-increasing seismicity caused by continuing oil&gas production in West Texas, this AI-facilitated framework significantly relieves the workload of TexNet analysts. We show the mean absolute error (MAE) of automatic magnitude estimation for the magnitude-above-two earthquakes is smaller than 0.15 in West Texas and MAEs of hypocenter locations within 2.6 km in both distance and depth estimates. This research provides more evidence that DL pickers can play a fundamental role in daily earthquake monitoring.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Floods in India are recurring natural disasters resulting from extreme precipitation during the summer monsoon season (June–September). The recent flood in North India in July 2023 caused substantial damage to lives, agriculture, and infrastructure. However, what led to the 2023 North India flood and the role of atmospheric and land drivers still need to be examined. Using in situ observations, satellite data, and ERA5 reanalysis combined with hydrological and hydrodynamical modeling, we examine the role of land and atmospheric drivers in flood occurrence and its impacts. Extreme precipitation in a large region during 7–10 July 2023 created favorable conditions for the flood in the hilly terrains and plains of north India. More than 300 mm of precipitation fell in just 4 days, which was eight times higher than the long-term average (2001–2022). Anomalously high moisture transport over northern India was recorded on 7 July 2023, making atmospheric conditions favorable for intense landfall. Increased column water vapor and specific humidity at different pressure levels confirmed the continuous moisture presence before the extreme rainfall that caused floods in northern India from 7 to 12 July 2023. Atmospheric and land (high antecedent soil moisture) conditions contributed to a more than 200% rise in streamflow at several gauge stations. Satellite-based flood extent shows a considerable flood inundation that caused damage in the Sutlej and Yamuna River basins. Our findings highlight the crucial role of the favorable land and atmospheric conditions that caused floods and flash floods in north India in July 2023.
{"title":"Land and Atmospheric Drivers of the 2023 Flood in India","authors":"Anuj Prakash Kushwaha, Hiren Solanki, Urmin Vegad, Shanti Shwarup Mahto, Vimal Mishra","doi":"10.1029/2024EA003750","DOIUrl":"https://doi.org/10.1029/2024EA003750","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Floods in India are recurring natural disasters resulting from extreme precipitation during the summer monsoon season (June–September). The recent flood in North India in July 2023 caused substantial damage to lives, agriculture, and infrastructure. However, what led to the 2023 North India flood and the role of atmospheric and land drivers still need to be examined. Using in situ observations, satellite data, and ERA5 reanalysis combined with hydrological and hydrodynamical modeling, we examine the role of land and atmospheric drivers in flood occurrence and its impacts. Extreme precipitation in a large region during 7–10 July 2023 created favorable conditions for the flood in the hilly terrains and plains of north India. More than 300 mm of precipitation fell in just 4 days, which was eight times higher than the long-term average (2001–2022). Anomalously high moisture transport over northern India was recorded on 7 July 2023, making atmospheric conditions favorable for intense landfall. Increased column water vapor and specific humidity at different pressure levels confirmed the continuous moisture presence before the extreme rainfall that caused floods in northern India from 7 to 12 July 2023. Atmospheric and land (high antecedent soil moisture) conditions contributed to a more than 200% rise in streamflow at several gauge stations. Satellite-based flood extent shows a considerable flood inundation that caused damage in the Sutlej and Yamuna River basins. Our findings highlight the crucial role of the favorable land and atmospheric conditions that caused floods and flash floods in north India in July 2023.</p>\u0000 </section>\u0000 </div>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jamie MacMahan, Ed Thornton, Stef Dressel, Mike Cook
