Jingyu Wang, Xiaokang Wang, Huiling Yuan, Chunguang Cui, Xiaofang Wang, Lin Liu
Forecasting and early warnings for summertime flash heavy rain events (FHREs) in the middle Yangtze River basin (MYRB) pose significant challenges. This study examined variations in lower tropospheric wind profiles hours before these FHREs using reanalysis data and wind profile radar observations. The findings highlight wind accelerations, directional shifts, and associated vertical shears preceding FHREs, providing valuable insight for severe weather warnings. During the summers of 2010–2019, FHREs occurred most frequently and contributed significantly to total precipitation during the Meiyu period, compared to before and after. Meiyu FHREs also exhibit longer durations and nocturnal peaks. Spatially, FHRE frequency increases from northwest to southeast, with higher frequencies in the topographic areas. The discernible moisture influx 4 hr before FHREs primarily comes from southwesterly and easterly winds below 700 hPa. Before FHRE, weaker easterly winds dominated western MYRB, while strong southerly winds prevailed in the east, influenced by mesoscale cyclonic shear and low-level jets. Detailed wind changes below 4 km altitude show that over the southeastern MYRB, accelerated west-southwest winds are observed 3–4 hr before FHREs, while southerly components near the boundary layer top intensified 2 hr earlier. Within 1 hr before FHREs, the wind speeds sharply increase to peak. East of the western mountains, southwesterly winds strengthen 5 hr prior, then weaken as they shift to northerlies just before FHREs, accompanied by reinforced northerlies near the surface. Over the western mountainous area, southeasterly components below 2 km altitude increase 4 hr before FHREs, although at lower speeds.
{"title":"Wind Profile Characteristics That Warn of Summertime Flash Heavy Rain Events Over the Middle Reaches of the Yangtze River Basin","authors":"Jingyu Wang, Xiaokang Wang, Huiling Yuan, Chunguang Cui, Xiaofang Wang, Lin Liu","doi":"10.1029/2024EA003902","DOIUrl":"https://doi.org/10.1029/2024EA003902","url":null,"abstract":"<p>Forecasting and early warnings for summertime flash heavy rain events (FHREs) in the middle Yangtze River basin (MYRB) pose significant challenges. This study examined variations in lower tropospheric wind profiles hours before these FHREs using reanalysis data and wind profile radar observations. The findings highlight wind accelerations, directional shifts, and associated vertical shears preceding FHREs, providing valuable insight for severe weather warnings. During the summers of 2010–2019, FHREs occurred most frequently and contributed significantly to total precipitation during the Meiyu period, compared to before and after. Meiyu FHREs also exhibit longer durations and nocturnal peaks. Spatially, FHRE frequency increases from northwest to southeast, with higher frequencies in the topographic areas. The discernible moisture influx 4 hr before FHREs primarily comes from southwesterly and easterly winds below 700 hPa. Before FHRE, weaker easterly winds dominated western MYRB, while strong southerly winds prevailed in the east, influenced by mesoscale cyclonic shear and low-level jets. Detailed wind changes below 4 km altitude show that over the southeastern MYRB, accelerated west-southwest winds are observed 3–4 hr before FHREs, while southerly components near the boundary layer top intensified 2 hr earlier. Within 1 hr before FHREs, the wind speeds sharply increase to peak. East of the western mountains, southwesterly winds strengthen 5 hr prior, then weaken as they shift to northerlies just before FHREs, accompanied by reinforced northerlies near the surface. Over the western mountainous area, southeasterly components below 2 km altitude increase 4 hr before FHREs, although at lower speeds.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003902","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388964","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}
Zolal Ayazpour, Gonzalo González Abad, Caroline R. Nowlan, Kang Sun, Hyeong-Ahn Kwon, Christopher Chan Miller, Heesung Chong, Huiqun Wang, Xiong Liu, Kelly Chance, Ewan O’Sullivan, Lei Zhu, Corinne Vigouroux, Isabelle De Smedt, Wolfgang Stremme, James W. Hannigan, Justus Notholt, Xiaoyu Sun, Mathias Palm, Cristof Petri, Kimberly Strong, Amelie N. Röhling, Emmanuel Mahieu, Dan Smale, Yao Té, Isamu Morino, Isao Murata, Tomoo Nagahama, Rigel Kivi, Maria Makarova, Nicholas Jones, Ralf Sussmann, Minqiang Zhou
<p>This study presents the ozone monitoring instrument (OMI) Collection 4 formaldehyde (HCHO) retrieval developed with the Smithsonian Astrophysical Observatory's (SAO) Making Earth System Data Records for Use in Research Environments (MEaSUREs) algorithm. The retrieval algorithm updates and makes improvements to the NASA operational OMI HCHO (OMI Collection 3 HCHO) algorithm, and has been transitioned to use OMI Collection 4 Level-1B radiances. This paper describes the updated retrieval algorithm and compares Collection 3 and Collection 4 data products. The OMI Collection 4 HCHO exhibits remarkably improved stability over time in comparison to the OMI Collection 3 HCHO product, with better precision and the elimination of artificial trends present in the Collection 3 during the later years of the mission. We validate the OMI Collection 4 HCHO data product using Fourier-Transform Infrared (FTIR) ground-based HCHO measurements. The climatological monthly averaged OMI Collection 4 HCHO vertical column densities (VCDs) agree well with the FTIR VCDs, with a correlation coefficient of 0.83, root-mean-square error (RMSE) of <span></span><math> <semantics> <mrow> <mn>2.98</mn> <mo>×</mo> <mn>1</mn> <msup> <mn>0</mn> <mn>15</mn> </msup> </mrow> <annotation> $2.98times 1{0}^{15}$</annotation> </semantics></math> molecules <span></span><math> <semantics> <mrow> <msup> <mtext>cm</mtext> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> <annotation> ${text{cm}}^{-2}$</annotation> </semantics></math>, regression slope of 0.79, and intercept of <span></span><math> <semantics> <mrow> <mn>8.21</mn> <mo>×</mo> <mn>1</mn> <msup> <mn>0</mn> <mn>14</mn> </msup> </mrow> <annotation> $8.21times 1{0}^{14}$</annotation> </semantics></math> molecules <span></span><math> <semantics> <mrow> <msup> <mtext>cm</mtext> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> <annotation> ${text{cm}}^{-2}$</annotation> </semantics></math>. Additionally, we compare the monthly averaged OMI Collection 4 HCHO VCDs to OMPS
{"title":"Aura Ozone Monitoring Instrument (OMI) Collection 4 Formaldehyde Products","authors":"Zolal Ayazpour, Gonzalo González Abad, Caroline R. Nowlan, Kang Sun, Hyeong-Ahn Kwon, Christopher Chan Miller, Heesung Chong, Huiqun Wang, Xiong Liu, Kelly Chance, Ewan O’Sullivan, Lei Zhu, Corinne Vigouroux, Isabelle De Smedt, Wolfgang Stremme, James W. Hannigan, Justus Notholt, Xiaoyu Sun, Mathias Palm, Cristof Petri, Kimberly Strong, Amelie N. Röhling, Emmanuel Mahieu, Dan Smale, Yao Té, Isamu Morino, Isao Murata, Tomoo Nagahama, Rigel Kivi, Maria Makarova, Nicholas Jones, Ralf Sussmann, Minqiang Zhou","doi":"10.1029/2024EA003792","DOIUrl":"https://doi.org/10.1029/2024EA003792","url":null,"abstract":"<p>This study presents the ozone monitoring instrument (OMI) Collection 4 formaldehyde (HCHO) retrieval developed with the Smithsonian Astrophysical Observatory's (SAO) Making Earth System Data Records for Use in Research Environments (MEaSUREs) algorithm. The retrieval algorithm updates and makes improvements to the NASA operational OMI HCHO (OMI Collection 3 HCHO) algorithm, and has been transitioned to use OMI Collection 4 Level-1B radiances. This