<div> <section> <p>Approximately 75% of the world's and California's shores are rocky. Rocky shores are of biological interest owing to their diverse and productive species assemblages, where waves and currents play a critical role in larval dispersal and recruitment. Surface variability for nearshore <span></span><math> <semantics> <mrow> <mo>(</mo> <mrow> <mn>5</mn> <mo>⪅</mo> <mi>d</mi> <mi>e</mi> <mi>p</mi> <mi>t</mi> <mi>h</mi> <mo>⪅</mo> <mn>60</mn> <mspace></mspace> <mi>m</mi> </mrow> <mo>)</mo> </mrow> <annotation> $(5lessapprox mathrm{d}mathrm{e}mathrm{p}mathrm{t}mathrm{h}lessapprox 60hspace*{.5em}mathrm{m})$</annotation> </semantics></math> rocky bottoms at intermediate wave scale <span></span><math> <semantics> <mrow> <mfenced> <mrow> <mn>1</mn> <mo>/</mo> <mn>750</mn> <mo><</mo> <mi>k</mi> <mo><</mo> <mn>1</mn> <mo>/</mo> <mn>4</mn> <mspace></mspace> <msup> <mi>m</mi> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </mfenced> </mrow> <annotation> $left(1/750< k< 1/4hspace*{.5em}{mathrm{m}}^{-1}right)$</annotation> </semantics></math> is described for the first time using existing high-resolution bathymetric surveys extending the length of California. The vertical variability of rocky shores is three times larger than measured coral reefs at the reef scale of <span></span><math> <semantics> <mrow> <mn>1</mn> <mo>/</mo> <mn>64</mn> <mo><</mo>
全世界和加利福尼亚州约有 75% 的海岸是岩石海岸。岩石海岸因其多样且富饶的物种组合而备受生物界关注,其中波浪和海流对幼虫的扩散和繁殖起着至关重要的作用。Surface variability for nearshore ( 5 ⪅ d e p t h ⪅ 60 m ) $(5lessapprox mathrm{d}mathrm{e}mathrm{p}mathrm{t}mathrm{h}lessapprox 60hspace*{.5em}mathrm{m})$ rocky bottoms at intermediate wave scale 1 / 750 < k < 1 / 4 m − 1 $left(1/750< k< 1/4hspace*{.5em}{mathrm{m}}^{-1}right)$ is described for the first time using existing high-resolution bathymetric surveys extending the length of California.在 1 / 64 < k < 1 / 4 m - 1 $1/64 < k < 1/4hspace*{.5em}{mathrm{m}}^{-1}$ 的珊瑚礁尺度下,岩石海岸的垂直变化比测量的珊瑚礁大三倍。经过归一化处理的集合平均波数高程谱具有自相似性,至少有两个负幂律斜率区域,1.3 表示粗糙的底部,2.75 的高波数表示较平滑的凹陷。岩质海底的集合统计量似乎是各向同性的,因为它们的跨岸和沿岸方差以及波数斜率谱在统计上是相等的。各向同性的原因是波浪随时间的侵蚀只发生在近岸海洋区域。为沿岩石海岸的波浪和海流驱动的水动力模型开发了海底边界条件 G‾z ′ ${overline{G}}_{{z}^{prime }}$ 的傅立叶谱表示法。相对于重建剖面和表面的观测光谱数据,该模型的误差小于 2%。
{"title":"Intermediate Wave Scale Rocky Bottom Variability for the Nearshore Along California","authors":"Jamie MacMahan, Ed Thornton, Stef Dressel, Mike Cook","doi":"10.1029/2023EA003475","DOIUrl":"https://doi.org/10.1029/2023EA003475","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Approximately 75% of the world's and California's shores are rocky. Rocky shores are of biological interest owing to their diverse and productive species assemblages, where waves and currents play a critical role in larval dispersal and recruitment. Surface variability for nearshore <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>(</mo>\u0000 <mrow>\u0000 <mn>5</mn>\u0000 <mo>⪅</mo>\u0000 <mi>d</mi>\u0000 <mi>e</mi>\u0000 <mi>p</mi>\u0000 <mi>t</mi>\u0000 <mi>h</mi>\u0000 <mo>⪅</mo>\u0000 <mn>60</mn>\u0000 <mspace></mspace>\u0000 <mi>m</mi>\u0000 </mrow>\u0000 <mo>)</mo>\u0000 </mrow>\u0000 <annotation> $(5lessapprox mathrm{d}mathrm{e}mathrm{p}mathrm{t}mathrm{h}lessapprox 60hspace*{.5em}mathrm{m})$</annotation>\u0000 </semantics></math> rocky bottoms at intermediate wave scale <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mfenced>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>/</mo>\u0000 <mn>750</mn>\u0000 <mo><</mo>\u0000 <mi>k</mi>\u0000 <mo><</mo>\u0000 <mn>1</mn>\u0000 <mo>/</mo>\u0000 <mn>4</mn>\u0000 <mspace></mspace>\u0000 <msup>\u0000 <mi>m</mi>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 </mfenced>\u0000 </mrow>\u0000 <annotation> $left(1/750< k< 1/4hspace*{.5em}{mathrm{m}}^{-1}right)$</annotation>\u0000 </semantics></math> is described for the first time using existing high-resolution bathymetric surveys extending the length of California. The vertical variability of rocky shores is three times larger than measured coral reefs at the reef scale of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 <mo>/</mo>\u0000 <mn>64</mn>\u0000 <mo><</mo>\u0000 ","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EA003475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ping Jiang, Yaoqin Xie, Sijia Wu, Tangsheng Wang, Yalan Li