paper describes the updated retrieval algorithm and compares Collection 3 and Collection 4 data products. The OMI Collection 4 HCHO exhibits remarkably improved stability over time in comparison to the OMI Collection 3 HCHO product, with better precision and the elimination of artificial trends present in the Collection 3 during the later years of the mission. We validate the OMI Collection 4 HCHO data product using Fourier-Transform Infrared (FTIR) ground-based HCHO measurements. The climatological monthly averaged OMI Collection 4 HCHO vertical column densities (VCDs) agree well with the FTIR VCDs, with a correlation coefficient of 0.83, root-mean-square error (RMSE) of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>2.98</mn>\u0000 <mo>×</mo>\u0000 <mn>1</mn>\u0000 <msup>\u0000 <mn>0</mn>\u0000 <mn>15</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> $2.98times 1{0}^{15}$</annotation>\u0000 </semantics></math> molecules <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>cm</mtext>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${text{cm}}^{-2}$</annotation>\u0000 </semantics></math>, regression slope of 0.79, and intercept of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>8.21</mn>\u0000 <mo>×</mo>\u0000 <mn>1</mn>\u0000 <msup>\u0000 <mn>0</mn>\u0000 <mn>14</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> $8.21times 1{0}^{14}$</annotation>\u0000 </semantics></math> molecules <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>cm</mtext>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>2</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${text{cm}}^{-2}$</annotation>\u0000 </semantics></math>. Additionally, we compare the monthly averaged OMI Collection 4 HCHO VCDs to OMPS ","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003792","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396874","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}
Srinivasarao Tanniru, Dhiraj Kumar Singh, Kamal Kant Singh, Raaj Ramsankaran
Snow depth (SD) exhibits high spatiotemporal heterogeneity in Western Himalaya (WH), and its knowledge is essential for applications related to water resources, disaster management, climate, etc. However, due to inclement weather and rugged topographical conditions, only a sparse network of SD monitoring stations exists in WH. Spaceborne passive microwave (PMW) remote sensing data sets provides valuable information about SD; however, only a limited PMW SD studies that cover subregions of WH are available. Different machine learning (ML) methods viz. support vector machine, random forest, and Extremely Randomized Trees (ERT) were tested for estimating SD. Based on our preliminary assessment of these ML approaches, the current study utilizes ERT approach to estimate daily SD over the entire WH region. The ERT SD model is developed using PMW brightness temperature data sets from Advanced Microwave Scanning Radiometer-2 (AMSR-2), snow cover duration (SCD), and other auxiliary parameters (i.e., location, elevation, vegetation cover, etc.) during the winter period between 2012–2013 and 2019–2020. The data between 2012–2013 and 2017–2018 is used for training the model, whereas the data between 2018–2019 and 2019–2020 is used for testing the model. The results demonstrate: (a) The ERT SD model has shown improved SD estimates compared to the available PMW remote sensing-based operational SD products and empirical PMW SD models. (b) In general, with an increase in SD, the mean absolute error of SD retrievals has increased in all SD products/models. (c) Unlike the operational AMSR2 SD product, and Northern Hemisphere Machine Learning SD product, the ERT SD model retrievals have shown better consistency with MODIS snow cover. (d) The developed model has shown a wider range in SD retrievals as compared to other products considered in this study.
{"title":"Exploring Machine Learning's Potential for Estimating High Resolution Daily Snow Depth in Western Himalaya Using Passive Microwave Remote Sensing Data Sets","authors":"Srinivasarao Tanniru, Dhiraj Kumar Singh, Kamal Kant Singh, Raaj Ramsankaran","doi":"10.1029/2024EA003849","DOIUrl":"https://doi.org/10.1029/2024EA003849","url":null,"abstract":"<p>Snow depth (SD) exhibits high spatiotemporal heterogeneity in Western Himalaya (WH), and its knowledge is essential for applications related to water resources, disaster management, climate, etc. However, due to inclement weather and rugged topographical conditions, only a sparse network of SD monitoring stations exists in WH. Spaceborne passive microwave (PMW) remote sensing data sets provides valuable information about SD; however, only a limited PMW SD studies that cover subregions of WH are available. Different machine learning (ML) methods viz. support vector machine, random forest, and Extremely Randomized Trees (ERT) were tested for estimating SD. Based on our preliminary assessment of these ML approaches, the current study utilizes ERT approach to estimate daily SD over the entire WH region. The ERT SD model is developed using PMW brightness temperature data sets from Advanced Microwave Scanning Radiometer-2 (AMSR-2), snow cover duration (SCD), and other auxiliary parameters (i.e., location, elevation, vegetation cover, etc.) during the winter period between 2012–2013 and 2019–2020. The data between 2012–2013 and 2017–2018 is used for training the model, whereas the data between 2018–2019 and 2019–2020 is used for testing the model. The results demonstrate: (a) The ERT SD model has shown improved SD estimates compared to the available PMW remote sensing-based operational SD products and empirical PMW SD models. (b) In general, with an increase in SD, the mean absolute error of SD retrievals has increased in all SD products/models. (c) Unlike the operational AMSR2 SD product, and Northern Hemisphere Machine Learning SD product, the ERT SD model retrievals have shown better consistency with MODIS snow cover. (d) The developed model has shown a wider range in SD retrievals as compared to other products considered in this study.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003849","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388968","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}
Isabell Stucke, Deborah Morgenstern, Gerhard Diendorfer, Georg J. Mayr, Hannes Pichler, Wolfgang Schulz, Thorsten Simon, Achim Zeileis
This study investigates lightning at tall objects and evaluates the risk of upward lightning (UL) over the eastern Alps and its surrounding areas. While uncommon, UL poses a threat, especially to wind turbines, as the long-duration current of UL can cause significant damage. Current risk assessment methods overlook the impact of meteorological conditions, potentially underestimating UL risks. Therefore, this study employs random forests, a machine learning technique, to analyze the relationship between UL measured at Gaisberg Tower (Austria) and 35 larger-scale meteorological variables. Of these, the larger-scale upward velocity, wind speed and direction at 10 m and cloud physics variables contribute most information. The random forests predict the risk of UL across the study area at a 1 resolution. Strong near-surface winds combined with upward deflection by elevated terrain increase UL risk. The diurnal cycle of the UL risk as well as high-risk areas shift seasonally. They are concentrated north/northeast of the Alps in winter due to prevailing northerly winds, and expanding southward, impacting northern Italy in the transitional and summer months. The model performs best in winter, with the highest predicted UL risk coinciding with observed peaks in measured lightning at tall objects. The highest concentration is north of the Alps, where most wind turbines are located, leading to an increase in overall lightning activity. Comprehensive meteorological information is essential for UL risk assessment, as lightning densities are a poor indicator of lightning at tall objects.