There are many problems in space debris monitoring with ground-based telescopes, such as too many stars in the same field of view, uneven background and optical distortion in the optical system. We propose a two-stage weak debris detection algorithm. In the first stage, wavelet transform is used to extract different components of three frames of images, and the median of corresponding components of the images is taken respectively to eliminate the influence of stars. In the second stage, an improved version of the faint space target extraction based on principal component analysis. The algorithm uses a smooth-detection idea to extract target information. Based on a 150 mm aperture telescope, we improved the existing method of faint space debris extraction based on principal component analysis by introducing the smooth-detection idea, and transformed the target detection problem into the separation problem of sparse matrix and low-rank matrix. We applied a certain preprocessing consisting of wavelet-based star removal and median pre-filtering to keep as little noise and other contaminants as possible. After experimental measurements by observers, the algorithm demonstrated advanced detection capabilities on multiple indicators.
{"title":"Faint Space Target Information Extraction Based on Small Aperture Telescope in Complex Background","authors":"Ping Jiang, Yaoqin Xie, Sijia Wu, Tangsheng Wang, Yalan Li","doi":"10.1029/2023EA003404","DOIUrl":"https://doi.org/10.1029/2023EA003404","url":null,"abstract":"<p>There are many problems in space debris monitoring with ground-based telescopes, such as too many stars in the same field of view, uneven background and optical distortion in the optical system. We propose a two-stage weak debris detection algorithm. In the first stage, wavelet transform is used to extract different components of three frames of images, and the median of corresponding components of the images is taken respectively to eliminate the influence of stars. In the second stage, an improved version of the faint space target extraction based on principal component analysis. The algorithm uses a smooth-detection idea to extract target information. Based on a 150 mm aperture telescope, we improved the existing method of faint space debris extraction based on principal component analysis by introducing the smooth-detection idea, and transformed the target detection problem into the separation problem of sparse matrix and low-rank matrix. We applied a certain preprocessing consisting of wavelet-based star removal and median pre-filtering to keep as little noise and other contaminants as possible. After experimental measurements by observers, the algorithm demonstrated advanced detection capabilities on multiple indicators.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EA003404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The estimation of the wet refractivity indices is crucial for applications like weather predictions or improving the accuracy of real-time positioning techniques. Traditionally, solving the inverse tomography problem to estimate these atmospheric parameters has been challenging due to its ill-posed nature and high computational demands, necessitating additional constraints. To overcome these challenges, the data assimilation method is proposed here to integrate Global Navigation Satellite System (GNSS) observations into a background model. In this study, the Ensemble Kalman Filter (EnKF) was served as the assimilation core to reduce the computational load and to enable the epoch-wise estimation of wet refractivity indices. The Global Pressure and Temperature 3 (GPT3w) model was utilized as the background, and wet refractivity indices at each epoch were transformed into B-spline coefficients, representing state vector parameters. Subsequently, GNSS derived zenith wet delay (ZWD) values were integrated into the model using the EnKF method. The study's region encompassed the western parts of Europe and incorporated approximately 893 GNSS stations. Evaluation spanned from 1 January 2017 to 31 December 2017. The estimated wet refractivity indices from the proposed method were compared with observations from 16 existing radiosonde stations, radio occultation data, and ZWD values from the 47 selected GNSS test stations. Additionally, calculated ZWD values, resulting from the integration of wet refractivity indices, were compared to the ZWD values from 47 test stations in the study region. The numerical results demonstrated that the proposed method achieved a root mean square error value of approximately 2.6 ppm, which was nearly 49% and 18% lower than that of the considered empirical and numerical atmospheric models, respectively.