{"title":"Spatio-Seasonal Risk Assessment of Upward Lightning at Tall Objects Using Meteorological Reanalysis Data","authors":"Isabell Stucke, Deborah Morgenstern, Gerhard Diendorfer, Georg J. Mayr, Hannes Pichler, Wolfgang Schulz, Thorsten Simon, Achim Zeileis","doi":"10.1029/2024EA003706","DOIUrl":"https://doi.org/10.1029/2024EA003706","url":null,"abstract":"<p>This study investigates lightning at tall objects and evaluates the risk of upward lightning (UL) over the eastern Alps and its surrounding areas. While uncommon, UL poses a threat, especially to wind turbines, as the long-duration current of UL can cause significant damage. Current risk assessment methods overlook the impact of meteorological conditions, potentially underestimating UL risks. Therefore, this study employs random forests, a machine learning technique, to analyze the relationship between UL measured at Gaisberg Tower (Austria) and 35 larger-scale meteorological variables. Of these, the larger-scale upward velocity, wind speed and direction at 10 m and cloud physics variables contribute most information. The random forests predict the risk of UL across the study area at a 1 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mtext>km</mtext>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation> ${text{km}}^{2}$</annotation>\u0000 </semantics></math> resolution. Strong near-surface winds combined with upward deflection by elevated terrain increase UL risk. The diurnal cycle of the UL risk as well as high-risk areas shift seasonally. They are concentrated north/northeast of the Alps in winter due to prevailing northerly winds, and expanding southward, impacting northern Italy in the transitional and summer months. The model performs best in winter, with the highest predicted UL risk coinciding with observed peaks in measured lightning at tall objects. The highest concentration is north of the Alps, where most wind turbines are located, leading to an increase in overall lightning activity. Comprehensive meteorological information is essential for UL risk assessment, as lightning densities are a poor indicator of lightning at tall objects.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388963","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}
This study aims to quantify and better understand the impact of an upgrade to the configuration of an FV3 (Finite Volume cubed-sphere) LAM (Limited Area Model) convection-allowing ensemble on the skill of the RF models trained on cases before the upgrade and forecast on cases after the upgrade. Specifically, Random Forest (RF) models were used to produce probabilistic forecasts of severe weather, significant severe weather, and individual hazards of wind, hail, and tornado for the purpose of day-1 convective outlook guidance. The RF models are trained and forecast on different subsets of the available data set of forecast cases from the spring seasons of 2019 and 2021 (before the FV3 LAM upgrade) and 2022 (after the upgrade) and evaluated both quantitatively and qualitatively. It is found for most predictands that the RF models forecasting 2022 (2019/2021) cases are statistically significantly more skillful when trained on other cases from the 2022 (2019/2021) data set using a leave-one-out approach. However, within the 2019/2021 data set, training on cases from a different year than the year being forecast also leads to statistically significant degradations of skill, apparently at least in part due to the different sample climate between 2019 and 2021. For this particular NWP (Numerical Weather Prediction) model configuration change, the consistency in sample climate between training and forecast cases is at least as important as consistency in model configuration. Finally, increases in skill resulting from increasing the number of forecast cases used to train the RF levels off around 30 forecast cases.
{"title":"Impact of NWP Model Configuration and Training Sample Characteristics on Random Forest-Based Day-1 Convective Outlook Guidance","authors":"Aaron Johnson, Xuguang Wang","doi":"10.1029/2024EA003822","DOIUrl":"https://doi.org/10.1029/2024EA003822","url":null,"abstract":"<p>This study aims to quantify and better understand the impact of an upgrade to the configuration of an FV3 (Finite Volume cubed-sphere) LAM (Limited Area Model) convection-allowing ensemble on the skill of the RF models trained on cases before the upgrade and forecast on cases after the upgrade. Specifically, Random Forest (RF) models were used to produce probabilistic forecasts of severe weather, significant severe weather, and individual hazards of wind, hail, and tornado for the purpose of day-1 convective outlook guidance. The RF models are trained and forecast on different subsets of the available data set of forecast cases from the spring seasons of 2019 and 2021 (before the FV3 LAM upgrade) and 2022 (after the upgrade) and evaluated both quantitatively and qualitatively. It is found for most predictands that the RF models forecasting 2022 (2019/2021) cases are statistically significantly more skillful when trained on other cases from the 2022 (2019/2021) data set using a leave-one-out approach. However, within the 2019/2021 data set, training on cases from a different year than the year being forecast also leads to statistically significant degradations of skill, apparently at least in part due to the different sample climate between 2019 and 2021. For this particular NWP (Numerical Weather Prediction) model configuration change, the consistency in sample climate between training and forecast cases is at least as important as consistency in model configuration. Finally, increases in skill resulting from increasing the number of forecast cases used to train the RF levels off around 30 forecast cases.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380831","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}
Cole A. Nypaver, Bradley J. Thomson, Jeffrey E. Moersch, David A. Kring
Impact cratering processes are ubiquitous throughout our solar system, and the distribution and modification of impact ejecta are sensitive to variable environmental and geologic surface conditions. Here we examine the scale dependency of orbital versus field-based remote sensing data sets of a terrestrial impact structure by comparing low-resolution (90 m/pixel) orbital with high-resolution (23 cm/pixel) drone-based thermophysical data to measure ejecta distribution patterns of Meteor Crater in northeast Arizona, USA. Our results indicate that the thermophysical properties of the Meteor Crater ejecta blanket are well constrained at the scale of orbital data resolution. However, when high-resolution, drone-based data are binned using previously mapped unit boundaries, no clear correlations between thermophysical properties and surface composition are observed. A trend of increasing apparent thermal inertia with surface rock population is observed. These results indicate that significant ejecta distribution variability can exist below the resolution of orbital thermophysical remote sensing data. In addition to providing insights into how remote sensing data can improve field-based geologic mapping campaigns and impact crater analyses, our results place constraints on how the accuracy of geologic maps may be affected by surface erosion.
{"title":"A Drone-Based Thermophysical Investigation of Barringer Meteorite Crater Ejecta","authors":"Cole A. Nypaver, Bradley J. Thomson, Jeffrey E. Moersch, David A. Kring","doi":"10.1029/2024EA003984","DOIUrl":"https://doi.org/10.1029/2024EA003984","url":null,"abstract":"<p>Impact cratering processes are ubiquitous throughout our solar system, and the distribution and modification of impact ejecta are sensitive to variable environmental and geologic surface conditions. Here we examine the scale dependency of orbital versus field-based remote sensing data sets of a terrestrial impact structure by comparing low-resolution (90 m/pixel) orbital with high-resolution (23 cm/pixel) drone-based thermophysical data to measure ejecta distribution patterns of Meteor Crater in northeast Arizona, USA. Our results indicate that the thermophysical properties of the Meteor Crater ejecta blanket are well constrained at the scale of orbital data resolution. However, when high-resolution, drone-based data are binned using previously mapped unit boundaries, no clear correlations between thermophysical properties and surface composition are observed. A trend of increasing apparent thermal inertia with surface rock population is observed. These results indicate that significant ejecta distribution variability can exist below the resolution of orbital thermophysical remote sensing data. In addition to providing insights into how remote sensing data can improve field-based geologic mapping campaigns and impact crater analyses, our results place constraints on how the accuracy of geologic maps may be affected by surface erosion.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380832","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 dissipation of elastic strain energy, or attenuation, in Earth materials contributes to a range of geophysical phenomena, such as the damping of seismic waves and tidal heating of planetary bodies. We present a new method for measuring attenuation in single crystals of minerals and in reference materials over a frequency range of 1–10−4 Hz via nanoindentation. In the experiments, we measure the phase lag between a sinusoidal load applied to the nanoindenter tip and the sinusoidal displacement of the tip into and out of the tested sample, which provides a measure of the inverse quality factor Q−1, or attenuation, of the sample. Experiments were conducted on polymethyl methacrylate (PMMA), indium, halite, olivine and quartz. Attenuation spectra from our tests on PMMA and indium are in excellent agreement with reported values from previous studies. We quantified the natural damping of the nanoindenter and showed that it becomes comparable to that of the samples only at frequencies greater than 0.1 Hz, and is much less than that of the samples at frequencies below 0.1 Hz.