{"title":"Ensemble Based Estimation of Wet Refractivity Indices Using a Functional Model Approach","authors":"Masoud Dehvari, Saeed Farzaneh, Ehsan Forootan","doi":"10.1029/2023EA003453","DOIUrl":"https://doi.org/10.1029/2023EA003453","url":null,"abstract":"<p>The estimation of the wet refractivity indices is crucial for applications like weather predictions or improving the accuracy of real-time positioning techniques. Traditionally, solving the inverse tomography problem to estimate these atmospheric parameters has been challenging due to its ill-posed nature and high computational demands, necessitating additional constraints. To overcome these challenges, the data assimilation method is proposed here to integrate Global Navigation Satellite System (GNSS) observations into a background model. In this study, the Ensemble Kalman Filter (EnKF) was served as the assimilation core to reduce the computational load and to enable the epoch-wise estimation of wet refractivity indices. The Global Pressure and Temperature 3 (GPT3w) model was utilized as the background, and wet refractivity indices at each epoch were transformed into B-spline coefficients, representing state vector parameters. Subsequently, GNSS derived zenith wet delay (ZWD) values were integrated into the model using the EnKF method. The study's region encompassed the western parts of Europe and incorporated approximately 893 GNSS stations. Evaluation spanned from 1 January 2017 to 31 December 2017. The estimated wet refractivity indices from the proposed method were compared with observations from 16 existing radiosonde stations, radio occultation data, and ZWD values from the 47 selected GNSS test stations. Additionally, calculated ZWD values, resulting from the integration of wet refractivity indices, were compared to the ZWD values from 47 test stations in the study region. The numerical results demonstrated that the proposed method achieved a root mean square error value of approximately 2.6 ppm, which was nearly 49% and 18% lower than that of the considered empirical and numerical atmospheric models, respectively.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2023EA003453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The stochastic EXtended finite-fault ground-motion SIMulation algorithm (EXSIM) has been widely applied in simulating and predicting broadband strong ground-motion. However, an increasingly number of researchers have found that EXSIM may overestimate ground-motions at low frequencies for some large-magnitude earthquakes and/or thrust earthquakes, for which the far-field source model has been explained by a double-corner-frequency model. Despite controversy, the double-corner-frequency model is now being accepted as one of the main categories of the far-field source model. This study demonstrated the limited applicability of EXSIM to earthquakes explained by the double-corner-frequency source model, by presenting the equivalence between motions generated by EXSIM and those generated by EXSIM's point-source version, SMSIM, which adopts the ω-square single-corner-frequency model. Furthermore, two improvements to EXSIM have been proposed: (a) the incorporation of the asperity-distributed stress-drop compound faults model and (b) the hybrid application of EXSIM with the proposed model. The effects of the two improvements have been verified by comparing EXSIM-generating motions with recorded ground-motions for the 2013 Mw 6.7 Lushan thrust earthquake. Significantly, consistent simulation accuracy has been achieved across high- and low-frequency bands as well as in far- and near-fields. The consistent accuracy of the improved EXSIM in simulating high- and low-frequency ground motions enables its direct and independent application to broadband ground motion simulations. Moreover, the first validation of this consistent accuracy in both near- and far-field scenarios further enhances its application in earthquake engineering practices.
{"title":"Improvements to EXSIM in Ground Motion Simulation for Earthquakes Explained by Double-Corner-Frequency Source Model","authors":"Wanjun Ma, Zhinan Xie, Lei Fu, Zhendong Shan, Jianqi Lu, Lihua Tang, Xubin Zhang","doi":"10.1029/2024EA003797","DOIUrl":"https://doi.org/10.1029/2024EA003797","url":null,"abstract":"<p>The stochastic EXtended finite-fault ground-motion SIMulation algorithm (EXSIM) has been widely applied in simulating and predicting broadband strong ground-motion. However, an increasingly number of researchers have found that EXSIM may overestimate ground-motions at low frequencies for some large-magnitude earthquakes and/or thrust earthquakes, for which the far-field source model has been explained by a double-corner-frequency model. Despite controversy, the double-corner-frequency model is now being accepted as one of the main categories of the far-field source model. This study demonstrated the limited applicability of EXSIM to earthquakes explained by the double-corner-frequency source model, by presenting the equivalence between motions generated by EXSIM and those generated by EXSIM's point-source version, SMSIM, which adopts the <i>ω</i>-square single-corner-frequency model. Furthermore, two improvements to EXSIM have been proposed: (a) the incorporation of the asperity-distributed stress-drop compound faults model and (b) the hybrid application of EXSIM with the proposed model. The effects of the two improvements have been verified by comparing EXSIM-generating motions with recorded ground-motions for the 2013 <i>M</i><sub>w</sub> 6.7 Lushan thrust earthquake. Significantly, consistent simulation accuracy has been achieved across high- and low-frequency bands as well as in far- and near-fields. The consistent accuracy of the improved EXSIM in simulating high- and low-frequency ground motions enables its direct and independent application to broadband ground motion simulations. Moreover, the first validation of this consistent accuracy in both near- and far-field scenarios further enhances its application in earthquake engineering practices.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Keil, H. Igel, M. Schimmel, F. Lindner, F. Bernauer
In the past few years, the remarkable progress of commercially operated spacecrafts, the success with reusable rocket engines, as well as the international competition to explore space, has led to a substantial acceleration of activities in the design and preparation of ambitious future lunar missions. In the search for ice and/or cavities imaging the shallow subsurface structure is of vital importance. Hereby, previous studies have shown that seismic interferometry is a promising method to investigate the subsurface properties from passive lunar data. In this study, we want to evaluate the potential of this method further by examining the required duration of seismic measurements and the influence of scattering on the Green's function retrieval. Therefore, we applied seismic interferometry to both measured Apollo 17 data and synthetic data. Our findings indicate that, under optimal conditions, a few hours of data are sufficient when using the method of time-scaled phase-weighted stack (ts-PWS). However, this strongly depends on the inter-station distance, the orientation toward the principal noise sources, and the timing of the measurement during the lunar cycle. Additionally, we were able to reproduce the measured data using numerical simulations in 2D. The synthetic results show that scattering effects clearly influence the Green's function extraction, especially for larger station distances.
{"title":"Investigating Subsurface Properties of the Shallow Lunar Crust Using Seismic Interferometry on Synthetic and Recorded Data","authors":"S. Keil, H. Igel, M. Schimmel, F. Lindner, F. Bernauer","doi":"10.1029/2024EA003742","DOIUrl":"https://doi.org/10.1029/2024EA003742","url":null,"abstract":"<p>In the past few years, the remarkable progress of commercially operated spacecrafts, the success with reusable rocket engines, as well as the international competition to explore space, has led to a substantial acceleration of activities in the design and preparation of ambitious future lunar missions. In the search for ice and/or cavities imaging the shallow subsurface structure is of vital importance. Hereby, previous studies have shown that seismic interferometry is a promising method to investigate the subsurface properties from passive lunar data. In this study, we want to evaluate the potential of this method further by examining the required duration of seismic measurements and the influence of scattering on the Green's function retrieval. Therefore, we applied seismic interferometry to both measured Apollo 17 data and synthetic data. Our findings indicate that, under optimal conditions, a few hours of data are sufficient when using the method of time-scaled phase-weighted stack (ts-PWS). However, this strongly depends on the inter-station distance, the orientation toward the principal noise sources, and the timing of the measurement during the lunar cycle. Additionally, we were able to reproduce the measured data using numerical simulations in 2D. The synthetic results show that scattering effects clearly influence the Green's function extraction, especially for larger station distances.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"11 10","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003742","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}