{"title":"A Nanoindentation Study of Attenuation in Geological Materials","authors":"Nir Z. Badt, Ron Maor, David L. Goldsby","doi":"10.1029/2024EA003870","DOIUrl":"https://doi.org/10.1029/2024EA003870","url":null,"abstract":"<p>The dissipation of elastic strain energy, or attenuation, in Earth materials contributes to a range of geophysical phenomena, such as the damping of seismic waves and tidal heating of planetary bodies. We present a new method for measuring attenuation in single crystals of minerals and in reference materials over a frequency range of 1–10<sup>−4</sup> Hz via nanoindentation. In the experiments, we measure the phase lag between a sinusoidal load applied to the nanoindenter tip and the sinusoidal displacement of the tip into and out of the tested sample, which provides a measure of the inverse quality factor Q<sup>−1</sup>, or attenuation, of the sample. Experiments were conducted on polymethyl methacrylate (PMMA), indium, halite, olivine and quartz. Attenuation spectra from our tests on PMMA and indium are in excellent agreement with reported values from previous studies. We quantified the natural damping of the nanoindenter and showed that it becomes comparable to that of the samples only at frequencies greater than 0.1 Hz, and is much less than that of the samples at frequencies below 0.1 Hz.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362688","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}
Ron Maor, Nir Z. Badt, Hugo N. Ulloa, David L. Goldsby
The phase lag between an applied forcing and a response to that forcing is a fundamental parameter in geophysical signal processing. For solid deforming materials, the phase lag between an oscillatory applied stress and the resulting strain response encapsulates information about the dynamical behavior of materials. The phase lag is not directly measured and must be extracted through multiple steps by carefully comparing two time-series signals. The extracted value of the phase lag is highly sensitive to the nature of the signals and the analysis method. Here, we propose a method for extracting the phase lag between two signals when either one or both include an underlying nonlinear trend, which is very common when measuring attenuation in creeping materials. We demonstrate the robustness of the method by analyzing artificial signals and quantifying their absolute and relative errors. We apply the method to two experimental datasets and compare our results with previous studies.
{"title":"A Method for Calculating Attenuation in Creeping Materials","authors":"Ron Maor, Nir Z. Badt, Hugo N. Ulloa, David L. Goldsby","doi":"10.1029/2024EA004127","DOIUrl":"https://doi.org/10.1029/2024EA004127","url":null,"abstract":"<p>The phase lag between an applied forcing and a response to that forcing is a fundamental parameter in geophysical signal processing. For solid deforming materials, the phase lag between an oscillatory applied stress and the resulting strain response encapsulates information about the dynamical behavior of materials. The phase lag is not directly measured and must be extracted through multiple steps by carefully comparing two time-series signals. The extracted value of the phase lag is highly sensitive to the nature of the signals and the analysis method. Here, we propose a method for extracting the phase lag between two signals when either one or both include an underlying nonlinear trend, which is very common when measuring attenuation in creeping materials. We demonstrate the robustness of the method by analyzing artificial signals and quantifying their absolute and relative errors. We apply the method to two experimental datasets and compare our results with previous studies.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362758","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}
A. Laloue, P. Schaeffer, M.-I. Pujol, P. Veillard, O. Andersen, D. Sandwell, A. Delepoulle, G. Dibarboure, Y. Faugère
In this paper, we compute a new hybrid mean sea surface (MSS) model by merging three recent models, CNES_CLS22, SCRIPPS_CLS22, and DTU21, and taking advantage of their respective features. The errors associated with these models were assessed using sea level anomalies for wavelengths ranging from 15 to 100 km from Sentinel-3A (S3A), SWOT KaRIn during its calibration phase and ICESat-2 in the Arctic ice-covered regions. The variance of the error associated with this new Hybrid23 MSS is estimated at 0.15 ± 0.04 cm2 with S3A. The greatest improvements observed on S3A sea level anomalies are mainly located in coastal regions and along geodetic structures: on average, the error is reduced by 23% within 200 km along the coast and by 35% in the Indonesian region compared with SCRIPPS_CLS22. Despite these improvements, the MSS error still impacts significantly sea level anomalies computed from altimetry: it explains 15% and 18% of the S3A and SWOT KaRIn respective global variance. It becomes predominant (>30%) if we consider the shorter wavelengths ([15, 30 km]). CNES_CLS15 (Pujol et al., 2018, https://doi.org/10.1029/2017jc013503), older, explains up to 88% of the variance of SWOT KaRIn at these wavelengths. MSS errors have become a major limiting factor to the accuracy of sea level anomalies, and hybridization even adds sub-mesoscale errors. SCRIPPS_CLS22 and DTU21 also remain better in certain regions of the North Atlantic above 60°N and in Arctic coastal areas. Finally, many efforts are still required to develop the MSS to a new level of precision, which we could soon achieve with SWOT KaRIn during the scientific phase.
{"title":"Merging Recent Mean Sea Surface Into a 2023 Hybrid Model (From Scripps, DTU, CLS, and CNES)","authors":"A. Laloue, P. Schaeffer, M.-I. Pujol, P. Veillard, O. Andersen, D. Sandwell, A. Delepoulle, G. Dibarboure, Y. Faugère","doi":"10.1029/2024EA003836","DOIUrl":"https://doi.org/10.1029/2024EA003836","url":null,"abstract":"<p>In this paper, we compute a new hybrid mean sea surface (MSS) model by merging three recent models, CNES_CLS22, SCRIPPS_CLS22, and DTU21, and taking advantage of their respective features. The errors associated with these models were assessed using sea level anomalies for wavelengths ranging from 15 to 100 km from Sentinel-3A (S3A), SWOT KaRIn during its calibration phase and ICESat-2 in the Arctic ice-covered regions. The variance of the error associated with this new Hybrid23 MSS is estimated at 0.15 ± 0.04 cm<sup>2</sup> with S3A. The greatest improvements observed on S3A sea level anomalies are mainly located in coastal regions and along geodetic structures: on average, the error is reduced by 23% within 200 km along the coast and by 35% in the Indonesian region compared with SCRIPPS_CLS22. Despite these improvements, the MSS error still impacts significantly sea level anomalies computed from altimetry: it explains 15% and 18% of the S3A and SWOT KaRIn respective global variance. It becomes predominant (>30%) if we consider the shorter wavelengths ([15, 30 km]). CNES_CLS15 (Pujol et al., 2018, https://doi.org/10.1029/2017jc013503), older, explains up to 88% of the variance of SWOT KaRIn at these wavelengths. MSS errors have become a major limiting factor to the accuracy of sea level anomalies, and hybridization even adds sub-mesoscale errors. SCRIPPS_CLS22 and DTU21 also remain better in certain regions of the North Atlantic above 60°N and in Arctic coastal areas. Finally, many efforts are still required to develop the MSS to a new level of precision, which we could soon achieve with SWOT KaRIn during the scientific phase.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA003836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362687","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}
L. W. Blum, F. Staples, A. Y. Drozdov, A. Michael, R. Millan, W. Tu, H. Zhao, A. Ukhorskiy, X. Fu
On 21 June 2022, during the annual Geospace Environment Modeling (GEM) workshop, a panel discussion titled “Radiation Belt Loss: The Long-Standing Debate Part II” was organized by the focus group “System Understanding of Radiation Belt Particle Dynamics.” The panel focused on unresolved questions regarding the mechanisms driving electron loss in Earth's radiation belts, discussing topics including magnetopause shadowing, outward radial transport, and wave-particle interactions driving particle precipitation. In this commentary, we provide an overview of the outcomes of this discussion and highlight future needs to better resolve outstanding questions.
{"title":"Radiation Belt Losses: The Long-Standing Debate Part II","authors":"L. W. Blum, F. Staples, A. Y. Drozdov, A. Michael, R. Millan, W. Tu, H. Zhao, A. Ukhorskiy, X. Fu","doi":"10.1029/2024EA004102","DOIUrl":"https://doi.org/10.1029/2024EA004102","url":null,"abstract":"<p>On 21 June 2022, during the annual Geospace Environment Modeling (GEM) workshop, a panel discussion titled “Radiation Belt Loss: The Long-Standing Debate Part II” was organized by the focus group “System Understanding of Radiation Belt Particle Dynamics.” The panel focused on unresolved questions regarding the mechanisms driving electron loss in Earth's radiation belts, discussing topics including magnetopause shadowing, outward radial transport, and wave-particle interactions driving particle precipitation. In this commentary, we provide an overview of the outcomes of this discussion and highlight future needs to better resolve outstanding questions.</p>","PeriodicalId":54286,"journal":{"name":"Earth and Space Science","volume":"12 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EA004102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112152","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}